Three-Dimensional Electroencephalographic Changes on Low-Resolution Brain Electromagnetic Tomography (LORETA) During the Sleep Onset Period.

PubMed

Park, Doo-Heum; Ha, Jee Hyun; Ryu, Seung-Ho; Yu, Jaehak; Shin, Chul-Jin

2015-10-01

Electroencephalographic (EEG) patterns during sleep are markedly different from those measured during the waking state, but the process of falling asleep is not fully understood in terms of biochemical and neurophysiological aspects. We sought to investigate EEG changes that occur during the transitional period from wakefulness to sleep in a 3-dimensional manner to gain a better understanding of the physiological meaning of sleep for the brain. We examined EEG 3-dimensionally using LORETA (low-resolution electromagnetic tomography), to localize the brain region associated with changes that occur during the sleep onset period (SOP). Thirty-channel EEG was recorded in 61 healthy subjects. EEG power spectra and intracortical standardized LORETA were compared between 4 types of 30-second states, including the wakeful stage, transition stage, early sleep stage 1, and late sleep stage 1. Sleep onset began with increased delta and theta power and decreased alpha-1 power in the occipital lobe, and increased theta power in the parietal lobe. Thereafter, global reductions of alpha-1 and alpha-2 powers and greater increases of theta power in the occipito-parietal lobe occurred. As sleep became deeper in sleep stage 1, beta-2 and beta-3, powers decreased mainly in the frontal lobe and some regions of the parieto-temporo-limbic area. These findings suggest that sleep onset includes at least 3 steps in a sequential manner, which include an increase in theta waves in the posterior region of the brain, a global decrease in alpha waves, and a decrease in beta waves in the fronto-central area. © EEG and Clinical Neuroscience Society (ECNS) 2014.

Scale-Free Brain-Wave Music from Simultaneously EEG and fMRI Recordings

PubMed Central

Lu, Jing; Wu, Dan; Yang, Hua; Luo, Cheng; Li, Chaoyi; Yao, Dezhong

2012-01-01

In the past years, a few methods have been developed to translate human EEG to music. In 2009, PloS One 4 e5915, we developed a method to generate scale-free brainwave music where the amplitude of EEG was translated to music pitch according to the power law followed by both of them, the period of an EEG waveform is translated directly to the duration of a note, and the logarithm of the average power change of EEG is translated to music intensity according to the Fechner's law. In this work, we proposed to adopt simultaneously-recorded fMRI signal to control the intensity of the EEG music, thus an EEG-fMRI music is generated by combining two different and simultaneous brain signals. And most importantly, this approach further realized power law for music intensity as fMRI signal follows it. Thus the EEG-fMRI music makes a step ahead in reflecting the physiological process of the scale-free brain. PMID:23166768

Robot Control Through Brain Computer Interface For Patterns Generation

NASA Astrophysics Data System (ADS)

Belluomo, P.; Bucolo, M.; Fortuna, L.; Frasca, M.

2011-09-01

A Brain Computer Interface (BCI) system processes and translates neuronal signals, that mainly comes from EEG instruments, into commands for controlling electronic devices. This system can allow people with motor disabilities to control external devices through the real-time modulation of their brain waves. In this context an EEG-based BCI system that allows creative luminous artistic representations is here presented. The system that has been designed and realized in our laboratory interfaces the BCI2000 platform performing real-time analysis of EEG signals with a couple of moving luminescent twin robots. Experiments are also presented.

Quantum neural network-based EEG filtering for a brain-computer interface.

PubMed

Gandhi, Vaibhav; Prasad, Girijesh; Coyle, Damien; Behera, Laxmidhar; McGinnity, Thomas Martin

2014-02-01

A novel neural information processing architecture inspired by quantum mechanics and incorporating the well-known Schrodinger wave equation is proposed in this paper. The proposed architecture referred to as recurrent quantum neural network (RQNN) can characterize a nonstationary stochastic signal as time-varying wave packets. A robust unsupervised learning algorithm enables the RQNN to effectively capture the statistical behavior of the input signal and facilitates the estimation of signal embedded in noise with unknown characteristics. The results from a number of benchmark tests show that simple signals such as dc, staircase dc, and sinusoidal signals embedded within high noise can be accurately filtered and particle swarm optimization can be employed to select model parameters. The RQNN filtering procedure is applied in a two-class motor imagery-based brain-computer interface where the objective was to filter electroencephalogram (EEG) signals before feature extraction and classification to increase signal separability. A two-step inner-outer fivefold cross-validation approach is utilized to select the algorithm parameters subject-specifically for nine subjects. It is shown that the subject-specific RQNN EEG filtering significantly improves brain-computer interface performance compared to using only the raw EEG or Savitzky-Golay filtered EEG across multiple sessions.

Kohlbergian Cosmic Perspective Responses, EEG Coherence and the TM and TM-Sidhi Programme.

ERIC Educational Resources Information Center

Nidich, Sanford I.; And Others

1983-01-01

This study compared the brain wave activity (EEG) of people who responded to the question "Why be moral?" with answers indicating a belief in the wholeness of man and nature with respondents who did not show a cosmic orientation. Results showed differences in EEG scores between groups. (Author/IS)

[Determination of irreversibility of clinical brain death. Electroencephalography and evoked potentials].

PubMed

Buchner, H; Ferbert, A

2016-02-01

Principally, in the fourth update of the rules for the procedure to finally determine the irreversible cessation of function of the cerebrum, the cerebellum and the brainstem, the importance of an electroencephalogram (EEG), somatosensory evoked potentials (SEP) and brainstem auditory evoked potentials (BAEP) are confirmed. This paper presents the reliability and validity of the electrophysiological diagnosis, discusses the amendments in the fourth version of the guidelines and introduces the practical application, problems and sources of error.An EEG is the best established supplementary diagnostic method for determining the irreversibility of clinical brain death syndrome. It should be noted that residual brain activity can often persist for many hours after the onset of brain death syndrome, particularly in patients with primary brainstem lesions. The derivation and analysis of an EEG requires a high level of expertise to be able to safely distinguish artefacts from primary brain activity. The registration of EEGs to demonstrate the irreversibility of clinical brain death syndrome is extremely time consuming.The BAEPs can only be used to confirm the irreversibility of brain death syndrome in serial examinations or in the rare cases of a sustained wave I or sustained waves I and II. Very often, an investigation cannot be reliably performed because of existing sound conduction disturbances or failure of all potentials even before the onset of clinical brain death syndrome. This explains why BAEPs are only used in exceptional cases.The SEPs of the median nerve can be very reliably derived, are technically simple and with few sources of error. A serial investigation is not required and the time needed for examination is short. For these reasons SEPs are given preference over EEGs and BAEPs for establishing the irreversibility of clinical brain death syndrome.

Electroencephalographic profiles for differentiation of disorders of consciousness

PubMed Central

2013-01-01

Background Electroencephalography (EEG) is best suited for long-term monitoring of brain functions in patients with disorders of consciousness (DOC). Mathematical tools are needed to facilitate efficient interpretation of long-duration sleep-wake EEG recordings. Methods Starting with matching pursuit (MP) decomposition, we automatically detect and parametrize sleep spindles, slow wave activity, K-complexes and alpha, beta and theta waves present in EEG recordings, and automatically construct profiles of their time evolution, relevant to the assessment of residual brain function in patients with DOC. Results Above proposed EEG profiles were computed for 32 patients diagnosed as minimally conscious state (MCS, 20 patients), vegetative state/unresponsive wakefulness syndrome (VS/UWS, 11 patients) and Locked-in Syndrome (LiS, 1 patient). Their interpretation revealed significant correlations between patients’ behavioral diagnosis and: (a) occurrence of sleep EEG patterns including sleep spindles, slow wave activity and light/deep sleep cycles, (b) appearance and variability across time of alpha, beta, and theta rhythms. Discrimination between MCS and VS/UWS based upon prominent features of these profiles classified correctly 87% of cases. Conclusions Proposed EEG profiles offer user-independent, repeatable, comprehensive and continuous representation of relevant EEG characteristics, intended as an aid in differentiation between VS/UWS and MCS states and diagnostic prognosis. To enable further development of this methodology into clinically usable tests, we share user-friendly software for MP decomposition of EEG (http://braintech.pl/svarog) and scripts used for creation of the presented profiles (attached to this article). PMID:24143892

Effects of Parkinson's disease on brain-wave phase synchronisation and cross-modulation

NASA Astrophysics Data System (ADS)

Stumpf, K.; Schumann, A. Y.; Plotnik, M.; Gans, F.; Penzel, T.; Fietze, I.; Hausdorff, J. M.; Kantelhardt, J. W.

2010-02-01

We study the effects of Parkinson's disease (PD) on phase synchronisation and cross-modulation of instantaneous amplitudes and frequencies for brain waves during sleep. Analysing data from 40 full-night EEGs (electro-encephalograms) of ten patients with PD and ten age-matched healthy controls we find that phase synchronisation between the left and right hemisphere of the brain is characteristically reduced in patients with PD. Since there is no such difference in phase synchronisation for EEGs from the same hemisphere, our results suggest the possibility of a relation with problems in coordinated motion of left and right limbs in some patients with PD. Using the novel technique of amplitude and frequency cross-modulation analysis, relating oscillations in different EEG bands and distinguishing both positive and negative modulation, we observe an even more significant decrease in patients for several band combinations.

Material and physical model for evaluation of deep brain activity contribution to EEG recordings

NASA Astrophysics Data System (ADS)

Ye, Yan; Li, Xiaoping; Wu, Tiecheng; Li, Zhe; Xie, Wenwen

2015-12-01

Deep brain activity is conventionally recorded with surgical implantation of electrodes. During the neurosurgery, brain tissue damage and the consequent side effects to patients are inevitably incurred. In order to eliminate undesired risks, we propose that deep brain activity should be measured using the noninvasive scalp electroencephalography (EEG) technique. However, the deeper the neuronal activity is located, the noisier the corresponding scalp EEG signals are. Thus, the present study aims to evaluate whether deep brain activity could be observed from EEG recordings. In the experiment, a three-layer cylindrical head model was constructed to mimic a human head. A single dipole source (sine wave, 10 Hz, altering amplitudes) was embedded inside the model to simulate neuronal activity. When the dipole source was activated, surface potential was measured via electrodes attached on the top surface of the model and raw data were recorded for signal analysis. Results show that the dipole source activity positioned at 66 mm depth in the model, equivalent to the depth of deep brain structures, is clearly observed from surface potential recordings. Therefore, it is highly possible that deep brain activity could be observed from EEG recordings and deep brain activity could be measured using the noninvasive scalp EEG technique.

Alpha brain-wave neurofeedback training reduces psychopathology in a cohort of male and female Canadian aboriginals.

PubMed

Hardt, James V

2012-01-01

The study was conducted to determine if alpha brain-wave neurofeedback training can have positive psychological results by reducing anxiety and other psychopathology. The cohort participated in alpha brain-wave neurofeedback training for 76 minutes (day 1) to 120 or more minutes (days 5-7) daily for 7 days. Electroencephalogram (EEG) electrodes were attached to the head with conductive gel according to the 10-20 International Electrode Placement System. During training, participants were seated in a comfortable armchair within a soundproof and lightproof room. Brain-wave signals were amplified for processing by analog-to-digital converters and polygraphs, then filtered to the pure delta, theta, alpha, beta, and gamma bands as well as subbands of these bands of the EEG. For 2-minute epochs, trainees sat with their eyes closed in the dark listening to their feedback tones as the filtered alpha brain-wave EEG signals controlled the loudness of the tones. Then a "ding" sounded and the tones stopped. For 8 seconds, a monitor lit up with dimly illuminated, static numbers, indicating the strength of their alpha brain waves, after which the feedback tones resumed and the process was repeated. 40 adult volunteers were recruited from the aboriginal population (First Nations, Métis, and Inuit) of Canada. The cohort ranged in age from 25 to 60 years and included males and females. The study was conducted at Biocybernaut Institute of Canada in Victoria, British Columbia. Data was obtained to determine the effectiveness of this training by giving four psychological tests (Minnesota Multi-Phasic Personality Inventory, and the trait forms of the Multiple Affect Adjective Check List, Clyde Mood Scale, and Profile of Mood States) on the first day prior to commencing training and on the seventh day upon completion of the training. EEG data was also compiled throughout the training and analyzed as a factor of the training process. Postintervention data showed positive results with reduction of psychopathology when compared to the data from testing prior to the training. Analysis of this data showed improvement in several areas of psychopathology. Alpha brain-wave neurofeedback training daily for 7 days does have positive psychological results in adult male and female Canadian aboriginals as measured by data from four psychological tests on the participants.

Quantitative modeling of multiscale neural activity

NASA Astrophysics Data System (ADS)

Robinson, Peter A.; Rennie, Christopher J.

2007-01-01

The electrical activity of the brain has been observed for over a century and is widely used to probe brain function and disorders, chiefly through the electroencephalogram (EEG) recorded by electrodes on the scalp. However, the connections between physiology and EEGs have been chiefly qualitative until recently, and most uses of the EEG have been based on phenomenological correlations. A quantitative mean-field model of brain electrical activity is described that spans the range of physiological and anatomical scales from microscopic synapses to the whole brain. Its parameters measure quantities such as synaptic strengths, signal delays, cellular time constants, and neural ranges, and are all constrained by independent physiological measurements. Application of standard techniques from wave physics allows successful predictions to be made of a wide range of EEG phenomena, including time series and spectra, evoked responses to stimuli, dependence on arousal state, seizure dynamics, and relationships to functional magnetic resonance imaging (fMRI). Fitting to experimental data also enables physiological parameters to be infered, giving a new noninvasive window into brain function, especially when referenced to a standardized database of subjects. Modifications of the core model to treat mm-scale patchy interconnections in the visual cortex are also described, and it is shown that resulting waves obey the Schroedinger equation. This opens the possibility of classical cortical analogs of quantum phenomena.

Studying frequency processing of the brain to enhance long-term memory and develop a human brain protocol.

PubMed

Friedrich, Wernher; Du, Shengzhi; Balt, Karlien

2015-01-01

The temporal lobe in conjunction with the hippocampus is responsible for memory processing. The gamma wave is involved with this process. To develop a human brain protocol, a better understanding of the relationship between gamma and long-term memory is vital. A more comprehensive understanding of the human brain and specific analogue waves it uses will support the development of a human brain protocol. Fifty-eight participants aged between 6 and 60 years participated in long-term memory experiments. It is envisaged that the brain could be stimulated through binaural beats (sound frequency) at 40 Hz (gamma) to enhance long-term memory capacity. EEG recordings have been transformed to sound and then to an information standard, namely ASCII. Statistical analysis showed a proportional relationship between long-term memory and gamma activity. Results from EEG recordings indicate a pattern. The pattern was obtained through the de-codification of an EEG recording to sound and then to ASCII. Stimulation of gamma should enhance long term memory capacity. More research is required to unlock the human brains' protocol key. This key will enable the processing of information directly to and from human memory via gamma, the hippocampus and the temporal lobe.

Design of an online EEG based neurofeedback game for enhancing attention and memory.

PubMed

Thomas, Kavitha P; Vinod, A P; Guan, Cuntai

2013-01-01

Brain-Computer Interface (BCI) is an alternative communication and control channel between brain and computer which finds applications in neuroprosthetics, brain wave controlled computer games etc. This paper proposes an Electroencephalogram (EEG) based neurofeedback computer game that allows the player to control the game with the help of attention based brain signals. The proposed game protocol requires the player to memorize a set of numbers in a matrix, and to correctly fill the matrix using his attention. The attention level of the player is quantified using sample entropy features of EEG. The statistically significant performance improvement of five healthy subjects after playing a number of game sessions demonstrates the effectiveness of the proposed game in enhancing their concentration and memory skills.

Urodynamic function during sleep-like brain states in urethane anesthetized rats.

PubMed

Crook, J; Lovick, T

2016-01-28

The aim was to investigate urodynamic parameters and functional excitability of the periaqueductal gray matter (PAG) during changes in sleep-like brain states in urethane anesthetized rats. Simultaneous recordings of detrusor pressure, external urethral sphincter (EUS) electromyogram (EMG), cortical electroencephalogram (EEG), and single-unit activity in the PAG were made during repeated voiding induced by continuous infusion of saline into the bladder. The EEG cycled between synchronized, high-amplitude slow wave activity (SWA) and desynchronized low-amplitude fast activity similar to slow wave and 'activated' sleep-like brain states. During (SWA, 0.5-1.5 Hz synchronized oscillation of the EEG waveform) voiding became more irregular than in the 'activated' brain state (2-5 Hz low-amplitude desynchronized EEG waveform) and detrusor void pressure threshold, void volume threshold and the duration of bursting activity in the external urethral sphincter EMG were raised. The spontaneous firing rate of 23/52 neurons recorded within the caudal PAG and adjacent tegmentum was linked to the EEG state, with the majority of responsive cells (92%) firing more slowly during SWA. Almost a quarter of the cells recorded (12/52) showed phasic changes in firing rate that were linked to the occurrence of voids. Inhibition (n=6), excitation (n=4) or excitation/inhibition (n=2) was seen. The spontaneous firing rate of 83% of the micturition-responsive cells was sensitive to changes in EEG state. In nine of the 12 responsive cells (75%) the responses were reduced during SWA. We propose that during different sleep-like brain states changes in urodynamic properties occur which may be linked to changing excitability of the micturition circuitry in the periaqueductal gray. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Human Brain Activity Patterns beyond the Isoelectric Line of Extreme Deep Coma

PubMed Central

Kroeger, Daniel; Florea, Bogdan; Amzica, Florin

2013-01-01

The electroencephalogram (EEG) reflects brain electrical activity. A flat (isoelectric) EEG, which is usually recorded during very deep coma, is considered to be a turning point between a living brain and a deceased brain. Therefore the isoelectric EEG constitutes, together with evidence of irreversible structural brain damage, one of the criteria for the assessment of brain death. In this study we use EEG recordings for humans on the one hand, and on the other hand double simultaneous intracellular recordings in the cortex and hippocampus, combined with EEG, in cats. They serve to demonstrate that a novel brain phenomenon is observable in both humans and animals during coma that is deeper than the one reflected by the isoelectric EEG, and that this state is characterized by brain activity generated within the hippocampal formation. This new state was induced either by medication applied to postanoxic coma (in human) or by application of high doses of anesthesia (isoflurane in animals) leading to an EEG activity of quasi-rhythmic sharp waves which henceforth we propose to call ν-complexes (Nu-complexes). Using simultaneous intracellular recordings in vivo in the cortex and hippocampus (especially in the CA3 region) we demonstrate that ν-complexes arise in the hippocampus and are subsequently transmitted to the cortex. The genesis of a hippocampal ν-complex depends upon another hippocampal activity, known as ripple activity, which is not overtly detectable at the cortical level. Based on our observations, we propose a scenario of how self-oscillations in hippocampal neurons can lead to a whole brain phenomenon during coma. PMID:24058669

Modulation of electroencephalograph activity by manual acupuncture stimulation in healthy subjects: An autoregressive spectral analysis

NASA Astrophysics Data System (ADS)

Yi, Guo-Sheng; Wang, Jiang; Deng, Bin; Wei, Xi-Le; Han, Chun-Xiao

2013-02-01

To investigate whether and how manual acupuncture (MA) modulates brain activities, we design an experiment where acupuncture at acupoint ST36 of the right leg is used to obtain electroencephalograph (EEG) signals in healthy subjects. We adopt the autoregressive (AR) Burg method to estimate the power spectrum of EEG signals and analyze the relative powers in delta (0 Hz-4 Hz), theta (4 Hz-8 Hz), alpha (8 Hz-13 Hz), and beta (13 Hz-30 Hz) bands. Our results show that MA at ST36 can significantly increase the EEG slow wave relative power (delta band) and reduce the fast wave relative powers (alpha and beta bands), while there are no statistical differences in theta band relative power between different acupuncture states. In order to quantify the ratio of slow to fast wave EEG activity, we compute the power ratio index. It is found that the MA can significantly increase the power ratio index, especially in frontal and central lobes. All the results highlight the modulation of brain activities with MA and may provide potential help for the clinical use of acupuncture. The proposed quantitative method of acupuncture signals may be further used to make MA more standardized.

Electroencephalograph (EEG) study of brain bistable illusion

NASA Astrophysics Data System (ADS)

Meng, Qinglei; Hong, Elliot; Choa, Fow-Sen

2015-05-01

Bistable illusion reflects two different kinds of interpretations for a single image, which is currently known as a competition between two groups of antagonism of neurons. Recent research indicates that these two groups of antagonism of neurons express different comprehension, while one group is emitting a pulse, the other group will be restrained. On the other hand, when this inhibition mechanism becomes weaker, the other antagonism neurons group will take over the interpretation. Since attention plays key roles controlling cognition, is highly interesting to find the location and frequency band used by brain (with either top-down or bottom-up control) to reach deterministic visual perceptions. In our study, we used a 16-channel EEG system to record brain signals from subjects while conducting bistable illusion testing. An extra channel of the EEG system was used for temporal marking. The moment when subjects reach a perception switch, they click the channel and mark the time. The recorded data were presented in form of brain electrical activity map (BEAM) with different frequency bands for analysis. It was found that the visual cortex in the on the right side between parietal and occipital areas was controlling the switching of perception. In the periods with stable perception, we can constantly observe all the delta, theta, alpha and beta waves. While the period perception is switching, almost all theta, alpha, and beta waves were suppressed by delta waves. This result suggests that delta wave may control the processing of perception switching.

Systemic lupus erythematosus with organic brain syndrome: serial electroencephalograms accurately evaluate therapeutic efficacy.

PubMed

Kato, Takashi; Shiratori, Kyoji; Kobashigawa, Tsuyoshi; Hidaka, Yuji

2006-01-01

A 48-year-old man with systemic lupus erythematosus developed organic brain syndrome. High-dose prednisolone was ineffective, and somnolence without focal signs rapidly developed. Electroencephalogram (EEG) demonstrated a slow basic rhythm (3 Hz), but brain magnetic resonance imaging was normal. Somnolence resolved soon after performing plasma exchange (two sessions). However, memory dysfunction persisted, with EEG demonstrating mild abnormalities (7-8 Hz basic rhythm). Double-filtration plasmapheresis (three sessions) was done, followed by intravenous cyclophosphamide. Immediately after the first plasmapheresis session, memory dysfunction began to improve. After the second dose of cyclophosphamide, intellectual function resolved completely and EEG findings also normalized (basic rhythm of 10 Hz waves). Serial EEG findings precisely reflected the neurological condition and therapeutic efficacy in this patient. In contrast, protein levels in cerebrospinal fluid remained high and did not seem to appropriately reflect the neurological condition in this patient.

Identifying stereotypic evolving micro-scale seizures (SEMS) in the hypoxic-ischemic EEG of the pre-term fetal sheep with a wavelet type-II fuzzy classifier.

PubMed

Abbasi, Hamid; Bennet, Laura; Gunn, Alistair J; Unsworth, Charles P

2016-08-01

Perinatal hypoxic-ischemic encephalopathy (HIE) around the time of birth due to lack of oxygen can lead to debilitating neurological conditions such as epilepsy and cerebral palsy. Experimental data have shown that brain injury evolves over time, but during the first 6-8 hours after HIE the brain has recovered oxidative metabolism in a latent phase, and brain injury is reversible. Treatments such as therapeutic cerebral hypothermia (brain cooling) are effective when started during the latent phase, and continued for several days. Effectiveness of hypothermia is lost if started after the latent phase. Post occlusion monitoring of particular micro-scale transients in the hypoxic-ischemic (HI) Electroencephalogram (EEG), from an asphyxiated fetal sheep model in utero, could provide precursory evidence to identify potential biomarkers of injury when brain damage is still treatable. In our studies, we have reported how it is possible to automatically detect HI EEG transients in the form of spikes and sharp waves during the latent phase of the HI EEG of the preterm fetal sheep. This paper describes how to identify stereotypic evolving micro-scale seizures (SEMS) which have a relatively abrupt onset and termination in a frequency range of 1.8-3Hz (Delta waves) superimposed on a suppressed EEG amplitude background post occlusion. This research demonstrates how a Wavelet Type-II Fuzzy Logic System (WT-Type-II-FLS) can be used to automatically identify subtle abnormal SEMS that occur during the latent phase with a preliminary average validation overall performance of 78.71%±6.63 over the 390 minutes of the latent phase, post insult, using in utero pre-term hypoxic fetal sheep models.

Longitudinal sleep EEG trajectories indicate complex patterns of adolescent brain maturation.

PubMed

Feinberg, Irwin; Campbell, Ian G

2013-02-15

New longitudinal sleep data spanning ages 6-10 yr are presented and combined with previous data to analyze maturational trajectories of delta and theta EEG across ages 6-18 yr in non-rapid eye movement (NREM) and rapid eye movement (REM) sleep. NREM delta power (DP) increased from age 6 to age 8 yr and then declined. Its highest rate of decline occurred between ages 12 and 16.5 yr. We attribute the delta EEG trajectories to changes in synaptic density. Whatever their neuronal underpinnings, these age curves can guide research into the molecular-genetic mechanisms that underlie adolescent brain development. The DP trajectories in NREM and REM sleep differed strikingly. DP in REM did not initially increase but declined steadily from age 6 to age 16 yr. We hypothesize that the DP decline in REM reflects maturation of the same brain arousal systems that eliminate delta waves in waking EEG. Whereas the DP age curves differed in NREM and REM sleep, theta age curves were similar in both, roughly paralleling the age trajectory of REM DP. The different maturational curves for NREM delta and theta indicate that they serve different brain functions despite having similar within-sleep dynamics and responses to sleep loss. Period-amplitude analysis of NREM and REM delta waveforms revealed that the age trends in DP were driven more by changes in wave amplitude rather than incidence. These data further document the powerful and complex link between sleep and brain maturation. Understanding this relationship would shed light on both brain development and the function of sleep.

Impact of playing American professional football on long-term brain function.

PubMed

Amen, Daniel G; Newberg, Andrew; Thatcher, Robert; Jin, Yi; Wu, Joseph; Keator, David; Willeumier, Kristen

2011-01-01

The authors recruited 100 active and former National Football League players, representing 27 teams and all positions. Players underwent a clinical history, brain SPECT imaging, qEEG, and multiple neuropsychological measures, including MicroCog. Relative to a healthy-comparison group, players showed global decreased perfusion, especially in the prefrontal, temporal, parietal, and occipital lobes, and cerebellar regions. Quantitative EEG findings were consistent, showing elevated slow waves in the frontal and temporal regions. Significant decreases from normal values were found in most neuropsychological tests. This is the first large-scale brain-imaging study to demonstrate significant differences consistent with a chronic brain trauma pattern in professional football players.

Comparison of simultaneously recorded [H2(15)O]-PET and LORETA during cognitive and pharmacological activation.

PubMed

Gamma, Alex; Lehmann, Dietrich; Frei, Edi; Iwata, Kazuki; Pascual-Marqui, Roberto D; Vollenweider, Franz X

2004-06-01

The complementary strengths and weaknesses of established functional brain imaging methods (high spatial, low temporal resolution) and EEG-based techniques (low spatial, high temporal resolution) make their combined use a promising avenue for studying brain processes at a more fine-grained level. However, this strategy requires a better understanding of the relationship between hemodynamic/metabolic and neuroelectric measures of brain activity. We investigated possible correspondences between cerebral blood flow (CBF) as measured by [H2O]-PET and intracerebral electric activity computed by Low Resolution Brain Electromagnetic Tomography (LORETA) from scalp-recorded multichannel EEG in healthy human subjects during cognitive and pharmacological stimulation. The two imaging modalities were compared by descriptive, correlational, and variance analyses, the latter carried out using statistical parametric mapping (SPM99). Descriptive visual comparison showed a partial overlap between the sets of active brain regions detected by the two modalities. A number of exclusively positive correlations of neuroelectric activity with regional CBF were found across the whole EEG frequency range, including slow wave activity, the latter finding being in contrast to most previous studies conducted in patients. Analysis of variance revealed an extensive lack of statistically significant correspondences between brain activity changes as measured by PET vs. EEG-LORETA. In general, correspondences, to the extent they were found, were dependent on experimental condition, brain region, and EEG frequency. Copyright 2004 Wiley-Liss, Inc.

Long-Range Correlation in alpha-Wave Predominant EEG in Human

NASA Astrophysics Data System (ADS)

Sharif, Asif; Chyan Lin, Der; Kwan, Hon; Borette, D. S.

2004-03-01

The background noise in the alpha-predominant EEG taken from eyes-open and eyes-closed neurophysiological states is studied. Scale-free characteristic is found in both cases using the wavelet approach developed by Simonsen and Nes [1]. The numerical results further show the scaling exponent during eyes-closed is consistently lower than eyes-open. We conjecture the origin of this difference is related to the temporal reconfiguration of the neural network in the brain. To further investigate the scaling structure of the EEG background noise, we extended the second order statistics to higher order moments using the EEG increment process. We found that the background fluctuation in the alpha-predominant EEG is predominantly monofractal. Preliminary results are given to support this finding and its implication in brain functioning is discussed. [1] A.H. Simonsen and O.M. Nes, Physical Review E, 58, 2779¡V2748 (1998).

Epileptic seizure prediction by non-linear methods

DOEpatents

Hively, Lee M.; Clapp, Ned E.; Daw, C. Stuart; Lawkins, William F.

1999-01-01

Methods and apparatus for automatically predicting epileptic seizures monitor and analyze brain wave (EEG or MEG) signals. Steps include: acquiring the brain wave data from the patient; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis tools; obtaining time serial trends in the nonlinear measures; comparison of the trend to known seizure predictors; and providing notification that a seizure is forthcoming.

Postictal psychosis and its electrophysiological correlates in invasive EEG: a case report study and literature review.

PubMed

Kuba, Robert; Brázdil, Milan; Rektor, Ivan

2012-04-01

We identified two patients with medically refractory temporal lobe epilepsy, from whom intracranial EEG recordings were obtained at the time of postictal psychosis. Both patients had mesial temporal epilepsy associated with hippocampal sclerosis. In both patients, the postictal psychosis was associated with a continual "epileptiform" EEG pattern that differed from their interictal and ictal EEG findings (rhythmical slow wave and "abortive" spike-slow wave complex activity in the right hippocampus and lateral temporal cortex in case 1 and a periodic pattern of triphasic waves in the contacts recording activity from the left anterior cingulate gyrus). Some cases of postictal psychosis might be caused by the transient impairment of several limbic system structures due to the "continual epileptiform discharge" in some brain regions. Case 2 is the first report of a patient with TLE in whom psychotic symptoms were associated with the epileptiform impairment of the anterior cingulate gyrus. Copyright © 2012 Elsevier Inc. All rights reserved.

Contribution of EEG in transient neurological deficits.

PubMed

Lozeron, Pierre; Tcheumeni, Nadine Carole; Turki, Sahar; Amiel, Hélène; Meppiel, Elodie; Masmoudi, Sana; Roos, Caroline; Crassard, Isabelle; Plaisance, Patrick; Benbetka, Houria; Guichard, Jean-Pierre; Houdart, Emmanuel; Baudoin, Hélène; Kubis, Nathalie

2018-01-01

Identification of stroke mimics and 'chameleons' among transient neurological deficits (TND) is critical. Diagnostic workup consists of a brain imaging study, for a vascular disease or a brain tumour and EEG, for epileptiform discharges. The precise role of EEG in this diagnostic workup has, however, never been clearly delineated. However, this could be crucial in cases of atypical or incomplete presentation with consequences on disease management and treatment. We analysed the EEG patterns on 95 consecutive patients referred for an EEG within 7 days of a TND with diagnostic uncertainty. Patients were classified at the discharge or the 3-month follow-up visit as: 'ischemic origin', 'migraine aura', 'focal seizure', and 'other'. All patients had a brain imaging study. EEG characteristics were correlated to the TND symptoms, imaging study, and final diagnosis. Sixty four (67%) were of acute onset. Median symptom duration was 45 min. Thirty two % were 'ischemic', 14% 'migraine aura', 19% 'focal seizure', and 36% 'other' cause. EEGs were recorded with a median delay of 1.6 day after symptoms onset. Forty EEGs (42%) were abnormal. Focal slow waves were the most common finding (43%), also in the ischemic group (43%), whether patients had a typical presentation or not. Epileptiform discharges were found in three patients, one with focal seizure and two with migraine aura. Non-specific EEG focal slowing is commonly found in TND, and may last several days. We found no difference in EEG presentation between stroke mimics and stroke chameleons, and between other diagnoses.

Quantitative EEG and its Correlation with Cardiovascular, Cognition and mood State: an Integrated Study in Simulated Microgravity

NASA Astrophysics Data System (ADS)

Zhang, Jianyuan; Hu, Bin; Chen, Wenjuan; Moore, Philip; Xu, Tingting; Dong, Qunxi; Liu, Zhenyu; Luo, Yuejia; Chen, Shanguang

2014-12-01

The focus of the study is the estimation of the effects of microgravity on the central nervous activity and its underlying influencing mechanisms. To validate the microgravity-induced physiological and psychological effects on EEG, quantitative EEG features, cardiovascular indicators, mood state, and cognitive performances data collection was achieved during a 45 day period using a -6°head-down bed rest (HDBR) integrated approach. The results demonstrated significant differences in EEG data, as an increased Theta wave, a decreased Beta wave and a reduced complexity of brain, accompanied with an increased heart rate and pulse rate, decreased positive emotion, and degraded emotion conflict monitoring performance. The canonical correlation analysis (CCA) based cardiovascular and cognitive related EEG model showed the cardiovascular effect on EEG mainly affected bilateral temporal region and the cognitive effect impacted parietal-occipital and frontal regions. The results obtained in the study support the use of an approach which combines a multi-factor influential mechanism hypothesis. The changes in the EEG data may be influenced by both cardiovascular and cognitive effects.

Epileptic seizure prediction by non-linear methods

DOEpatents

Hively, L.M.; Clapp, N.E.; Day, C.S.; Lawkins, W.F.

1999-01-12

This research discloses methods and apparatus for automatically predicting epileptic seizures monitor and analyze brain wave (EEG or MEG) signals. Steps include: acquiring the brain wave data from the patient; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis tools; obtaining time serial trends in the nonlinear measures; comparison of the trend to known seizure predictors; and providing notification that a seizure is forthcoming. 76 figs.

Cannabis Essential Oil: A Preliminary Study for the Evaluation of the Brain Effects

PubMed Central

Loiacono, Idalba; Lanzo, Giovanni; Gori, Luigi; Macchi, Claudio; Epifani, Francesco

2018-01-01

We examined the effects of essential oil from legal (THC <0.2% w/v) hemp variety on the nervous system in 5 healthy volunteers. GC/EIMS and GC/FID analysis of the EO showed that the main components were myrcene and β-caryophyllene. The experiment consisted of measuring autonomic nervous system (ANS) parameters; evaluations of the mood state; and electroencephalography (EEG) recording before treatment, during treatment, and after hemp inhalation periods as compared with control conditions. The results revealed decreased diastolic blood pressure, increased heart rate, and significant increased skin temperature. The subjects described themselves as more energetic, relaxed, and calm. The analysis EEG showed a significant increase in the mean frequency of alpha (8–13 Hz) and significant decreased mean frequency and relative power of beta 2 (18,5–30 Hz) waves. Moreover, an increased power, relative power, and amplitude of theta (4–8 Hz) and alpha brain waves activities and an increment in the delta wave (0,5–4 Hz) power and relative power was recorded in the posterior region of the brain. These results suggest that the brain wave activity and ANS are affected by the inhalation of the EO of Cannabis sativa suggesting a neuromodular activity in cases of stress, depression, and anxiety. PMID:29576792

Cannabis Essential Oil: A Preliminary Study for the Evaluation of the Brain Effects.

PubMed

Gulluni, Nadia; Re, Tania; Loiacono, Idalba; Lanzo, Giovanni; Gori, Luigi; Macchi, Claudio; Epifani, Francesco; Bragazzi, Nicola; Firenzuoli, Fabio

2018-01-01

We examined the effects of essential oil from legal (THC <0.2% w/v) hemp variety on the nervous system in 5 healthy volunteers. GC/EIMS and GC/FID analysis of the EO showed that the main components were myrcene and β -caryophyllene. The experiment consisted of measuring autonomic nervous system (ANS) parameters; evaluations of the mood state; and electroencephalography (EEG) recording before treatment, during treatment, and after hemp inhalation periods as compared with control conditions. The results revealed decreased diastolic blood pressure, increased heart rate, and significant increased skin temperature. The subjects described themselves as more energetic, relaxed, and calm. The analysis EEG showed a significant increase in the mean frequency of alpha (8-13 Hz) and significant decreased mean frequency and relative power of beta 2 (18,5-30 Hz) waves. Moreover, an increased power, relative power, and amplitude of theta (4-8 Hz) and alpha brain waves activities and an increment in the delta wave (0,5-4 Hz) power and relative power was recorded in the posterior region of the brain. These results suggest that the brain wave activity and ANS are affected by the inhalation of the EO of Cannabis sativa suggesting a neuromodular activity in cases of stress, depression, and anxiety.

Neural activity based biofeedback therapy for Autism spectrum disorder through wearable wireless textile EEG monitoring system

NASA Astrophysics Data System (ADS)

Sahi, Ahna; Rai, Pratyush; Oh, Sechang; Ramasamy, Mouli; Harbaugh, Robert E.; Varadan, Vijay K.

2014-04-01

Mu waves, also known as mu rhythms, comb or wicket rhythms are synchronized patterns of electrical activity involving large numbers of neurons, in the part of the brain that controls voluntary functions. Controlling, manipulating, or gaining greater awareness of these functions can be done through the process of Biofeedback. Biofeedback is a process that enables an individual to learn how to change voluntary movements for purposes of improving health and performance through the means of instruments such as EEG which rapidly and accurately 'feedback' information to the user. Biofeedback is used for therapeutic purpose for Autism Spectrum Disorder (ASD) by focusing on Mu waves for detecting anomalies in brain wave patterns of mirror neurons. Conventional EEG measurement systems use gel based gold cup electrodes, attached to the scalp with adhesive. It is obtrusive and wires sticking out of the electrodes to signal acquisition system make them impractical for use in sensitive subjects like infants and children with ASD. To remedy this, sensors can be incorporated with skull cap and baseball cap that are commonly used for infants and children. Feasibility of Textile based Sensor system has been investigated here. Textile based multi-electrode EEG, EOG and EMG monitoring system with embedded electronics for data acquisition and wireless transmission has been seamlessly integrated into fabric of these items for continuous detection of Mu waves. Textile electrodes were placed on positions C3, CZ, C4 according to 10-20 international system and their capability to detect Mu waves was tested. The system is ergonomic and can potentially be used for early diagnosis in infants and planning therapy for ASD patients.

Rewarming affects EEG background in term newborns with hypoxic-ischemic encephalopathy undergoing therapeutic hypothermia.

PubMed

Birca, Ala; Lortie, Anne; Birca, Veronica; Decarie, Jean-Claude; Veilleux, Annie; Gallagher, Anne; Dehaes, Mathieu; Lodygensky, Gregory A; Carmant, Lionel

2016-04-01

To investigate how rewarming impacts the evolution of EEG background in neonates with hypoxic-ischemic encephalopathy (HIE) undergoing therapeutic hypothermia (TH). We recruited a retrospective cohort of 15 consecutive newborns with moderate (9) and severe (6) HIE monitored with a continuous EEG during TH and at least 12h after its end. EEG background was analyzed using conventional visual and quantitative EEG analysis methods including EEG discontinuity, absolute and relative spectral magnitudes. One patient with seizures on rewarming was excluded from analyses. Visual and quantitative analyses demonstrated significant changes in EEG background from pre- to post-rewarming, characterized by an increased EEG discontinuity, more pronounced in newborns with severe compared to moderate HIE. Neonates with moderate HIE also had an increase in the relative magnitude of slower delta and a decrease in higher frequency theta and alpha waves with rewarming. Rewarming affects EEG background in HIE newborns undergoing TH, which may represent a transient adaptive response or reflect an evolving brain injury. EEG background impairment induced by rewarming may represent a biomarker of evolving encephalopathy in HIE newborns undergoing TH and underscores the importance of continuously monitoring the brain health in critically ill neonates. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Therapeutic effects of antimotion sickness medications on the secondary symptoms of motion sickness

NASA Technical Reports Server (NTRS)

Wood, C. D.; Stewart, J. J.; Wood, M. J.; Manno, J. E.; Manno, B. R.

1990-01-01

In addition to nausea and vomiting, motion sickness involves slowing of brain waves, loss of performance, inhibition of gastric motility and the Sopite Syndrome. The therapeutic effects of antimotion sickness drugs on these reactions were evaluated. The subjects were rotated to the M-III end-point of motion sickness. Intramuscular (IM) medications were then administered. Side effects before and after rotation were reported on the Cornell Medical Index. Brain waves were recorded on a Grass Model 6 Electroencephalograph (EEG), and gastric emptying was studied after an oral dose of 1 mCi Technetium 99m DTPA in 10 oz. isotonic saline. An increase in dizziness and drowsiness was reported with placebo after rotation. This was not prevented by IM scopolamine 0.1 mg or ephedrine 25 mg. EEG recordings indicated a slowing of alpha waves with some thea and delta waves from the frontal areas after rotation. IM ephedine and dimenhydrinate counteracted the slowing while 0.3 mg scopolamine had an additive effect. Alterations of performance on the pursuit meter correlated with the brain wave changes. Gastric emptying was restored by IM metoclopramide. Ephedrine IM but not scopolamine is effective for some of the secondary effects of motion sickness after it is established.

[Electrophysiological correlates of efficacy of nootropic drugs in the treatment of consequences of traumatic brain injury in adolescents].

PubMed

Iznak, E V; Iznak, A F; Pankratova, E A; Zavadenko, N N; Guzilova, L S; Guzilova, Iu I

2010-01-01

To assess objectively a dynamics of brain functional state, EEG spectral power and peak latency of the P300 component of cognitive auditory evoked potentials have been analyzed in adolescents during the course of nootropic therapy of residual asthenic consequences of traumatic brain injury (ICD-10 F07.2). The study included 76 adolescents, aged 12-18 years, who have undergone severe closed head trauma with brain commotion 1/2--5 years ago. Patients have been divided into 3 groups treated during one month with cerebrolysin, piracetam or magne-B6, respectively. After the end of the nootropic therapy, 77% of patients treated with cerebrolysin as well as 50% of patients treated with piracetam and magne-B6 have demonstrated the positive dynamics of their brain functional state that manifested itself in the appearance of occipital EEG alpha rhythm or in the increase of its spectral power; in the normalization of alpha rhythm frequency; in the decrease in the spectral power of slow wave (theta and delta) EEG activity, in the amount (up to the disappearance) of paroxysmal EEG activity, in the EEG response to hyperventilation and in the shortening of the P300 peak latency. Such positive changes of neurophysiological parameters have been associated with the improvement of clinical conditions of patients and correlated significantly with the dynamics of psychometric scores of attention and memory.

[The noncoherent components of evoked brain activity].

PubMed

Kovalev, V P; Novototskiĭ-Vlasov, V Iu

1998-01-01

Poststimulus spectral EEG changes and their correlation with evoked potential (EP) were analyzed. The non-stationary components of the brain evoked activity were revealed in 32 volunteers during simple motor reaction and choice reaction to visual stimuli. This nonstationary activity was manifested in poststimulus changes in the mean wave half-period duration (MWHPD) and mean wave half-period power of the delta- and beta-frequency oscillations computed in the EEG realizations after the EP subtraction. The latencies of high-frequency EP components fell into the intervals of the MWHPD decrease and increase in the power of beta-oscillations, and the latencies of low-frequency EP components coincided with the intervals of the MWHPD increase and decrease in the power of delta and beta-oscillations, which pointed to correlation of these changes with the EP.

Interhemispheric differences of the correlation dimension in a human sleep electroencephalogram.

PubMed

Kobayashi, Toshio; Madokoro, Shigeki; Misaki, Kiwamu; Murayama, Jyunichi; Nakagawa, Hiroki; Wada, Yuji

2002-06-01

The interhemispheric differences of the correlation dimension (D2) in the sleep electroencephalogram (EEG) of eight healthy right-handed students was investigated. During slow wave sleep (SWS) the D2 of the central EEG and the temporal left hemisphere (LH) EEG were significantly higher than those in the right hemisphere (RH) EEG; but during rapid eye movement (REM) sleep, the D2 of the central EEG and the occipital RH EEG were significantly higher. The D2 of EEG in the left temporal site during REM sleep were significantly higher than in the right during the first and third sleep cycles, but these were significantly lower during the fourth and fifth sleep cycles. During REM sleep, temporal brain activity may shift from the LH to the RH as morning approaches.

Methods for using a biometric parameter in the identification of persons

DOEpatents

Hively, Lee M [Philadelphia, TN

2011-11-22

Brain waves are used as a biometric parameter to provide for authentication and identification of personnel. The brain waves are sampled using EEG equipment and are processed using phase-space distribution functions to compare digital signature data from enrollment of authorized individuals to data taken from a test subject to determine if the data from the test subject matches the signature data to a degree to support positive identification.

Apparatus and method for epileptic seizure detection using non-linear techniques

DOEpatents

Hively, Lee M.; Clapp, Ned E.; Daw, C. Stuart; Lawkins, William F.

1998-01-01

Methods and apparatus for automatically detecting epileptic seizures by monitoring and analyzing brain wave (EEG or MEG) signals. Steps include: acquiring the brain wave data from the patient; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis; obtaining time serial trends in the nonlinear measures; determining that one or more trends in the nonlinear measures indicate a seizure, and providing notification of seizure occurrence.

Complexity of cardiac signals for predicting changes in alpha-waves after stress in patients undergoing cardiac catheterization

NASA Astrophysics Data System (ADS)

Chiu, Hung-Chih; Lin, Yen-Hung; Lo, Men-Tzung; Tang, Sung-Chun; Wang, Tzung-Dau; Lu, Hung-Chun; Ho, Yi-Lwun; Ma, Hsi-Pin; Peng, Chung-Kang

2015-08-01

The hierarchical interaction between electrical signals of the brain and heart is not fully understood. We hypothesized that the complexity of cardiac electrical activity can be used to predict changes in encephalic electricity after stress. Most methods for analyzing the interaction between the heart rate variability (HRV) and electroencephalography (EEG) require a computation-intensive mathematical model. To overcome these limitations and increase the predictive accuracy of human relaxing states, we developed a method to test our hypothesis. In addition to routine linear analysis, multiscale entropy and detrended fluctuation analysis of the HRV were used to quantify nonstationary and nonlinear dynamic changes in the heart rate time series. Short-time Fourier transform was applied to quantify the power of EEG. The clinical, HRV, and EEG parameters of postcatheterization EEG alpha waves were analyzed using change-score analysis and generalized additive models. In conclusion, the complexity of cardiac electrical signals can be used to predict EEG changes after stress.

Complexity of cardiac signals for predicting changes in alpha-waves after stress in patients undergoing cardiac catheterization

PubMed Central

Chiu, Hung-Chih; Lin, Yen-Hung; Lo, Men-Tzung; Tang, Sung-Chun; Wang, Tzung-Dau; Lu, Hung-Chun; Ho, Yi-Lwun; Ma, Hsi-Pin; Peng, Chung-Kang

2015-01-01

The hierarchical interaction between electrical signals of the brain and heart is not fully understood. We hypothesized that the complexity of cardiac electrical activity can be used to predict changes in encephalic electricity after stress. Most methods for analyzing the interaction between the heart rate variability (HRV) and electroencephalography (EEG) require a computation-intensive mathematical model. To overcome these limitations and increase the predictive accuracy of human relaxing states, we developed a method to test our hypothesis. In addition to routine linear analysis, multiscale entropy and detrended fluctuation analysis of the HRV were used to quantify nonstationary and nonlinear dynamic changes in the heart rate time series. Short-time Fourier transform was applied to quantify the power of EEG. The clinical, HRV, and EEG parameters of postcatheterization EEG alpha waves were analyzed using change-score analysis and generalized additive models. In conclusion, the complexity of cardiac electrical signals can be used to predict EEG changes after stress. PMID:26286628

Effets des radiofréquences sur le système nerveux central chez lʼhomme : EEG, sommeil, cognition, vascularisation

NASA Astrophysics Data System (ADS)

Ghosn, Rania; Villégier, Anne-Sophie; Selmaoui, Brahim; Thuróczy, Georges; de Sèze, René

2013-05-01

Most of clinical studies on radiofrequency electromagnetic fields (RF) were directed at mobile phone-related exposures, usually at the level of the head, at their effect on some physiological functions including sleep, brain electrical activity (EEG), cognitive processes, brain vascularisation, and more generally on the cardiovascular and endocrine systems. They were frequently carried out on healthy adults. Effects on the amplitude of EEG alpha waves, mainly during sleep, look reproducible. It would however be important to define more precisely whether and how the absence of electromagnetic disturbance between RF exposure and the recording systems is checked. No consensus arises about cognitive effects. Some effects on cerebral vascularisation need complementary work.

[Effect of oxysophoridine on electric activities and its power spectrum of reticular formation in rats].

PubMed

Yu, Jianqiang; Li, Yuxiang; Zhao, Chengjun; Gong, Xin; Liu, Jianping; Wang, Feng; Jiang, Yuanxu

2010-05-01

To observe the effect of oxysophoridine (OSR) on the EEG and its power spectrum of reticulum formation in mesencephalon of anaesthetized rat. Utilizing the technique of brain stereotactic apparatus, electrodes were implanted into reticulum formation of mesencephalon. Monopolar lead and computerized FFT technique were employed to record and analyse the index of EEG, power spectrum and frequency distribution in order to study the effect of oxysophoridine on the bioelectricity change of mesencephalon reticulum formation in rats. After administrating(icy) with oxysophoridine at the dose of 2.5,5, 10 mg/rat, the EEG of mesencephalon reticulum formation mainly characterized with low amplitude and slow waves accompanied by spindle-formed sleeping waves with a significant decrease of total power of EEG (P < 0.05) while the ratio of theta, alpha waves increased in total frequency of rats (P < 0.05). Oxysophoridine possesses central inhibitory effects and its inhibitory mechanism may associate with the reduction of bioelectricity in mesencephalon reticulum formation. Mesencephalon reticulum formation may serve as one part of the structure serving as the circuit conducting the central inhibitory effect of oxysophoridine. [Key words] oxysophoridine; reticulum formation; electroencephalogram (EEG) ; rats

Resting EEG deficits in accused murderers with schizophrenia.

PubMed

Schug, Robert A; Yang, Yaling; Raine, Adrian; Han, Chenbo; Liu, Jianghong; Li, Liejia

2011-10-31

Empirical evidence continues to suggest a biologically distinct violent subtype of schizophrenia. The present study examined whether murderers with schizophrenia would demonstrate resting EEG deficits distinguishing them from both non-violent schizophrenia patients and murderers without schizophrenia. Resting EEG data were collected from five diagnostic groups (normal controls, non-murderers with schizophrenia, murderers with schizophrenia, murderers without schizophrenia, and murderers with psychiatric conditions other than schizophrenia) at a brain hospital in Nanjing, China. Murderers with schizophrenia were characterized by increased left-hemispheric fast-wave EEG activity relative to non-violent schizophrenia patients, while non-violent schizophrenia patients instead demonstrated increased diffuse slow-wave activity compared to all other groups. Results are discussed within the framework of a proposed left-hemispheric over-processing hypothesis specific to violent individuals with schizophrenia, involving left hemispheric hyperarousal deficits, which may lead to a homicidally violent schizophrenia outcome. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Dynamics of large-scale brain activity in normal arousal states and epileptic seizures

NASA Astrophysics Data System (ADS)

Robinson, P. A.; Rennie, C. J.; Rowe, D. L.

2002-04-01

Links between electroencephalograms (EEGs) and underlying aspects of neurophysiology and anatomy are poorly understood. Here a nonlinear continuum model of large-scale brain electrical activity is used to analyze arousal states and their stability and nonlinear dynamics for physiologically realistic parameters. A simple ordered arousal sequence in a reduced parameter space is inferred and found to be consistent with experimentally determined parameters of waking states. Instabilities arise at spectral peaks of the major clinically observed EEG rhythms-mainly slow wave, delta, theta, alpha, and sleep spindle-with each instability zone lying near its most common experimental precursor arousal states in the reduced space. Theta, alpha, and spindle instabilities evolve toward low-dimensional nonlinear limit cycles that correspond closely to EEGs of petit mal seizures for theta instability, and grand mal seizures for the other types. Nonlinear stimulus-induced entrainment and seizures are also seen, EEG spectra and potentials evoked by stimuli are reproduced, and numerous other points of experimental agreement are found. Inverse modeling enables physiological parameters underlying observed EEGs to be determined by a new, noninvasive route. This model thus provides a single, powerful framework for quantitative understanding of a wide variety of brain phenomena.

Confused or not Confused?: Disentangling Brain Activity from EEG Data Using Bidirectional LSTM Recurrent Neural Networks.

PubMed

Ni, Zhaoheng; Yuksel, Ahmet Cem; Ni, Xiuyan; Mandel, Michael I; Xie, Lei

2017-08-01

Brain fog, also known as confusion, is one of the main reasons for low performance in the learning process or any kind of daily task that involves and requires thinking. Detecting confusion in a human's mind in real time is a challenging and important task that can be applied to online education, driver fatigue detection and so on. In this paper, we apply Bidirectional LSTM Recurrent Neural Networks to classify students' confusion in watching online course videos from EEG data. The results show that Bidirectional LSTM model achieves the state-of-the-art performance compared with other machine learning approaches, and shows strong robustness as evaluated by cross-validation. We can predict whether or not a student is confused in the accuracy of 73.3%. Furthermore, we find the most important feature to detecting the brain confusion is the gamma 1 wave of EEG signal. Our results suggest that machine learning is a potentially powerful tool to model and understand brain activity.

Integrating EEG and fMRI in epilepsy.

PubMed

Formaggio, Emanuela; Storti, Silvia Francesca; Bertoldo, Alessandra; Manganotti, Paolo; Fiaschi, Antonio; Toffolo, Gianna Maria

2011-02-14

Integrating electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) studies enables to non-invasively investigate human brain function and to find the direct correlation of these two important measures of brain activity. Presurgical evaluation of patients with epilepsy is one of the areas where EEG and fMRI integration has considerable clinical relevance for localizing the brain regions generating interictal epileptiform activity. The conventional analysis of EEG-fMRI data is based on the visual identification of the interictal epileptiform discharges (IEDs) on scalp EEG. The convolution of these EEG events, represented as stick functions, with a model of the fMRI response, i.e. the hemodynamic response function, provides the regressor for general linear model (GLM) analysis of fMRI data. However, the conventional analysis is not automatic and suffers of some subjectivity in IEDs classification. Here, we present an easy-to-use and automatic approach for combined EEG-fMRI analysis able to improve IEDs identification based on Independent Component Analysis and wavelet analysis. EEG signal due to IED is reconstructed and its wavelet power is used as a regressor in GLM. The method was validated on simulated data and then applied on real data set consisting of 2 normal subjects and 5 patients with partial epilepsy. In all continuous EEG-fMRI recording sessions a good quality EEG was obtained allowing the detection of spontaneous IEDs and the analysis of the related BOLD activation. The main clinical finding in EEG-fMRI studies of patients with partial epilepsy is that focal interictal slow-wave activity was invariably associated with increased focal BOLD responses in a spatially related brain area. Our study extends current knowledge on epileptic foci localization and confirms previous reports suggesting that BOLD activation associated with slow activity might have a role in localizing the epileptogenic region even in the absence of clear interictal spikes. Copyright © 2010 Elsevier Inc. All rights reserved.

Exploring resting-state EEG brain oscillatory activity in relation to cognitive functioning in multiple sclerosis.

PubMed

Keune, Philipp M; Hansen, Sascha; Weber, Emily; Zapf, Franziska; Habich, Juliane; Muenssinger, Jana; Wolf, Sebastian; Schönenberg, Michael; Oschmann, Patrick

2017-09-01

Neurophysiologic monitoring parameters related to cognition in Multiple Sclerosis (MS) are sparse. Previous work reported an association between magnetoencephalographic (MEG) alpha-1 activity and information processing speed. While this remains to be replicated by more available electroencephalographic (EEG) methods, also other established EEG markers, e.g. the slow-wave/fast-wave ratio (theta/beta ratio), remain to be explored in this context. Performance on standard tests addressing information processing speed and attention (Symbol-Digit Modalities Test, SDMT; Test of Attention Performance, TAP) was examined in relation to resting-state EEG alpha-1 and alpha-2 activity and the theta/beta ratio in 25MS patients. Increased global alpha-1 and alpha-2 activity and an increased frontal theta/beta ratio (pronounced slow-wave relative to fast-wave activity) were associated with lower SDMT processing speed. In an exploratory analysis, clinically impaired attention was associated with a significantly increased frontal theta/beta ratio whereas alpha power did not show sensitivity to clinical impairment. EEG global alpha power and the frontal theta/beta ratio were both associated with attention. The theta/beta ratio involved potential clinical sensitivity. Resting-state EEG recordings can be obtained during the routine clinical process. The examined resting-state measures may represent feasible monitoring parameters in MS. This notion should be explored in future intervention studies. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

On the Synchronization of EEG Spindle Waves

NASA Astrophysics Data System (ADS)

Long, Wen; Zhang, ChengFu; Zhao, SiLan; Shi, RuiHong

2000-06-01

Based on recently sleeping cellular substrates, a network model synaptically coupled by N three-cell circuits is provided. Simulation results show that: (i) the dynamic behavior of every circuit is chaotic; (ii) the synchronization of the network is incomplete; (iii) the incomplete synchronization can integrate burst firings of cortical cells into waxing-and-wanning EEG spindle waves. These results enlighten us that this kind of incomplete synchronization may integrate microscopic, electrical activities of neurons in billions into macroscopic, functional states in human brain. In addition, the effects of coupling strength, connectional mode and noise to the synchronization are discussed.

Retinoic Acid Signaling Affects Cortical Synchrony During Sleep

NASA Astrophysics Data System (ADS)

Maret, Stéphanie; Franken, Paul; Dauvilliers, Yves; Ghyselinck, Norbert B.; Chambon, Pierre; Tafti, Mehdi

2005-10-01

Delta oscillations, characteristic of the electroencephalogram (EEG) of slow wave sleep, estimate sleep depth and need and are thought to be closely linked to the recovery function of sleep. The cellular mechanisms underlying the generation of delta waves at the cortical and thalamic levels are well documented, but the molecular regulatory mechanisms remain elusive. Here we demonstrate in the mouse that the gene encoding the retinoic acid receptor beta determines the contribution of delta oscillations to the sleep EEG. Thus, retinoic acid signaling, which is involved in the patterning of the brain and dopaminergic pathways, regulates cortical synchrony in the adult.

Early and Later Life Stress Alter Brain Activity and Sleep in Rats

PubMed Central

Mrdalj, Jelena; Pallesen, Ståle; Milde, Anne Marita; Jellestad, Finn Konow; Murison, Robert; Ursin, Reidun; Bjorvatn, Bjørn; Grønli, Janne

2013-01-01

Exposure to early life stress may profoundly influence the developing brain in lasting ways. Neuropsychiatric disorders associated with early life adversity may involve neural changes reflected in EEG power as a measure of brain activity and disturbed sleep. The main aim of the present study was for the first time to characterize possible changes in adult EEG power after postnatal maternal separation in rats. Furthermore, in the same animals, we investigated how EEG power and sleep architecture were affected after exposure to a chronic mild stress protocol. During postnatal day 2–14 male rats were exposed to either long maternal separation (180 min) or brief maternal separation (10 min). Long maternally separated offspring showed a sleep-wake nonspecific reduction in adult EEG power at the frontal EEG derivation compared to the brief maternally separated group. The quality of slow wave sleep differed as the long maternally separated group showed lower delta power in the frontal-frontal EEG and a slower reduction of the sleep pressure. Exposure to chronic mild stress led to a lower EEG power in both groups. Chronic exposure to mild stressors affected sleep differently in the two groups of maternal separation. Long maternally separated offspring showed more total sleep time, more episodes of rapid eye movement sleep and higher percentage of non-rapid eye movement episodes ending in rapid eye movement sleep compared to brief maternal separation. Chronic stress affected similarly other sleep parameters and flattened the sleep homeostasis curves in all offspring. The results confirm that early environmental conditions modulate the brain functioning in a long-lasting way. PMID:23922857

Apparatus and method for epileptic seizure detection using non-linear techniques

DOEpatents

Hively, L.M.; Clapp, N.E.; Daw, C.S.; Lawkins, W.F.

1998-04-28

Methods and apparatus are disclosed for automatically detecting epileptic seizures by monitoring and analyzing brain wave (EEG or MEG) signals. Steps include: acquiring the brain wave data from the patient; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis; obtaining time serial trends in the nonlinear measures; determining that one or more trends in the nonlinear measures indicate a seizure, and providing notification of seizure occurrence. 76 figs.

Meanings of Waves: Electroencephalography and Society in Mexico City, 1940-1950.

PubMed

Pérez, Nuria Valverde

2016-12-01

Argument This paper focuses on the uses of electroencephalograms (EEGs) in Mexico during their introductory decade from 1940 to 1950. Following Borck (2006), I argue that EEGs adapted to fit local circumstances and that this adjustment led to the consolidation of different ways of making science and the emergence of new objects of study and social types. I also maintain that the way EEGs were introduced into the institutional networks of Mexico entangled them in discussions about the objective and juridical definitions of social groups, thereby preempting concerns about their technical and epistemic limitations. This ultimately enabled the use of EEGs as normative machines and dispositifs. To this end, the paper follows the arrival of EEGs and the creation of institutional networks then analyzes the extent to which the styles of thinking behind the uses of EEGs and attempts to reify a notion of normal electrical brain behavior-particularly by applying EEGs to a community of Otomí Indians-correlated with the difficulties of defining the socio-anthropological notions that articulated legal and disciplinary projects of the time. Finally, it unveils the shortcomings of alternative attempts to define a brain model and to resist the production of ontological determinations.

Recording brain waves at the supermarket: what can we learn from a shopper's brain?

PubMed

Sands, Stephen F; Sands, J Andrew

2012-01-01

Communication and marketing campaigns have traditionally been divided into two lines: above the line (ATL) and below the line (BTL). ATL campaigns refer to communications such as TV, print, and outdoor displays that are intended to reach large audiences. The effects of ATL are inherently difficult to measure; we do not see the direct consequences of viewing an advertisement (i.e., a talking baby giving financial advice) and actual purchase of the product. ATL is intended to indirectly improve the impression of a brand. BTL campaigns refer to promotions and in-store displays and are designed to affect the point-of-purchase behavior. The effects of BTL are easier to measure; we see direct consequences of viewing a display (i.e., “Today Only, Two for the Price of One”) and eventual purchase of the product. BTL is intended to directly improve the impression of a brand. Neuroscience plays an important role in measuring the effects of marketing campaigns. Traditional methods of measurement (such as surveys and interviews) depend on the verbal ability of the consumer to articulate their motivations for purchasing a product. It is well known that participants are poor at introspective reasoning, leading to an eventual purchase that omits emotional elements. Recently, methods normally employed in cognitive neuroscience have been adapted for use in the evaluation of campaign effectiveness. These methods have increased our understanding of factors leading to economic decisions. The application of neuroscience in ATL campaigns is relatively straightforward. Participants view TV commercials, for example, seated in a comfortable setting with minimal movement while electroencephalogram (EEG) measures are monitored. These brain waves reveal cognitive events related to the media. Participants are exposed to a functional magnetic resonance imaging (fMRI) scanner to monitor changes in blood flow in various regions of the brain. Both of these methods are sensitive to underlying cognitive and emotional activity and are complimentary. EEG is more sensitive to time-locked events (i.e., story lines), whereas fMRI is more sensitive to the brain regions involved. The application of neuroscience in BTL campaigns is significantly more difficult to achieve. Participants move unconstrained in a shopping environment while EEG and eye movements are monitored. In this scenario, fMRI is not possible. fMRI can be used with virtual store mock-ups, but it is expensive and seldom used. We have developed a technology that allows for the measurement of EEG in an unobtrusive manner. The intent is to record the brain waves of participants during their day-to-day shopping experience. A miniaturized video recorder, EEG amplifiers, and eye-tracking systems are used. Digital signal processing is employed to remove the substantial artifact generated by eye movements and motion. Eye fixations identify specific viewings of products and displays, and they are used for synchronizing the behavior with EEG response. The location of EEG sources is determined by the use of a source reconstruction software.

Brain-computer interface design using alpha wave

NASA Astrophysics Data System (ADS)

Zhao, Hai-bin; Wang, Hong; Liu, Chong; Li, Chun-sheng

2010-01-01

A brain-computer interface (BCI) is a novel communication system that translates brain activity into commands for a computer or other electronic devices. BCI system based on non-invasive scalp electroencephalogram (EEG) has become a hot research area in recent years. BCI technology can help improve the quality of life and restore function for people with severe motor disabilities. In this study, we design a real-time asynchronous BCI system using Alpha wave. The basic theory of this BCI system is alpha wave-block phenomenon. Alpha wave is the most prominent wave in the whole realm of brain activity. This system includes data acquisition, feature selection and classification. The subject can use this system easily and freely choose anyone of four commands with only short-time training. The results of the experiment show that this BCI system has high classification accuracy, and has potential application for clinical engineering and is valuable for further research.

Going local: insights from EEG and stereo-EEG studies of the human sleep-wake cycle.

PubMed

Ferrara, Michele; De Gennaro, Luigi

2011-01-01

In the present paper, we reviewed a large body of evidence, mainly from quantitative EEG studies of our laboratory, supporting the notion that sleep is a local and use-dependent process. Quantitative analyses of sleep EEG recorded from multiple cortical derivations clearly indicate that every sleep phenomenon, from sleep onset to the awakening, is strictly local in nature. Sleep onset first occurs in frontal areas, and a frontal predominance of low-frequency power persists in the first part of the night, when the homeostatic processes mainly occur, and then it vanishes. Upon awakening, we showed an asynchronous EEG activation of different cortical areas, the more anterior ones being the first to wake up. During extended periods of wakefulness, the increase of sleepiness-related low-EEG frequencies is again evident over the frontal derivations. Similarly, experimental manipulations of sleep length by total sleep deprivation, partial sleep curtailment or even selective slow-wave sleep deprivation lead to a slow-wave activity rebound localized especially on the anterior derivations. Thus, frontal areas are crucially involved in sleep homeostasis. According to the local use-dependent theory, this would derive from a higher sleep need of the frontal cortex, which in turn is due to its higher levels of activity during wakefulness. The fact that different brain regions can simultaneously exhibit different sleep intensities indicates that sleep is not a spatially global and uniform state, as hypothesized in the theory. We have also reviewed recent evidence of localized effects of learning and plasticity on EEG sleep measures. These studies provide crucial support to a key concept in the theory, the one claiming that local sleep characteristics should be use-dependent. Finally, we have reported data corroborating the notion that sleep is not necessarily present simultaneously in the entire brain. Our stereo-EEG recordings clearly indicate that sleep and wakefulness can co-exist in different areas, suggesting that vigilance states are not necessarily temporally discrete states. We conclude that understanding local variations in sleep propensity and depth, especially as a result of brain plasticity, may provide in the near future insightful hints into the fundamental functions of sleep.

Source analysis of MEG activities during sleep (abstract)

NASA Astrophysics Data System (ADS)

Ueno, S.; Iramina, K.

1991-04-01

The present study focuses on magnetic fields of the brain activities during sleep, in particular on K-complexes, vertex waves, and sleep spindles in human subjects. We analyzed these waveforms based on both topographic EEG (electroencephalographic) maps and magnetic fields measurements, called MEGs (magnetoencephalograms). The components of magnetic fields perpendicular to the surface of the head were measured using a dc SQUID magnetometer with a second derivative gradiometer. In our computer simulation, the head is assumed to be a homogeneous spherical volume conductor, with electric sources of brain activity modeled as current dipoles. Comparison of computer simulations with the measured data, particularly the MEG, suggests that the source of K-complexes can be modeled by two current dipoles. A source for the vertex wave is modeled by a single current dipole which orients along the body axis out of the head. By again measuring the simultaneous MEG and EEG signals, it is possible to uniquely determine the orientation of this dipole, particularly when it is tilted slightly off-axis. In sleep stage 2, fast waves of magnetic fields consistently appeared, but EEG spindles appeared intermittently. The results suggest that there exist sources which are undetectable by electrical measurement but are detectable by magnetic-field measurement. Such source can be described by a pair of opposing dipoles of which directions are oppositely oriented.

Adolescent Changes in Homeostatic Regulation of EEG Activity in the Delta and Theta Frequency Bands during NREM Sleep

PubMed Central

Campbell, Ian G.; Darchia, Nato; Higgins, Lisa M.; Dykan, Igor V.; Davis, Nicole M.; de Bie, Evan; Feinberg, Irwin

2011-01-01

Study Objectives: Slow wave EEG activity in NREM sleep decreases by more than 60% between ages 10 and 20 years. Slow wave EEG activity also declines across NREM periods (NREMPs) within a night, and this decline is thought to represent the dynamics of sleep homeostasis. We used longitudinal data to determine whether these homeostatic dynamics change across adolescence. Design: All-night sleep EEG was recorded semiannually for 6 years. Setting: EEG was recorded with ambulatory recorders in the subjects' homes. Participants: Sixty-seven subjects in 2 cohorts, one starting at age 9 and one starting at age 12 years. Measurements and Results: For NREM delta (1-4 Hz) and theta (4-8 Hz) EEG, we tested whether the proportion of spectral energy contained in the first NREMP changes with age. We also tested for age changes in the parameters of the process S exponential decline. For both delta and theta, the proportion of energy in the first NREMP declined significantly across ages 9 to 18 years. Process S parameters SWA0 and TWA0, respectively, represent slow wave (delta) activity and theta wave activity at the beginning of the night. SWA0 and TWA0 declined significantly (P < 0.0001) across ages 9 to 18. Conclusions: These declines indicate that the intensity of the homeostatic or restorative processes at the beginning of sleep diminished across adolescence. We propose that this change in sleep regulation is caused by the synaptic pruning that occurs during adolescent brain maturation. Citation: Campbell IG; Darchia N; Higgins LM; Dykan IV; Davis NM; de Bie E; Feinberg I. Adolescent changes in homeostatic regulation of EEG activity in the delta and theta frequency bands during NREM sleep. SLEEP 2011;34(1):83-91. PMID:21203377

Preliminary study of Alzheimer's Disease diagnosis based on brain electrical signals using wireless EEG

NASA Astrophysics Data System (ADS)

Handayani, N.; Akbar, Y.; Khotimah, S. N.; Haryanto, F.; Arif, I.; Taruno, W. P.

2016-03-01

This research aims to study brain's electrical signals recorded using EEG as a basis for the diagnosis of patients with Alzheimer's Disease (AD). The subjects consisted of patients with AD, and normal subjects are used as the control. Brain signals are recorded for 3 minutes in a relaxed condition and with eyes closed. The data is processed using power spectral analysis, brain mapping and chaos test to observe the level of complexity of EEG's data. The results show a shift in the power spectral in the low frequency band (delta and theta) in AD patients. The increase of delta and theta occurs in lobus frontal area and lobus parietal respectively. However, there is a decrease of alpha activity in AD patients where in the case of normal subjects with relaxed condition, brain alpha wave dominates the posterior area. This is confirmed by the results of brain mapping. While the results of chaos analysis show that the average value of MMLE is lower in AD patients than in normal subjects. The level of chaos associated with neural complexity in AD patients with lower neural complexity is due to neuronal damage caused by the beta amyloid plaques and tau protein in neurons.

Paradoxical ictal EEG lateralization in children with unilateral encephaloclastic lesions.

PubMed

Garzon, Eliana; Gupta, Ajay; Bingaman, William; Sakamoto, Americo C; Lüders, Hans

2009-09-01

Describe an ictal EEG pattern of paradoxical lateralization in children with unilateral encephaloclastic hemispheric lesion acquired early in life. Of 68 children who underwent hemispherectomy during 2003-2005, scalp video-EEG and brain MRI of six children with an ictal scalp EEG pattern discordant to the clinical and imaging data were reanalyzed. Medical charts were reviewed for clinical findings and seizure outcome. Age of seizure onset was 1 day-4 years. The destructive MRI lesion was an ischemic stroke in 2, a post-infectious encephalomalacia in 2, and a perinatal trauma and hemiconvulsive-hemiplegic syndrome in one patient each. Ictal EEG pattern was characterized by prominent ictal rhythms with either 3-7 Hz spike and wave complexes or beta frequency sharp waves (paroxysmal fast) over the unaffected (contralesional) hemisphere. Scalp video-EEG was discordant, however, other findings of motor deficits (hemiparesis; five severe, one mild), seizure semiology (4/6), interictal EEG abnormalities (3/6), and unilateral burden of MRI lesion guided the decision for hemispherectomy. After 12-39 months of post-surgery follow up, five of six patients were seizure free and one has brief staring spells. We describe a paradoxical lateralization of the EEG to the "good" hemisphere in children with unihemispheric encephaloclastic lesions. This EEG pattern is compatible with seizure free outcome after surgery, provided other clinical findings and tests are concordant with origin from the abnormal hemisphere.

Neocortical dynamics at multiple scales: EEG standing waves, statistical mechanics, and physical analogs.

PubMed

Ingber, Lester; Nunez, Paul L

2011-02-01

The dynamic behavior of scalp potentials (EEG) is apparently due to some combination of global and local processes with important top-down and bottom-up interactions across spatial scales. In treating global mechanisms, we stress the importance of myelinated axon propagation delays and periodic boundary conditions in the cortical-white matter system, which is topologically close to a spherical shell. By contrast, the proposed local mechanisms are multiscale interactions between cortical columns via short-ranged non-myelinated fibers. A mechanical model consisting of a stretched string with attached nonlinear springs demonstrates the general idea. The string produces standing waves analogous to large-scale coherent EEG observed in some brain states. The attached springs are analogous to the smaller (mesoscopic) scale columnar dynamics. Generally, we expect string displacement and EEG at all scales to result from both global and local phenomena. A statistical mechanics of neocortical interactions (SMNI) calculates oscillatory behavior consistent with typical EEG, within columns, between neighboring columns via short-ranged non-myelinated fibers, across cortical regions via myelinated fibers, and also derives a string equation consistent with the global EEG model. Copyright © 2010 Elsevier Inc. All rights reserved.

A Comparative Study of Standardized Infinity Reference and Average Reference for EEG of Three Typical Brain States

PubMed Central

Zheng, Gaoxing; Qi, Xiaoying; Li, Yuzhu; Zhang, Wei; Yu, Yuguo

2018-01-01

The choice of different reference electrodes plays an important role in deciphering the functional meaning of electroencephalography (EEG) signals. In recent years, the infinity zero reference using the reference electrode standard technique (REST) has been increasingly applied, while the average reference (AR) was generally advocated as the best available reference option in previous classical EEG studies. Here, we designed EEG experiments and performed a direct comparison between the influences of REST and AR on EEG-revealed brain activity features for three typical brain behavior states (eyes-closed, eyes-open and music-listening). The analysis results revealed the following observations: (1) there is no significant difference in the alpha-wave-blocking effect during the eyes-open state compared with the eyes-closed state for both REST and AR references; (2) there was clear frontal EEG asymmetry during the resting state, and the degree of lateralization under REST was higher than that under AR; (3) the global brain functional connectivity density (FCD) and local FCD have higher values for REST than for AR under different behavior states; and (4) the value of the small-world network characteristic in the eyes-closed state is significantly (in full, alpha, beta and gamma frequency bands) higher than that in the eyes-open state, and the small-world effect under the REST reference is higher than that under AR. In addition, the music-listening state has a higher small-world network effect than the eyes-closed state. The above results suggest that typical EEG features might be more clearly presented by applying the REST reference than by applying AR when using a 64-channel recording. PMID:29593490

Brain-computer interface for alertness estimation and improving

NASA Astrophysics Data System (ADS)

Hramov, Alexander; Maksimenko, Vladimir; Hramova, Marina

2018-02-01

Using wavelet analysis of the signals of electrical brain activity (EEG), we study the processes of neural activity, associated with perception of visual stimuli. We demonstrate that the brain can process visual stimuli in two scenarios: (i) perception is characterized by destruction of the alpha-waves and increase in the high-frequency (beta) activity, (ii) the beta-rhythm is not well pronounced, while the alpha-wave energy remains unchanged. The special experiments show that the motivation factor initiates the first scenario, explained by the increasing alertness. Based on the obtained results we build the brain-computer interface and demonstrate how the degree of the alertness can be estimated and controlled in real experiment.

High Resolution Topography of Age-Related Changes in Non-Rapid Eye Movement Sleep Electroencephalography

PubMed Central

Sprecher, Kate E.; Riedner, Brady A.; Smith, Richard F.; Tononi, Giulio; Davidson, Richard J.; Benca, Ruth M.

2016-01-01

Sleeping brain activity reflects brain anatomy and physiology. The aim of this study was to use high density (256 channel) electroencephalography (EEG) during sleep to characterize topographic changes in sleep EEG power across normal aging, with high spatial resolution. Sleep was evaluated in 92 healthy adults aged 18–65 years old using full polysomnography and high density EEG. After artifact removal, spectral power density was calculated for standard frequency bands for all channels, averaged across the NREM periods of the first 3 sleep cycles. To quantify topographic changes with age, maps were generated of the Pearson’s coefficient of the correlation between power and age at each electrode. Significant correlations were determined by statistical non-parametric mapping. Absolute slow wave power declined significantly with increasing age across the entire scalp, whereas declines in theta and sigma power were significant only in frontal regions. Power in fast spindle frequencies declined significantly with increasing age frontally, whereas absolute power of slow spindle frequencies showed no significant change with age. When EEG power was normalized across the scalp, a left centro-parietal region showed significantly less age-related decline in power than the rest of the scalp. This partial preservation was particularly significant in the slow wave and sigma bands. The effect of age on sleep EEG varies substantially by region and frequency band. This non-uniformity should inform the design of future investigations of aging and sleep. This study provides normative data on the effect of age on sleep EEG topography, and provides a basis from which to explore the mechanisms of normal aging as well as neurodegenerative disorders for which age is a risk factor. PMID:26901503

Estimating mental fatigue based on electroencephalogram and heart rate variability

NASA Astrophysics Data System (ADS)

Zhang, Chong; Yu, Xiaolin

2010-01-01

The effects of long term mental arithmetic task on psychology are investigated by subjective self-reporting measures and action performance test. Based on electroencephalogram (EEG) and heart rate variability (HRV), the impacts of prolonged cognitive activity on central nervous system and autonomic nervous system are observed and analyzed. Wavelet packet parameters of EEG and power spectral indices of HRV are combined to estimate the change of mental fatigue. Then wavelet packet parameters of EEG which change significantly are extracted as the features of brain activity in different mental fatigue state, support vector machine (SVM) algorithm is applied to differentiate two mental fatigue states. The experimental results show that long term mental arithmetic task induces the mental fatigue. The wavelet packet parameters of EEG and power spectral indices of HRV are strongly correlated with mental fatigue. The predominant activity of autonomic nervous system of subjects turns to the sympathetic activity from parasympathetic activity after the task. Moreover, the slow waves of EEG increase, the fast waves of EEG and the degree of disorder of brain decrease compared with the pre-task. The SVM algorithm can effectively differentiate two mental fatigue states, which achieves the maximum classification accuracy (91%). The SVM algorithm could be a promising tool for the evaluation of mental fatigue. Fatigue, especially mental fatigue, is a common phenomenon in modern life, is a persistent occupational hazard for professional. Mental fatigue is usually accompanied with a sense of weariness, reduced alertness, and reduced mental performance, which would lead the accidents in life, decrease productivity in workplace and harm the health. Therefore, the evaluation of mental fatigue is important for the occupational risk protection, productivity, and occupational health.

Can the Brain Be Trained? Comparing the Literature on the Use of EEG Biofeedback/Neurofeedback as an Alternative or Complementary Therapy for Attention Deficit Hyperactivity Disorder (ADHD)

ERIC Educational Resources Information Center

Stankus, Tony

2008-01-01

Psychologists, social workers, and school counselors are increasingly adding neurofeedback (NFT), a controversial alternative or complementary therapy to their treatment plans for patients with Attention Deficit Hyperactivity Disorder. NFT involves training the patient in self-regulation of brain wave patterns, employing a standard diagnostic…

Scale-Free Music of the Brain

PubMed Central

Wu, Dan; Li, Chao-Yi; Yao, De-Zhong

2009-01-01

Background There is growing interest in the relation between the brain and music. The appealing similarity between brainwaves and the rhythms of music has motivated many scientists to seek a connection between them. A variety of transferring rules has been utilized to convert the brainwaves into music; and most of them are mainly based on spectra feature of EEG. Methodology/Principal Findings In this study, audibly recognizable scale-free music was deduced from individual Electroencephalogram (EEG) waveforms. The translation rules include the direct mapping from the period of an EEG waveform to the duration of a note, the logarithmic mapping of the change of average power of EEG to music intensity according to the Fechner's law, and a scale-free based mapping from the amplitude of EEG to music pitch according to the power law. To show the actual effect, we applied the deduced sonification rules to EEG segments recorded during rapid-eye movement sleep (REM) and slow-wave sleep (SWS). The resulting music is vivid and different between the two mental states; the melody during REM sleep sounds fast and lively, whereas that in SWS sleep is slow and tranquil. 60 volunteers evaluated 25 music pieces, 10 from REM, 10 from SWS and 5 from white noise (WN), 74.3% experienced a happy emotion from REM and felt boring and drowsy when listening to SWS, and the average accuracy for all the music pieces identification is 86.8%(κ = 0.800, P<0.001). We also applied the method to the EEG data from eyes closed, eyes open and epileptic EEG, and the results showed these mental states can be identified by listeners. Conclusions/Significance The sonification rules may identify the mental states of the brain, which provide a real-time strategy for monitoring brain activities and are potentially useful to neurofeedback therapy. PMID:19526057

The Brain and Learning: Examining the Connection between Brain Activity, Spatial Intelligence, and Learning Outcomes in Online Visual Instruction

ERIC Educational Resources Information Center

Lee, Hyangsook

2013-01-01

The purpose of the study was to compare 2D and 3D visual presentation styles, both still frame and animation, on subjects' brain activity measured by the amplitude of EEG alpha wave and on their recall to see if alpha power and recall differ significantly by depth and movement of visual presentation style and by spatial intelligence. In addition,…

Real-Time Optical Diagnosis of the Rat Brain Exposed to a Laser-Induced Shock Wave: Observation of Spreading Depolarization, Vasoconstriction and Hypoxemia-Oligemia

PubMed Central

Sato, Shunichi; Kawauchi, Satoko; Okuda, Wataru; Nishidate, Izumi; Nawashiro, Hiroshi; Tsumatori, Gentaro

2014-01-01

Despite many efforts, the pathophysiology and mechanism of blast-induced traumatic brain injury (bTBI) have not yet been elucidated, partially due to the difficulty of real-time diagnosis and extremely complex factors determining the outcome. In this study, we topically applied a laser-induced shock wave (LISW) to the rat brain through the skull, for which real-time measurements of optical diffuse reflectance and electroencephalogram (EEG) were performed. Even under conditions showing no clear changes in systemic physiological parameters, the brain showed a drastic light scattering change accompanied by EEG suppression, which indicated the occurrence of spreading depression, long-lasting hypoxemia and signal change indicating mitochondrial energy impairment. Under the standard LISW conditions examined, hemorrhage and contusion were not apparent in the cortex. To investigate events associated with spreading depression, measurement of direct current (DC) potential, light scattering imaging and stereomicroscopic observation of blood vessels were also conducted for the brain. After LISW application, we observed a distinct negative shift in the DC potential, which temporally coincided with the transit of a light scattering wave, showing the occurrence of spreading depolarization and concomitant change in light scattering. Blood vessels in the brain surface initially showed vasodilatation for 3–4 min, which was followed by long-lasting vasoconstriction, corresponding to hypoxemia. Computer simulation based on the inverse Monte Carlo method showed that hemoglobin oxygen saturation declined to as low as ∼35% in the long-term hypoxemic phase. Overall, we found that topical application of a shock wave to the brain caused spreading depolarization/depression and prolonged severe hypoxemia-oligemia, which might lead to pathological conditions in the brain. Although further study is needed, our findings suggest that spreading depolarization/depression is one of the key events determining the outcome in bTBI. Furthermore, a rat exposed to an LISW(s) can be a reliable laboratory animal model for blast injury research. PMID:24416150

Night and day variations of sleep in patients with disorders of consciousness.

PubMed

Wislowska, Malgorzata; Del Giudice, Renata; Lechinger, Julia; Wielek, Tomasz; Heib, Dominik P J; Pitiot, Alain; Pichler, Gerald; Michitsch, Gabriele; Donis, Johann; Schabus, Manuel

2017-03-21

Brain injuries substantially change the entire landscape of oscillatory dynamics and render detection of typical sleep patterns difficult. Yet, sleep is characterized not only by specific EEG waveforms, but also by its circadian organization. In the present study we investigated whether brain dynamics of patients with disorders of consciousness systematically change between day and night. We recorded ~24 h EEG at the bedside of 18 patients diagnosed to be vigilant but unaware (Unresponsive Wakefulness Syndrome) and 17 patients revealing signs of fluctuating consciousness (Minimally Conscious State). The day-to-night changes in (i) spectral power, (ii) sleep-specific oscillatory patterns and (iii) signal complexity were analyzed and compared to 26 healthy control subjects. Surprisingly, the prevalence of sleep spindles and slow waves did not systematically vary between day and night in patients, whereas day-night changes in EEG power spectra and signal complexity were revealed in minimally conscious but not unaware patients.

BNDF heterozygosity is associated with memory deficits and alterations in cortical and hippocampal EEG power.

PubMed

Geist, Phillip A; Dulka, Brooke N; Barnes, Abigail; Totty, Michael; Datta, Subimal

2017-08-14

Brain derived neurotrophic factor (BDNF) plays a pivotal role in structural plasticity, learning, and memory. Electroencephalogram (EEG) spectral power in the cortex and hippocampus has also been correlated with learning and memory. In this study, we investigated the effect of globally reduced BDNF levels on learning behavior and EEG power via BDNF heterozygous (KO) rats. We employed several behavioral tests that are thought to depend on cortical and hippocampal plasticity to varying degrees: novel object recognition, a test that is reliant on a variety of cognitive systems; contextual fear, which is highly hippocampal-dependent; and cued fear, which has been shown to be amygdala-dependent. We also examined the effects of BDNF reduction on cortical and hippocampal EEG spectral power via chronically implanted electrodes in the motor cortex and dorsal hippocampus. We found that BDNF KO rats were impaired in novelty recognition and fear memory retention, while hippocampal EEG power was decreased in slow waves and increased in fast waves. Interestingly, our results, for the first time, show sexual dimorphism in each of our tests. These results support the hypothesis that BDNF drives both cognitive plasticity and coordinates EEG activity patterns, potentially serving as a link between the two. Copyright © 2017 Elsevier B.V. All rights reserved.

Electroencephalographic, behavioral and receptor binding correlates of phencyclinoids in the rat

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mattia, A.; Marquis, K.L.; Leccese, A.P.

1988-08-01

The pharmacology and structure-activity relationship of phencyclidine (PCP)-like drugs (phencyclinoids) were studied using electroencephalographic (EEG), behavioral and receptor binding techniques. The effects of PCP, 1-phenylcyclohexylamine HCl, N-methyl-1-phenycyclohexylamine HCl, N-ethyl-1-phenylcyclohexylamine HCl, N-(s-butyl)-1-phenylcyclohexylamine HCL, 1-(1-phenylcyclo-hexyl)-pyrrolidine HCl, 1-(1-(2-thienyl)cyclohexyl) piperidine HCl, 1-(1-(2-thienyl)cyclohexyl)-pyrrolidine HCl, ketamine and (+/-)-SKF 10047 were evaluated on the direct EEG and EEG spectra after acute i.v. injections (0.1-17.8 mg/kg). Similarities and differences were noted in the EEG dose-response curves. At lower doses of PCP and its analogs, low-amplitude theta waves predominated; however, at higher doses, high-amplitude, lower-frequency waves predominated. Qualitatively, the N-piperidine derivatives were similar to PCP and differed primarily inmore » potency. The benzomorphan (+/-)-SKF 10047 produced only theta activity at doses up to 12.8 mg/kg. These EEG effects occurred in conjunction with overt behaviors including locomotion, stereotypy and ataxia, concurrently assessed via observer-based rating scales. A strong correlation (r = 0.98) was obtained between the EEG and behavioral effects and the IC50 values from (/sup 3/H)PCP displacement experiments using crude rat brain homogenates.« less

Quantitative electroencephalographic changes due to middle cerebral artery occlusion by endothelin 1 in conscious rats.

PubMed

Moyanova, S; Kortenska, L; Kirov, R; Iliev, I

1998-12-01

The powerful vasoconstrictor peptide endothelin-1 (ET1) has been shown to reduce local cerebral blood flow in brain areas supplied by the middle cerebral artery (MCA) to a pathologically low level upon intracerebral injection adjacent to the MCA. This reduction manifests itself as an ischemic infarct, that is fully developed within 3 days after ET1 injection. The aim of the present study is to examine the effect of ET1 on electroencephalographic (EEG) activity. ET1 was microinjected unilaterally at a dose of 60 pmol in 3 microl of saline to the MCA in conscious rats. EEG signals were recorded from the frontoparietal cortical area, supplied by MCA, from the first up to the fourteenth day after ET1 injection. EEG activity was analyzed by the fast Fourier transformation. A significant shift to a lower EEG frequency, i.e., augmentation of slow waves and a reduction of alpha-like and faster EEG waves was found post-ET1. This effect was maximal after 3-7 days when the most severe destruction of neurons in this cortical area occurs, as has been previously demonstrated. The results suggest that the quantitative EEG analysis may provide useful additional information about the functional disturbances associated with focal cerebral ischemia.

Developmental trajectories of EEG sleep slow wave activity as a marker for motor skill development during adolescence: a pilot study.

PubMed

Lustenberger, Caroline; Mouthon, Anne-Laure; Tesler, Noemi; Kurth, Salome; Ringli, Maya; Buchmann, Andreas; Jenni, Oskar G; Huber, Reto

2017-01-01

Reliable markers for brain maturation are important to identify neural deviations that eventually predict the development of mental illnesses. Recent studies have proposed topographical EEG-derived slow wave activity (SWA) during NREM sleep as a mirror of cortical development. However, studies about the longitudinal stability as well as the relationship with behavioral skills are needed before SWA topography may be considered such a reliable marker. We examined six subjects longitudinally (over 5.1 years) using high-density EEG and a visuomotor learning task. All subjects showed a steady increase of SWA at a frontal electrode and a decrease in central electrodes. Despite these large changes in EEG power, SWA topography was relatively stable within each subject during development indicating individual trait-like characteristics. Moreover, the SWA changes in the central cluster were related to the development of specific visuomotor skills. Taken together with the previous work in this domain, our results suggest that EEG sleep SWA represents a marker for motor skill development and further supports the idea that SWA mirrors cortical development during childhood and adolescence. © 2016 Wiley Periodicals, Inc.

Soft drink effects on sensorimotor rhythm brain computer interface performance and resting-state spectral power.

PubMed

Mundahl, John; Jianjun Meng; He, Jeffrey; Bin He

2016-08-01

Brain-computer interface (BCI) systems allow users to directly control computers and other machines by modulating their brain waves. In the present study, we investigated the effect of soft drinks on resting state (RS) EEG signals and BCI control. Eight healthy human volunteers each participated in three sessions of BCI cursor tasks and resting state EEG. During each session, the subjects drank an unlabeled soft drink with either sugar, caffeine, or neither ingredient. A comparison of resting state spectral power shows a substantial decrease in alpha and beta power after caffeine consumption relative to control. Despite attenuation of the frequency range used for the control signal, caffeine average BCI performance was the same as control. Our work provides a useful characterization of caffeine, the world's most popular stimulant, on brain signal frequencies and their effect on BCI performance.

EEG Functional Connectivity Prior to Sleepwalking: Evidence of Interplay Between Sleep and Wakefulness

PubMed Central

Desjardins, Marie-Ève; Carrier, Julie; Lina, Jean-Marc; Fortin, Maxime; Gosselin, Nadia; Montplaisir, Jacques

2017-01-01

Abstract Study Objectives: Although sleepwalking (somnambulism) affects up to 4% of adults, its pathophysiology remains poorly understood. Sleepwalking can be preceded by fluctuations in slow-wave sleep EEG signals, but the significance of these pre-episode changes remains unknown and methods based on EEG functional connectivity have yet to be used to better comprehend the disorder. Methods: We investigated the sleep EEG of 27 adult sleepwalkers (mean age: 29 ± 7.6 years) who experienced a somnambulistic episode during slow-wave sleep. The 20-second segment of sleep EEG immediately preceding each patient’s episode was compared with the 20-second segment occurring 2 minutes prior to episode onset. Results: Results from spectral analyses revealed increased delta and theta spectral power in the 20 seconds preceding the episodes’ onset as compared to the 20 seconds occurring 2 minutes before the episodes. The imaginary part of the coherence immediately prior to episode onset revealed (1) decreased delta EEG functional connectivity in parietal and occipital regions, (2) increased alpha connectivity over a fronto-parietal network, and (3) increased beta connectivity involving symmetric inter-hemispheric networks implicating frontotemporal, parietal and occipital areas. Conclusions: Taken together, these modifications in EEG functional connectivity suggest that somnambulistic episodes are preceded by brain processes characterized by the co-existence of arousal and deep sleep. PMID:28204773

Brain waves-based index for workload estimation and mental effort engagement recognition

NASA Astrophysics Data System (ADS)

Zammouri, A.; Chraa-Mesbahi, S.; Ait Moussa, A.; Zerouali, S.; Sahnoun, M.; Tairi, H.; Mahraz, A. M.

2017-10-01

The advent of the communication systems and considering the complexity that some impose in their use, it is necessary to incorporate and equip these systems with a certain intelligence which takes into account the cognitive and mental capacities of the human operator. In this work, we address the issue of estimating the mental effort of an operator according to the cognitive tasks difficulty levels. Based on the Electroencephalogram (EEG) measurements, the proposed approach analyzes the user’s brain activity from different brain regions while performing cognitive tasks with several levels of difficulty. At a first time, we propose a variances comparison-based classifier (VCC) that makes use of the Power Spectral Density (PSD) of the EEG signal. The aim of using such a classifier is to highlight the brain regions that enter into interaction according to the cognitive task difficulty. In a second time, we present and describe a new EEG-based index for the estimation of mental efforts. The designed index is based on information recorded from two EEG channels. Results from the VCC demonstrate that powers of the Theta [4-7 Hz] (θ) and Alpha [8-12 Hz] (α) oscillations decrease while increasing the cognitive task difficulty. These decreases are mainly located in parietal and temporal brain regions. Based on the Kappa coefficients, decisions of the introduced index are compared to those obtained from an existing index. This performance assessment method revealed strong agreements. Hence the efficiency of the introduced index.

EEG slow-wave coherence changes in propofol-induced general anesthesia: experiment and theory

PubMed Central

Wang, Kaier; Steyn-Ross, Moira L.; Steyn-Ross, D. A.; Wilson, Marcus T.; Sleigh, Jamie W.

2014-01-01

The electroencephalogram (EEG) patterns recorded during general anesthetic-induced coma are closely similar to those seen during slow-wave sleep, the deepest stage of natural sleep; both states show patterns dominated by large amplitude slow waves. Slow oscillations are believed to be important for memory consolidation during natural sleep. Tracking the emergence of slow-wave oscillations during transition to unconsciousness may help us to identify drug-induced alterations of the underlying brain state, and provide insight into the mechanisms of general anesthesia. Although cellular-based mechanisms have been proposed, the origin of the slow oscillation has not yet been unambiguously established. A recent theoretical study by Steyn-Ross et al. (2013) proposes that the slow oscillation is a network, rather than cellular phenomenon. Modeling anesthesia as a moderate reduction in gap-junction interneuronal coupling, they predict an unconscious state signposted by emergent low-frequency oscillations with chaotic dynamics in space and time. They suggest that anesthetic slow-waves arise from a competitive interaction between symmetry-breaking instabilities in space (Turing) and time (Hopf), modulated by gap-junction coupling strength. A significant prediction of their model is that EEG phase coherence will decrease as the cortex transits from Turing–Hopf balance (wake) to Hopf-dominated chaotic slow-waves (unconsciousness). Here, we investigate changes in phase coherence during induction of general anesthesia. After examining 128-channel EEG traces recorded from five volunteers undergoing propofol anesthesia, we report a significant drop in sub-delta band (0.05–1.5 Hz) slow-wave coherence between frontal, occipital, and frontal–occipital electrode pairs, with the most pronounced wake-vs.-unconscious coherence changes occurring at the frontal cortex. PMID:25400558

Blood-Brain Barrier Breakdown Following Traumatic Brain Injury: A Possible Role in Posttraumatic Epilepsy

PubMed Central

Tomkins, Oren; Feintuch, Akiva; Benifla, Moni; Cohen, Avi; Friedman, Alon; Shelef, Ilan

2011-01-01

Recent animal experiments indicate a critical role for opening of the blood-brain barrier (BBB) in the pathogenesis of post-traumatic epilepsy (PTE). This study aimed to investigate the frequency, extent, and functional correlates of BBB disruption in epileptic patients following mild traumatic brain injury (TBI). Thirty-seven TBI patients were included in this study, 19 of whom suffered from PTE. All underwent electroencephalographic (EEG) recordings and brain magnetic resonance imaging (bMRI). bMRIs were evaluated for BBB disruption using novel quantitative techniques. Cortical dysfunction was localized using standardized low-resolution brain electromagnetic tomography (sLORETA). TBI patients displayed significant EEG slowing compared to controls with no significant differences between PTE and nonepileptic patients. BBB disruption was found in 82.4% of PTE compared to 25% of non-epileptic patients (P = .001) and could be observed even years following the trauma. The volume of cerebral cortex with BBB disruption was significantly larger in PTE patients (P = .001). Slow wave EEG activity was localized to the same region of BBB disruption in 70% of patients and correlated to the volume of BBB disrupted cortex. We finally present a patient suffering from early cortical dysfunction and BBB breakdown with a gradual and parallel resolution of both pathologies. Our findings demonstrate that BBB pathology is frequently found following mild TBI. Lasting BBB breakdown is found with increased frequency and extent in PTE patients. Based on recent animal studies and the colocalization found between the region of disrupted BBB and abnormal EEG activity, we suggest a role for a vascular lesion in the pathogenesis of PTE. PMID:21436875

A Study on Analysis of EEG Caused by Grating Stimulation Imaging

NASA Astrophysics Data System (ADS)

Urakawa, Hiroshi; Nishimura, Toshihiro; Tsubai, Masayoshi; Itoh, Kenji

Recently, many researchers have studied a visual perception. Focus is attended to studies of the visual perception phenomenon by using the grating stimulation images. The previous researches have suggested that a subset of retinal ganglion cells responds to motion in the receptive field center, but only if the wider surround moves with a different trajectory. We discuss the function of human retina, and measure and analysis EEG(electroencephalography) of a normal subject who looks on grating stimulation images. We confirmed the visual perception of human by EEG signal analysis. We also have obtained that a sinusoidal grating stimulation was given, asymmetry was observed the α wave element in EEG of the symmetric part in a left hemisphere and a right hemisphere of the brain. Therefore, it is presumed that projected image is even when the still picture is seen and the image projected onto retinas of right and left eyes is not even for the dynamic scene. It evaluated it by taking the envelope curve for the detected α wave, and using the average and standard deviation.

Understanding the pathophysiology of reflex epilepsy using simultaneous EEG-fMRI.

PubMed

Sandhya, Manglore; Bharath, Rose Dawn; Panda, Rajanikant; Chandra, S R; Kumar, Naveen; George, Lija; Thamodharan, A; Gupta, Arun Kumar; Satishchandra, P

2014-03-01

Measuring neuro-haemodynamic correlates in the brain of epilepsy patients using EEG-fMRI has opened new avenues in clinical neuroscience, as these are two complementary methods for understanding brain function. In this study, we investigated three patients with drug-resistant reflex epilepsy using EEG-fMRI. Different types of reflex epilepsy such as eating, startle myoclonus, and hot water epilepsy were included in the study. The analysis of EEG-fMRI data was based on the visual identification of interictal epileptiform discharges on scalp EEG. The convolution of onset time and duration of these epilepsy spikes was estimated, and using these condition-specific effects in a general linear model approach, we evaluated activation of fMRI. Patients with startle myoclonus epilepsy experienced epilepsy in response to sudden sound or touch, in association with increased delta and theta activity with a spike-and-slow-wave pattern of interictal epileptiform discharges on EEG and fronto-parietal network activation pattern on SPECT and EEG-fMRI. Eating epilepsy was triggered by sight or smell of food and fronto-temporal discharges were noted on video-EEG (VEEG). Similarly, fronto-temporo-parietal involvement was noted on SPECT and EEG-fMRI. Hot water epilepsy was triggered by contact with hot water either in the bath or by hand immersion, and VEEG showed fronto-parietal involvement. SPECT and EEG fMRI revealed a similar fronto-parietal-occipital involvement. From these results, we conclude that continuous EEG recording can improve the modelling of BOLD changes related to interictal epileptic activity and this can thus be used to understand the neuro-haemodynamic substrates involved in reflex epilepsy.

The antinociceptive effects of Monechma ciliatum and changes in EEG waves following oral and intrathecal administration in rats

NASA Astrophysics Data System (ADS)

Meraiyebu, Ajibola B.; Adelaiye, Alexander B.; O, Odeh S.

2010-02-01

The research work was carried out to study the effect of Oral and Intrathecal Monechma Ciliatum on antinociception and EEG readings in Wistar Rats. Traditionally the extract is given to women in labour believed to reduce pain and ease parturition, though past works show that it has oesteogenic and oxytotic effects. The rats were divided into 5 major groups. Group 1 served as oral control group while groups 2 and 3 served as oral experimental groups and were treated with 500mg/kg and 1000mg/kg monechma ciliatum respectively. Group 4 served as intrathecal control group treated with intrathecal dextrose and group 5 received 1000mg/kg Monechma Ciliatrum intrathecally. The antinociceptive effect was analysed using a Von Frey's aesthesiometer. Monechma Ciliatum showed significant antinociceptive effect both orally and intrathecally, although it had a greater effect orally and during the first 15 minutes of intrathecal administration. EEG readings were also taken for all the groups and there was a decrease in amplitude and an increase in frequency for high dose (1000mg/ml) experimental groups and the mid brain electrodes produced a change from theta waves (3.5 - 7 waves per second) to alpha waves (7.5 - 13 waves per second) as seen in relaxed persons and caused decreased amplitudes and change in distribution seen in beta waves. Properties similarly accentuated by sedativehypnotic drugs.

Comment on the Nanoparticle Conclusions in Crüts et al. (2008), "Exposure to diesel exhaust induces changes in EEG in human volunteers"

PubMed Central

Valberg, Peter A; Long, Christopher M; Hesterberg, Thomas W

2008-01-01

A recent publication in this journal reported interesting changes in electroencephalographic (EEG) waves that occurred in 10 young, male volunteers following inhalation for one hour of elevated levels of diesel-engine exhaust fumes [1]. The authors then proposed a chain of causal events that they hypothesized underlay their observed EEG changes. Their reasoning linked the observed results to nanoparticles in diesel-engine exhaust (DEE), and went on to suggest that associations between changes in ambient particulate matter (PM) levels and changes in health statistics might be due to the effects of diesel-engine exhaust (DEE) nanoparticles on EEG. We suggest that the extrapolations of the Crüts et al. EEG findings to casual mechanisms about how ambient levels of DEE particulate might affect electrical signals in the brain, and subsequently to how DEE particulate might alter disease risk, are premature. PMID:18652692

High-accuracy user identification using EEG biometrics.

PubMed

Koike-Akino, Toshiaki; Mahajan, Ruhi; Marks, Tim K; Ye Wang; Watanabe, Shinji; Tuzel, Oncel; Orlik, Philip

2016-08-01

We analyze brain waves acquired through a consumer-grade EEG device to investigate its capabilities for user identification and authentication. First, we show the statistical significance of the P300 component in event-related potential (ERP) data from 14-channel EEGs across 25 subjects. We then apply a variety of machine learning techniques, comparing the user identification performance of various different combinations of a dimensionality reduction technique followed by a classification algorithm. Experimental results show that an identification accuracy of 72% can be achieved using only a single 800 ms ERP epoch. In addition, we demonstrate that the user identification accuracy can be significantly improved to more than 96.7% by joint classification of multiple epochs.

[Time-organization of EEG patterns' structure in anxiety and phobic disorders].

PubMed

Sviatogor, I A; Mokhovikova, I A

2005-01-01

Thirty-five patients, aged 19-48 years (mean age 38 years) with anxiety and phobic disorders were examined. According to ICD-10 criteria--social phobia (F40.1), panic disorder (F41.0), somatoform autonomic dysfunction (F45.3) were diagnosed. Using electroencephalography data, qualitative and quantitative characteristics of the time- and spatial-organization of brain EEG activity in anxiety and phobic disorders of different severity were established. It were determined 4 types of wave interactions between EEG components, which reflected a different extent of the regulatory mechanisms lesions: 2 structures with one core component (alpha or beta), a structure with two core components and a non-organized structure.

The Antiepileptic Drug Levetiracetam Suppresses Non-Convulsive Seizure Activity and Reduces Ischemic Brain Damage in Rats Subjected to Permanent Middle Cerebral Artery Occlusion

PubMed Central

Cuomo, Ornella; Rispoli, Vincenzo; Leo, Antonio; Politi, Giovanni Bosco; Vinciguerra, Antonio; di Renzo, Gianfranco; Cataldi, Mauro

2013-01-01

The antiepileptic drug Levetiracetam (Lev) has neuroprotective properties in experimental stroke, cerebral hemorrhage and neurotrauma. In these conditions, non-convulsive seizures (NCSs) propagate from the core of the focal lesion into perilesional tissue, enlarging the damaged area and promoting epileptogenesis. Here, we explore whether Lev neuroprotective effect is accompanied by changes in NCS generation or propagation. In particular, we performed continuous EEG recordings before and after the permanent occlusion of the middle cerebral artery (pMCAO) in rats that received Lev (100 mg/kg) or its vehicle immediately before surgery. Both in Lev-treated and in control rats, EEG activity was suppressed after pMCAO. In control but not in Lev-treated rats, EEG activity reappeared approximately 30-45 min after pMCAO. It initially consisted in single spikes and, then, evolved into spike-and-wave and polyspike-and-wave discharges. In Lev-treated rats, only rare spike events were observed and the EEG power was significantly smaller than in controls. Approximately 24 hours after pMCAO, EEG activity increased in Lev-treated rats because of the appearance of polyspike events whose power was, however, significantly smaller than in controls. In rats sacrificed 24 hours after pMCAO, the ischemic lesion was approximately 50% smaller in Lev-treated than in control rats. A similar neuroprotection was observed in rats sacrificed 72 hours after pMCAO. In conclusion, in rats subjected to pMCAO, a single Lev injection suppresses NCS occurrence for at least 24 hours. This electrophysiological effect could explain the long lasting reduction of ischemic brain damage caused by this drug. PMID:24236205

EEG Functional Connectivity Prior to Sleepwalking: Evidence of Interplay Between Sleep and Wakefulness.

PubMed

Desjardins, Marie-Ève; Carrier, Julie; Lina, Jean-Marc; Fortin, Maxime; Gosselin, Nadia; Montplaisir, Jacques; Zadra, Antonio

2017-04-01

Although sleepwalking (somnambulism) affects up to 4% of adults, its pathophysiology remains poorly understood. Sleepwalking can be preceded by fluctuations in slow-wave sleep EEG signals, but the significance of these pre-episode changes remains unknown and methods based on EEG functional connectivity have yet to be used to better comprehend the disorder. We investigated the sleep EEG of 27 adult sleepwalkers (mean age: 29 ± 7.6 years) who experienced a somnambulistic episode during slow-wave sleep. The 20-second segment of sleep EEG immediately preceding each patient's episode was compared with the 20-second segment occurring 2 minutes prior to episode onset. Results from spectral analyses revealed increased delta and theta spectral power in the 20 seconds preceding the episodes' onset as compared to the 20 seconds occurring 2 minutes before the episodes. The imaginary part of the coherence immediately prior to episode onset revealed (1) decreased delta EEG functional connectivity in parietal and occipital regions, (2) increased alpha connectivity over a fronto-parietal network, and (3) increased beta connectivity involving symmetric inter-hemispheric networks implicating frontotemporal, parietal and occipital areas. Taken together, these modifications in EEG functional connectivity suggest that somnambulistic episodes are preceded by brain processes characterized by the co-existence of arousal and deep sleep. © Sleep Research Society 2017. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities.

PubMed

Sik, Hin Hung; Gao, Junling; Fan, Jicong; Wu, Bonnie Wai Yan; Leung, Hang Kin; Hung, Yeung Sam

2017-05-10

In both the East and West, traditional teachings say that the mind and heart are somehow closely correlated, especially during spiritual practice. One difficulty in proving this objectively is that the natures of brain and heart activities are quite different. In this paper, we propose a methodology that uses wavelet entropy to measure the chaotic levels of both electroencephalogram (EEG) and electrocardiogram (ECG) data and show how this may be used to explore the potential coordination between the mind and heart under different experimental conditions. Furthermore, Statistical Parametric Mapping (SPM) was used to identify the brain regions in which the EEG wavelet entropy was the most affected by the experimental conditions. As an illustration, the EEG and ECG were recorded under two different conditions (normal rest and mindful breathing) at the beginning of an 8-week standard Mindfulness-based Stress Reduction (MBSR) training course (pretest) and after the course (posttest). Using the proposed method, the results consistently showed that the wavelet entropy of the brain EEG decreased during the MBSR mindful breathing state as compared to that during the closed-eye resting state. Similarly, a lower wavelet entropy of heartrate was found during MBSR mindful breathing. However, no difference in wavelet entropy during MBSR mindful breathing was found between the pretest and posttest. No correlation was observed between the entropy of brain waves and the entropy of heartrate during normal rest in all participants, whereas a significant correlation was observed during MBSR mindful breathing. Additionally, the most well-correlated brain regions were located in the central areas of the brain. This study provides a methodology for the establishment of evidence that mindfulness practice (i.e., mindful breathing) may increase the coordination between mind and heart activities.

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

PubMed Central

Sik, Hin Hung; Gao, Junling; Fan, Jicong; Wu, Bonnie Wai Yan; Leung, Hang Kin; Hung, Yeung Sam

2017-01-01

In both the East and West, traditional teachings say that the mind and heart are somehow closely correlated, especially during spiritual practice. One difficulty in proving this objectively is that the natures of brain and heart activities are quite different. In this paper, we propose a methodology that uses wavelet entropy to measure the chaotic levels of both electroencephalogram (EEG) and electrocardiogram (ECG) data and show how this may be used to explore the potential coordination between the mind and heart under different experimental conditions. Furthermore, Statistical Parametric Mapping (SPM) was used to identify the brain regions in which the EEG wavelet entropy was the most affected by the experimental conditions. As an illustration, the EEG and ECG were recorded under two different conditions (normal rest and mindful breathing) at the beginning of an 8-week standard Mindfulness-based Stress Reduction (MBSR) training course (pretest) and after the course (posttest). Using the proposed method, the results consistently showed that the wavelet entropy of the brain EEG decreased during the MBSR mindful breathing state as compared to that during the closed-eye resting state. Similarly, a lower wavelet entropy of heartrate was found during MBSR mindful breathing. However, no difference in wavelet entropy during MBSR mindful breathing was found between the pretest and posttest. No correlation was observed between the entropy of brain waves and the entropy of heartrate during normal rest in all participants, whereas a significant correlation was observed during MBSR mindful breathing. Additionally, the most well-correlated brain regions were located in the central areas of the brain. This study provides a methodology for the establishment of evidence that mindfulness practice (i.e., mindful breathing) may increase the coordination between mind and heart activities. PMID:28518101

Processing of emotional words measured simultaneously with steady-state visually evoked potentials and near-infrared diffusing-wave spectroscopy.

PubMed

Koban, Leonie; Ninck, Markus; Li, Jun; Gisler, Thomas; Kissler, Johanna

2010-07-27

Emotional stimuli are preferentially processed compared to neutral ones. Measuring the magnetic resonance blood-oxygen level dependent (BOLD) response or EEG event-related potentials, this has also been demonstrated for emotional versus neutral words. However, it is currently unclear whether emotion effects in word processing can also be detected with other measures such as EEG steady-state visual evoked potentials (SSVEPs) or optical brain imaging techniques. In the present study, we simultaneously performed SSVEP measurements and near-infrared diffusing-wave spectroscopy (DWS), a new optical technique for the non-invasive measurement of brain function, to measure brain responses to neutral, pleasant, and unpleasant nouns flickering at a frequency of 7.5 Hz. The power of the SSVEP signal was significantly modulated by the words' emotional content at occipital electrodes, showing reduced SSVEP power during stimulation with pleasant compared to neutral nouns. By contrast, the DWS signal measured over the visual cortex showed significant differences between stimulation with flickering words and baseline periods, but no modulation in response to the words' emotional significance. This study is the first investigation of brain responses to emotional words using simultaneous measurements of SSVEPs and DWS. Emotional modulation of word processing was detected with EEG SSVEPs, but not by DWS. SSVEP power for emotional, specifically pleasant, compared to neutral words was reduced, which contrasts with previous results obtained when presenting emotional pictures. This appears to reflect processing differences between symbolic and pictorial emotional stimuli. While pictures prompt sustained perceptual processing, decoding the significance of emotional words requires more internal associative processing. Reasons for an absence of emotion effects in the DWS signal are discussed.

The Effects of Fifa 2015 Computer Games on Changes in Cognitive, Hormonal and Brain Waves Functions of Young Men Volunteers

PubMed Central

Aliyari, Hamed; Kazemi, Masoomeh; Tekieh, Elaheh; Salehi, Maryam; Sahraei, Hedayat; Daliri, Mohammad Reza; Agaei, Hassan; Minaei-Bidgoli, Behrouz; Lashgari, Reza; Srahian, Nahid; Hadipour, Mohammad Mehdi; Salehi, Mostafa; Ranjbar Aghdam, Asghar

2015-01-01

Introduction: Computer games have attracted remarkable attentions in general publics with different cultures and their effects are subject of research by cognitive neuroscientists. In the present study, possible effects of the game Fifa 2015 on cognitive performance, hormonal levels, and electroencephalographic (EEG) signals were evaluated in young male volunteers. Methods: Thirty two subjects aged 20 years on average participated mutually in playing computer game Fifa 2015. Identification information and general knowledge about the game were collected. Saliva samples from the contestants were obtained before and after the competition. Perceptive and cognitive performance including the general cognitive health, response delay, attention maintenance, and mental fatigue were measured using PASAT test. EEG were recorded during the play using EEG device and analyzed later using QEEG. Simultaneously, the players’ behavior were recorded using a video camera. Saliva cortisol levels were assessed by ELISA kit. Data were analyzed by SPSS program. Results: The impact of playing computer games on cortisol concentration of saliva before and after the game showed that the amount of saliva plasma after playing the game has dropped significantly. Also the impact of playing computer games on mental health, before and after the game indicated that the number of correct answers has not changed significantly. This indicates that sustained attention has increased in participants after the game in comparison with before that. Also it is shown that mental fatigue measured by PASAT test, did not changed significantly after the game in comparison to before that. The impact of game on changes in brain waves showed that the subjects in high activity state during playing the game had higher power of the EEG signals in most of the channels in lower frequency bands in compared to normal state. Discussion: The present study showed that computer games can positively affect the stress system and the perceptual-cognitive system. Even though this impact was not significant in most cases, the changes in cognitive and hormonal test and also in brain waves were visible. Hence, due to the importance of this matter, it is necessary to create control systems in selecting the types of games for playing. PMID:26904177

The Effects of Fifa 2015 Computer Games on Changes in Cognitive, Hormonal and Brain Waves Functions of Young Men Volunteers.

PubMed

Aliyari, Hamed; Kazemi, Masoomeh; Tekieh, Elaheh; Salehi, Maryam; Sahraei, Hedayat; Daliri, Mohammad Reza; Agaei, Hassan; Minaei-Bidgoli, Behrouz; Lashgari, Reza; Srahian, Nahid; Hadipour, Mohammad Mehdi; Salehi, Mostafa; Ranjbar Aghdam, Asghar

2015-07-01

Computer games have attracted remarkable attentions in general publics with different cultures and their effects are subject of research by cognitive neuroscientists. In the present study, possible effects of the game Fifa 2015 on cognitive performance, hormonal levels, and electroencephalographic (EEG) signals were evaluated in young male volunteers. Thirty two subjects aged 20 years on average participated mutually in playing computer game Fifa 2015. Identification information and general knowledge about the game were collected. Saliva samples from the contestants were obtained before and after the competition. Perceptive and cognitive performance including the general cognitive health, response delay, attention maintenance, and mental fatigue were measured using PASAT test. EEG were recorded during the play using EEG device and analyzed later using QEEG. Simultaneously, the players' behavior were recorded using a video camera. Saliva cortisol levels were assessed by ELISA kit. Data were analyzed by SPSS program. The impact of playing computer games on cortisol concentration of saliva before and after the game showed that the amount of saliva plasma after playing the game has dropped significantly. Also the impact of playing computer games on mental health, before and after the game indicated that the number of correct answers has not changed significantly. This indicates that sustained attention has increased in participants after the game in comparison with before that. Also it is shown that mental fatigue measured by PASAT test, did not changed significantly after the game in comparison to before that. The impact of game on changes in brain waves showed that the subjects in high activity state during playing the game had higher power of the EEG signals in most of the channels in lower frequency bands in compared to normal state. The present study showed that computer games can positively affect the stress system and the perceptual-cognitive system. Even though this impact was not significant in most cases, the changes in cognitive and hormonal test and also in brain waves were visible. Hence, due to the importance of this matter, it is necessary to create control systems in selecting the types of games for playing.

Old Brains Come Uncoupled in Sleep: Slow Wave-Spindle Synchrony, Brain Atrophy, and Forgetting.

PubMed

Helfrich, Randolph F; Mander, Bryce A; Jagust, William J; Knight, Robert T; Walker, Matthew P

2018-01-03

The coupled interaction between slow-wave oscillations and sleep spindles during non-rapid-eye-movement (NREM) sleep has been proposed to support memory consolidation. However, little evidence in humans supports this theory. Moreover, whether such dynamic coupling is impaired as a consequence of brain aging in later life, contributing to cognitive and memory decline, is unknown. Combining electroencephalography (EEG), structural MRI, and sleep-dependent memory assessment, we addressed these questions in cognitively normal young and older adults. Directional cross-frequency coupling analyses demonstrated that the slow wave governs a precise temporal coordination of sleep spindles, the quality of which predicts overnight memory retention. Moreover, selective atrophy within the medial frontal cortex in older adults predicted a temporal dispersion of this slow wave-spindle coupling, impairing overnight memory consolidation and leading to forgetting. Prefrontal-dependent deficits in the spatiotemporal coordination of NREM sleep oscillations therefore represent one pathway explaining age-related memory decline. Copyright © 2017 Elsevier Inc. All rights reserved.

Effect of low-level laser stimulation on EEG.

PubMed

Wu, Jih-Huah; Chang, Wen-Dien; Hsieh, Chang-Wei; Jiang, Joe-Air; Fang, Wei; Shan, Yi-Chia; Chang, Yang-Chyuan

2012-01-01

Conventional laser stimulation at the acupoint can induce significant brain activation, and the activation is theoretically conveyed by the sensory afferents. Whether the insensible low-level Laser stimulation outside the acupoint could also evoke electroencephalographic (EEG) changes is not known. We designed a low-level laser array stimulator (6 pcs laser diode, wavelength 830 nm, output power 7 mW, and operation frequency 10 Hz) to deliver insensible laser stimulations to the palm. EEG activities before, during, and after the laser stimulation were collected. The amplitude powers of each EEG frequency band were analyzed. We found that the low-level laser stimulation was able to increase the power of alpha rhythms and theta waves, mainly in the posterior head regions. These effects lasted at least 15 minutes after cessation of the laser stimulation. The amplitude power of beta activities in the anterior head regions decreased after laser stimulation. We thought these EEG changes comparable to those in meditation.

EEG Alpha and Beta Activity in Normal and Deaf Subjects.

ERIC Educational Resources Information Center

Waldron, Manjula; And Others

Electroencephalogram and task performance data were collected from three groups of young adult males: profoundly deaf Ss who signed from an early age, profoundly deaf Ss who only used oral (speech and speedreading) methods of communication, and normal hearing Ss. Alpha and Beta brain wave patterns over the Wernicke's area were compared across…

Designing EEG Neurofeedback Procedures to Enhance Open-Ended versus Closed-Ended Creative Potentials

ERIC Educational Resources Information Center

Lin, Wei-Lun; Shih, Yi-Ling

2016-01-01

Recent empirical evidence demonstrated that open-ended creativity (which refers to creativity measures that require various and numerous responses, such as divergent thinking) correlated with alpha brain wave activation, whereas closed-ended creativity (which refers to creativity measures that ask for one final correct answer, such as insight…

EEG Negativity in Fixations Used for Gaze-Based Control: Toward Converting Intentions into Actions with an Eye-Brain-Computer Interface

PubMed Central

Shishkin, Sergei L.; Nuzhdin, Yuri O.; Svirin, Evgeny P.; Trofimov, Alexander G.; Fedorova, Anastasia A.; Kozyrskiy, Bogdan L.; Velichkovsky, Boris M.

2016-01-01

We usually look at an object when we are going to manipulate it. Thus, eye tracking can be used to communicate intended actions. An effective human-machine interface, however, should be able to differentiate intentional and spontaneous eye movements. We report an electroencephalogram (EEG) marker that differentiates gaze fixations used for control from spontaneous fixations involved in visual exploration. Eight healthy participants played a game with their eye movements only. Their gaze-synchronized EEG data (fixation-related potentials, FRPs) were collected during game's control-on and control-off conditions. A slow negative wave with a maximum in the parietooccipital region was present in each participant's averaged FRPs in the control-on conditions and was absent or had much lower amplitude in the control-off condition. This wave was similar but not identical to stimulus-preceding negativity, a slow negative wave that can be observed during feedback expectation. Classification of intentional vs. spontaneous fixations was based on amplitude features from 13 EEG channels using 300 ms length segments free from electrooculogram contamination (200–500 ms relative to the fixation onset). For the first fixations in the fixation triplets required to make moves in the game, classified against control-off data, a committee of greedy classifiers provided 0.90 ± 0.07 specificity and 0.38 ± 0.14 sensitivity. Similar (slightly lower) results were obtained for the shrinkage Linear Discriminate Analysis (LDA) classifier. The second and third fixations in the triplets were classified at lower rate. We expect that, with improved feature sets and classifiers, a hybrid dwell-based Eye-Brain-Computer Interface (EBCI) can be built using the FRP difference between the intended and spontaneous fixations. If this direction of BCI development will be successful, such a multimodal interface may improve the fluency of interaction and can possibly become the basis for a new input device for paralyzed and healthy users, the EBCI “Wish Mouse.” PMID:27917105

Electroencephalography Amplitude Modulation Analysis for Automated Affective Tagging of Music Video Clips

PubMed Central

Clerico, Andrea; Tiwari, Abhishek; Gupta, Rishabh; Jayaraman, Srinivasan; Falk, Tiago H.

2018-01-01

The quantity of music content is rapidly increasing and automated affective tagging of music video clips can enable the development of intelligent retrieval, music recommendation, automatic playlist generators, and music browsing interfaces tuned to the users' current desires, preferences, or affective states. To achieve this goal, the field of affective computing has emerged, in particular the development of so-called affective brain-computer interfaces, which measure the user's affective state directly from measured brain waves using non-invasive tools, such as electroencephalography (EEG). Typically, conventional features extracted from the EEG signal have been used, such as frequency subband powers and/or inter-hemispheric power asymmetry indices. More recently, the coupling between EEG and peripheral physiological signals, such as the galvanic skin response (GSR), have also been proposed. Here, we show the importance of EEG amplitude modulations and propose several new features that measure the amplitude-amplitude cross-frequency coupling per EEG electrode, as well as linear and non-linear connections between multiple electrode pairs. When tested on a publicly available dataset of music video clips tagged with subjective affective ratings, support vector classifiers trained on the proposed features were shown to outperform those trained on conventional benchmark EEG features by as much as 6, 20, 8, and 7% for arousal, valence, dominance and liking, respectively. Moreover, fusion of the proposed features with EEG-GSR coupling features showed to be particularly useful for arousal (feature-level fusion) and liking (decision-level fusion) prediction. Together, these findings show the importance of the proposed features to characterize human affective states during music clip watching. PMID:29367844

Visual brain activity patterns classification with simultaneous EEG-fMRI: A multimodal approach.

PubMed

Ahmad, Rana Fayyaz; Malik, Aamir Saeed; Kamel, Nidal; Reza, Faruque; Amin, Hafeez Ullah; Hussain, Muhammad

2017-01-01

Classification of the visual information from the brain activity data is a challenging task. Many studies reported in the literature are based on the brain activity patterns using either fMRI or EEG/MEG only. EEG and fMRI considered as two complementary neuroimaging modalities in terms of their temporal and spatial resolution to map the brain activity. For getting a high spatial and temporal resolution of the brain at the same time, simultaneous EEG-fMRI seems to be fruitful. In this article, we propose a new method based on simultaneous EEG-fMRI data and machine learning approach to classify the visual brain activity patterns. We acquired EEG-fMRI data simultaneously on the ten healthy human participants by showing them visual stimuli. Data fusion approach is used to merge EEG and fMRI data. Machine learning classifier is used for the classification purposes. Results showed that superior classification performance has been achieved with simultaneous EEG-fMRI data as compared to the EEG and fMRI data standalone. This shows that multimodal approach improved the classification accuracy results as compared with other approaches reported in the literature. The proposed simultaneous EEG-fMRI approach for classifying the brain activity patterns can be helpful to predict or fully decode the brain activity patterns.

Towards the utilization of EEG as a brain imaging tool.

PubMed

Michel, Christoph M; Murray, Micah M

2012-06-01

Recent advances in signal analysis have engendered EEG with the status of a true brain mapping and brain imaging method capable of providing spatio-temporal information regarding brain (dys)function. Because of the increasing interest in the temporal dynamics of brain networks, and because of the straightforward compatibility of the EEG with other brain imaging techniques, EEG is increasingly used in the neuroimaging community. However, the full capability of EEG is highly underestimated. Many combined EEG-fMRI studies use the EEG only as a spike-counter or an oscilloscope. Many cognitive and clinical EEG studies use the EEG still in its traditional way and analyze grapho-elements at certain electrodes and latencies. We here show that this way of using the EEG is not only dangerous because it leads to misinterpretations, but it is also largely ignoring the spatial aspects of the signals. In fact, EEG primarily measures the electric potential field at the scalp surface in the same way as MEG measures the magnetic field. By properly sampling and correctly analyzing this electric field, EEG can provide reliable information about the neuronal activity in the brain and the temporal dynamics of this activity in the millisecond range. This review explains some of these analysis methods and illustrates their potential in clinical and experimental applications. Copyright © 2011 Elsevier Inc. All rights reserved.

Does arousal interfere with operant conditioning of spike-wave discharges in genetic epileptic rats?

PubMed

Osterhagen, Lasse; Breteler, Marinus; van Luijtelaar, Gilles

2010-06-01

One of the ways in which brain computer interfaces can be used is neurofeedback (NF). Subjects use their brain activation to control an external device, and with this technique it is also possible to learn to control aspects of the brain activity by operant conditioning. Beneficial effects of NF training on seizure occurrence have been described in epileptic patients. Little research has been done about differentiating NF effectiveness by type of epilepsy, particularly, whether idiopathic generalized seizures are susceptible to NF. In this experiment, seizures that manifest themselves as spike-wave discharges (SWDs) in the EEG were reinforced during 10 sessions in 6 rats of the WAG/Rij strain, an animal model for absence epilepsy. EEG's were recorded before and after the training sessions. Reinforcing SWDs let to decreased SWD occurrences during training; however, the changes during training were not persistent in the post-training sessions. Because behavioural states are known to have an influence on the occurrence of SWDs, it is proposed that the reinforcement situation increased arousal which resulted in fewer SWDs. Additional tests supported this hypothesis. The outcomes have implications for the possibility to train SWDs with operant learning techniques. Copyright (c) 2010 Elsevier B.V. All rights reserved.

Chaotic time series analysis of vision evoked EEG

NASA Astrophysics Data System (ADS)

Zhang, Ningning; Wang, Hong

2010-01-01

To investigate the human brain activities for aesthetic processing, beautiful woman face picture and ugly buffoon face picture were applied. Twelve subjects were assigned the aesthetic processing task while the electroencephalogram (EEG) was recorded. Event-related brain potential (ERP) was required from the 32 scalp electrodes and the ugly buffoon picture produced larger amplitudes for the N1, P2, N2, and late slow wave components. Average ERP from the ugly buffoon picture were larger than that from the beautiful woman picture. The ERP signals shows that the ugly buffoon elite higher emotion waves than the beautiful woman face, because some expression is on the face of the buffoon. Then, chaos time series analysis was carried out to calculate the largest Lyapunov exponent using small data set method and the correlation dimension using G-P algorithm. The results show that the largest Lyapunov exponents of the ERP signals are greater than zero, which indicate that the ERP signals may be chaotic. The correlations dimensions coming from the beautiful woman picture are larger than that from the ugly buffoon picture. The comparison of the correlations dimensions shows that the beautiful face can excite the brain nerve cells. The research in the paper is a persuasive proof to the opinion that cerebrum's work is chaotic under some picture stimuli.

Heart rate calculation from ensemble brain wave using wavelet and Teager-Kaiser energy operator.

PubMed

Srinivasan, Jayaraman; Adithya, V

2015-01-01

Electroencephalogram (EEG) signal artifacts are caused by various factors, such as, Electro-oculogram (EOG), Electromyogram (EMG), Electrocardiogram (ECG), movement artifact and line interference. The relatively high electrical energy cardiac activity causes EEG artifacts. In EEG signal processing the general approach is to remove the ECG signal. In this paper, we introduce an automated method to extract the ECG signal from EEG using wavelet and Teager-Kaiser energy operator for R-peak enhancement and detection. From the detected R-peaks the heart rate (HR) is calculated for clinical diagnosis. To check the efficiency of our method, we compare the HR calculated from ECG signal recorded in synchronous with EEG. The proposed method yields a mean error of 1.4% for the heart rate and 1.7% for mean R-R interval. The result illustrates that, proposed method can be used for ECG extraction from single channel EEG and used in clinical diagnosis like estimation for stress analysis, fatigue, and sleep stages classification studies as a multi-model system. In addition, this method eliminates the dependence of additional synchronous ECG in extraction of ECG from EEG signal process.

Determining the degree of synchronism for intermittent phase synchronization in human electroencephalography data

NASA Astrophysics Data System (ADS)

Koloskova, A. D.; Moskalenko, O. I.

2017-05-01

The phenomenon of intermittent phase synchronization during development of epileptic activity in human beings has been discovered based on EEG data. The presence of synchronous behavior phases has been detected both during spike-wave discharges and in the regions of background activity of the brain. The degree of synchronism in the intermittent phase-synchronization regime in both cases has been determined, and it has been established that spike-wave discharges are characterized by a higher degree of synchronism than exists in the regions of background activity of the brain. To determine the degree of synchronism, a modified method of evaluating zero conditional Lyapunov exponents from time series is proposed.

Wearable electroencephalography. What is it, why is it needed, and what does it entail?

PubMed

Casson, Alexander; Yates, David; Smith, Shelagh; Duncan, John; Rodriguez-Villegas, Esther

2010-01-01

The electroencephalogram (EEG) is a classic noninvasive method for measuring a person's brain waves and is used in a large number of fields: from epilepsy and sleep disorder diagnosis to brain-computer interfaces (BCIs). Electrodes are placed on the scalp to detect the microvolt-sized signals that result from synchronized neuronal activity within the brain. Current long-term EEG monitoring is generally either carried out as an inpatient in combination with video recording and long cables to an amplifier and recording unit or is ambulatory. In the latter, the EEG recorder is portable but bulky, and in principle, the subject can go about their normal daily life during the recording. In practice, however, this is rarely the case. It is quite common for people undergoing ambulatory EEG monitoring to take time off work and stay at home rather than be seen in public with such a device. Wearable EEG is envisioned as the evolution of ambulatory EEG units from the bulky, limited lifetime devices available today to small devices present only on the head that can record EEG for days, weeks, or months at a time. Such miniaturized units could enable prolonged monitoring of chronic conditions such as epilepsy and greatly improve the end-user acceptance of BCI systems. In this article, we aim to provide a review and overview of wearable EEG technology, answering the questions: What is it, why is it needed, and what does it entail? We first investigate the requirements of portable EEG systems and then link these to the core applications of wearable EEG technology: epilepsy diagnosis, sleep disorder diagnosis, and BCIs. As a part of our review, we asked 21 neurologists (as a key user group) for their views on wearable EEG. This group highlighted that wearable EEG will be an essential future tool. Our descriptions here will focus mainly on epilepsy and the medical applications of wearable EEG, as this is the historical background of the EEG, our area of expertise, and a core motivating area in itself, but we will also discuss the other application areas. We continue by considering the forthcoming research challenges, principally new electrode technology and lower power electronics, and we outline our approach for dealing with the electronic power issues. We believe that the optimal approach to realizing wearable EEG technology is not to optimize any one part but to find the best set of tradeoffs at both the system and implementation level. In this article, we discuss two of these tradeoffs in detail: investigating the online compression of EEG data to reduce the system power consumption and the optimal method for providing this data compression.

Effects of Soft Drinks on Resting State EEG and Brain-Computer Interface Performance.

PubMed

Meng, Jianjun; Mundahl, John; Streitz, Taylor; Maile, Kaitlin; Gulachek, Nicholas; He, Jeffrey; He, Bin

2017-01-01

Motor imagery-based (MI based) brain-computer interface (BCI) using electroencephalography (EEG) allows users to directly control a computer or external device by modulating and decoding the brain waves. A variety of factors could potentially affect the performance of BCI such as the health status of subjects or the environment. In this study, we investigated the effects of soft drinks and regular coffee on EEG signals under resting state and on the performance of MI based BCI. Twenty-six healthy human subjects participated in three or four BCI sessions with a resting period in each session. During each session, the subjects drank an unlabeled soft drink with either sugar (Caffeine Free Coca-Cola), caffeine (Diet Coke), neither ingredient (Caffeine Free Diet Coke), or a regular coffee if there was a fourth session. The resting state spectral power in each condition was compared; the analysis showed that power in alpha and beta band after caffeine consumption were decreased substantially compared to control and sugar condition. Although the attenuation of powers in the frequency range used for the online BCI control signal was shown, group averaged BCI online performance after consuming caffeine was similar to those of other conditions. This work, for the first time, shows the effect of caffeine, sugar intake on the online BCI performance and resting state brain signal.

Uncovering clinical and radiological associations of triphasic waves in acute encephalopathy: a case-control study.

PubMed

Sutter, R; Kaplan, P W

2014-04-01

Triphasic waves (TWs) are archetypal waveforms seen on electroencephalography (EEG) in some forms of encephalopathy. Their particular underlying pathological substrates are largely unexplored. This case-control study was designed to identify and quantify specific clinical and neuroradiological associations underlying TWs and to determine if TWs predicate outcome. From 2004 to 2012, adult encephalopathic patients with TWs (cases) were matched 1:1 with encephalopathic patients without TWs (controls) by Glasgow Coma Scale (GCS) and the frequency range of EEG background activity. Clinical characteristics, neuroimaging and outcomes were assessed. The mean age of 190 patients (95 with and 95 without TWs) was 66.6 years (±15.6). In multivariable analyses, patients with TWs had significantly higher odds for liver insufficiency [odds ratio (OR) = 8.10, 95% confidence interval (CI) 1.98-33.08], alcohol abuse (OR = 3.65, 95% CI 1.25-10.63), subcortical brain atrophy (OR = 2.82, 95% CI 1.39-5.71) and respiratory tract infections (OR = 1.28, 95% CI 1.01-4.71). With each additional independent predictor, the odds increased for the occurrence of TWs (1 predictor, OR = 2.40, 95% CI 1.16-5.13; ≥2 predictors, OR = 9.20, 95% CI 3.27-25.62). Mortality was 15% and tended to be higher in patients with TWs (19% with vs. 11% without TWs). Alcohol abuse, liver insufficiency, infections and subcortical brain atrophy were independently associated with TWs in patients matched for clinical and EEG features of encephalopathy. These associations strengthen the hypothesis that TWs evolve from an interplay of pathological neurostructural, metabolic and toxic conditions. When matched for EEG background activity and GCS, TWs were not associated with death. © 2014 The Author(s) European Journal of Neurology © 2014 EFNS.

The contribution of posterior circulation to memory function during the intracarotid amobarbital procedure.

PubMed

Zijlmans, M; Huibers, C J A; Huiskamp, G J; de Kort, G A P; Alpherts, W C J; Leijten, F S S; Hendrikse, J

2012-08-01

The purpose of this study was to evaluate the contribution of posterior circulation to memory function by comparing memory scores between patients with and without a foetal-type posterior cerebral artery (FTP) during the intracarotid amobarbital procedure (IAP) in epilepsy patients. Patients undergoing bilateral IAP between January 2004 and January 2010 were retrospectively included. Pre-test angiograms were assessed for the presence of a FTP. Memory function scores (% correct) after right and left injections were obtained. Functional significance of FTP was affirmed by relative occipital versus parietal EEG slow-wave increase during IAP. Memory and EEG scores were compared between patients with and without FTP (Mann-Whitney U test). A total of 106 patients were included, 73 with posterior cerebral arteries (PCA) without FTP ('non-FTP'), 28 patients with unilateral FTP and 5 with a bilateral FTP. Memory scores were lower when amytal was injected to the hemisphere contralateral to the presumed seizure focus (on the right decreasing from 98.3 to 59.1, and on the left decreasing from 89.1 to 72.4; p < 0.001). When IAP was performed on the side of FTP memory scores were significantly lower (70.8) compared to non-FTP (82.0; p = 0.02). Relative occipital EEG changes were 0.44 for FTP cases and 0.36 for non-FTP patients (p = 0.01). A relationship between vasculature and brain function was demonstrated by lower memory scores and more slow-wave activity on occipital EEG during IAP in patients with foetal-type PCA compared to patients with non-FTP. This suggests an important contribution of brain areas supplied by the PCA to memory function.

Central nervous system hyperexcitability associated with glutamate dehydrogenase gain of function mutations.

PubMed

Raizen, David M; Brooks-Kayal, Amy; Steinkrauss, Linda; Tennekoon, Gihan I; Stanley, Charles A; Kelly, Andrea

2005-03-01

To describe seizure phenotypes associated with the hyperinsulinism/hyperammonemia syndrome (HI/HA), which is caused by gain of function mutations in the enzyme glutamate dehydrogenase (GDH). A retrospective review of records of 14 patients with HI/HA. Nine patients had seizures as the first symptom of HI/HA, and six had seizures in the absence of hypoglycemia. No electroencephalogram (EEG) background abnormalities were identified. In four patients, EEG recordings during seizures in the setting of normal blood glucose contained generalized epileptiform discharges. EEGs of three of these patients showed 0.5- to 2-second generalized irregular spike-and-wave discharge at 3 to 6 Hz corresponding to eye blinks, eye rolling, or staring. The EEG of the fourth patient consisted of 20 seconds of generalized regular spike-and-wave discharge at 3 Hz in the clinical context of staring and unresponsiveness. In two patients, seizure control worsened with carbamezapine or oxcarbezapine treatment. In patients with HI/HA, generalized seizures are common and can occur in the absence of hypoglycemia. The drugs carbamazepine and oxcarbazepine should be used with caution for treatment. Pathogenesis of epilepsy in these patients may be related to effects of GDH mutations in the brain, perhaps in combination with effects of recurrent hypoglycemia and chronic hyperammonemia.

Positive temporal sharp waves in preterm infants with and without brain ultrasound lesions.

PubMed

Castro Conde, José Ramón; Martínez, Eduardo Doménech; Campo, Candelaria González; Pérez, Arturo Méndez; McLean, Michael Lee

2004-11-01

Clinical significance of neonatal positive temporal sharp waves (PTS) is controversial. The aim of this work is to study (1) PTS incidence in preterm infants with or without major ultrasound lesion (MUL) per gestational age (GA), and (2) the relationship between PTS in both sleep states and other electroencephalographic (EEG) findings with poor prognoses. 97 preterm infants of <27-36 weeks GA, and 12 full-term healthy infants were presented. Prospective study included (1) neurodevelopmental assessment at 40-42 weeks conceptional age (CA), (2) serial neurosonography, and (3) EEG recording at postnatal week 1, 2, 4 and at 40-42 weeks CA. In 50 neonates without MUL, peak PTS was at 31-32 weeks GA. In 47 neonates with MUL, PTS increased significantly from week 2 after birth, descending at the 4th. Neonates of <33 weeks GA with MUL showed significantly increased PTS at term. A significant relationship was found between PTS and other EEG abnormalities with poor neurologic prognoses. PTS incidence varied with sleep states, being predominant in indeterminate sleep in neonates with MUL. PTS increased significantly in infants with MUL, mainly at week 2 of postnatal life, persisting high until term CA, and correlated with other abnormal EEG findings. PTS are highly sensitive to MUL.

Cortical activity during cued picture naming predicts individual differences in stuttering frequency

PubMed Central

Mock, Jeffrey R.; Foundas, Anne L.; Golob, Edward J.

2016-01-01

Objective Developmental stuttering is characterized by fluent speech punctuated by stuttering events, the frequency of which varies among individuals and contexts. Most stuttering events occur at the beginning of an utterance, suggesting neural dynamics associated with stuttering may be evident during speech preparation. Methods This study used EEG to measure cortical activity during speech preparation in men who stutter, and compared the EEG measures to individual differences in stuttering rate as well as to a fluent control group. Each trial contained a cue followed by an acoustic probe at one of two onset times (early or late), and then a picture. There were two conditions: a speech condition where cues induced speech preparation of the picture’s name and a control condition that minimized speech preparation. Results Across conditions stuttering frequency correlated to cue-related EEG beta power and auditory ERP slow waves from early onset acoustic probes. Conclusions The findings reveal two new cortical markers of stuttering frequency that were present in both conditions, manifest at different times, are elicited by different stimuli (visual cue, auditory probe), and have different EEG responses (beta power, ERP slow wave). Significance The cue-target paradigm evoked brain responses that correlated to pre-experimental stuttering rate. PMID:27472545

Cortical activity during cued picture naming predicts individual differences in stuttering frequency.

PubMed

Mock, Jeffrey R; Foundas, Anne L; Golob, Edward J

2016-09-01

Developmental stuttering is characterized by fluent speech punctuated by stuttering events, the frequency of which varies among individuals and contexts. Most stuttering events occur at the beginning of an utterance, suggesting neural dynamics associated with stuttering may be evident during speech preparation. This study used EEG to measure cortical activity during speech preparation in men who stutter, and compared the EEG measures to individual differences in stuttering rate as well as to a fluent control group. Each trial contained a cue followed by an acoustic probe at one of two onset times (early or late), and then a picture. There were two conditions: a speech condition where cues induced speech preparation of the picture's name and a control condition that minimized speech preparation. Across conditions stuttering frequency correlated to cue-related EEG beta power and auditory ERP slow waves from early onset acoustic probes. The findings reveal two new cortical markers of stuttering frequency that were present in both conditions, manifest at different times, are elicited by different stimuli (visual cue, auditory probe), and have different EEG responses (beta power, ERP slow wave). The cue-target paradigm evoked brain responses that correlated to pre-experimental stuttering rate. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

EEG in Sarcoidosis Patients Without Neurological Findings.

PubMed

Bilgin Topçuoğlu, Özgür; Kavas, Murat; Öztaş, Selahattin; Arınç, Sibel; Afşar, Gülgün; Saraç, Sema; Midi, İpek

2017-01-01

Sarcoidosis is a multisystem granulomatous disease affecting nervous system in 5% to 10% of patients. Magnetic resonance imaging (MRI) is accepted as the most sensitive method for detecting neurosarcoidosis. However, the most common findings in MRI are the nonspecific white matter lesions, which may be unrelated to sarcoidosis and can occur because of hypertension, diabetes mellitus, smoking, and other inflammatory or infectious disorders, as well. Autopsy studies report more frequent neurological involvement than the ante mortem studies. The aim of this study is to assess electroencephalography (EEG) in sarcoidosis patients without neurological findings in order to display asymptomatic neurological dysfunction. We performed EEG on 30 sarcoidosis patients without diagnosis of neurosarcoidosis or prior neurological comorbidities. Fourteen patients (46.7%) showed intermittant focal and/or generalized slowings while awake and not mentally activated. Seven (50%) of these 14 patients with EEG slowings had nonspecific white matter changes while the other half showed EEG slowings in the absence of MRI changes. We conclude that EEG slowings, when normal variants (psychomotor variant, temporal theta of elderly, frontal theta waves) are eliminated, may be an indicator of dysfunction in brain activity even in the absence of MRI findings. Hence, EEG may contribute toward detecting asymptomatic neurological dysfunction or probable future neurological involvement in sarcoidosis patients. © EEG and Clinical Neuroscience Society (ECNS) 2016.

Intracranial electroencephalography power and phase synchronization changes during monaural and binaural beat stimulation.

PubMed

Becher, Ann-Katrin; Höhne, Marlene; Axmacher, Nikolai; Chaieb, Leila; Elger, Christian E; Fell, Juergen

2015-01-01

Auditory stimulation with monaural or binaural auditory beats (i.e. sine waves with nearby frequencies presented either to both ears or to each ear separately) represents a non-invasive approach to influence electrical brain activity. It is still unclear exactly which brain sites are affected by beat stimulation. In particular, an impact of beat stimulation on mediotemporal brain areas could possibly provide new options for memory enhancement or seizure control. Therefore, we examined how electroencephalography (EEG) power and phase synchronization are modulated by auditory stimulation with beat frequencies corresponding to dominant EEG rhythms based on intracranial recordings in presurgical epilepsy patients. Monaural and binaural beat stimuli with beat frequencies of 5, 10, 40 and 80 Hz and non-superposed control signals were administered with low amplitudes (60 dB SPL) and for short durations (5 s). EEG power was intracranially recorded from mediotemporal, temporo-basal and temporo-lateral and surface sites. Evoked and total EEG power and phase synchronization during beat vs. control stimulation were compared by the use of Bonferroni-corrected non-parametric label-permutation tests. We found that power and phase synchronization were significantly modulated by beat stimulation not only at temporo-basal, temporo-lateral and surface sites, but also at mediotemporal sites. Generally, more significant decreases than increases were observed. The most prominent power increases were seen after stimulation with monaural 40-Hz beats. The most pronounced power and synchronization decreases resulted from stimulation with monaural 5-Hz and binaural 80-Hz beats. Our results suggest that beat stimulation offers a non-invasive approach for the modulation of intracranial EEG characteristics. © 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Opioid system of the brain and ethanol.

PubMed

Gogichadze, M; Mgaloblishvili-Nemsadze, M; Oniani, N; Emukhvary, N; Basishvili, T

2009-04-01

Influence of blocking of opioid receptors with concomitant intraperitoneal injections of Naloxone (20 mg/kg) (non-selective antagonist of opioid system) on the outcomes of anesthetic dose of ethanol (4,25 ml /kg 25% solution) was investigated in the rats. The sleep-wakefulness cycle (SWC) was used as a model for identification of the effects. Alterations of the SWC structure adequately reflect the neuro-chemical changes, which may develop during pharmacological and non-pharmacological impact. Administration of anesthetic dose of ethanol evoked considerable modification of spontaneous EEG activity of the neocortex. The EEG activity was depressed and full inhibition of spinal reflexes and somatic muscular relaxation did occur. During EEG depression regular SWC did not develop. All phases of SWC were reduced. The disturbances of SWC, such as decrease of slow wave sleep and paradoxical sleep duration and increase of wakefulness, remained for several days. At concomitant administration of Naloxone and ethanol, duration of EEG depression decreased significantly. Generation of normal SWC was observed on the same experimental day. However, it should be noted that complete abolishment of ethanol effects by Naloxone was not observed. The results obtained suggest that Naloxone partially blocks ethanol depressogenic effects and duration of this effect is mediated by GABA-ergic system of the brain.

Scalp and Source Power Topography in Sleepwalking and Sleep Terrors: A High-Density EEG Study

PubMed Central

Castelnovo, Anna; Riedner, Brady A.; Smith, Richard F.; Tononi, Giulio; Boly, Melanie; Benca, Ruth M.

2016-01-01

Study Objectives: To examine scalp and source power topography in sleep arousals disorders (SADs) using high-density EEG (hdEEG). Methods: Fifteen adult subjects with sleep arousal disorders (SADs) and 15 age- and gender-matched good sleeping healthy controls were recorded in a sleep laboratory setting using a 256 channel EEG system. Results: Scalp EEG analysis of all night NREM sleep revealed a localized decrease in slow wave activity (SWA) power (1–4 Hz) over centro-parietal regions relative to the rest of the brain in SADs compared to good sleeping healthy controls. Source modelling analysis of 5-minute segments taken from N3 during the first half of the night revealed that the local decrease in SWA power was prominent at the level of the cingulate, motor, and sensori-motor associative cortices. Similar patterns were also evident during REM sleep and wake. These differences in local sleep were present in the absence of any detectable clinical or electrophysiological sign of arousal. Conclusions: Overall, results suggest the presence of local sleep differences in the brain of SADs patients during nights without clinical episodes. The persistence of similar topographical changes in local EEG power during REM sleep and wakefulness points to trait-like functional changes that cross the boundaries of NREM sleep. The regions identified by source imaging are consistent with the current neurophysiological understanding of SADs as a disorder caused by local arousals in motor and cingulate cortices. Persistent localized changes in neuronal excitability may predispose affected subjects to clinical episodes. Citation: Castelnovo A, Riedner BA, Smith RF, Tononi G, Boly M, Benca RM. Scalp and source power topography in sleepwalking and sleep terrors: a high-density EEG study. SLEEP 2016;39(10):1815–1825. PMID:27568805

Infrasounds and biorhythms of the human brain

NASA Astrophysics Data System (ADS)

Panuszka, Ryszard; Damijan, Zbigniew; Kasprzak, Cezary; McGlothlin, James

2002-05-01

Low Frequency Noise (LFN) and infrasound has begun a new public health hazard. Evaluations of annoyance of (LFN) on human occupational health were based on standards where reactions of human auditory system and vibrations of parts of human body were small. Significant sensitivity has been observed on the central nervous system from infrasonic waves especially below 10 Hz. Observed follow-up effects in the brain gives incentive to study the relationship between parameters of waves and reactions obtained of biorhythms (EEG) and heart action (EKG). New results show the impact of LFN on the electrical potentials of the brain are dependent on the pressure waves on the human body. Electrical activity of circulatory system was also affected. Signals recorded in industrial workplaces were duplicated by loudspeakers and used to record data from a typical LFN spectra with 5 and 7 Hz in a laboratory chamber. External noise, electromagnetic fields, temperature, dust, and other elements were controlled. Results show not only a follow-up effect in the brain but also a result similar to arrhythmia in the heart. Relaxations effects were observed of people impacted by waves generated from natural sources such as streams and waterfalls.

Spatio-temporal reconstruction of brain dynamics from EEG with a Markov prior.

PubMed

Hansen, Sofie Therese; Hansen, Lars Kai

2017-03-01

Electroencephalography (EEG) can capture brain dynamics in high temporal resolution. By projecting the scalp EEG signal back to its origin in the brain also high spatial resolution can be achieved. Source localized EEG therefore has potential to be a very powerful tool for understanding the functional dynamics of the brain. Solving the inverse problem of EEG is however highly ill-posed as there are many more potential locations of the EEG generators than EEG measurement points. Several well-known properties of brain dynamics can be exploited to alleviate this problem. More short ranging connections exist in the brain than long ranging, arguing for spatially focal sources. Additionally, recent work (Delorme et al., 2012) argues that EEG can be decomposed into components having sparse source distributions. On the temporal side both short and long term stationarity of brain activation are seen. We summarize these insights in an inverse solver, the so-called "Variational Garrote" (Kappen and Gómez, 2013). Using a Markov prior we can incorporate flexible degrees of temporal stationarity. Through spatial basis functions spatially smooth distributions are obtained. Sparsity of these are inherent to the Variational Garrote solver. We name our method the MarkoVG and demonstrate its ability to adapt to the temporal smoothness and spatial sparsity in simulated EEG data. Finally a benchmark EEG dataset is used to demonstrate MarkoVG's ability to recover non-stationary brain dynamics. Copyright © 2016 Elsevier Inc. All rights reserved.

Electroencephalographic Variation during End Maintenance and Emergence from Surgical Anesthesia

PubMed Central

MacColl, Jono N.; Illing, Sam; Sleigh, Jamie W.

2014-01-01

The re-establishment of conscious awareness after discontinuing general anesthesia has often been assumed to be the inverse of loss of consciousness. This is despite the obvious asymmetry in the initiation and termination of natural sleep. In order to characterize the restoration of consciousness after surgery, we recorded frontal electroencephalograph (EEG) from 100 patients in the operating room during maintenance and emergence from general anesthesia. We have defined, for the first time, 4 steady-state patterns of anesthetic maintenance based on the relative EEG power in the slow-wave (<14 Hz) frequency bands that dominate sleep and anesthesia. Unlike single-drug experiments performed in healthy volunteers, we found that surgical patients exhibited greater electroencephalographic heterogeneity while re-establishing conscious awareness after drug discontinuation. Moreover, these emergence patterns could be broadly grouped according to the duration and rapidity of transitions amongst these slow-wave dominated brain states that precede awakening. Most patients progressed gradually from a pattern characterized by strong peaks of delta (0.5–4 Hz) and alpha/spindle (8–14 Hz) power (‘Slow-Wave Anesthesia’) to a state marked by low delta-spindle power (‘Non Slow-Wave Anesthesia’) before awakening. However, 31% of patients transitioned abruptly from Slow-Wave Anesthesia to waking; they were also more likely to express pain in the post-operative period. Our results, based on sleep-staging classification, provide the first systematized nomenclature for tracking brain states under general anesthesia from maintenance to emergence, and suggest that these transitions may correlate with post-operative outcomes such as pain. PMID:25264892

Design of a 32-Channel EEG System for Brain Control Interface Applications

PubMed Central

Wang, Ching-Sung

2012-01-01

This study integrates the hardware circuit design and the development support of the software interface to achieve a 32-channel EEG system for BCI applications. Since the EEG signals of human bodies are generally very weak, in addition to preventing noise interference, it also requires avoiding the waveform distortion as well as waveform offset and so on; therefore, the design of a preamplifier with high common-mode rejection ratio and high signal-to-noise ratio is very important. Moreover, the friction between the electrode pads and the skin as well as the design of dual power supply will generate DC bias which affects the measurement signals. For this reason, this study specially designs an improved single-power AC-coupled circuit, which effectively reduces the DC bias and improves the error caused by the effects of part errors. At the same time, the digital way is applied to design the adjustable amplification and filter function, which can design for different EEG frequency bands. For the analog circuit, a frequency band will be taken out through the filtering circuit and then the digital filtering design will be used to adjust the extracted frequency band for the target frequency band, combining with MATLAB to design man-machine interface for displaying brain wave. Finally the measured signals are compared to the traditional 32-channel EEG signals. In addition to meeting the IFCN standards, the system design also conducted measurement verification in the standard EEG isolation room in order to demonstrate the accuracy and reliability of this system design. PMID:22778545

Design of a 32-channel EEG system for brain control interface applications.

PubMed

Wang, Ching-Sung

2012-01-01

This study integrates the hardware circuit design and the development support of the software interface to achieve a 32-channel EEG system for BCI applications. Since the EEG signals of human bodies are generally very weak, in addition to preventing noise interference, it also requires avoiding the waveform distortion as well as waveform offset and so on; therefore, the design of a preamplifier with high common-mode rejection ratio and high signal-to-noise ratio is very important. Moreover, the friction between the electrode pads and the skin as well as the design of dual power supply will generate DC bias which affects the measurement signals. For this reason, this study specially designs an improved single-power AC-coupled circuit, which effectively reduces the DC bias and improves the error caused by the effects of part errors. At the same time, the digital way is applied to design the adjustable amplification and filter function, which can design for different EEG frequency bands. For the analog circuit, a frequency band will be taken out through the filtering circuit and then the digital filtering design will be used to adjust the extracted frequency band for the target frequency band, combining with MATLAB to design man-machine interface for displaying brain wave. Finally the measured signals are compared to the traditional 32-channel EEG signals. In addition to meeting the IFCN standards, the system design also conducted measurement verification in the standard EEG isolation room in order to demonstrate the accuracy and reliability of this system design.

Scalp and Source Power Topography in Sleepwalking and Sleep Terrors: A High-Density EEG Study.

PubMed

Castelnovo, Anna; Riedner, Brady A; Smith, Richard F; Tononi, Giulio; Boly, Melanie; Benca, Ruth M

2016-10-01

To examine scalp and source power topography in sleep arousals disorders (SADs) using high-density EEG (hdEEG). Fifteen adult subjects with sleep arousal disorders (SADs) and 15 age- and gender-matched good sleeping healthy controls were recorded in a sleep laboratory setting using a 256 channel EEG system. Scalp EEG analysis of all night NREM sleep revealed a localized decrease in slow wave activity (SWA) power (1-4 Hz) over centro-parietal regions relative to the rest of the brain in SADs compared to good sleeping healthy controls. Source modelling analysis of 5-minute segments taken from N3 during the first half of the night revealed that the local decrease in SWA power was prominent at the level of the cingulate, motor, and sensori-motor associative cortices. Similar patterns were also evident during REM sleep and wake. These differences in local sleep were present in the absence of any detectable clinical or electrophysiological sign of arousal. Overall, results suggest the presence of local sleep differences in the brain of SADs patients during nights without clinical episodes. The persistence of similar topographical changes in local EEG power during REM sleep and wakefulness points to trait-like functional changes that cross the boundaries of NREM sleep. The regions identified by source imaging are consistent with the current neurophysiological understanding of SADs as a disorder caused by local arousals in motor and cingulate cortices. Persistent localized changes in neuronal excitability may predispose affected subjects to clinical episodes. © 2016 Associated Professional Sleep Societies, LLC.

Alteration in Memory and Electroencephalogram Waves with Sub-acute Noise Stress in Albino Rats and Safeguarded by Scoparia dulcis.

PubMed

Loganathan, Sundareswaran; Rathinasamy, Sheeladevi

2016-01-01

Noise stress has different effects on memory and novelty and the link between them with an electroencephalogram (EEG) has not yet been reported. To find the effect of sub-acute noise stress on the memory and novelty along with EEG and neurotransmitter changes. Eight-arm maze (EAM) and Y-maze to analyze the memory and novelty by novel object test. Four groups of rats were used: Control, control treated with Scoparia dulcis extract, noise exposed, and noise exposed which received Scoparia extract. The results showed no marked difference observed between control and control treated with Scoparia extract on EAM, Y-maze, novel object test, and EEG in both prefrontal and occipital region, however, noise stress exposed rats showed significant increase in the reference memory and working memory error in EAM and latency delay, triad errors in Y-maze, and prefrontal and occipital EEG frequency rate with the corresponding increase in plasma corticosterone and epinephrine, and significant reduction in the novelty test, and significant reduction in the novelty test, amplitude of prefrontal, occipital EEG, and acetylcholine. These noise stress induced changes in EAM, Y-maze, novel object test, and neurotransmitters were significantly prevented when treated with Scoparia extract and these changes may be due to the normalizing action of Scoparia extract on the brain, which altered due to noise stress. Noise stress exposure causes EEG, behavior, and neurotransmitter alteration in the frontoparietal and occipital regions mainly involved in planning and recognition memoryOnly the noise stress exposed animals showed the significant alteration in the EEG, behavior, and neurotransmittersHowever, these noise stress induced changes in EEG behavior and neurotransmitters were significantly prevented when treated with Scoparia extractThese changes may be due to the normalizing action of Scoparia dulcis (adoptogen) on the brain which altered by noise stress. Abbreviations used: EEG: Electroencephalogram, dB: Decibel, EPI: Epinephrine, ACH: Acetylcholine, EAM: Eight-arm maze.

Alteration in Memory and Electroencephalogram Waves with Sub-acute Noise Stress in Albino Rats and Safeguarded by Scoparia dulcis

PubMed Central

Loganathan, Sundareswaran; Rathinasamy, Sheeladevi

2016-01-01

Background: Noise stress has different effects on memory and novelty and the link between them with an electroencephalogram (EEG) has not yet been reported. Objective: To find the effect of sub-acute noise stress on the memory and novelty along with EEG and neurotransmitter changes. Materials and Methods: Eight-arm maze (EAM) and Y-maze to analyze the memory and novelty by novel object test. Four groups of rats were used: Control, control treated with Scoparia dulcis extract, noise exposed, and noise exposed which received Scoparia extract. Results: The results showed no marked difference observed between control and control treated with Scoparia extract on EAM, Y-maze, novel object test, and EEG in both prefrontal and occipital region, however, noise stress exposed rats showed significant increase in the reference memory and working memory error in EAM and latency delay, triad errors in Y-maze, and prefrontal and occipital EEG frequency rate with the corresponding increase in plasma corticosterone and epinephrine, and significant reduction in the novelty test, and significant reduction in the novelty test, amplitude of prefrontal, occipital EEG, and acetylcholine. Conclusion: These noise stress induced changes in EAM, Y-maze, novel object test, and neurotransmitters were significantly prevented when treated with Scoparia extract and these changes may be due to the normalizing action of Scoparia extract on the brain, which altered due to noise stress. SUMMARY Noise stress exposure causes EEG, behavior, and neurotransmitter alteration in the frontoparietal and occipital regions mainly involved in planning and recognition memoryOnly the noise stress exposed animals showed the significant alteration in the EEG, behavior, and neurotransmittersHowever, these noise stress induced changes in EEG behavior and neurotransmitters were significantly prevented when treated with Scoparia extractThese changes may be due to the normalizing action of Scoparia dulcis (adoptogen) on the brain which altered by noise stress. Abbreviations used: EEG: Electroencephalogram, dB: Decibel, EPI: Epinephrine, ACH: Acetylcholine, EAM: Eight-arm maze PMID:27041862

[The electroencephalographic correlates of neurological disorders in the late periods of exposure to ionizing radiation (the aftereffects of the accident at the Chernobyl Atomic Electric Power Station)].

PubMed

Zhavoronkova, L A; Kholodova, N B; Zubovskiĭ, G A; Smirnov, Iu N; Koptelov, Iu M; Ryzhov, N I

1994-01-01

EEG mapping and three-dimensional localization of epileptic activity sources together with a neurological analysis were carried out in subjects having taken part in 1986-1987 in the liquidation of consequences of the Chernobyl accident. Experimental group included 40 right-handed 25-45 years-old men having received a radiation dose of 15-51 Ber stated officially. Control group consisted of 20 healthy men. Neurological examination of the patients revealed vegetative-vascular and endocrine dysfunctions as well as diffuse neurological symptoms. EEG of one group of patients (25 persons) was characterized by slow alpha- and theta-band foci and epileptic waves in the central-frontal regions; epileptic sources were localized at the diencephalic level mainly in the midline being shifted to the right hemisphere. In the EEG of another group (15 persons) delta-waves were recorded in the frontal regions at the background of diffuse beta-activity. The sources of epileptic activity of a diffuse character were localized at the basal level of the brain and in the cortex (predominantly) in the left hemisphere. The results obtained together with SPECT mapping and CT data permit to suppose the organic damage of different brain structures (at the cortical and the midline levels) in the patients, with participation of diencephalic structures in the pathological process hypothalamic-hypophysial system being probably connected with adaptive processes in the CNS.

Detection of Drug Effects on Brain Activity using EEG-P300 with Similar Stimuli

NASA Astrophysics Data System (ADS)

Turnip, Arjon; Dwi Esti, K.; Faizal Amri, M.; Simbolon, Artha I.; Agung Suhendra, M.; IsKandar, Shelly; Wirakusumah, Firman F.

2017-07-01

Drug addiction poses a serious problem to our species. The worry that our significant family might be involved in drug use and are concerned to know how to detect drug use. Examinations of thirty taped EEG recordings were performed. The subjects consist of three group: addictive, methadone treatment (rehabilitation), and control (normal) which 10 subjects for each group. Statistical analysis was performed for the relative frequency of wave bands. The higher average amplitude is obtained from the addiction subjects. In the comparison with the signals source, channels P3 provide slightly higher average amplitude than other channels for all of subjects.

Effects of oral temazepam on slow waves during non-rapid eye movement sleep in healthy young adults: A high-density EEG investigation.

PubMed

Plante, D T; Goldstein, M R; Cook, J D; Smith, R; Riedner, B A; Rumble, M E; Jelenchick, L; Roth, A; Tononi, G; Benca, R M; Peterson, M J

2016-03-01

Slow waves are characteristic waveforms that occur during non-rapid eye movement (NREM) sleep that play an integral role in sleep quality and brain plasticity. Benzodiazepines are commonly used medications that alter slow waves, however, their effects may depend on the time of night and measure used to characterize slow waves. Prior investigations have utilized minimal scalp derivations to evaluate the effects of benzodiazepines on slow waves, and thus the topography of changes to slow waves induced by benzodiazepines has yet to be fully elucidated. This study used high-density electroencephalography (hdEEG) to evaluate the effects of oral temazepam on slow wave activity, incidence, and morphology during NREM sleep in 18 healthy adults relative to placebo. Temazepam was associated with significant decreases in slow wave activity and incidence, which were most prominent in the latter portions of the sleep period. However, temazepam was also associated with a decrease in the magnitude of high-amplitude slow waves and their slopes in the first NREM sleep episode, which was most prominent in frontal derivations. These findings suggest that benzodiazepines produce changes in slow waves throughout the night that vary depending on cortical topography and measures used to characterize slow waves. Further research that explores the relationships between benzodiazepine-induced changes to slow waves and the functional effects of these waveforms is indicated. Copyright © 2016 Elsevier B.V. All rights reserved.

The Nature and Process of Development in Averaged Visually Evoked Potentials: Discussion on Pattern Structure.

ERIC Educational Resources Information Center

Izawa, Shuji; Mizutani, Tohru

This paper examines the development of visually evoked EEG patterns in retarded and normal subjects. The paper focuses on the averaged visually evoked potentials (AVEP) in the central and occipital regions of the brain in eyes closed and eyes open conditions. Wave pattern, amplitude, and latency are examined. The first section of the paper reviews…

The Perspectives of Primary Mathematics Teacher Candidates about Equal Sign: The EEG Case

ERIC Educational Resources Information Center

Ayvaz,Ülkü; Yaman, Hakan; Mersin, Nazan; Yilmaz, Yasemin; Durmus, Soner

2017-01-01

In this study, it was aimed to investigate the primary mathematics teacher candidates' perceptions about the equal sign within the scope of neuroscience studies. To reveal their perceptions about the equal sign, three types of addition operations were asked to the participants: a+b=[], []=a+b, a+b=[]+c. Their brain waves were recorded by EEG…

The multiple time scales of sleep dynamics as a challenge for modelling the sleeping brain.

PubMed

Olbrich, Eckehard; Claussen, Jens Christian; Achermann, Peter

2011-10-13

A particular property of the sleeping brain is that it exhibits dynamics on very different time scales ranging from the typical sleep oscillations such as sleep spindles and slow waves that can be observed in electroencephalogram (EEG) segments of several seconds duration over the transitions between the different sleep stages on a time scale of minutes to the dynamical processes involved in sleep regulation with typical time constants in the range of hours. There is an increasing body of work on mathematical and computational models addressing these different dynamics, however, usually considering only processes on a single time scale. In this paper, we review and present a new analysis of the dynamics of human sleep EEG at the different time scales and relate the findings to recent modelling efforts pointing out both the achievements and remaining challenges.

Modulatory Effect of Association of Brain Stimulation by Light and Binaural Beats in Specific Brain Waves

PubMed Central

Calomeni, Mauricio Rocha; Furtado da Silva, Vernon; Velasques, Bruna Brandão; Feijó, Olavo Guimarães; Bittencourt, Juliana Marques; Ribeiro de Souza e Silva, Alair Pedro

2017-01-01

Introduction: One of the positive effects of brain stimulation is interhemispheric modulation as shown in some scientific studies. This study examined if a type of noninvasive stimulation using binaural beats with led-lights and sound would show different modulatory effects upon Alfa and SMR brain waves of elderlies and children with some disease types. Subjects: The sample included 75 individuals of both genders, being, randomly, divided in 6 groups. Groups were named elderly without dementia diagnosis (EWD), n=15, 76±8 years, elderly diagnosed with Parkinson’s disease (EDP), n=15, 72±7 years, elderly diagnosed with Alzheimer’s disease (EDA), n=15, 81±6 years. The other groups were named children with Autism (CA), n=10, 11±4 years, children with Intellectual Impairment (CII), n=10, 12 ±5 years and children with normal cognitive development (CND), n=10, 11±4 years. Instruments and procedure: Instruments were the Mini Mental State Examination Test (MMSE), EEG-Neurocomputer instrument for brain waves registration, brain stimulator, Digit Span Test and a Protocol for working memory training. Data collection followed a pre and post-conjugated stimulation version. Results: The results of the inferential statistics showed that the stimulation protocol had different effects on Alpha and SMR brain waves of the patients. Also, indicated gains in memory functions, for both, children and elderlies as related to gains in brain waves modulation. Conclusion: The results may receive and provide support to a range of studies examining brain modulation and synaptic plasticity. Also, it was emphasized in the results discussion that there was the possibility of the technique serving as an accessory instrument to alternative brain therapies. PMID:29238390

Modulatory Effect of Association of Brain Stimulation by Light and Binaural Beats in Specific Brain Waves.

PubMed

Calomeni, Mauricio Rocha; Furtado da Silva, Vernon; Velasques, Bruna Brandão; Feijó, Olavo Guimarães; Bittencourt, Juliana Marques; Ribeiro de Souza E Silva, Alair Pedro

2017-01-01

One of the positive effects of brain stimulation is interhemispheric modulation as shown in some scientific studies. This study examined if a type of noninvasive stimulation using binaural beats with led-lights and sound would show different modulatory effects upon Alfa and SMR brain waves of elderlies and children with some disease types. The sample included 75 individuals of both genders, being, randomly, divided in 6 groups. Groups were named elderly without dementia diagnosis (EWD), n=15, 76±8 years, elderly diagnosed with Parkinson's disease (EDP), n=15, 72±7 years, elderly diagnosed with Alzheimer's disease (EDA), n=15, 81±6 years. The other groups were named children with Autism (CA), n=10, 11±4 years, children with Intellectual Impairment (CII), n=10, 12 ±5 years and children with normal cognitive development (CND), n=10, 11±4 years. Instruments were the Mini Mental State Examination Test (MMSE), EEG-Neurocomputer instrument for brain waves registration, brain stimulator, Digit Span Test and a Protocol for working memory training. Data collection followed a pre and post-conjugated stimulation version. The results of the inferential statistics showed that the stimulation protocol had different effects on Alpha and SMR brain waves of the patients. Also, indicated gains in memory functions, for both, children and elderlies as related to gains in brain waves modulation. The results may receive and provide support to a range of studies examining brain modulation and synaptic plasticity. Also, it was emphasized in the results discussion that there was the possibility of the technique serving as an accessory instrument to alternative brain therapies.

Quantitative electroencephalography in a swine model of blast-induced brain injury.

PubMed

Chen, Chaoyang; Zhou, Chengpeng; Cavanaugh, John M; Kallakuri, Srinivasu; Desai, Alok; Zhang, Liying; King, Albert I

2017-01-01

Electroencephalography (EEG) was used to examine brain activity abnormalities earlier after blast exposure using a swine model to develop a qEEG data analysis protocol. Anaesthetized swine were exposed to 420-450 Kpa blast overpressure and survived for 3 days after blast. EEG recordings were performed at 15 minutes before the blast and 15 minutes, 30 minutes, 2 hours and 1, 2 and 3 days post-blast using surface recording electrodes and a Biopac 4-channel data acquisition system. Off-line quantitative EEG (qEEG) data analysis was performed to determine qEEG changes. Blast induced qEEG changes earlier after blast exposure, including a decrease of mean amplitude (MAMP), an increase of delta band power, a decrease of alpha band root mean square (RMS) and a decrease of 90% spectral edge frequency (SEF90). This study demonstrated that qEEG is sensitive for cerebral injury. The changes of qEEG earlier after the blast indicate the potential of utilization of multiple parameters of qEEG for diagnosis of blast-induced brain injury. Early detection of blast induced brain injury will allow early screening and assessment of brain abnormalities in soldiers to enable timely therapeutic intervention.

Neural complexity in patients with poststroke depression: A resting EEG study.

PubMed

Zhang, Ying; Wang, Chunfang; Sun, Changcheng; Zhang, Xi; Wang, Yongjun; Qi, Hongzhi; He, Feng; Zhao, Xin; Wan, Baikun; Du, Jingang; Ming, Dong

2015-12-01

Poststroke depression (PSD) is one of the most common emotional disorders affecting post-stroke patients. However, the neurophysiological mechanism remains elusive. This study was aimed to study the relationship between complexity of neural electrical activity and PSD. Resting state eye-closed electroencephalogram (EEG) signals of 16 electrodes were recorded in 21 ischemic poststroke depression (PSD) patients, 22 ischemic poststroke non-depression (PSND) patients and 15 healthy controls (CONT). Lempel-Ziv Complexity (LZC) was used to evaluate changes in EEG complexity in PSD patients. Statistical analysis was performed to explore difference among different groups and electrodes. Correlation between the severity of depression (HDRS) and EEG complexity was determined with pearson correlation coefficients. Receiver operating characteristic (ROC) and binary logistic regression analysis were conducted to estimate the discriminating ability of LZC for PSD in specificity, sensitivity and accuracy. PSD patients showed lower neural complexity compared with PSND and CONT subjects in the whole brain regions. There was no significant difference among different brain regions, and no interactions between group and electrodes. None of the LZC significantly correlated with overall depression severity or differentiated symptom severity of 7 items in PSD patients, but in stroke patients, significant correlation was found between HDRS and LZC in the whole brain regions, especially in frontal and temporal. LZC parameters used for PSD recognition possessed more than 85% in specificity, sensitivity and accuracy, suggesting the feasibility of LZC to serve as screening indicators for PSD. Increased slow wave rhythms were found in PSD patients and clearly correlation was confirmed between neuronal complexity and spectral power of the four EEG rhythms. Lesion location of stroke patients in the study distributed in different brain regions, and most of the PSD patients were mild or moderate in depressive severity. Compared with conventional spectral analysis, complexity of neural activity using LZC was more sensitive and stationary in the measurement of abnormal brain activity in PSD patients and may offer a potential approach to facilitate clinical screening of this disease. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Building an EEG-fMRI Multi-Modal Brain Graph: A Concurrent EEG-fMRI Study

PubMed Central

Yu, Qingbao; Wu, Lei; Bridwell, David A.; Erhardt, Erik B.; Du, Yuhui; He, Hao; Chen, Jiayu; Liu, Peng; Sui, Jing; Pearlson, Godfrey; Calhoun, Vince D.

2016-01-01

The topological architecture of brain connectivity has been well-characterized by graph theory based analysis. However, previous studies have primarily built brain graphs based on a single modality of brain imaging data. Here we develop a framework to construct multi-modal brain graphs using concurrent EEG-fMRI data which are simultaneously collected during eyes open (EO) and eyes closed (EC) resting states. FMRI data are decomposed into independent components with associated time courses by group independent component analysis (ICA). EEG time series are segmented, and then spectral power time courses are computed and averaged within 5 frequency bands (delta; theta; alpha; beta; low gamma). EEG-fMRI brain graphs, with EEG electrodes and fMRI brain components serving as nodes, are built by computing correlations within and between fMRI ICA time courses and EEG spectral power time courses. Dynamic EEG-fMRI graphs are built using a sliding window method, versus static ones treating the entire time course as stationary. In global level, static graph measures and properties of dynamic graph measures are different across frequency bands and are mainly showing higher values in eyes closed than eyes open. Nodal level graph measures of a few brain components are also showing higher values during eyes closed in specific frequency bands. Overall, these findings incorporate fMRI spatial localization and EEG frequency information which could not be obtained by examining only one modality. This work provides a new approach to examine EEG-fMRI associations within a graph theoretic framework with potential application to many topics. PMID:27733821

A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis

PubMed Central

Wagatsuma, Hiroaki

2017-01-01

EEG signals contain a large amount of ocular artifacts with different time-frequency properties mixing together in EEGs of interest. The artifact removal has been substantially dealt with by existing decomposition methods known as PCA and ICA based on the orthogonality of signal vectors or statistical independence of signal components. We focused on the signal morphology and proposed a systematic decomposition method to identify the type of signal components on the basis of sparsity in the time-frequency domain based on Morphological Component Analysis (MCA), which provides a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases in accordance with the concept of “dictionary.” MCA was applied to decompose the real EEG signal and clarified the best combination of dictionaries for this purpose. In our proposed semirealistic biological signal analysis with iEEGs recorded from the brain intracranially, those signals were successfully decomposed into original types by a linear expansion of waveforms, such as redundant transforms: UDWT, DCT, LDCT, DST, and DIRAC. Our result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST, and DIRAC to represent the baseline envelope, multifrequency wave-forms, and spiking activities individually as representative types of EEG morphologies. PMID:28194221

Scatterplot analysis of EEG slow-wave magnitude and heart rate variability: an integrative exploration of cerebral cortical and autonomic functions.

PubMed

Kuo, Terry B J; Yang, Cheryl C H

2004-06-15

To explore interactions between cerebral cortical and autonomic functions in different sleep-wake states. Active waking (AW), quiet sleep (QS), and paradoxical sleep (PS) of adult male Wistar-Kyoto rats (WKY) on their daytime sleep were compared. Ten WKY. All rats had electrodes implanted for polygraphic recordings. One week later, a 6-hour daytime sleep-wakefulness recording session was performed. A scatterplot analysis of electroencephalogram (EEG) slow-wave magnitude (0.5-4 Hz) and heart rate variability (HRV) was applied in each rat. The EEG slow-wave-RR interval scatterplot from all of the recordings revealed a propeller-like pattern. If the scatterplot was divided into AW, PS, and QS according to the corresponding EEG mean power frequency and nuchal electromyogram, the EEG slow wave-RR interval relationship became nil, negative, and positive for AW, PS, and QS, respectively. A significant negative relationship was found for EEG slow-wave and high-frequency power of HRV (HF) coupling during PS and for EEG slow wave and low-frequency power of HRV to HF ratio (LF/HF) coupling during QS. The optimal time lags for the slow wave-LF/HF relationship were different between PS and QS. Bradycardia noted in QS and PS was related to sympathetic suppression and vagal excitation, respectively. The EEG slow wave-HRV scatterplot may provide unique insights into studies of sleep, and such a relationship may delineate the sleep-state-dependent fluctuations in autonomic nervous system activity.

Plastic changes following imitation-based speech and language therapy for aphasia: a high-density sleep EEG study.

PubMed

Sarasso, Simone; Määttä, Sara; Ferrarelli, Fabio; Poryazova, Rositsa; Tononi, Giulio; Small, Steven L

2014-02-01

BACKGROUND OBJECTIVE: measurement of plastic brain changes induced by a novel rehabilitative approach is a key requirement for validating its biological rationale linking the potential therapeutic gains to the changes in brain physiology. Based on an emerging notion linking cortical plastic changes to EEG sleep slow-wave activity (SWA) regulation, we aimed to assess the acute plastic changes induced by an imitation-based speech therapy in individuals with aphasia by comparing sleep SWA changes before and after therapy. A total of 13 left-hemispheric stroke patients underwent language assessment with the Western Aphasia Battery (WAB) before and after 2 consecutive high-density (hd) EEG sleep recordings interleaved by a daytime session of imitation-based speech therapy (Intensive Mouth Imitation and Talking for Aphasia Therapeutic Effects [IMITATE]). This protocol is thought to stimulate bilateral connections between the inferior parietal lobule and the ventral premotor areas. A single exposure to IMITATE resulted in increases in local EEG SWA during subsequent sleep over the same regions predicted by the therapeutic rationale, particularly over the right hemisphere (unaffected by the lesion). Furthermore, changes in SWA over the left-precentral areas predicted changes in WAB repetition scores in our group, supporting the role of perilesional areas in predicting positive functional responses. Our results suggest that SWA changes occurring in brain areas activated during imitation-based aphasia therapy may reflect the acute plastic changes induced by this intervention. Further testing will be needed to evaluate SWA as a non-invasive assessment of changes induced by the therapy and as a predictor of positive long-term clinical outcome.

Information dynamics of brain-heart physiological networks during sleep

NASA Astrophysics Data System (ADS)

Faes, L.; Nollo, G.; Jurysta, F.; Marinazzo, D.

2014-10-01

This study proposes an integrated approach, framed in the emerging fields of network physiology and information dynamics, for the quantitative analysis of brain-heart interaction networks during sleep. With this approach, the time series of cardiac vagal autonomic activity and brain wave activities measured respectively as the normalized high frequency component of heart rate variability and the EEG power in the δ, θ, α, σ, and β bands, are considered as realizations of the stochastic processes describing the dynamics of the heart system and of different brain sub-systems. Entropy-based measures are exploited to quantify the predictive information carried by each (sub)system, and to dissect this information into a part actively stored in the system and a part transferred to it from the other connected systems. The application of this approach to polysomnographic recordings of ten healthy subjects led us to identify a structured network of sleep brain-brain and brain-heart interactions, with the node described by the β EEG power acting as a hub which conveys the largest amount of information flowing between the heart and brain nodes. This network was found to be sustained mostly by the transitions across different sleep stages, as the information transfer was weaker during specific stages than during the whole night, and vanished progressively when moving from light sleep to deep sleep and to REM sleep.

[Digital electroencephalography in brain death diagnostics : Technical requirements and results of a survey on the compatibility with medical guidelines of digital EEG systems from providers in Germany].

PubMed

Walter, U; Noachtar, S; Hinrichs, H

2018-02-01

The guidelines of the German Medical Association and the German Society for Clinical Neurophysiology and Functional Imaging (DGKN) require a high procedural and technical standard for electroencephalography (EEG) as an ancillary method for diagnosing the irreversible cessation of brain function (brain death). Nowadays, digital EEG systems are increasingly being applied in hospitals. So far it is unclear to what extent the digital EEG systems currently marketed in Germany meet the guidelines for diagnosing brain death. In the present article, the technical und safety-related requirements for digital EEG systems and the EEG documentation for diagnosing brain death are described in detail. On behalf of the DGKN, the authors sent out a questionnaire to all identified distributors of digital EEG systems in Germany with respect to the following technical demands: repeated recording of the calibration signals during an ongoing EEG recording, repeated recording of all electrode impedances during an ongoing EEG recording, assessability of intrasystem noise and galvanic isolation of measurement earthing from earthing conductor (floating input). For 15 of the identified 20 different digital EEG systems the specifications were provided by the distributors (among them all distributors based in Germany). All of these EEG systems are provided with a galvanic isolation (floating input). The internal noise can be tested with all systems; however, some systems do not allow repeated recording of the calibration signals and/or the electrode impedances during an ongoing EEG recording. The majority but not all of the currently available digital EEG systems offered for clinical use are eligible for use in brain death diagnostics as per German guidelines.

Characterizing and Modulating Brain Circuitry through Transcranial Magnetic Stimulation Combined with Electroencephalography.

PubMed

Farzan, Faranak; Vernet, Marine; Shafi, Mouhsin M D; Rotenberg, Alexander; Daskalakis, Zafiris J; Pascual-Leone, Alvaro

2016-01-01

The concurrent combination of transcranial magnetic stimulation (TMS) with electroencephalography (TMS-EEG) is a powerful technology for characterizing and modulating brain networks across developmental, behavioral, and disease states. Given the global initiatives in mapping the human brain, recognition of the utility of this technique is growing across neuroscience disciplines. Importantly, TMS-EEG offers translational biomarkers that can be applied in health and disease, across the lifespan, and in humans and animals, bridging the gap between animal models and human studies. However, to utilize the full potential of TMS-EEG methodology, standardization of TMS-EEG study protocols is needed. In this article, we review the principles of TMS-EEG methodology, factors impacting TMS-EEG outcome measures, and the techniques for preventing and correcting artifacts in TMS-EEG data. To promote the standardization of this technique, we provide comprehensive guides for designing TMS-EEG studies and conducting TMS-EEG experiments. We conclude by reviewing the application of TMS-EEG in basic, cognitive and clinical neurosciences, and evaluate the potential of this emerging technology in brain research.

Characterizing and Modulating Brain Circuitry through Transcranial Magnetic Stimulation Combined with Electroencephalography

PubMed Central

Farzan, Faranak; Vernet, Marine; Shafi, Mouhsin M. D.; Rotenberg, Alexander; Daskalakis, Zafiris J.; Pascual-Leone, Alvaro

2016-01-01

The concurrent combination of transcranial magnetic stimulation (TMS) with electroencephalography (TMS-EEG) is a powerful technology for characterizing and modulating brain networks across developmental, behavioral, and disease states. Given the global initiatives in mapping the human brain, recognition of the utility of this technique is growing across neuroscience disciplines. Importantly, TMS-EEG offers translational biomarkers that can be applied in health and disease, across the lifespan, and in humans and animals, bridging the gap between animal models and human studies. However, to utilize the full potential of TMS-EEG methodology, standardization of TMS-EEG study protocols is needed. In this article, we review the principles of TMS-EEG methodology, factors impacting TMS-EEG outcome measures, and the techniques for preventing and correcting artifacts in TMS-EEG data. To promote the standardization of this technique, we provide comprehensive guides for designing TMS-EEG studies and conducting TMS-EEG experiments. We conclude by reviewing the application of TMS-EEG in basic, cognitive and clinical neurosciences, and evaluate the potential of this emerging technology in brain research. PMID:27713691

Continuous electroencephalogram monitoring in the intensive care unit.

PubMed

Friedman, Daniel; Claassen, Jan; Hirsch, Lawrence J

2009-08-01

Because of recent technical advances, it is now possible to record and monitor the continuous digital electroencephalogram (EEG) of many critically ill patients simultaneously. Continuous EEG monitoring (cEEG) provides dynamic information about brain function that permits early detection of changes in neurologic status, which is especially useful when the clinical examination is limited. Nonconvulsive seizures are common in comatose critically ill patients and can have multiple negative effects on the injured brain. The majority of seizures in these patients cannot be detected without cEEG. cEEG monitoring is most commonly used to detect and guide treatment of nonconvulsive seizures, including after convulsive status epilepticus. In addition, cEEG is used to guide management of pharmacological coma for treatment of increased intracranial pressure. An emerging application for cEEG is to detect new or worsening brain ischemia in patients at high risk, especially those with subarachnoid hemorrhage. Improving quantitative EEG software is helping to make it feasible for cEEG (using full scalp coverage) to provide continuous information about changes in brain function in real time at the bedside and to alert clinicians to any acute brain event, including seizures, ischemia, increasing intracranial pressure, hemorrhage, and even systemic abnormalities affecting the brain, such as hypoxia, hypotension, acidosis, and others. Monitoring using only a few electrodes or using full scalp coverage, but without expert review of the raw EEG, must be done with extreme caution as false positives and false negatives are common. Intracranial EEG recording is being performed in a few centers to better detect seizures, ischemia, and peri-injury depolarizations, all of which may contribute to secondary injury. When cEEG is combined with individualized, physiologically driven decision making via multimodality brain monitoring, intensivists can identify when the brain is at risk for injury or when neuronal injury is already occurring and intervene before there is permanent damage. The exact role and cost-effectiveness of cEEG at the current time remains unclear, but we believe it has significant potential to improve neurologic outcomes in a variety of settings.

Shared Genetic Control of Brain Activity During Sleep and Insulin Secretion: A Laboratory-Based Family Study.

PubMed

Morselli, Lisa L; Gamazon, Eric R; Tasali, Esra; Cox, Nancy J; Van Cauter, Eve; Davis, Lea K

2018-01-01

Over the past 20 years, a large body of experimental and epidemiologic evidence has linked sleep duration and quality to glucose homeostasis, although the mechanistic pathways remain unclear. The aim of the current study was to determine whether genetic variation influencing both sleep and glucose regulation could underlie their functional relationship. We hypothesized that the genetic regulation of electroencephalographic (EEG) activity during non-rapid eye movement sleep, a highly heritable trait with fingerprint reproducibility, is correlated with the genetic control of metabolic traits including insulin sensitivity and β-cell function. We tested our hypotheses through univariate and bivariate heritability analyses in a three-generation pedigree with in-depth phenotyping of both sleep EEG and metabolic traits in 48 family members. Our analyses accounted for age, sex, adiposity, and the use of psychoactive medications. In univariate analyses, we found significant heritability for measures of fasting insulin sensitivity and β-cell function, for time spent in slow-wave sleep, and for EEG spectral power in the delta, theta, and sigma ranges. Bivariate heritability analyses provided the first evidence for a shared genetic control of brain activity during deep sleep and fasting insulin secretion rate. © 2017 by the American Diabetes Association.

Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation.

PubMed

Marcel, Sébastien; Millán, José Del R

2007-04-01

In this paper, we investigate the use of brain activity for person authentication. It has been shown in previous studies that the brain-wave pattern of every individual is unique and that the electroencephalogram (EEG) can be used for biometric identification. EEG-based biometry is an emerging research topic and we believe that it may open new research directions and applications in the future. However, very little work has been done in this area and was focusing mainly on person identification but not on person authentication. Person authentication aims to accept or to reject a person claiming an identity, i.e., comparing a biometric data to one template, while the goal of person identification is to match the biometric data against all the records in a database. We propose the use of a statistical framework based on Gaussian Mixture Models and Maximum A Posteriori model adaptation, successfully applied to speaker and face authentication, which can deal with only one training session. We perform intensive experimental simulations using several strict train/test protocols to show the potential of our method. We also show that there are some mental tasks that are more appropriate for person authentication than others.

Long-term history and immediate preceding state affect EEG slow wave characteristics at NREM sleep onset in C57BL/6 mice.

PubMed

Cui, N; Mckillop, L E; Fisher, S P; Oliver, P L; Vyazovskiy, V V

2014-01-01

The dynamics of cortical activity across the 24-h day and at vigilance state transitions is regulated by an interaction between global subcortical neuromodulatory influences and local shifts in network synchrony and excitability. To address the role of long-term and immediate preceding history in local and global cortical dynamics, we investigated cortical EEG recorded from both frontal and occipital regions during an undisturbed 24-h recording in mice. As expected, at the beginning of the light period, under physiologically increased sleep pressure, EEG slow waves were more frequent and had higher amplitude and slopes, compared to the rest of the light period. Within discrete NREM sleep episodes, the incidence, amplitude and slopes of individual slow waves increased progressively after episode onset in both derivations by approximately 10-30%. Interestingly, at the beginning of NREM sleep episodes slow waves in the frontal and occipital derivations frequently occurred in isolation, as quantified by longer latencies between consecutive slow waves in the two regions. Notably, slow waves during the initial period of NREM sleep following REM sleep episodes were significantly less frequent, lower in amplitude and exhibited shallower slopes, compared to those that occurred in NREM episodes after prolonged waking. Moreover, the latencies between consecutive frontal and occipital NREM slow waves were substantially longer when they occurred directly after REM sleep compared to following consolidated wakefulness. Overall these data reveal a complex picture, where both time of day and preceding state contribute to the characteristics and dynamics of slow waves within NREM sleep. These findings suggest that NREM sleep initiates in a more "local" fashion when it occurs following REM sleep episodes as opposed to sustained waking bouts. While the mechanisms and functional significance of such a re-setting of brain state after individual REM sleep episodes remains to be investigated, we suggest that it may be an essential feature of physiological sleep regulation.

Preterm EEG: a multimodal neurophysiological protocol.

PubMed

Stjerna, Susanna; Voipio, Juha; Metsäranta, Marjo; Kaila, Kai; Vanhatalo, Sampsa

2012-02-18

Since its introduction in early 1950s, electroencephalography (EEG) has been widely used in the neonatal intensive care units (NICU) for assessment and monitoring of brain function in preterm and term babies. Most common indications are the diagnosis of epileptic seizures, assessment of brain maturity, and recovery from hypoxic-ischemic events. EEG recording techniques and the understanding of neonatal EEG signals have dramatically improved, but these advances have been slow to penetrate through the clinical traditions. The aim of this presentation is to bring theory and practice of advanced EEG recording available for neonatal units. In the theoretical part, we will present animations to illustrate how a preterm brain gives rise to spontaneous and evoked EEG activities, both of which are unique to this developmental phase, as well as crucial for a proper brain maturation. Recent animal work has shown that the structural brain development is clearly reflected in early EEG activity. Most important structures in this regard are the growing long range connections and the transient cortical structure, subplate. Sensory stimuli in a preterm baby will generate responses that are seen at a single trial level, and they have underpinnings in the subplate-cortex interaction. This brings neonatal EEG readily into a multimodal study, where EEG is not only recording cortical function, but it also tests subplate function via different sensory modalities. Finally, introduction of clinically suitable dense array EEG caps, as well as amplifiers capable of recording low frequencies, have disclosed multitude of brain activities that have as yet been overlooked. In the practical part of this video, we show how a multimodal, dense array EEG study is performed in neonatal intensive care unit from a preterm baby in the incubator. The video demonstrates preparation of the baby and incubator, application of the EEG cap, and performance of the sensory stimulations.

Effects of neurofeedback therapy in healthy young subjects.

PubMed

Altan, Sümeyra; Berberoglu, Bercim; Canan, Sinan; Dane, Şenol

2016-12-01

Neurofeedback refers to a form of operant conditioning of electrical brain activity, in which desirable brain activity is rewarded and undesirable brain activity is inhibited. The research team aimed to examine the efficacy of neurofeedback therapy on electroencephalogram (EEG) for heart rate, electrocardiogram (ECG) and galvanic skin resistance (GSR) parameters in a healthy young male population. Forty healthy young male subjects aged between 18 to 30 years participated in this study. Neurofeedback application of one session was made with bipolar electrodes placed on T3 and T4 (temporal 3 and 4) regions and with reference electrode placed on PF1 (prefrontal 1). Electroencephalogram (EEG), electrocardiogram (ECG) and galvanic skin resistance (GSR) were assessed during Othmer neurofeedback application of one session to regulate slow wave activity for forty minutes thorough the session. Data assessed before neurofeedback application for 5 minutes and during neurofeedback application of 30 minutes and after neurofeedback application for 5 minutes throughout the session of 40 minutes. Means for each 5 minutes, that is to say, a total 8 data points for each subjects over 40 minutes, were assessed. Galvanic skin resistance increased and heart rate decreased after neurofeedback therapy. Beta activity in EEG increased and alfa activity decreased after neurofeedback therapy. These results suggest that neurofeedback can be used to restore sympathovagal imbalances. Also, it may be accepted as a preventive therapy for psychological and neurological problems.

[Extraction of evoked related potentials by using the combination of independent component analysis and wavelet analysis].

PubMed

Zou, Ling; Chen, Shuyue; Sun, Yuqiang; Ma, Zhenghua

2010-08-01

In this paper we present a new method of combining Independent Component Analysis (ICA) and Wavelet de-noising algorithm to extract Evoked Related Potentials (ERPs). First, the extended Infomax-ICA algorithm is used to analyze EEG signals and obtain the independent components (Ics); Then, the Wave Shrink (WS) method is applied to the demixed Ics as an intermediate step; the EEG data were rebuilt by using the inverse ICA based on the new Ics; the ERPs were extracted by using de-noised EEG data after being averaged several trials. The experimental results showed that the combined method and ICA method could remove eye artifacts and muscle artifacts mixed in the ERPs, while the combined method could retain the brain neural activity mixed in the noise Ics and could extract the weak ERPs efficiently from strong background artifacts.

Electroencephalography as a Tool for Assessment of Brain Ischemic Alterations after Open Heart Operations

PubMed Central

Golukhova, Elena Z.; Polunina, Anna G.; Lefterova, Natalia P.; Begachev, Alexey V.

2011-01-01

Cardiac surgery is commonly associated with brain ischemia. Few studies addressed brain electric activity changes after on-pump operations. Eyes closed EEG was performed in 22 patients (mean age: 45.2 ± 11.2) before and two weeks after valve replacement. Spouses of patients were invited to participate as controls. Generalized increase of beta power most prominent in beta-1 band was an unambiguous pathological sign of postoperative cortex dysfunction, probably, manifesting due to gamma-activity slowing (“beta buzz” symptom). Generalized postoperative increase of delta-1 mean frequency along with increase of slow-wave activity in right posterior region may be hypothesized to be a consequence of intraoperative ischemia as well. At the same time, significant changes of alpha activity were observed in both patient and control groups, and, therefore, may be considered as physiological. Unexpectedly, controls showed prominent increase of electric activity in left temporal region whereas patients were deficient in left hemisphere activity in comparison with controls at postoperative followup. Further research is needed in order to determine the true neurological meaning of the EEG findings after on-pump operations. PMID:21776370

Non-invasive transmission of sensorimotor information in humans using an EEG/focused ultrasound brain-to-brain interface

PubMed Central

Lee, Wonhye; Kim, Suji; Kim, Byeongnam; Lee, Chungki; Chung, Yong An; Kim, Laehyun; Yoo, Seung-Schik

2017-01-01

We present non-invasive means that detect unilateral hand motor brain activity from one individual and subsequently stimulate the somatosensory area of another individual, thus, enabling the remote hemispheric link between each brain hemisphere in humans. Healthy participants were paired as a sender and a receiver. A sender performed a motor imagery task of either right or left hand, and associated changes in the electroencephalogram (EEG) mu rhythm (8–10 Hz) originating from either hemisphere were programmed to move a computer cursor to a target that appeared in either left or right of the computer screen. When the cursor reaches its target, the outcome was transmitted to another computer over the internet, and actuated the focused ultrasound (FUS) devices that selectively and non-invasively stimulated either the right or left hand somatosensory area of the receiver. Small FUS transducers effectively allowed for the independent administration of stimulatory ultrasonic waves to somatosensory areas. The stimulation elicited unilateral tactile sensation of the hand from the receiver, thus establishing the hemispheric brain-to-brain interface (BBI). Although there was a degree of variability in task accuracy, six pairs of volunteers performed the BBI task in high accuracy, transferring approximately eight commands per minute. Linkage between the hemispheric brain activities among individuals suggests the possibility for expansion of the information bandwidth in the context of BBI. PMID:28598972

Increased Sleep Depth in Developing Neural Networks: New Insights from Sleep Restriction in Children

PubMed Central

Kurth, Salome; Dean, Douglas C.; Achermann, Peter; O’Muircheartaigh, Jonathan; Huber, Reto; Deoni, Sean C. L.; LeBourgeois, Monique K.

2016-01-01

Brain networks respond to sleep deprivation or restriction with increased sleep depth, which is quantified as slow-wave activity (SWA) in the sleep electroencephalogram (EEG). When adults are sleep deprived, this homeostatic response is most pronounced over prefrontal brain regions. However, it is unknown how children’s developing brain networks respond to acute sleep restriction, and whether this response is linked to myelination, an ongoing process in childhood that is critical for brain development and cortical integration. We implemented a bedtime delay protocol in 5- to 12-year-old children to obtain partial sleep restriction (1-night; 50% of their habitual sleep). High-density sleep EEG was assessed during habitual and restricted sleep and brain myelin content was obtained using mcDESPOT magnetic resonance imaging. The effect of sleep restriction was analyzed using statistical non-parametric mapping with supra-threshold cluster analysis. We observed a localized homeostatic SWA response following sleep restriction in a specific parieto-occipital region. The restricted/habitual SWA ratio was negatively associated with myelin water fraction in the optic radiation, a developing fiber bundle. This relationship occurred bilaterally over parieto-temporal areas and was adjacent to, but did not overlap with the parieto-occipital region showing the most pronounced homeostatic SWA response. These results provide evidence for increased sleep need in posterior neural networks in children. Sleep need in parieto-temporal areas is related to myelin content, yet it remains speculative whether age-related myelin growth drives the fading of the posterior homeostatic SWA response during the transition to adulthood. Whether chronic insufficient sleep in the sensitive period of early life alters the anatomical generators of deep sleep slow-waves is an important unanswered question. PMID:27708567

Sleep spindles in humans: insights from intracranial EEG and unit recordings

PubMed Central

Andrillon, Thomas; Nir, Yuval; Staba, Richard J.; Ferrarelli, Fabio; Cirelli, Chiara; Tononi, Giulio; Fried, Itzhak

2012-01-01

Sleep spindles are an electroencephalographic (EEG) hallmark of non-rapid eye movement (NREM) sleep and are believed to mediate many sleep-related functions, from memory consolidation to cortical development. Spindles differ in location, frequency, and association with slow waves, but whether this heterogeneity may reflect different physiological processes and potentially serve different functional roles remains unclear. Here we utilized a unique opportunity to record intracranial depth EEG and single-unit activity in multiple brain regions of neurosurgical patients to better characterize spindle activity in human sleep. We find that spindles occur across multiple neocortical regions, and less frequently also in the parahippocampal gyrus and hippocampus. Most spindles are spatially restricted to specific brain regions. In addition, spindle frequency is topographically organized with a sharp transition around the supplementary motor area between fast (13-15Hz) centroparietal spindles often occurring with slow wave up-states, and slow (9-12Hz) frontal spindles occurring 200ms later on average. Spindle variability across regions may reflect the underlying thalamocortical projections. We also find that during individual spindles, frequency decreases within and between regions. In addition, deeper sleep is associated with a reduction in spindle occurrence and spindle frequency. Frequency changes between regions, during individual spindles, and across sleep may reflect the same phenomenon, the underlying level of thalamocortical hyperpolarization. Finally, during spindles neuronal firing rates are not consistently modulated, although some neurons exhibit phase-locked discharges. Overall, anatomical considerations can account well for regional spindle characteristics, while variable hyperpolarization levels can explain differences in spindle frequency. PMID:22159098

Impact of brain injury on functional measures of amplitude-integrated EEG at term equivalent age in premature infants.

PubMed

El Ters, N M; Vesoulis, Z A; Liao, S M; Smyser, C D; Mathur, A M

2017-08-01

To evaluate the association between qualitative and quantitative amplitude-integrated EEG (aEEG) measures at term equivalent age (TEA) and brain injury on magnetic resonance imaging (MRI) in preterm infants. A cohort of premature infants born at <30 weeks of gestation and with moderate-to-severe MRI injury on a TEA MRI scan was identified. A contemporaneous group of gestational age-matched control infants also born at <30 weeks of gestation with none/mild injury on MRI was also recruited. Quantitative aEEG measures, including maximum and minimum amplitudes, bandwidth span and spectral edge frequency (SEF 90 ), were calculated using an offline software package. The aEEG recordings were qualitatively scored using the Burdjalov system. MRI scans, performed on the same day as aEEG, occurred at a mean postmenstrual age of 38.0 (range 37 to 42) weeks and were scored for abnormality in a blinded manner using an established MRI scoring system. Twenty-eight (46.7%) infants had a normal MRI or mild brain abnormality, while 32 (53.3%) infants had moderate-to-severe brain abnormality. Univariate regression analysis demonstrated an association between severity of brain abnormality and quantitative measures of left and right SEF 90 and bandwidth span (β=-0.38, -0.40 and 0.30, respectively) and qualitative measures of cyclicity, continuity and total Burdjalov score (β=-0.10, -0.14 and -0.12, respectively). After correcting for confounding variables, the relationship between MRI abnormality score and aEEG measures of SEF 90 , bandwidth span and Burdjalov score remained significant. Brain abnormalities on MRI at TEA in premature infants are associated with abnormalities on term aEEG measures, suggesting that anatomical brain injury may contribute to delay in functional brain maturation as assessed using aEEG.

Optimal spatiotemporal representation of multichannel EEG for recognition of brain states associated with distinct visual stimulus

NASA Astrophysics Data System (ADS)

Hramov, Alexander; Musatov, Vyacheslav Yu.; Runnova, Anastasija E.; Efremova, Tatiana Yu.; Koronovskii, Alexey A.; Pisarchik, Alexander N.

2018-04-01

In the paper we propose an approach based on artificial neural networks for recognition of different human brain states associated with distinct visual stimulus. Based on the developed numerical technique and the analysis of obtained experimental multichannel EEG data, we optimize the spatiotemporal representation of multichannel EEG to provide close to 97% accuracy in recognition of the EEG brain states during visual perception. Different interpretations of an ambiguous image produce different oscillatory patterns in the human EEG with similar features for every interpretation. Since these features are inherent to all subjects, a single artificial network can classify with high quality the associated brain states of other subjects.

Different quantitative EEG alterations induced by TBI among patients with different APOE genotypes.

PubMed

Jiang, Li; Yin, Xiaohong; Yin, Cheng; Zhou, Shuai; Dan, Wei; Sun, Xiaochuan

2011-11-14

Although several studies have revealed the EEG alterations in AD and TBI patients, the influence of APOE (apolipoprotein E) genotype in EEG at the early stage of TBI has not been reported yet. We have previously studied EEG alterations caused by TBI among different APOE genotype carriers. In this study, we firstly investigated the relationship between APOE polymorphisms and quantitative EEG (QEEG) changes after TBI. A total of 118 consecutive TBI patients with a Glasgow Coma Scale (GCS) of 9 or higher were recruited, and 40 normal adults were also included as a control group. APOE genotype was determined by PCR-RFLP for each subject, and QEEG recordings were performed in rest, relaxed, awake and with eyes closed in normal subjects and TBI patients during 1-3 days after TBI. In the normal control group, both APOEɛ4 carriers and non-carriers had normal EEG, and no significant difference of QEEG data was found between APOEɛ4 carriers and non-carriers. But in the TBI group, APOEɛ4 carriers had more focal or global irregular slow wave activities than APOEɛ4 non-carriers. APOE gene did not influence brain electrical activity under normal conditions, but TBI can induce different alterations among different APOE gene carriers, and APOEɛ4 allele enhances the EEG abnormalities at the early stage of TBI. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

High-density EEG characterization of brain responses to auditory rhythmic stimuli during wakefulness and NREM sleep.

PubMed

Lustenberger, Caroline; Patel, Yogi A; Alagapan, Sankaraleengam; Page, Jessica M; Price, Betsy; Boyle, Michael R; Fröhlich, Flavio

2018-04-01

Auditory rhythmic sensory stimulation modulates brain oscillations by increasing phase-locking to the temporal structure of the stimuli and by increasing the power of specific frequency bands, resulting in Auditory Steady State Responses (ASSR). The ASSR is altered in different diseases of the central nervous system such as schizophrenia. However, in order to use the ASSR as biological markers for disease states, it needs to be understood how different vigilance states and underlying brain activity affect the ASSR. Here, we compared the effects of auditory rhythmic stimuli on EEG brain activity during wake and NREM sleep, investigated the influence of the presence of dominant sleep rhythms on the ASSR, and delineated the topographical distribution of these modulations. Participants (14 healthy males, 20-33 years) completed on the same day a 60 min nap session and two 30 min wakefulness sessions (before and after the nap). During these sessions, amplitude modulated (AM) white noise auditory stimuli at different frequencies were applied. High-density EEG was continuously recorded and time-frequency analyses were performed to assess ASSR during wakefulness and NREM periods. Our analysis revealed that depending on the electrode location, stimulation frequency applied and window/frequencies analysed the ASSR was significantly modulated by sleep pressure (before and after sleep), vigilance state (wake vs. NREM sleep), and the presence of slow wave activity and sleep spindles. Furthermore, AM stimuli increased spindle activity during NREM sleep but not during wakefulness. Thus, (1) electrode location, sleep history, vigilance state and ongoing brain activity needs to be carefully considered when investigating ASSR and (2) auditory rhythmic stimuli during sleep might represent a powerful tool to boost sleep spindles. Copyright © 2017 Elsevier Inc. All rights reserved.

[A case of Creutzfeldt-Jakob in the Mexican north-east and review of current concepts on prion disease].

PubMed

Calderón-Garcidueñas, A L; Sagastegui-Rodríguez, J A; Canales-Ibarra, C; Farías-García, R

2001-01-01

The case reported here is that of a 50-year-old man from Saltillo, Coahuila, Mexico, who during the previous 15 months developed a demential syndrome and myoclonia. The brain biopsy led to establish a diagnosis of spongiform encephalopathy. The EEG showed periodic sharp wave complexes over the right hemisphere. A review on about prion diseases is included.

β oscillation during slow wave sleep and rapid eye movement sleep in the electroencephalogram of a transgenic mouse model of Huntington's disease.

PubMed

Jeantet, Yannick; Cayzac, Sebastien; Cho, Yoon H

2013-01-01

To search for early abnormalities in electroencephalogram (EEG) during sleep which may precede motor symptoms in a transgenic mouse model of hereditary neurodegenerative Huntington's disease (HD). In the R6/1 transgenic mouse model of HD, rhythmic brain activity in EEG recordings was monitored longitudinally and across vigilance states through the onset and progression of disease. Mice with chronic electrode implants were recorded monthly over wake-sleep cycles (4 hours), beginning at 9-11 weeks (presymptomatic period) through 6-7 months (symptomatic period). Recording data revealed a unique β rhythm (20-35 Hz), present only in R6/1 transgenic mice, which evolves in close parallel with the disease. In addition, there was an unusual relationship between this β oscillation and vigilance states: while nearly absent during the active waking state, the β oscillation appeared with drowsiness and during slow wave sleep (SWS) and, interestingly, strengthened rather than dissipating when the brain returned to an activated state during rapid eye movement (REM) sleep. In addition to providing a new in vivo biomarker and insight into Huntington's disease pathophysiology, this serendipitous observation opens a window onto the rarely explored neurophysiology of the cortico-basal ganglia circuit during SWS and REM sleep.

The smartphone brain scanner: a portable real-time neuroimaging system.

PubMed

Stopczynski, Arkadiusz; Stahlhut, Carsten; Larsen, Jakob Eg; Petersen, Michael Kai; Hansen, Lars Kai

2014-01-01

Combining low-cost wireless EEG sensors with smartphones offers novel opportunities for mobile brain imaging in an everyday context. Here we present the technical details and validation of a framework for building multi-platform, portable EEG applications with real-time 3D source reconstruction. The system--Smartphone Brain Scanner--combines an off-the-shelf neuroheadset or EEG cap with a smartphone or tablet, and as such represents the first fully portable system for real-time 3D EEG imaging. We discuss the benefits and challenges, including technical limitations as well as details of real-time reconstruction of 3D images of brain activity. We present examples of brain activity captured in a simple experiment involving imagined finger tapping, which shows that the acquired signal in a relevant brain region is similar to that obtained with standard EEG lab equipment. Although the quality of the signal in a mobile solution using an off-the-shelf consumer neuroheadset is lower than the signal obtained using high-density standard EEG equipment, we propose mobile application development may offset the disadvantages and provide completely new opportunities for neuroimaging in natural settings.

Computational Electromagnetic Analysis in a Human Head Model with EEG Electrodes and Leads Exposed to RF-Field Sources at 915 MHz and 1748 MHz

PubMed Central

Angelone, Leonardo M.; Bit-Babik, Giorgi; Chou, Chung-Kwang

2010-01-01

An electromagnetic analysis of a human head with EEG electrodes and leads exposed to RF-field sources was performed by means of Finite-Difference Time-Domain simulations on a 1-mm3 MRI-based human head model. RF-field source models included a half-wave dipole, a patch antenna, and a realistic CAD-based mobile phone at 915 MHz and 1748 MHz. EEG electrodes/leads models included two configurations of EEG leads, both a standard 10–20 montage with 19 electrodes and a 32-electrode cap, and metallic and high resistive leads. Whole-head and peak 10-g average SAR showed less than 20% changes with and without leads. Peak 1-g and 10-g average SARs were below the ICNIRP and IEEE guideline limits. Conversely, a comprehensive volumetric assessment of changes in the RF field with and without metallic EEG leads showed an increase of two orders of magnitude in single-voxel power absorption in the epidermis and a 40-fold increase in the brain during exposure to the 915 MHz mobile phone. Results varied with the geometry and conductivity of EEG electrodes/leads. This enhancement confirms the validity of the question whether any observed effects in studies involving EEG recordings during RF-field exposure are directly related to the RF fields generated by the source or indirectly to the RF-field-induced currents due to the presence of conductive EEG leads. PMID:20681803

Demonstration of brain noise on human EEG signals in perception of bistable images

NASA Astrophysics Data System (ADS)

Grubov, Vadim V.; Runnova, Anastasiya E.; Kurovskaya, Maria K.; Pavlov, Alexey N.; Koronovskii, Alexey A.; Hramov, Alexander E.

2016-03-01

In this report we studied human brain activity in the case of bistable visual perception. We proposed a new approach for quantitative characterization of this activity based on analysis of EEG oscillatory patterns and evoked potentials. Accordingly to theoretical background, obtained experimental EEG data and results of its analysis we studied a characteristics of brain activity during decision-making. Also we have shown that decisionmaking process has the special patterns on the EEG data.

Effect of essential oil and supercritical carbon dioxide extract from the root of Angelica gigas on human EEG activity.

PubMed

Sowndhararajan, Kandhasamy; Seo, Min; Kim, Minju; Kim, Heeyeon; Kim, Songmun

2017-08-01

The present study aimed to investigate the effect of inhalation of essential oil (EO) and supercritical carbon dioxide extract (SC-CO 2 ) from the root of A. gigas on human electroencephalographic (EEG) activity. For this purpose, the EO was obtained from the root of A. gigas by steam distillation and SC-CO 2 was obtained at 50 °C and 400 bar for 1 h. The EEG readings were recorded using the QEEG-8 system from 8 electrode sites according to the International 10-20 system. In the EEG study, the absolute low beta (left temporal and left parietal) activity significantly increased during the inhalation of EO. In the case of SC-CO 2 inhalation, there was no significant change in absolute waves. The results revealed that the EO of A. gigas root produced significant changes in the absolute low beta activity and these changes may enhance the language learning abilities of human brain. Copyright © 2017. Published by Elsevier Ltd.

Electroencephalographic imaging of higher brain function

NASA Technical Reports Server (NTRS)

Gevins, A.; Smith, M. E.; McEvoy, L. K.; Leong, H.; Le, J.

1999-01-01

High temporal resolution is necessary to resolve the rapidly changing patterns of brain activity that underlie mental function. Electroencephalography (EEG) provides temporal resolution in the millisecond range. However, traditional EEG technology and practice provide insufficient spatial detail to identify relationships between brain electrical events and structures and functions visualized by magnetic resonance imaging or positron emission tomography. Recent advances help to overcome this problem by recording EEGs from more electrodes, by registering EEG data with anatomical images, and by correcting the distortion caused by volume conduction of EEG signals through the skull and scalp. In addition, statistical measurements of sub-second interdependences between EEG time-series recorded from different locations can help to generate hypotheses about the instantaneous functional networks that form between different cortical regions during perception, thought and action. Example applications are presented from studies of language, attention and working memory. Along with its unique ability to monitor brain function as people perform everyday activities in the real world, these advances make modern EEG an invaluable complement to other functional neuroimaging modalities.

Prevention of Blast-Related Injuries

DTIC Science & Technology

2017-09-01

allow early screening and assessment of brain abnormality in soldiers to enable timely therapeutic intervention. The current study reports on the...use of qEEG in blast-induced brain injury using a swine model. The purposes are to determine if qEEG can detect brain activity abnormalities early...brain functional abnormalities and deficits in absence of any clinical mTBI symptoms. Methods such as EEG-wavelet entropy measures [36] and Shannon

Use Case Analysis: The Ambulatory EEG in Navy Medicine for Traumatic Brain Injuries

DTIC Science & Technology

2016-12-01

best uses of the device for naval medicine. 14. SUBJECT TERMS traumatic brain injuries, electroencephalography, EEG, use case study 15. NUMBER OF...Traumatic Brain Injury NCS Non-Convulsive Seizures PD Parkinson’s Disease QEEG Quantitative EEG SPECT Single-Photon Emission Computerized Tomography...INTENTIONALLY LEFT BLANK 1 I. INTRODUCTION This study examines the diagnosis of traumatic brain injuries (TBI). Early detection and diagnosis is

Electroencephalography and quantitative electroencephalography in mild traumatic brain injury.

PubMed

Haneef, Zulfi; Levin, Harvey S; Frost, James D; Mizrahi, Eli M

2013-04-15

Mild traumatic brain injury (mTBI) causes brain injury resulting in electrophysiologic abnormalities visible in electroencephalography (EEG) recordings. Quantitative EEG (qEEG) makes use of quantitative techniques to analyze EEG characteristics such as frequency, amplitude, coherence, power, phase, and symmetry over time independently or in combination. QEEG has been evaluated for its use in making a diagnosis of mTBI and assessing prognosis, including the likelihood of progressing to the postconcussive syndrome (PCS) phase. We review the EEG and qEEG changes of mTBI described in the literature. An attempt is made to separate the findings seen during the acute, subacute, and chronic phases after mTBI. Brief mention is also made of the neurobiological correlates of qEEG using neuroimaging techniques or in histopathology. Although the literature indicates the promise of qEEG in making a diagnosis and indicating prognosis of mTBI, further study is needed to corroborate and refine these methods.

Electroencephalography and Quantitative Electroencephalography in Mild Traumatic Brain Injury

PubMed Central

Levin, Harvey S.; Frost, James D.; Mizrahi, Eli M.

2013-01-01

Abstract Mild traumatic brain injury (mTBI) causes brain injury resulting in electrophysiologic abnormalities visible in electroencephalography (EEG) recordings. Quantitative EEG (qEEG) makes use of quantitative techniques to analyze EEG characteristics such as frequency, amplitude, coherence, power, phase, and symmetry over time independently or in combination. QEEG has been evaluated for its use in making a diagnosis of mTBI and assessing prognosis, including the likelihood of progressing to the postconcussive syndrome (PCS) phase. We review the EEG and qEEG changes of mTBI described in the literature. An attempt is made to separate the findings seen during the acute, subacute, and chronic phases after mTBI. Brief mention is also made of the neurobiological correlates of qEEG using neuroimaging techniques or in histopathology. Although the literature indicates the promise of qEEG in making a diagnosis and indicating prognosis of mTBI, further study is needed to corroborate and refine these methods. PMID:23249295

How do children fall asleep? A high-density EEG study of slow waves in the transition from wake to sleep.

PubMed

Spiess, Mathilde; Bernardi, Giulio; Kurth, Salome; Ringli, Maya; Wehrle, Flavia M; Jenni, Oskar G; Huber, Reto; Siclari, Francesca

2018-05-17

Slow waves, the hallmarks of non-rapid eye-movement (NREM) sleep, are thought to reflect maturational changes that occur in the cerebral cortex throughout childhood and adolescence. Recent work in adults has revealed evidence for two distinct synchronization processes involved in the generation of slow waves, which sequentially come into play in the transition to sleep. In order to understand how these two processes are affected by developmental changes, we compared slow waves between children and young adults in the falling asleep period. The sleep onset period (starting 30s before end of alpha activity and ending at the first slow wave sequence) was extracted from 72 sleep onset high-density EEG recordings (128 electrodes) of 49 healthy subjects (age 8-25). Using an automatic slow wave detection algorithm, the number, amplitude and slope of slow waves were analyzed and compared between children (age 8-11) and young adults (age 20-25). Slow wave number and amplitude increased linearly in the falling asleep period in children, while in young adults, isolated high-amplitude slow waves (type I) dominated initially and numerous smaller slow waves (type II) with progressively increasing amplitude occurred later. Compared to young adults, children displayed faster increases in slow wave amplitude and number across the falling asleep period in central and posterior brain regions, respectively, and also showed larger slow waves during wakefulness immediately prior to sleep. Children do not display the two temporally dissociated slow wave synchronization processes in the falling asleep period observed in adults, suggesting that maturational factors underlie the temporal segregation of these two processes. Our findings provide novel perspectives for studying how sleep-related behaviors and dreaming differ between children and adults. Copyright © 2018 Elsevier Inc. All rights reserved.

Biosensor Technologies for Augmented Brain-Computer Interfaces in the Next Decades

DTIC Science & Technology

2012-05-13

Research Triangle Park, NC 27709-2211 Augmented brain–computer interface (ABCI);biosensor; cognitive-state monitoring; electroencephalogram( EEG ); human...biosensor; cognitive-state monitoring; electroencephalogram ( EEG ); human brain imaging Manuscript received November 28, 2011; accepted December 20...magnetic reso- nance imaging (fMRI) [1], positron emission tomography (PET) [2], electroencephalograms ( EEGs ) and optical brain imaging techniques (i.e

Effects of oral amines on the EEG.

PubMed

Scott, D F; Moffett, A M; Swash, M

1977-02-01

Oral tyramine activated pre-existing episodic EEG abnormalities--namely, sharp waves, spike and wave, and localised theta activity--in epileptic patients. Little change was found in the EEGs of migrainous subjects after chocolate or beta-phenylethylamine. The implications of the findings with tyramine are discussed.

Changes in the electroencephalogram during anaesthesia and their physiological basis.

PubMed

Hagihira, S

2015-07-01

The use of EEG monitors to assess the level of hypnosis during anaesthesia has become widespread. Anaesthetists, however, do not usually observe the raw EEG data: they generally pay attention only to the Bispectral Index (BIS™) and other indices calculated by EEG monitors. This abstracted information only partially characterizes EEG features. To properly appreciate the availability and reliability of EEG-derived indices, it is necessary to understand how raw EEG changes during anaesthesia. With hemi-frontal lead EEGs obtained under volatile anaesthesia or propofol anaesthesia, the dominant EEG frequency decreases and the amplitude increases with increasing concentrations of anaesthetic. Looking more closely, the EEG changes are more complicated. At surgical concentrations of anaesthesia, spindle waves (alpha range) become dominant. At deeper levels, this activity decreases, and theta and delta waves predominate. At even deeper levels, EEG waveform changes into a burst and suppression pattern, and finally becomes flat. EEG waveforms vary in the presence of noxious stimuli (surgical skin incision), which is not always reflected in BIS™, or other processed EEG indices. Spindle waves are adequately sensitive, however, to noxious stimuli: under surgical anaesthesia they disappear when noxious stimuli are applied, and reappear when adequate analgesia is obtained. To prevent awareness during anaesthesia, I speculate that the most effective strategy is to administer anaesthetic agents in such a way as to maintain anaesthesia at a level where spindle waves predominate. © The Author 2015. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands.

PubMed

Deligianni, Fani; Centeno, Maria; Carmichael, David W; Clayden, Jonathan D

2014-01-01

Whole brain functional connectomes hold promise for understanding human brain activity across a range of cognitive, developmental and pathological states. So called resting-state (rs) functional MRI studies have contributed to the brain being considered at a macroscopic scale as a set of interacting regions. Interactions are defined as correlation-based signal measurements driven by blood oxygenation level dependent (BOLD) contrast. Understanding the neurophysiological basis of these measurements is important in conveying useful information about brain function. Local coupling between BOLD fMRI and neurophysiological measurements is relatively well defined, with evidence that gamma (range) frequency EEG signals are the closest correlate of BOLD fMRI changes during cognitive processing. However, it is less clear how whole-brain network interactions relate during rest where lower frequency signals have been suggested to play a key role. Simultaneous EEG-fMRI offers the opportunity to observe brain network dynamics with high spatio-temporal resolution. We utilize these measurements to compare the connectomes derived from rs-fMRI and EEG band limited power (BLP). Merging this multi-modal information requires the development of an appropriate statistical framework. We relate the covariance matrices of the Hilbert envelope of the source localized EEG signal across bands to the covariance matrices derived from rs-fMRI with the means of statistical prediction based on sparse Canonical Correlation Analysis (sCCA). Subsequently, we identify the most prominent connections that contribute to this relationship. We compare whole-brain functional connectomes based on their geodesic distance to reliably estimate the performance of the prediction. The performance of predicting fMRI from EEG connectomes is considerably better than predicting EEG from fMRI across all bands, whereas the connectomes derived in low frequency EEG bands resemble best rs-fMRI connectivity.

Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands

PubMed Central

Deligianni, Fani; Centeno, Maria; Carmichael, David W.; Clayden, Jonathan D.

2014-01-01

Whole brain functional connectomes hold promise for understanding human brain activity across a range of cognitive, developmental and pathological states. So called resting-state (rs) functional MRI studies have contributed to the brain being considered at a macroscopic scale as a set of interacting regions. Interactions are defined as correlation-based signal measurements driven by blood oxygenation level dependent (BOLD) contrast. Understanding the neurophysiological basis of these measurements is important in conveying useful information about brain function. Local coupling between BOLD fMRI and neurophysiological measurements is relatively well defined, with evidence that gamma (range) frequency EEG signals are the closest correlate of BOLD fMRI changes during cognitive processing. However, it is less clear how whole-brain network interactions relate during rest where lower frequency signals have been suggested to play a key role. Simultaneous EEG-fMRI offers the opportunity to observe brain network dynamics with high spatio-temporal resolution. We utilize these measurements to compare the connectomes derived from rs-fMRI and EEG band limited power (BLP). Merging this multi-modal information requires the development of an appropriate statistical framework. We relate the covariance matrices of the Hilbert envelope of the source localized EEG signal across bands to the covariance matrices derived from rs-fMRI with the means of statistical prediction based on sparse Canonical Correlation Analysis (sCCA). Subsequently, we identify the most prominent connections that contribute to this relationship. We compare whole-brain functional connectomes based on their geodesic distance to reliably estimate the performance of the prediction. The performance of predicting fMRI from EEG connectomes is considerably better than predicting EEG from fMRI across all bands, whereas the connectomes derived in low frequency EEG bands resemble best rs-fMRI connectivity. PMID:25221467

Changes of brain response induced by simulated weightlessness

NASA Astrophysics Data System (ADS)

Wei, Jinhe; Yan, Gongdong; Guan, Zhiqiang

The characteristics change of brain response was studied during 15° head-down tilt (HDT) comparing with 45° head-up tilt (HUT). The brain responses evaluated included the EEG power spectra change at rest and during mental arithmetic, and the event-related potentials (ERPs) of somatosensory, selective attention and mental arithmetic activities. The prominent feature of brain response change during HDT revealed that the brain function was inhibited to some extent. Such inhibition included that the significant increment of "40Hz" activity during HUT arithmetic almost disappeared during HDT arithmetic, and that the positive-potential effect induced by HDT presented in all kinds of ERPs measured, but the slow negative wave reflecting mental arithmetic and memory process was elongated. These data suggest that the brain function be affected profoundly by the simulated weightlessness, therefore, the brain function change during space flight should be studied systematically.

Graph Theory at the Service of Electroencephalograms.

PubMed

Iakovidou, Nantia D

2017-04-01

The brain is one of the largest and most complex organs in the human body and EEG is a noninvasive electrophysiological monitoring method that is used to record the electrical activity of the brain. Lately, the functional connectivity in human brain has been regarded and studied as a complex network using EEG signals. This means that the brain is studied as a connected system where nodes, or units, represent different specialized brain regions and links, or connections, represent communication pathways between the nodes. Graph theory and theory of complex networks provide a variety of measures, methods, and tools that can be useful to efficiently model, analyze, and study EEG networks. This article is addressed to computer scientists who wish to be acquainted and deal with the study of EEG data and also to neuroscientists who would like to become familiar with graph theoretic approaches and tools to analyze EEG data.

Combined EEG-fNIRS decoding of motor attempt and imagery for brain switch control: an offline study in patients with tetraplegia.

PubMed

Blokland, Yvonne; Spyrou, Loukianos; Thijssen, Dick; Eijsvogels, Thijs; Colier, Willy; Floor-Westerdijk, Marianne; Vlek, Rutger; Bruhn, Jorgen; Farquhar, Jason

2014-03-01

Combining electrophysiological and hemodynamic features is a novel approach for improving current performance of brain switches based on sensorimotor rhythms (SMR). This study was conducted with a dual purpose: to test the feasibility of using a combined electroencephalogram/functional near-infrared spectroscopy (EEG-fNIRS) SMR-based brain switch in patients with tetraplegia, and to examine the performance difference between motor imagery and motor attempt for this user group. A general improvement was found when using both EEG and fNIRS features for classification as compared to using the single-modality EEG classifier, with average classification rates of 79% for attempted movement and 70% for imagined movement. For the control group, rates of 87% and 79% were obtained, respectively, where the "attempted movement" condition was replaced with "actual movement." A combined EEG-fNIRS system might be especially beneficial for users who lack sufficient control of current EEG-based brain switches. The average classification performance in the patient group for attempted movement was significantly higher than for imagined movement using the EEG-only as well as the combined classifier, arguing for the case of a paradigm shift in current brain switch research.

Determination of awareness in patients with severe brain injury using EEG power spectral analysis

PubMed Central

Goldfine, Andrew M.; Victor, Jonathan D.; Conte, Mary M.; Bardin, Jonathan C.; Schiff, Nicholas D.

2011-01-01

Objective To determine whether EEG spectral analysis could be used to demonstrate awareness in patients with severe brain injury. Methods We recorded EEG from healthy controls and three patients with severe brain injury, ranging from minimally conscious state (MCS) to locked-in-state (LIS), while they were asked to imagine motor and spatial navigation tasks. We assessed EEG spectral differences from 4 to 24 Hz with univariate comparisons (individual frequencies) and multivariate comparisons (patterns across the frequency range). Results In controls, EEG spectral power differed at multiple frequency bands and channels during performance of both tasks compared to a resting baseline. As patterns of signal change were inconsistent between controls, we defined a positive response in patient subjects as consistent spectral changes across task performances. One patient in MCS and one in LIS showed evidence of motor imagery task performance, though with patterns of spectral change different from the controls. Conclusion EEG power spectral analysis demonstrates evidence for performance of mental imagery tasks in healthy controls and patients with severe brain injury. Significance EEG power spectral analysis can be used as a flexible bedside tool to demonstrate awareness in brain-injured patients who are otherwise unable to communicate. PMID:21514214

Development and genetics of brain temporal stability related to attention problems in adolescent twins.

PubMed

Smit, Dirk J A; Anokhin, Andrey P

2017-05-01

The brain continuously develops and reorganizes to support an expanding repertoire of behaviors and increasingly complex cognition. These processes may, however, also result in the appearance or disappearance of specific neurodevelopmental disorders such as attention problems. To investigate whether brain activity changed during adolescence, how genetics shape this change, and how these changes were related to attention problems, we measured EEG activity in 759 twins and siblings, assessed longitudinally in four waves (12, 14, 16, and 18years of age). Attention problems were assessed with the SWAN at waves 12, 14, and 16. To characterize functional brain development, we used a measure of temporal stability (TS) of brain oscillations over the recording time of 5min reflecting the tendency of a brain to maintain the same oscillatory state for longer or shorter periods. Increased TS may reflect the brain's tendency to maintain stability, achieve focused attention, and thus reduce "mind wandering" and attention problems. The results indicate that brain TS is increased across the scalp from 12 to 18. TS showed large individual differences that were heritable. Change in TS (alpha oscillations) was heritable between 12 and 14 and between 14 and 16 for the frontal brain areas. Absolute levels of brain TS at each wave were positively correlated with attention problems but not significantly. High and low attention problems subjects showed different developmental trajectories in TS, which was significant in a cluster of frontal leads. These results indicate that trajectories in brain TS development are a biomarker for the developing brain. TS in brain oscillations is highly heritable, and age-related change in TS is also heritable in selected brain areas. These results suggest that high and low attention problems subjects are at different stages of brain development. Copyright © 2016. Published by Elsevier B.V.

Change in the electroencephalogram delta wave in the frontal cranial region of rats with the hyperventilation.

PubMed

Kim, Young Sik; An, Sun Joung; Lee, Hu Jang; Choi, Hyun Ju

2012-04-30

Hyperventilation is one way to cause activation on the electroencephalogram (EEG) to diagnose brain disorders. The hyperventilation is also known to affect on the delta power in EEG. This study divided the total delta wave into low, middle, and high bands corresponding to the wave frequency. The power in these three delta wave bands was examined in the frontal cranial region of adult male Sprague-Dawley rats hyperventilated with ventilation (VE) of 360, 540, and 720 ml/min for 5 min. The control group was ventilated normally with a volume of 160 ml/min. The results show that the relative power of the low delta band in the rats hyperventilated at 360 ml/min VE was significantly increased compared with powers of pre-hyperventilation (p<0.05). The relative power of the middle delta band was not significantly affected by hyperventilation at any VE, and in the high delta band, all of the relative powers were decreased significantly in all hyperventilated rats compared with powers of pre-hyperventilation (p<0.05). We concluded that hyperventilation affects the frontal cranial region, by increasing the low delta band and decreasing the high delta band. Copyright © 2012 Elsevier B.V. All rights reserved.

Towards deep brain monitoring with superficial EEG sensors plus neuromodulatory focused ultrasound

PubMed Central

Darvas, F; Mehić, E; Caler, CJ; Ojemann, JG; Mourad, PD

2017-01-01

Noninvasive recordings of electrophysiological activity have limited anatomical specificity and depth. We hypothesized that spatially tagging a small volume of brain with a unique electroencephalogram (EEG) signal induced by pulsed focused ultrasound (pFU) could overcome those limitations. As a first step towards testing this hypothesis, we applied transcranial ultrasound (2 MHz, 200 microsecond-long pulses applied at 1050 Hz for one second at a spatial peak temporal average intensity of 1.4 W/cm2) to the brains of anesthetized rats while simultaneously recording EEG signals. We observed a significant 1050 Hz electrophysiological signal only when ultrasound was applied to living brain. Moreover, amplitude demodulation of the EEG signal at 1050 Hz yielded measurement of gamma band (>30 Hz) brain activity consistent with direct measurements of that activity. These results represent preliminary support for use of pFU as a spatial tagging mechanism for non-invasive EEG-based mapping of deep brain activity with high spatial resolution. PMID:27181686

Effects of oral amines on the EEG.

PubMed Central

Scott, D F; Moffett, A M; Swash, M

1977-01-01

Oral tyramine activated pre-existing episodic EEG abnormalities--namely, sharp waves, spike and wave, and localised theta activity--in epileptic patients. Little change was found in the EEGs of migrainous subjects after chocolate or beta-phenylethylamine. The implications of the findings with tyramine are discussed. Images PMID:864482

Video-EEG recordings in full-term neonates of diabetic mothers: observational study.

PubMed

Castro Conde, José Ramón; González González, Nieves Luisa; González Barrios, Desiré; González Campo, Candelaria; Suárez Hernández, Yaiza; Sosa Comino, Elena

2013-11-01

To determine whether full-term newborn infants of diabetic mothers (IDM) present immature/disorganised EEG patterns in the immediate neonatal period, and whether there was any relationship with maternal glycaemic control. Cohort study with an incidental sample performed in a tertiary hospital neonatal unit. 23 IDM and 22 healthy newborns born between 2010 and 2013. All underwent video-EEG recording lasting >90 min at 48-72 h of life. We analysed the percentage of indeterminate sleep, transient sharp waves per hour and mature-for-gestational age EEG patterns (discontinuity, maximum duration of interburst interval (IBI), asynchrony, asymmetry, δ brushes, encoches frontales and α/θ rolandic activity). The group of IDM was divided into two subgroups according to maternal HbA1c: (1) HbA1c≥6% and (2) HbA1c<6%. Compared with healthy newborns, IDM presented significantly higher percentage of indeterminate sleep (57% vs 25%; p<0.001), discontinuity (2.5% vs 0%; p=0.044) and δ brushes in the bursts (6% vs 3%; p=0.024); higher duration of IBI (0.3 s vs 0 s; p=0.017); fewer encoches frontales (7/h vs 35/h; p<0.001), reduced θ/α rolandic activity (3/h vs 9/h; p<0.001); and more transient sharp waves (25/h vs 5/h; p<0.001). IDM with maternal HbA1c≥6% showed greater percentage of δ brushes in the burst (14% vs 4%; p=0.007). Full-term IDM newborns showed video-EEG features of abnormal development of brain function. Maternal HbA1c levels<6% during pregnancy could minimise the risk of cerebral dysmaturity.

Restricting Time in Bed in Early Adolescence Reduces Both NREM and REM Sleep but Does Not Increase Slow Wave EEG

PubMed Central

Campbell, Ian G.; Kraus, Amanda M.; Burright, Christopher S.; Feinberg, Irwin

2016-01-01

Study Objectives: School night total sleep time decreases across adolescence (9–18 years) by 10 min/year. This decline is comprised entirely of a selective decrease in NREM sleep; REM sleep actually increases slightly. Decreasing sleep duration across adolescence is often attributed to insufficient time in bed. Here we tested whether sleep restriction in early adolescence produces the same sleep stage changes observed on school nights across adolescence. Methods: All-night sleep EEG was recorded in 76 children ranging in age from 9.9 to 14.0 years. Each participant kept 3 different sleep schedules that consisted of 3 nights of 8.5 h in bed followed by 4 nights of either 7, 8.5, or 10 h in bed. Sleep stage durations and NREM delta EEG activity were compared across the 3 time in bed conditions. Results: Shortening time in bed from 10 to 7 hours reduced sleep duration by approximately 2 hours, roughly equal to the decrease in sleep duration we recorded longitudinally across adolescence. However, sleep restriction significantly reduced both NREM (by 83 min) and REM (by 47 min) sleep. Sleep restriction did not affect NREM delta EEG activity. Conclusions: Our findings suggest that the selective NREM reduction and the small increase in REM we observed longitudinally across 9–18 years are not produced by sleep restriction. We hypothesize that the selective NREM decline reflects adolescent brain maturation (synaptic elimination) that reduces the need for the restorative processes of NREM sleep. Citation: Campbell IG, Kraus AM, Burright CS, Feinberg I. Restricting time in bed in early adolescence reduces both NREM and REM sleep but does not increase slow wave EEG. SLEEP 2016;39(9):1663–1670. PMID:27397569

Data acquisition instrument for EEG based on embedded system

NASA Astrophysics Data System (ADS)

Toresano, La Ode Husein Z.; Wijaya, Sastra Kusuma; Prawito, Sudarmaji, Arief; Syakura, Abdan; Badri, Cholid

2017-02-01

An electroencephalogram (EEG) is a device for measuring and recording the electrical activity of brain. The EEG data of signal can be used as a source of analysis for human brain function. The purpose of this study was to design a portable multichannel EEG based on embedded system and ADS1299. The ADS1299 is an analog front-end to be used as an Analog to Digital Converter (ADC) to convert analog signal of electrical activity of brain, a filter of electrical signal to reduce the noise on low-frequency band and a data communication to the microcontroller. The system has been tested to capture brain signal within a range of 1-20 Hz using the NETECH EEG simulator 330. The developed system was relatively high accuracy of more than 82.5%. The EEG Instrument has been successfully implemented to acquire the brain signal activity using a PC (Personal Computer) connection for displaying the recorded data. The final result of data acquisition has been processed using OpenBCI GUI (Graphical User Interface) based through real-time process for 8-channel signal acquisition, brain-mapping and power spectral decomposition signal using the standard FFT (Fast Fourier Transform) algorithm.

The Smartphone Brain Scanner: A Portable Real-Time Neuroimaging System

PubMed Central

Stopczynski, Arkadiusz; Stahlhut, Carsten; Larsen, Jakob Eg; Petersen, Michael Kai; Hansen, Lars Kai

2014-01-01

Combining low-cost wireless EEG sensors with smartphones offers novel opportunities for mobile brain imaging in an everyday context. Here we present the technical details and validation of a framework for building multi-platform, portable EEG applications with real-time 3D source reconstruction. The system – Smartphone Brain Scanner – combines an off-the-shelf neuroheadset or EEG cap with a smartphone or tablet, and as such represents the first fully portable system for real-time 3D EEG imaging. We discuss the benefits and challenges, including technical limitations as well as details of real-time reconstruction of 3D images of brain activity. We present examples of brain activity captured in a simple experiment involving imagined finger tapping, which shows that the acquired signal in a relevant brain region is similar to that obtained with standard EEG lab equipment. Although the quality of the signal in a mobile solution using an off-the-shelf consumer neuroheadset is lower than the signal obtained using high-density standard EEG equipment, we propose mobile application development may offset the disadvantages and provide completely new opportunities for neuroimaging in natural settings. PMID:24505263

Recording of amplitude-integrated electroencephalography, oxygen saturation, pulse rate, and cerebral blood flow during massage of premature infants.

PubMed

Rudnicki, Jacek; Boberski, Marek; Butrymowicz, Ewa; Niedbalski, Paweł; Ogniewski, Paweł; Niedbalski, Marek; Niedbalski, Zbigniew; Podraza, Wojciech; Podraza, Hanna

2012-08-01

Stimulation of the nervous system plays an important role in brain function and psychomotor development of children. Massage can benefit premature infants, but has limitations. The authors conducted a study to verify the direct effects of massage on amplitude-integrated electroencephalography (aEEG), oxygen saturation (SaO(2)), and pulse analyzed by color cerebral function monitor (CCFM) and cerebral blood flow assessed by the Doppler technique. The amplitude of the aEEG trend during massage significantly increased. Massage also impacted the dominant frequency δ waves. Frequency significantly increased during the massage and return to baseline after treatment. SaO(2) significantly decreased during massage. In four premature infants, massage was discontinued due to desaturation below 85%. Pulse frequency during the massage decreased but remained within physiological limits of greater than 100 beats per minute in all infants. Doppler flow values in the anterior cerebral artery measured before and after massage did not show statistically significant changes. Resistance index after massage decreased, which might provide greater perfusion of the brain, but this difference was not statistically significant. Use of the CCFM device allows for monitoring of three basic physiologic functions, namely aEEG, SaO(2), and pulse, and increases the safety of massage in preterm infants. Copyright © 2012 by Thieme Medical Publishers

Imagined Hand Clenching Force and Speed Modulate Brain Activity and Are Classified by NIRS Combined With EEG.

PubMed

Fu, Yunfa; Xiong, Xin; Jiang, Changhao; Xu, Baolei; Li, Yongcheng; Li, Hongyi

2017-09-01

Simultaneous acquisition of brain activity signals from the sensorimotor area using NIRS combined with EEG, imagined hand clenching force and speed modulation of brain activity, as well as 6-class classification of these imagined motor parameters by NIRS-EEG were explored. Near infrared probes were aligned with C3 and C4, and EEG electrodes were placed midway between the NIRS probes. NIRS and EEG signals were acquired from six healthy subjects during six imagined hand clenching force and speed tasks involving the right hand. The results showed that NIRS combined with EEG is effective for simultaneously measuring brain activity of the sensorimotor area. The study also showed that in the duration of (0, 10) s for imagined force and speed of hand clenching, HbO first exhibited a negative variation trend, which was followed by a negative peak. After the negative peak, it exhibited a positive variation trend with a positive peak about 6-8 s after termination of imagined movement. During (-2, 1) s, the EEG may have indicated neural processing during the preparation, execution, and monitoring of a given imagined force and speed of hand clenching. The instantaneous phase, frequency, and amplitude feature of the EEG were calculated by Hilbert transform; HbO and the difference between HbO and Hb concentrations were extracted. The features of NIRS and EEG were combined to classify three levels of imagined force [at 20/50/80% MVGF (maximum voluntary grip force)] and speed (at 0.5/1/2 Hz) of hand clenching by SVM. The average classification accuracy of the NIRS-EEG fusion feature was 0.74 ± 0.02. These results may provide increased control commands of force and speed for a brain-controlled robot based on NIRS-EEG.

Early Oxygen-Utilization and Brain Activity in Preterm Infants

PubMed Central

de Vries, Linda S.; Groenendaal, Floris; Toet, Mona C.; Lemmers, Petra M. A.; Vosse van de, Renè E.; van Bel, Frank; Benders, Manon J. N. L.

2015-01-01

The combined monitoring of oxygen supply and delivery using Near-InfraRed spectroscopy (NIRS) and cerebral activity using amplitude-integrated EEG (aEEG) could yield new insights into brain metabolism and detect potentially vulnerable conditions soon after birth. The relationship between NIRS and quantitative aEEG/EEG parameters has not yet been investigated. Our aim was to study the association between oxygen utilization during the first 6 h after birth and simultaneously continuously monitored brain activity measured by aEEG/EEG. Forty-four hemodynamically stable babies with a GA < 28 weeks, with good quality NIRS and aEEG/EEG data available and who did not receive morphine were included in the study. aEEG and NIRS monitoring started at NICU admission. The relation between regional cerebral oxygen saturation (rScO2) and cerebral fractional tissue oxygen extraction (cFTOE), and quantitative measurements of brain activity such as number of spontaneous activity transients (SAT) per minute (SAT rate), the interval in seconds (i.e. time) between SATs (ISI) and the minimum amplitude of the EEG in μV (min aEEG) were evaluated. rScO2 was negatively associated with SAT rate (β=-3.45 [CI=-5.76- -1.15], p=0.004) and positively associated with ISI (β=1.45 [CI=0.44-2.45], p=0.006). cFTOE was positively associated with SAT rate (β=0.034 [CI=0.009-0.059], p=0.008) and negatively associated with ISI (β=-0.015 [CI=-0.026- -0.004], p=0.007). Oxygen delivery and utilization, as indicated by rScO2 and cFTOE, are directly related to functional brain activity, expressed by SAT rate and ISI during the first hours after birth, showing an increase in oxygen extraction in preterm infants with increased early electro-cerebral activity. NIRS monitored oxygenation may be a useful biomarker of brain vulnerability in high-risk infants. PMID:25965343

Fast detection of covert visuospatial attention using hybrid N2pc and SSVEP features

NASA Astrophysics Data System (ADS)

Xu, Minpeng; Wang, Yijun; Nakanishi, Masaki; Wang, Yu-Te; Qi, Hongzhi; Jung, Tzyy-Ping; Ming, Dong

2016-12-01

Objective. Detecting the shift of covert visuospatial attention (CVSA) is vital for gaze-independent brain-computer interfaces (BCIs), which might be the only communication approach for severely disabled patients who cannot move their eyes. Although previous studies had demonstrated that it is feasible to use CVSA-related electroencephalography (EEG) features to control a BCI system, the communication speed remains very low. This study aims to improve the speed and accuracy of CVSA detection by fusing EEG features of N2pc and steady-state visual evoked potential (SSVEP). Approach. A new paradigm was designed to code the left and right CVSA with the N2pc and SSVEP features, which were then decoded by a classification strategy based on canonical correlation analysis. Eleven subjects were recruited to perform an offline experiment in this study. Temporal waves, amplitudes, and topographies for brain responses related to N2pc and SSVEP were analyzed. The classification accuracy derived from the hybrid EEG features (SSVEP and N2pc) was compared with those using the single EEG features (SSVEP or N2pc). Main results. The N2pc could be significantly enhanced under certain conditions of SSVEP modulations. The hybrid EEG features achieved significantly higher accuracy than the single features. It obtained an average accuracy of 72.9% by using a data length of 400 ms after the attention shift. Moreover, the average accuracy reached ˜80% (peak values above 90%) when using 2 s long data. Significance. The results indicate that the combination of N2pc and SSVEP is effective for fast detection of CVSA. The proposed method could be a promising approach for implementing a gaze-independent BCI.

Source reconstruction via the spatiotemporal Kalman filter and LORETA from EEG time series with 32 or fewer electrodes.

PubMed

Hamid, Laith; Al Farawn, Ali; Merlet, Isabelle; Japaridze, Natia; Heute, Ulrich; Stephani, Ulrich; Galka, Andreas; Wendling, Fabrice; Siniatchkin, Michael

2017-07-01

The clinical routine of non-invasive electroencephalography (EEG) is usually performed with 8-40 electrodes, especially in long-term monitoring, infants or emergency care. There is a need in clinical and scientific brain imaging to develop inverse solution methods that can reconstruct brain sources from these low-density EEG recordings. In this proof-of-principle paper we investigate the performance of the spatiotemporal Kalman filter (STKF) in EEG source reconstruction with 9-, 19- and 32- electrodes. We used simulated EEG data of epileptic spikes generated from lateral frontal and lateral temporal brain sources using state-of-the-art neuronal population models. For validation of source reconstruction, we compared STKF results to the location of the simulated source and to the results of low-resolution brain electromagnetic tomography (LORETA) standard inverse solution. STKF consistently showed less localization bias compared to LORETA, especially when the number of electrodes was decreased. The results encourage further research into the application of the STKF in source reconstruction of brain activity from low-density EEG recordings.

Use of the Stockwell Transform in the Detection of P300 Evoked Potentials with Low-Cost Brain Sensors.

PubMed

Pérez-Vidal, Alan F; Garcia-Beltran, Carlos D; Martínez-Sibaja, Albino; Posada-Gómez, Rubén

2018-05-09

The evoked potential is a neuronal activity that originates when a stimulus is presented. To achieve its detection, various techniques of brain signal processing can be used. One of the most studied evoked potentials is the P300 brain wave, which usually appears between 300 and 500 ms after the stimulus. Currently, the detection of P300 evoked potentials is of great importance due to its unique properties that allow the development of applications such as spellers, lie detectors, and diagnosis of psychiatric disorders. The present study was developed to demonstrate the usefulness of the Stockwell transform in the process of identifying P300 evoked potentials using a low-cost electroencephalography (EEG) device with only two brain sensors. The acquisition of signals was carried out using the Emotiv EPOC ® device—a wireless EEG headset. In the feature extraction, the Stockwell transform was used to obtain time-frequency information. The algorithms of linear discriminant analysis and a support vector machine were used in the classification process. The experiments were carried out with 10 participants; men with an average age of 25.3 years in good health. In general, a good performance (75⁻92%) was obtained in identifying P300 evoked potentials.

Guiding transcranial brain stimulation by EEG/MEG to interact with ongoing brain activity and associated functions: A position paper

PubMed Central

Thut, Gregor; Bergmann, Til Ole; Fröhlich, Flavio; Soekadar, Surjo R.; Brittain, John-Stuart; Valero-Cabré, Antoni; Sack, Alexander; Miniussi, Carlo; Antal, Andrea; Siebner, Hartwig Roman; Ziemann, Ulf; Herrmann, Christoph S.

2017-01-01

Non-invasive transcranial brain stimulation (NTBS) techniques have a wide range of applications but also suffer from a number of limitations mainly related to poor specificity of intervention and variable effect size. These limitations motivated recent efforts to focus on the temporal dimension of NTBS with respect to the ongoing brain activity. Temporal patterns of ongoing neuronal activity, in particular brain oscillations and their fluctuations, can be traced with electro- or magnetoencephalography (EEG/MEG), to guide the timing as well as the stimulation settings of NTBS. These novel, online and offline EEG/MEG-guided NTBS-approaches are tailored to specifically interact with the underlying brain activity. Online EEG/MEG has been used to guide the timing of NTBS (i.e., when to stimulate): by taking into account instantaneous phase or power of oscillatory brain activity, NTBS can be aligned to fluctuations in excitability states. Moreover, offline EEG/MEG recordings prior to interventions can inform researchers and clinicians how to stimulate: by frequency-tuning NTBS to the oscillation of interest, intrinsic brain oscillations can be up- or down-regulated. In this paper, we provide an overview of existing approaches and ideas of EEG/MEG-guided interventions, and their promises and caveats. We point out potential future lines of research to address challenges. PMID:28233641

Correlations between personality traits and specific groups of alpha waves in the human EEG.

PubMed

Johannisson, Tomas

2016-01-01

Background. Different individuals have alpha waves with different wavelengths. The distribution of the wavelengths is assumed to be bell-shaped and smooth. Although this view is generally accepted, it is still just an assumption and has never been critically tested. When exploring the relationship between alpha waves and personality traits, it makes a huge difference if the distribution of the alpha waves is smooth or if specific groups of alpha waves can be demonstrated. Previous studies have not considered the possibility that specific groups of alpha waves may exist. Methods. Computerized EEGs have become standard, but wavelength measurements are problematic when based on averaging procedures using the Fourier transformation because such procedures cause a large systematic error. If the actual wavelength is of interest, it is necessary to go back to basic physiology and use raw EEG signals. In the present study, measurements were made directly from sequences of alpha waves where every wave could be identified. Personality dimensions were measured using an inventory derived from the International Personality Item Pool. Results. Recordings from 200 healthy individuals revealed that there are three main groups of alpha waves. These groups had frequencies around 8, 10, and 12 waves per second. The middle group had a bimodal distribution, and a subdivision gave a total of four alpha groups. In the center of each group, the degree of extraversion was high and the degree of neuroticism was low. Many small differences in personality traits were found when the centers were compared with one another. This gave four personality profiles that resemble the four classical temperaments. When people in the surrounding zones were compared with those in the centers, relatively large differences in personality traits were found. Conclusions. Specific groups of alpha waves exist, and these groups have to be taken into account when correlations are made to personality dimensions and temperament types. There is a link between alpha waves and personality traits, and this link implies that there is an underlying relationship. To explain the nature of this relationship, there are two hypotheses that can be applied. One of these deals with the general organization of the forebrain and the other explains why the brain generates alpha waves.

Simultaneous EEG and MEG source reconstruction in sparse electromagnetic source imaging.

PubMed

Ding, Lei; Yuan, Han

2013-04-01

Electroencephalography (EEG) and magnetoencephalography (MEG) have different sensitivities to differently configured brain activations, making them complimentary in providing independent information for better detection and inverse reconstruction of brain sources. In the present study, we developed an integrative approach, which integrates a novel sparse electromagnetic source imaging method, i.e., variation-based cortical current density (VB-SCCD), together with the combined use of EEG and MEG data in reconstructing complex brain activity. To perform simultaneous analysis of multimodal data, we proposed to normalize EEG and MEG signals according to their individual noise levels to create unit-free measures. Our Monte Carlo simulations demonstrated that this integrative approach is capable of reconstructing complex cortical brain activations (up to 10 simultaneously activated and randomly located sources). Results from experimental data showed that complex brain activations evoked in a face recognition task were successfully reconstructed using the integrative approach, which were consistent with other research findings and validated by independent data from functional magnetic resonance imaging using the same stimulus protocol. Reconstructed cortical brain activations from both simulations and experimental data provided precise source localizations as well as accurate spatial extents of localized sources. In comparison with studies using EEG or MEG alone, the performance of cortical source reconstructions using combined EEG and MEG was significantly improved. We demonstrated that this new sparse ESI methodology with integrated analysis of EEG and MEG data could accurately probe spatiotemporal processes of complex human brain activations. This is promising for noninvasively studying large-scale brain networks of high clinical and scientific significance. Copyright © 2011 Wiley Periodicals, Inc.

Detection of EEG-patterns associated with real and imaginary movements using detrended fluctuation analysis

NASA Astrophysics Data System (ADS)

Pavlov, Alexey N.; Runnova, Anastasiya E.; Maksimenko, Vladimir A.; Grishina, Daria S.; Hramov, Alexander E.

2018-02-01

Authentic recognition of specific patterns of electroencephalograms (EEGs) associated with real and imagi- nary movements is an important stage for the development of brain-computer interfaces. In experiments with untrained participants, the ability to detect the motor-related brain activity based on the multichannel EEG processing is demonstrated. Using the detrended fluctuation analysis, changes in the EEG patterns during the imagination of hand movements are reported. It is discussed how the ability to recognize brain activity related to motor executions depends on the electrode position.

Prognostic value of electroencephalography (EEG) for brain injury after cardiopulmonary resuscitation.

PubMed

Feng, Guibo; Jiang, Guohui; Li, Zhiwei; Wang, Xuefeng

2016-06-01

Cardiac arrest (CA) patients can experience neurological sequelae or even death after successful cardiopulmonary resuscitation (CPR) due to cerebral hypoxia- and ischemia-reperfusion-mediated brain injury. Thus, it is important to perform early prognostic evaluations in CA patients. Electroencephalography (EEG) is an important tool for determining the prognosis of hypoxic-ischemic encephalopathy due to its real-time measurement of brain function. Based on EEG, burst suppression, a burst suppression ratio >0.239, periodic discharges, status epilepticus, stimulus-induced rhythmic, periodic or ictal discharges, non-reactive EEG, and the BIS value based on quantitative EEG may be associated with the prognosis of CA after successful CPR. As measures of neural network integrity, the values of small-world characteristics of the neural network derived from EEG patterns have potential applications.

Symbolic joint entropy reveals the coupling of various brain regions

NASA Astrophysics Data System (ADS)

Ma, Xiaofei; Huang, Xiaolin; Du, Sidan; Liu, Hongxing; Ning, Xinbao

2018-01-01

The convergence and divergence of oscillatory behavior of different brain regions are very important for the procedure of information processing. Measurements of coupling or correlation are very useful to study the difference of brain activities. In this study, EEG signals were collected from ten subjects under two conditions, i.e. eyes closed state and idle with eyes open. We propose a nonlinear algorithm, symbolic joint entropy, to compare the coupling strength among the frontal, temporal, parietal and occipital lobes and between two different states. Instead of decomposing the EEG into different frequency bands (theta, alpha, beta, gamma etc.), the novel algorithm is to investigate the coupling from the entire spectrum of brain wave activities above 4Hz. The coupling coefficients in two states with different time delay steps are compared and the group statistics are presented as well. We find that the coupling coefficient of eyes open state with delay consistently lower than that of eyes close state across the group except for one subject, whereas the results without delay are not consistent. The differences between two brain states with non-zero delay can reveal the intrinsic inter-region coupling better. We also use the well-known Hénon map data to validate the algorithm proposed in this paper. The result shows that the method is robust and has a great potential for other physiologic time series.

Time is of the Essence: A Review of Electroencephalography (EEG) and Event-Related Brain Potentials (ERPs) in Language Research.

PubMed

Beres, Anna M

2017-12-01

The discovery of electroencephalography (EEG) over a century ago has changed the way we understand brain structure and function, in terms of both clinical and research applications. This paper starts with a short description of EEG and then focuses on the event-related brain potentials (ERPs), and their use in experimental settings. It describes the typical set-up of an ERP experiment. A description of a number of ERP components typically involved in language research is presented. Finally, the advantages and disadvantages of using ERPs in language research are discussed. EEG has an extensive use in today's world, including medical, psychology, or linguistic research. The excellent temporal resolution of EEG information allows one to track a brain response in milliseconds and therefore makes it uniquely suited to research concerning language processing.

Age-Dependency of Location of Epileptic Foci in "Continuous Spike-and-Waves during Sleep": A Parallel to the Posterior-Anterior Trajectory of Slow Wave Activity.

PubMed

Bölsterli Heinzle, Bigna Katrin; Bast, Thomas; Critelli, Hanne; Huber, Reto; Schmitt, Bernhard

2017-02-01

Epileptic encephalopathy with continuous spike-and-waves during sleep (CSWS) occurs during childhood and is characterized by an activation of spike wave complexes during slow wave sleep. The location of epileptic foci is variable, as is etiology. A relationship between the epileptic focus and age has been shown in various focal epilepsies following a posterior-anterior trajectory, and a link to brain maturation has been proposed. We hypothesize that in CSWS, maximal spike wave activity, corresponding to the epileptic focus, is related to age and shows a posterior-anterior evolution. In a retrospective cross-sectional study on CSWS (22 EEGs of 22 patients aged 3.1–13.5 years), the location of the epileptic focus is related to age and follows a posterior-anterior course. Younger patients are more likely to have posterior foci than older ones. We propose that the posterior-anterior trajectory of maximal spike waves in CSWS might reflect maturational changes of maximal expression of sleep slow waves, which follow a comparable course. Epileptic spike waves, that is, “hyper-synchronized slow waves” may occur at the place where the highest and therefore most synchronized slow waves meet brain tissue with an increased susceptibility to synchronization. Georg Thieme Verlag KG Stuttgart · New York.

Nonlinear Recurrent Dynamics and Long-Term Nonstationarities in EEG Alpha Cortical Activity: Implications for Choosing Adequate Segment Length in Nonlinear EEG Analyses.

PubMed

Cerquera, Alexander; Vollebregt, Madelon A; Arns, Martijn

2018-03-01

Nonlinear analysis of EEG recordings allows detection of characteristics that would probably be neglected by linear methods. This study aimed to determine a suitable epoch length for nonlinear analysis of EEG data based on its recurrence rate in EEG alpha activity (electrodes Fz, Oz, and Pz) from 28 healthy and 64 major depressive disorder subjects. Two nonlinear metrics, Lempel-Ziv complexity and scaling index, were applied in sliding windows of 20 seconds shifted every 1 second and in nonoverlapping windows of 1 minute. In addition, linear spectral analysis was carried out for comparison with the nonlinear results. The analysis with sliding windows showed that the cortical dynamics underlying alpha activity had a recurrence period of around 40 seconds in both groups. In the analysis with nonoverlapping windows, long-term nonstationarities entailed changes over time in the nonlinear dynamics that became significantly different between epochs across time, which was not detected with the linear spectral analysis. Findings suggest that epoch lengths shorter than 40 seconds neglect information in EEG nonlinear studies. In turn, linear analysis did not detect characteristics from long-term nonstationarities in EEG alpha waves of control subjects and patients with major depressive disorder patients. We recommend that application of nonlinear metrics in EEG time series, particularly of alpha activity, should be carried out with epochs around 60 seconds. In addition, this study aimed to demonstrate that long-term nonlinearities are inherent to the cortical brain dynamics regardless of the presence or absence of a mental disorder.

Predictable internal brain dynamics in EEG and its relation to conscious states

PubMed Central

Yoo, Jaewook; Kwon, Jaerock; Choe, Yoonsuck

2014-01-01

Consciousness is a complex and multi-faceted phenomenon defying scientific explanation. Part of the reason why this is the case is due to its subjective nature. In our previous computational experiments, to avoid such a subjective trap, we took a strategy to investigate objective necessary conditions of consciousness. Our basic hypothesis was that predictive internal dynamics serves as such a condition. This is in line with theories of consciousness that treat retention (memory), protention (anticipation), and primary impression as the tripartite temporal structure of consciousness. To test our hypothesis, we analyzed publicly available sleep and awake electroencephalogram (EEG) data. Our results show that EEG signals from awake or rapid eye movement (REM) sleep states have more predictable dynamics compared to those from slow-wave sleep (SWS). Since awakeness and REM sleep are associated with conscious states and SWS with unconscious or less consciousness states, these results support our hypothesis. The results suggest an intricate relationship among prediction, consciousness, and time, with potential applications to time perception and neurorobotics. PMID:24917813

Modular, bluetooth enabled, wireless electroencephalograph (EEG) platform.

PubMed

Lovelace, Joseph A; Witt, Tyler S; Beyette, Fred R

2013-01-01

A design for a modular, compact, and accurate wireless electroencephalograph (EEG) system is proposed. EEG is the only non-invasive measure for neuronal function of the brain. Using a number of digital signal processing (DSP) techniques, this neuronal function can be acquired and processed into meaningful representations of brain activity. The system described here utilizes Bluetooth to wirelessly transmit the digitized brain signal for an end application use. In this way, the system is portable, and modular in terms of the device to which it can interface. Brain Computer Interface (BCI) has become a popular extension of EEG systems in modern research. This design serves as a platform for applications using BCI capability.

[EFFECTS OF ELECTRICAL STIMULATION OF NUCLEUS RETICULARIS PONTIS ORALIS ON THE SLEEP-WAKING STATES IN KRUSHINSKII-MOLODKINA STRAIN RATS].

PubMed

Vataev, S I; Malgina, N A; Oganesyan, G A

2015-07-01

The effects of electrical stimulation of nucleus reticularis pontis oralis on the behavior and brain electrical activity during all phases of the sleep-waking cycle was studied in Krushinskii-Molodkina strain rats, which have an inherited predisposition to audiogenic seizures. Electrical stimulation with 7 Hz frequency in the deep stage of slow-wave sleep cause appearance the fast-wave sleep. Similar stimulation during fast-wave sleep periods did not effects on the electrographic patterns and EEG spectral characteristics of hippocampus, visual, auditory and somatocnen nrnrenc nf the cnrtey ThPe sfimul1stinns did nnt break a fast-wave sleenhut increased almost twice due the duration of these sleep episodes. After electrical stimulation by same frequency during the wakeftlness and superficial slow-wave sleep states, the patterns and spectral characteristics of brain electrical activity in rats showed no significant changes as compared with controls. The results of this study indicate that the state of the animals sleep-waking cycle at the time of stimulation is a critical variable that influences the responses which are induced by electrical stimulation of the nucleus reticularis pontis oralis.

Intracranial EEG fluctuates over months after implanting electrodes in human brain

NASA Astrophysics Data System (ADS)

Ung, Hoameng; Baldassano, Steven N.; Bink, Hank; Krieger, Abba M.; Williams, Shawniqua; Vitale, Flavia; Wu, Chengyuan; Freestone, Dean; Nurse, Ewan; Leyde, Kent; Davis, Kathryn A.; Cook, Mark; Litt, Brian

2017-10-01

Objective. Implanting subdural and penetrating electrodes in the brain causes acute trauma and inflammation that affect intracranial electroencephalographic (iEEG) recordings. This behavior and its potential impact on clinical decision-making and algorithms for implanted devices have not been assessed in detail. In this study we aim to characterize the temporal and spatial variability of continuous, prolonged human iEEG recordings. Approach. Intracranial electroencephalography from 15 patients with drug-refractory epilepsy, each implanted with 16 subdural electrodes and continuously monitored for an average of 18 months, was included in this study. Time and spectral domain features were computed each day for each channel for the duration of each patient’s recording. Metrics to capture post-implantation feature changes and inflexion points were computed on group and individual levels. A linear mixed model was used to characterize transient group-level changes in feature values post-implantation and independent linear models were used to describe individual variability. Main results. A significant decline in features important to seizure detection and prediction algorithms (mean line length, energy, and half-wave), as well as mean power in the Berger and high gamma bands, was observed in many patients over 100 d following implantation. In addition, spatial variability across electrodes declines post-implantation following a similar timeframe. All selected features decreased by 14-50% in the initial 75 d of recording on the group level, and at least one feature demonstrated this pattern in 13 of the 15 patients. Our findings indicate that iEEG signal features demonstrate increased variability following implantation, most notably in the weeks immediately post-implant. Significance. These findings suggest that conclusions drawn from iEEG, both clinically and for research, should account for spatiotemporal signal variability and that properly assessing the iEEG in patients, depending upon the application, may require extended monitoring.

Neuronal Networks during Burst Suppression as Revealed by Source Analysis

PubMed Central

Reinicke, Christine; Moeller, Friederike; Anwar, Abdul Rauf; Mideksa, Kidist Gebremariam; Pressler, Ronit; Deuschl, Günther; Stephani, Ulrich; Siniatchkin, Michael

2015-01-01

Introduction Burst-suppression (BS) is an electroencephalography (EEG) pattern consisting of alternant periods of slow waves of high amplitude (burst) and periods of so called flat EEG (suppression). It is generally associated with coma of various etiologies (hypoxia, drug-related intoxication, hypothermia, and childhood encephalopathies, but also anesthesia). Animal studies suggest that both the cortex and the thalamus are involved in the generation of BS. However, very little is known about mechanisms of BS in humans. The aim of this study was to identify the neuronal network underlying both burst and suppression phases using source reconstruction and analysis of functional and effective connectivity in EEG. Material/Methods Dynamic imaging of coherent sources (DICS) was applied to EEG segments of 13 neonates and infants with burst and suppression EEG pattern. The brain area with the strongest power in the analyzed frequency (1–4 Hz) range was defined as the reference region. DICS was used to compute the coherence between this reference region and the entire brain. The renormalized partial directed coherence (RPDC) was used to describe the informational flow between the identified sources. Results/Conclusion Delta activity during the burst phases was associated with coherent sources in the thalamus and brainstem as well as bilateral sources in cortical regions mainly frontal and parietal, whereas suppression phases were associated with coherent sources only in cortical regions. Results of the RPDC analyses showed an upwards informational flow from the brainstem towards the thalamus and from the thalamus to cortical regions, which was absent during the suppression phases. These findings may support the theory that a “cortical deafferentiation” between the cortex and sub-cortical structures exists especially in suppression phases compared to burst phases in burst suppression EEGs. Such a deafferentiation may play a role in the poor neurological outcome of children with these encephalopathies. PMID:25927439

Joint Estimation of Effective Brain Wave Activation Modes Using EEG/MEG Sensor Arrays and Multimodal MRI Volumes.

PubMed

Galinsky, Vitaly L; Martinez, Antigona; Paulus, Martin P; Frank, Lawrence R

2018-04-13

In this letter, we present a new method for integration of sensor-based multifrequency bands of electroencephalography and magnetoencephalography data sets into a voxel-based structural-temporal magnetic resonance imaging analysis by utilizing the general joint estimation using entropy regularization (JESTER) framework. This allows enhancement of the spatial-temporal localization of brain function and the ability to relate it to morphological features and structural connectivity. This method has broad implications for both basic neuroscience research and clinical neuroscience focused on identifying disease-relevant biomarkers by enhancing the spatial-temporal resolution of the estimates derived from current neuroimaging modalities, thereby providing a better picture of the normal human brain in basic neuroimaging experiments and variations associated with disease states.

Propofol Anesthesia and Sleep: A High-Density EEG Study

PubMed Central

Murphy, Michael; Bruno, Marie-Aurelie; Riedner, Brady A.; Boveroux, Pierre; Noirhomme, Quentin; Landsness, Eric C.; Brichant, Jean-Francois; Phillips, Christophe; Massimini, Marcello; Laureys, Steven; Tononi, Giulio; Boly, Melanie

2011-01-01

Study Objectives: The electrophysiological correlates of anesthetic sedation remain poorly understood. We used high-density electroencephalography (hd-EEG) and source modeling to investigate the cortical processes underlying propofol anesthesia and compare them to sleep. Design: 256-channel EEG recordings in humans during propofol anesthesia. Setting: Hospital operating room. Patients or Participants: 8 healthy subjects (4 males) Interventions: N/A Measurements and Results: Initially, propofol induced increases in EEG power from 12–25 Hz. Loss of consciousness (LOC) was accompanied by the appearance of EEG slow waves that resembled the slow waves of NREM sleep. We compared slow waves in propofol to slow waves recorded during natural sleep and found that both populations of waves share similar cortical origins and preferentially propagate along the mesial components of the default network. However, propofol slow waves were spatially blurred compared to sleep slow waves and failed to effectively entrain spindle activity. Propofol also caused an increase in gamma (25–40 Hz) power that persisted throughout LOC. Source modeling analysis showed that this increase in gamma power originated from the anterior and posterior cingulate cortices. During LOC, we found increased gamma functional connectivity between these regions compared to the wakefulness. Conclusions: Propofol anesthesia is a sleep-like state and slow waves are associated with diminished consciousness even in the presence of high gamma activity. Citation: Murphy M; Bruno MA; Riedner BA; Boveroux P; Noirhomme Q; Landsness EC; Brichant JF; Phillips C; Massimini M; Laureys S; Tononi G; Boly M. Propofol anesthesia and sleep: a high-density EEG study. SLEEP 2011;34(3):283-291. PMID:21358845

Electroencephalography(EEG)-based instinctive brain-control of a quadruped locomotion robot.

PubMed

Jia, Wenchuan; Huang, Dandan; Luo, Xin; Pu, Huayan; Chen, Xuedong; Bai, Ou

2012-01-01

Artificial intelligence and bionic control have been applied in electroencephalography (EEG)-based robot system, to execute complex brain-control task. Nevertheless, due to technical limitations of the EEG decoding, the brain-computer interface (BCI) protocol is often complex, and the mapping between the EEG signal and the practical instructions lack of logic associated, which restrict the user's actual use. This paper presents a strategy that can be used to control a quadruped locomotion robot by user's instinctive action, based on five kinds of movement related neurophysiological signal. In actual use, the user drives or imagines the limbs/wrists action to generate EEG signal to adjust the real movement of the robot according to his/her own motor reflex of the robot locomotion. This method is easy for real use, as the user generates the brain-control signal through the instinctive reaction. By adopting the behavioral control of learning and evolution based on the proposed strategy, complex movement task may be realized by instinctive brain-control.

EEG Oscillatory States: Universality, Uniqueness and Specificity across Healthy-Normal, Altered and Pathological Brain Conditions

PubMed Central

Fingelkurts, Alexander A.; Fingelkurts, Andrew A.

2014-01-01

For the first time the dynamic repertoires and oscillatory types of local EEG states in 13 diverse conditions (examined over 9 studies) that covered healthy-normal, altered and pathological brain states were quantified within the same methodological and conceptual framework. EEG oscillatory states were assessed by the probability-classification analysis of short-term EEG spectral patterns. The results demonstrated that brain activity consists of a limited repertoire of local EEG states in any of the examined conditions. The size of the state repertoires was associated with changes in cognition and vigilance or neuropsychopathologic conditions. Additionally universal, optional and unique EEG states across 13 diverse conditions were observed. It was demonstrated also that EEG oscillations which constituted EEG states were characteristic for different groups of conditions in accordance to oscillations’ functional significance. The results suggested that (a) there is a limit in the number of local states available to the cortex and many ways in which these local states can rearrange themselves and still produce the same global state and (b) EEG individuality is determined by varying proportions of universal, optional and unique oscillatory states. The results enriched our understanding about dynamic microstructure of EEG-signal. PMID:24505292

Resected Brain Tissue, Seizure Onset Zone and Quantitative EEG Measures: Towards Prediction of Post-Surgical Seizure Control

PubMed Central

Andrzejak, Ralph G.; Hauf, Martinus; Pollo, Claudio; Müller, Markus; Weisstanner, Christian; Wiest, Roland; Schindler, Kaspar

2015-01-01

Background Epilepsy surgery is a potentially curative treatment option for pharmacoresistent patients. If non-invasive methods alone do not allow to delineate the epileptogenic brain areas the surgical candidates undergo long-term monitoring with intracranial EEG. Visual EEG analysis is then used to identify the seizure onset zone for targeted resection as a standard procedure. Methods Despite of its great potential to assess the epileptogenicty of brain tissue, quantitative EEG analysis has not yet found its way into routine clinical practice. To demonstrate that quantitative EEG may yield clinically highly relevant information we retrospectively investigated how post-operative seizure control is associated with four selected EEG measures evaluated in the resected brain tissue and the seizure onset zone. Importantly, the exact spatial location of the intracranial electrodes was determined by coregistration of pre-operative MRI and post-implantation CT and coregistration with post-resection MRI was used to delineate the extent of tissue resection. Using data-driven thresholding, quantitative EEG results were separated into normally contributing and salient channels. Results In patients with favorable post-surgical seizure control a significantly larger fraction of salient channels in three of the four quantitative EEG measures was resected than in patients with unfavorable outcome in terms of seizure control (median over the whole peri-ictal recordings). The same statistics revealed no association with post-operative seizure control when EEG channels contributing to the seizure onset zone were studied. Conclusions We conclude that quantitative EEG measures provide clinically relevant and objective markers of target tissue, which may be used to optimize epilepsy surgery. The finding that differentiation between favorable and unfavorable outcome was better for the fraction of salient values in the resected brain tissue than in the seizure onset zone is consistent with growing evidence that spatially extended networks might be more relevant for seizure generation, evolution and termination than a single highly localized brain region (i.e. a “focus”) where seizures start. PMID:26513359

Importance of Video-EEG Monitoring in the Diagnosis of Epilepsy in a Psychiatric Patient

PubMed Central

Kirmani, Batool F.

2013-01-01

Epilepsy is a chronic medical condition which is disabling to both patients and caregivers. The differential diagnosis of epilepsy includes psychogenic nonepileptic spells or “pseudoseizures.” Epilepsy is due to abnormal electrical activity in the brain, and pseudoseizure is a form of conversion disorder. The brain waves remain normal in pseudoseizures. The problem arises when a patient with significant psychiatric history presents with seizures. Pseudoseizures become high on the differential diagnosis without extensive work up. This is a case of woman with significant psychiatric issues which resulted in a delay in the diagnosis of epilepsy. PMID:23585974

Concealed, Unobtrusive Ear-Centered EEG Acquisition: cEEGrids for Transparent EEG

PubMed Central

Bleichner, Martin G.; Debener, Stefan

2017-01-01

Electroencephalography (EEG) is an important clinical tool and frequently used to study the brain-behavior relationship in humans noninvasively. Traditionally, EEG signals are recorded by positioning electrodes on the scalp and keeping them in place with glue, rubber bands, or elastic caps. This setup provides good coverage of the head, but is impractical for EEG acquisition in natural daily-life situations. Here, we propose the transparent EEG concept. Transparent EEG aims for motion tolerant, highly portable, unobtrusive, and near invisible data acquisition with minimum disturbance of a user's daily activities. In recent years several ear-centered EEG solutions that are compatible with the transparent EEG concept have been presented. We discuss work showing that miniature electrodes placed in and around the human ear are a feasible solution, as they are sensitive enough to pick up electrical signals stemming from various brain and non-brain sources. We also describe the cEEGrid flex-printed sensor array, which enables unobtrusive multi-channel EEG acquisition from around the ear. In a number of validation studies we found that the cEEGrid enables the recording of meaningful continuous EEG, event-related potentials and neural oscillations. Here, we explain the rationale underlying the cEEGrid ear-EEG solution, present possible use cases and identify open issues that need to be solved on the way toward transparent EEG. PMID:28439233

Exploring the Human Computer Interface and Photic Driving.

DTIC Science & Technology

1999-03-01

monitor emanations. Due to previous research results in photo- stimulation , the author chose to attempt modification of the subjects’ EEG through the CRT...emanations. 2 D. THESIS QUESTIONS This thesis will attempt to answer the following questions: "* Can photo- stimulation be used to alter a subject’s...is only through direct observation of the subject while they are being exposed to photo- stimulation can we determine if driving of the brain waves or

β Oscillation during Slow Wave Sleep and Rapid Eye Movement Sleep in the Electroencephalogram of a Transgenic Mouse Model of Huntington’s Disease

PubMed Central

Jeantet, Yannick; Cayzac, Sebastien; Cho, Yoon H.

2013-01-01

Study objectives To search for early abnormalities in electroencephalogram (EEG) during sleep which may precede motor symptoms in a transgenic mouse model of hereditary neurodegenerative Huntington’s disease (HD). Design In the R6/1 transgenic mouse model of HD, rhythmic brain activity in EEG recordings was monitored longitudinally and across vigilance states through the onset and progression of disease. Measurements and results Mice with chronic electrode implants were recorded monthly over wake-sleep cycles (4 hours), beginning at 9–11 weeks (presymptomatic period) through 6–7 months (symptomatic period). Recording data revealed a unique β rhythm (20–35 Hz), present only in R6/1 transgenic mice, which evolves in close parallel with the disease. In addition, there was an unusual relationship between this β oscillation and vigilance states: while nearly absent during the active waking state, the β oscillation appeared with drowsiness and during slow wave sleep (SWS) and, interestingly, strengthened rather than dissipating when the brain returned to an activated state during rapid eye movement (REM) sleep. Conclusions In addition to providing a new in vivo biomarker and insight into Huntington's disease pathophysiology, this serendipitous observation opens a window onto the rarely explored neurophysiology of the cortico-basal ganglia circuit during SWS and REM sleep. PMID:24244517

Entropy is more resistant to artifacts than bispectral index in brain-dead organ donors.

PubMed

Wennervirta, Johanna; Salmi, Tapani; Hynynen, Markku; Yli-Hankala, Arvi; Koivusalo, Anna-Maria; Van Gils, Mark; Pöyhiä, Reino; Vakkuri, Anne

2007-01-01

To evaluate the usefulness of entropy and the bispectral index (BIS) in brain-dead subjects. A prospective, open, nonselective, observational study in the university hospital. 16 brain-dead organ donors. Time-domain electroencephalography (EEG), spectral entropy of the EEG, and BIS were recorded during solid organ harvest. State entropy differed significantly from 0 (isoelectric EEG) 28%, response entropy 29%, and BIS 68% of the total recorded time. The median values during the operation were state entropy 0.0, response entropy 0.0, and BIS 3.0. In four of 16 organ donors studied the EEG was not isoelectric, and nonreactive rhythmic activity was noted in time-domain EEG. After excluding the results from subjects with persistent residual EEG activity state entropy, response entropy, and BIS values differed from zero 17%, 18%, and 62% of the recorded time, respectively. Median values were 0.0, 0.0, and 2.0 for state entropy, response entropy, and BIS, respectively. The highest index values in entropy and BIS monitoring were recorded without neuromuscular blockade. The main sources of artifacts were electrocauterization, 50-Hz artifact, handling of the donor, ballistocardiography, electromyography, and electrocardiography. Both entropy and BIS showed nonzero values due to artifacts after brain death diagnosis. BIS was more liable to artifacts than entropy. Neither of these indices are diagnostic tools, and care should be taken when interpreting EEG and EEG-derived indices in the evaluation of brain death.

Identifying the effects of microsaccades in tripolar EEG signals.

PubMed

Bellisle, Rachel; Steele, Preston; Bartels, Rachel; Lei Ding; Sunderam, Sridhar; Besio, Walter

2017-07-01

Microsaccades are tiny, involuntary eye movements that occur during fixation, and they are necessary to human sight to maintain a sharp image and correct the effects of other fixational movements. Researchers have theorized and studied the effects of microsaccades on electroencephalography (EEG) signals to understand and eliminate the unwanted artifacts from EEG. The tripolar concentric ring electrode (TCRE) sensors are used to acquire TCRE EEG (tEEG). The tEEG detects extremely focal signals from directly below the TCRE sensor. We have noticed a slow wave frequency found in some tEEG recordings. Therefore, we conducted the current work to determine if there was a correlation between the slow wave in the tEEG and the microsaccades. This was done by analyzing the coherence of the frequency spectrums of both tEEG and eye movement in recordings where microsaccades are present. Our preliminary findings show that there is a correlation between the two.

Joint Maximum Likelihood Time Delay Estimation of Unknown Event-Related Potential Signals for EEG Sensor Signal Quality Enhancement

PubMed Central

Kim, Kyungsoo; Lim, Sung-Ho; Lee, Jaeseok; Kang, Won-Seok; Moon, Cheil; Choi, Ji-Woong

2016-01-01

Electroencephalograms (EEGs) measure a brain signal that contains abundant information about the human brain function and health. For this reason, recent clinical brain research and brain computer interface (BCI) studies use EEG signals in many applications. Due to the significant noise in EEG traces, signal processing to enhance the signal to noise power ratio (SNR) is necessary for EEG analysis, especially for non-invasive EEG. A typical method to improve the SNR is averaging many trials of event related potential (ERP) signal that represents a brain’s response to a particular stimulus or a task. The averaging, however, is very sensitive to variable delays. In this study, we propose two time delay estimation (TDE) schemes based on a joint maximum likelihood (ML) criterion to compensate the uncertain delays which may be different in each trial. We evaluate the performance for different types of signals such as random, deterministic, and real EEG signals. The results show that the proposed schemes provide better performance than other conventional schemes employing averaged signal as a reference, e.g., up to 4 dB gain at the expected delay error of 10°. PMID:27322267

Methodological aspects of EEG and body dynamics measurements during motion

PubMed Central

Reis, Pedro M. R.; Hebenstreit, Felix; Gabsteiger, Florian; von Tscharner, Vinzenz; Lochmann, Matthias

2014-01-01

EEG involves the recording, analysis, and interpretation of voltages recorded on the human scalp which originate from brain gray matter. EEG is one of the most popular methods of studying and understanding the processes that underlie behavior. This is so, because EEG is relatively cheap, easy to wear, light weight and has high temporal resolution. In terms of behavior, this encompasses actions, such as movements that are performed in response to the environment. However, there are methodological difficulties which can occur when recording EEG during movement such as movement artifacts. Thus, most studies about the human brain have examined activations during static conditions. This article attempts to compile and describe relevant methodological solutions that emerged in order to measure body and brain dynamics during motion. These descriptions cover suggestions on how to avoid and reduce motion artifacts, hardware, software and techniques for synchronously recording EEG, EMG, kinematics, kinetics, and eye movements during motion. Additionally, we present various recording systems, EEG electrodes, caps and methods for determinating real/custom electrode positions. In the end we will conclude that it is possible to record and analyze synchronized brain and body dynamics related to movement or exercise tasks. PMID:24715858

Enhanced Burst-Suppression and Disruption of Local Field Potential Synchrony in a Mouse Model of Focal Cortical Dysplasia Exhibiting Spike-Wave Seizures.

PubMed

Williams, Anthony J; Zhou, Chen; Sun, Qian-Quan

2016-01-01

Focal cortical dysplasias (FCDs) are a common cause of brain seizures and are often associated with intractable epilepsy. Here we evaluated aberrant brain neurophysiology in an in vivo mouse model of FCD induced by neonatal freeze lesions (FLs) to the right cortical hemisphere (near S1). Linear multi-electrode arrays were used to record extracellular potentials from cortical and subcortical brain regions near the FL in anesthetized mice (5-13 months old) followed by 24 h cortical electroencephalogram (EEG) recordings. Results indicated that FL animals exhibit a high prevalence of spontaneous spike-wave discharges (SWDs), predominately during sleep (EEG), and an increase in the incidence of hyper-excitable burst/suppression activity under general anesthesia (extracellular recordings, 0.5%-3.0% isoflurane). Brief periods of burst activity in the local field potential (LFP) typically presented as an arrhythmic pattern of increased theta-alpha spectral peaks (4-12 Hz) on a background of low-amplitude delta activity (1-4 Hz), were associated with an increase in spontaneous spiking of cortical neurons, and were highly synchronized in control animals across recording sites in both cortical and subcortical layers (average cross-correlation values ranging from +0.73 to +1.0) with minimal phase shift between electrodes. However, in FL animals, cortical vs. subcortical burst activity was strongly out of phase with significantly lower cross-correlation values compared to controls (average values of -0.1 to +0.5, P < 0.05 between groups). In particular, a marked reduction in the level of synchronous burst activity was observed, the closer the recording electrodes were to the malformation (Pearson's Correlation = 0.525, P < 0.05). In a subset of FL animals (3/9), burst activity also included a spike or spike-wave pattern similar to the SWDs observed in unanesthetized animals. In summary, neonatal FLs increased the hyperexcitable pattern of burst activity induced by anesthesia and disrupted field potential synchrony between cortical and subcortical brain regions near the site of the cortical malformation. Monitoring the altered electrophysiology of burst activity under general anesthesia with multi-dimensional micro-electrode arrays may serve to define distinct neurophysiological biomarkers of epileptogenesis in human brain and improve techniques for surgical resection of epileptogenic malformed brain tissue.

Different event-related patterns of gamma-band power in brain waves of fast- and slow-reacting subjects.

PubMed Central

Jokeit, H; Makeig, S

1994-01-01

Fast- and slow-reacting subjects exhibit different patterns of gamma-band electroencephalogram (EEG) activity when responding as quickly as possible to auditory stimuli. This result appears to confirm long-standing speculations of Wundt that fast- and slow-reacting subjects produce speeded reactions in different ways and demonstrates that analysis of event-related changes in the amplitude of EEG activity recorded from the human scalp can reveal information about event-related brain processes unavailable using event-related potential measures. Time-varying spectral power in a selected (35- to 43-Hz) gamma frequency band was averaged across trials in two experimental conditions: passive listening and speeded reacting to binaural clicks, forming 40-Hz event-related spectral responses. Factor analysis of between-subject event-related spectral response differences split subjects into two near-equal groups composed of faster- and slower-reacting subjects. In faster-reacting subjects, 40-Hz power peaked near 200 ms and 400 ms poststimulus in the react condition, whereas in slower-reacting subjects, 40-Hz power just before stimulus delivery was larger in the react condition. These group differences were preserved in separate averages of relatively long and short reaction-time epochs for each group. gamma-band (20-60 Hz)-filtered event-related potential response averages did not differ between the two groups or conditions. Because of this and because gamma-band power in the auditory event-related potential is small compared with the EEG, the observed event-related spectral response features must represent gamma-band EEG activity reliably induced by, but not phase-locked to, experimental stimuli or events. PMID:8022783

Nonlinear aspects of the EEG during sleep in children

NASA Astrophysics Data System (ADS)

Berryman, Matthew J.; Coussens, Scott W.; Pamula, Yvonne; Kennedy, Declan; Lushington, Kurt; Shalizi, Cosma; Allison, Andrew; Martin, A. James; Saint, David; Abbott, Derek

2005-05-01

Electroencephalograph (EEG) analysis enables the dynamic behavior of the brain to be examined. If the behavior is nonlinear then nonlinear tools can be used to glean information on brain behavior, and aid in the diagnosis of sleep abnormalities such as obstructive sleep apnea syndrome (OSAS). In this paper the sleep EEGs of a set of normal children and children with mild OSAS are evaluated for nonlinear brain behaviour. We found that there were differences in the nonlinearity of the brain behaviour between different sleep stages, and between the two groups of children.

[Clinical and electroencephalographic characteristic of noopept in patients with mild cognitive impairment of posttraumatic and vascular origin].

PubMed

Bochkarev, V K; Teleshova, E S; Siuniakov, S A; Davydova, D V; Neznamov, G G

2008-01-01

An effect of a new nootropic drug noopept on the dynamics of main EEG rhythms and narrow-band spectral EEG characteristics in patients with cerebral asthenic and cognitive disturbances caused by traumas or vascular brain diseases has been studied. Noopept caused the EEG changes characteristic of the action of nootropics: the increase of alpha- and beta-rhythms power and reduction of delta-rhythms power. The reaction of alpha-rhythm was provided mostly by the dynamics of its low and medium frequencies (6,7-10,2 Hz), the changes of beta-rhythm were augmented in frontal and attenuated in occipital areas. The analysis of frequency and spatial structure of EEG changes reveals that noopept exerts a nonspecific activation and anxyolytic effect. The differences in EEG changes depending on the brain pathology were found. The EEG indices of nootropic effect of the drug were most obvious in cerebral vascular diseases. The EEG changes in posttraumatic brain lesion were less typical.

Inverse scattering approach to improving pattern recognition

NASA Astrophysics Data System (ADS)

Chapline, George; Fu, Chi-Yung

2005-05-01

The Helmholtz machine provides what may be the best existing model for how the mammalian brain recognizes patterns. Based on the observation that the "wake-sleep" algorithm for training a Helmholtz machine is similar to the problem of finding the potential for a multi-channel Schrodinger equation, we propose that the construction of a Schrodinger potential using inverse scattering methods can serve as a model for how the mammalian brain learns to extract essential information from sensory data. In particular, inverse scattering theory provides a conceptual framework for imagining how one might use EEG and MEG observations of brain-waves together with sensory feedback to improve human learning and pattern recognition. Longer term, implementation of inverse scattering algorithms on a digital or optical computer could be a step towards mimicking the seamless information fusion of the mammalian brain.

Inverse Scattering Approach to Improving Pattern Recognition

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chapline, G; Fu, C

2005-02-15

The Helmholtz machine provides what may be the best existing model for how the mammalian brain recognizes patterns. Based on the observation that the ''wake-sleep'' algorithm for training a Helmholtz machine is similar to the problem of finding the potential for a multi-channel Schrodinger equation, we propose that the construction of a Schrodinger potential using inverse scattering methods can serve as a model for how the mammalian brain learns to extract essential information from sensory data. In particular, inverse scattering theory provides a conceptual framework for imagining how one might use EEG and MEG observations of brain-waves together with sensorymore » feedback to improve human learning and pattern recognition. Longer term, implementation of inverse scattering algorithms on a digital or optical computer could be a step towards mimicking the seamless information fusion of the mammalian brain.« less

Brain Functional Connectivity in MS: An EEG-NIRS Study

DTIC Science & Technology

2015-10-01

electrical (EEG) and blood volume and blood oxygen-based (NIRS and fMRI ) signals, and to use the results to help optimize blood oxygen level...dependent (BOLD) fMRI analyses of brain activity. Participants will be patients with MS (n=25) and healthy demographically matched controls (n=25) who will...undergo standardized evaluations and imaging using combined EEG-NIRS- fMRI . EEG-NIRS data will be used to construct maps of neurovascular coupling

Deletion of the Snord116/SNORD116 Alters Sleep in Mice and Patients with Prader-Willi Syndrome.

PubMed

Lassi, Glenda; Priano, Lorenzo; Maggi, Silvia; Garcia-Garcia, Celina; Balzani, Edoardo; El-Assawy, Nadia; Pagani, Marco; Tinarelli, Federico; Giardino, Daniela; Mauro, Alessandro; Peters, Jo; Gozzi, Alessandro; Grugni, Graziano; Tucci, Valter

2016-03-01

Sleep-wake disturbances are often reported in Prader-Willi syndrome (PWS), a rare neurodevelopmental syndrome that is associated with paternally-expressed genomic imprinting defects within the human chromosome region 15q11-13. One of the candidate genes, prevalently expressed in the brain, is the small nucleolar ribonucleic acid-116 (SNORD116). Here we conducted a translational study into the sleep abnormalities of PWS, testing the hypothesis that SNORD116 is responsible for sleep defects that characterize the syndrome. We studied sleep in mutant mice that carry a deletion of Snord116 at the orthologous locus (mouse chromosome 7) of the human PWS critical region (PWScr). In particular, we assessed EEG and temperature profiles, across 24-h, in PWScr (m+/p-) heterozygous mutants compared to wild-type littermates. High-resolution magnetic resonance imaging (MRI) was performed to explore morphoanatomical differences according to the genotype. Moreover, we complemented the mouse work by presenting two patients with a diagnosis of PWS and characterized by atypical small deletions of SNORD116. We compared the individual EEG parameters of patients with healthy subjects and with a cohort of obese subjects. By studying the mouse mutant line PWScr(m+/p-), we observed specific rapid eye movement (REM) sleep alterations including abnormal electroencephalograph (EEG) theta waves. Remarkably, we observed identical sleep/EEG defects in the two PWS cases. We report brain morphological abnormalities that are associated with the EEG alterations. In particular, mouse mutants have a bilateral reduction of the gray matter volume in the ventral hippocampus and in the septum areas, which are pivotal structures for maintaining theta rhythms throughout the brain. In PWScr(m+/p-) mice we also observed increased body temperature that is coherent with REM sleep alterations in mice and human patients. Our study indicates that paternally expressed Snord116 is involved in the 24-h regulation of sleep physiological measures, suggesting that it is a candidate gene for the sleep disturbances that most individuals with PWS experience. © 2016 Associated Professional Sleep Societies, LLC.

Donders is dead: cortical traveling waves and the limits of mental chronometry in cognitive neuroscience.

PubMed

Alexander, David M; Trengove, Chris; van Leeuwen, Cees

2015-11-01

An assumption nearly all researchers in cognitive neuroscience tacitly adhere to is that of space-time separability. Historically, it forms the basis of Donders' difference method, and to date, it underwrites all difference imaging and trial-averaging of cortical activity, including the customary techniques for analyzing fMRI and EEG/MEG data. We describe the assumption and how it licenses common methods in cognitive neuroscience; in particular, we show how it plays out in signal differencing and averaging, and how it misleads us into seeing the brain as a set of static activity sources. In fact, rather than being static, the domains of cortical activity change from moment to moment: Recent research has suggested the importance of traveling waves of activation in the cortex. Traveling waves have been described at a range of different spatial scales in the cortex; they explain a large proportion of the variance in phase measurements of EEG, MEG and ECoG, and are important for understanding cortical function. Critically, traveling waves are not space-time separable. Their prominence suggests that the correct frame of reference for analyzing cortical activity is the dynamical trajectory of the system, rather than the time and space coordinates of measurements. We illustrate what the failure of space-time separability implies for cortical activation, and what consequences this should have for cognitive neuroscience.

Study on bayes discriminant analysis of EEG data.

PubMed

Shi, Yuan; He, DanDan; Qin, Fang

2014-01-01

In this paper, we have done Bayes Discriminant analysis to EEG data of experiment objects which are recorded impersonally come up with a relatively accurate method used in feature extraction and classification decisions. In accordance with the strength of α wave, the head electrodes are divided into four species. In use of part of 21 electrodes EEG data of 63 people, we have done Bayes Discriminant analysis to EEG data of six objects. Results In use of part of EEG data of 63 people, we have done Bayes Discriminant analysis, the electrode classification accuracy rates is 64.4%. Bayes Discriminant has higher prediction accuracy, EEG features (mainly αwave) extract more accurate. Bayes Discriminant would be better applied to the feature extraction and classification decisions of EEG data.

Detection of nonlinear interactions of EEG alpha waves in the brain by a new coherence measure and its application to epilepsy and anti-epileptic drug therapy.

PubMed

Sherman, David; Zhang, Ning; Garg, Shikha; Thakor, Nitish V; Mirski, Marek A; White, Mirinda Anderson; Hinich, Melvin J

2011-04-01

EEG and field potential rhythms established in the cortex and thalamus may accommodate the propagation of seizures. This article describes the interaction between thalamus and cortex during pentylenetetrazol (PTZ) seizures in rats with and without prior treatment with ethosuximide (ESM), a well-known antiepileptic drug (AED) that raises the threshold for seizures, was given before PTZ. The AED was given before PTZ convulsant administration. We track this thalamo-cortical association with a novel measure we have called the cross-bicoherence gain, or BISCOH. This quantity allows us to measure the spectral coherence in a purely higher order spectralmethodology. BISCOH is able to track the formation of nonlinearities at specific frequencies in the recorded EEG. BISCOH showed a strong increase in low alpha wave harmonic generationat 10 and 12.5 Hz after ESM treatment (p < 0.02 and p < 0.007, respectively). Conventional coherence failed to show distinctive and significant changes in thalamo-cortical coupling after ESM treatment at those frequencies and instead showed changes at 5 Hz. This rise in cortical rhythms is evidence of harmonic generation or new frequency formation in the thalamo-cortical system withAED therapy. BISCOH could become a powerful tool in unraveling changes in coherence due to neuroelectric modulation resulting from drug treatment or electrical stimulation.

Midazolam or diazepam administration during electroencephalography helps to diagnose subacute sclerosing panencephalitis (SSPE).

PubMed

Yilmaz, Kutluhan; Sahin, Derya Aydin

2010-08-01

Although diagnostic contribution of intravenous diazepam administration during electroencephalography (EEG) recording in subacute sclerosing panencephalitis has been known, no another drug with less potential side effects has been studied in this procedure. In this study, diazepam is compared with midazolam in 25 subacute sclerosing panencephalitis-diagnosed children and 10 children with subacute sclerosing panencephalitis-suggesting symptoms, normal EEG findings and no certain diagnosis. Neither midazolam nor diazepam affected typical periodic slow-wave complexes. However, in the patients with atypical EEG abnormalities, midazolam, like diazepam, attenuated sharp or sharp-and-slow waves, and therefore made the identification of periodic slow-wave paroxysms easier. In the patients with normal EEGs, both midazolam and diazepam revealed typical periodic complexes on EEG recording in the same 3 patients. Cerebrospinal fluid examination verified the diagnosis of subacute sclerosing panencephalitis. The findings suggest that midazolam or diazepam administration increases the contribution of EEG recording in atypical cases with subacute sclerosing panencephalitis.

Multifractal analysis of real and imaginary movements: EEG study

NASA Astrophysics Data System (ADS)

Pavlov, Alexey N.; Maksimenko, Vladimir A.; Runnova, Anastasiya E.; Khramova, Marina V.; Pisarchik, Alexander N.

2018-04-01

We study abilities of the wavelet-based multifractal analysis in recognition specific dynamics of electrical brain activity associated with real and imaginary movements. Based on the singularity spectra we analyze electroencephalograms (EEGs) acquired in untrained humans (operators) during imagination of hands movements, and show a possibility to distinguish between the related EEG patterns and the recordings performed during real movements or the background electrical brain activity. We discuss how such recognition depends on the selected brain region.

A Spiking Neural Network Methodology and System for Learning and Comparative Analysis of EEG Data From Healthy Versus Addiction Treated Versus Addiction Not Treated Subjects.

PubMed

Doborjeh, Maryam Gholami; Wang, Grace Y; Kasabov, Nikola K; Kydd, Robert; Russell, Bruce

2016-09-01

This paper introduces a method utilizing spiking neural networks (SNN) for learning, classification, and comparative analysis of brain data. As a case study, the method was applied to electroencephalography (EEG) data collected during a GO/NOGO cognitive task performed by untreated opiate addicts, those undergoing methadone maintenance treatment (MMT) for opiate dependence and a healthy control group. the method is based on an SNN architecture called NeuCube, trained on spatiotemporal EEG data. NeuCube was used to classify EEG data across subject groups and across GO versus NOGO trials, but also facilitated a deeper comparative analysis of the dynamic brain processes. This analysis results in a better understanding of human brain functioning across subject groups when performing a cognitive task. In terms of the EEG data classification, a NeuCube model obtained better results (the maximum obtained accuracy: 90.91%) when compared with traditional statistical and artificial intelligence methods (the maximum obtained accuracy: 50.55%). more importantly, new information about the effects of MMT on cognitive brain functions is revealed through the analysis of the SNN model connectivity and its dynamics. this paper presented a new method for EEG data modeling and revealed new knowledge on brain functions associated with mental activity which is different from the brain activity observed in a resting state of the same subjects.

Analyzing large data sets acquired through telemetry from rats exposed to organophosphorous compounds: an EEG study.

PubMed

de Araujo Furtado, Marcio; Zheng, Andy; Sedigh-Sarvestani, Madineh; Lumley, Lucille; Lichtenstein, Spencer; Yourick, Debra

2009-10-30

The organophosphorous compound soman is an acetylcholinesterase inhibitor that causes damage to the brain. Exposure to soman causes neuropathology as a result of prolonged and recurrent seizures. In the present study, long-term recordings of cortical EEG were used to develop an unbiased means to quantify measures of seizure activity in a large data set while excluding other signal types. Rats were implanted with telemetry transmitters and exposed to soman followed by treatment with therapeutics similar to those administered in the field after nerve agent exposure. EEG, activity and temperature were recorded continuously for a minimum of 2 days pre-exposure and 15 days post-exposure. A set of automatic MATLAB algorithms have been developed to remove artifacts and measure the characteristics of long-term EEG recordings. The algorithms use short-time Fourier transforms to compute the power spectrum of the signal for 2-s intervals. The spectrum is then divided into the delta, theta, alpha, and beta frequency bands. A linear fit to the power spectrum is used to distinguish normal EEG activity from artifacts and high amplitude spike wave activity. Changes in time spent in seizure over a prolonged period are a powerful indicator of the effects of novel therapeutics against seizures. A graphical user interface has been created that simultaneously plots the raw EEG in the time domain, the power spectrum, and the wavelet transform. Motor activity and temperature are associated with EEG changes. The accuracy of this algorithm is also verified against visual inspection of video recordings up to 3 days after exposure.

Algorithm to find high density EEG scalp coordinates and analysis of their correspondence to structural and functional regions of the brain

PubMed Central

Giacometti, Paolo; Perdue, Katherine L.; Diamond, Solomon G.

2014-01-01

Background Interpretation and analysis of electroencephalography (EEG) measurements relies on the correspondence of electrode scalp coordinates to structural and functional regions of the brain. New Method An algorithm is introduced for automatic calculation of the International 10–20, 10-10, and 10-5 scalp coordinates of EEG electrodes on a boundary element mesh of a human head. The EEG electrode positions are then used to generate parcellation regions of the cerebral cortex based on proximity to the EEG electrodes. Results The scalp electrode calculation method presented in this study effectively and efficiently identifies EEG locations without prior digitization of coordinates. The average of electrode proximity parcellations of the cortex were tabulated with respect to structural and functional regions of the brain in a population of 20 adult subjects. Comparison with Existing Methods Parcellations based on electrode proximity and EEG sensitivity were compared. The parcellation regions based on sensitivity and proximity were found to have 44.0 ± 11.3% agreement when demarcated by the International 10–20, 32.4 ± 12.6% by the 10-10, and 24.7 ± 16.3% by the 10-5 electrode positioning system. Conclusions The EEG positioning algorithm is a fast and easy method of locating EEG scalp coordinates without the need for digitized electrode positions. The parcellation method presented summarizes the EEG scalp locations with respect to brain regions without computation of a full EEG forward model solution. The reference table of electrode proximity versus cortical regions may be used by experimenters to select electrodes that correspond to anatomical and functional regions of interest. PMID:24769168

Algorithm to find high density EEG scalp coordinates and analysis of their correspondence to structural and functional regions of the brain.

PubMed

Giacometti, Paolo; Perdue, Katherine L; Diamond, Solomon G

2014-05-30

Interpretation and analysis of electroencephalography (EEG) measurements relies on the correspondence of electrode scalp coordinates to structural and functional regions of the brain. An algorithm is introduced for automatic calculation of the International 10-20, 10-10, and 10-5 scalp coordinates of EEG electrodes on a boundary element mesh of a human head. The EEG electrode positions are then used to generate parcellation regions of the cerebral cortex based on proximity to the EEG electrodes. The scalp electrode calculation method presented in this study effectively and efficiently identifies EEG locations without prior digitization of coordinates. The average of electrode proximity parcellations of the cortex were tabulated with respect to structural and functional regions of the brain in a population of 20 adult subjects. Parcellations based on electrode proximity and EEG sensitivity were compared. The parcellation regions based on sensitivity and proximity were found to have 44.0 ± 11.3% agreement when demarcated by the International 10-20, 32.4 ± 12.6% by the 10-10, and 24.7 ± 16.3% by the 10-5 electrode positioning system. The EEG positioning algorithm is a fast and easy method of locating EEG scalp coordinates without the need for digitized electrode positions. The parcellation method presented summarizes the EEG scalp locations with respect to brain regions without computation of a full EEG forward model solution. The reference table of electrode proximity versus cortical regions may be used by experimenters to select electrodes that correspond to anatomical and functional regions of interest. Copyright © 2014 Elsevier B.V. All rights reserved.

A Wearable Channel Selection-Based Brain-Computer Interface for Motor Imagery Detection.

PubMed

Lo, Chi-Chun; Chien, Tsung-Yi; Chen, Yu-Chun; Tsai, Shang-Ho; Fang, Wai-Chi; Lin, Bor-Shyh

2016-02-06

Motor imagery-based brain-computer interface (BCI) is a communication interface between an external machine and the brain. Many kinds of spatial filters are used in BCIs to enhance the electroencephalography (EEG) features related to motor imagery. The approach of channel selection, developed to reserve meaningful EEG channels, is also an important technique for the development of BCIs. However, current BCI systems require a conventional EEG machine and EEG electrodes with conductive gel to acquire multi-channel EEG signals and then transmit these EEG signals to the back-end computer to perform the approach of channel selection. This reduces the convenience of use in daily life and increases the limitations of BCI applications. In order to improve the above issues, a novel wearable channel selection-based brain-computer interface is proposed. Here, retractable comb-shaped active dry electrodes are designed to measure the EEG signals on a hairy site, without conductive gel. By the design of analog CAR spatial filters and the firmware of EEG acquisition module, the function of spatial filters could be performed without any calculation, and channel selection could be performed in the front-end device to improve the practicability of detecting motor imagery in the wearable EEG device directly or in commercial mobile phones or tablets, which may have relatively low system specifications. Finally, the performance of the proposed BCI is investigated, and the experimental results show that the proposed system is a good wearable BCI system prototype.

Functional brain networks in healthy subjects under acupuncture stimulation: An EEG study based on nonlinear synchronization likelihood analysis

NASA Astrophysics Data System (ADS)

Yu, Haitao; Liu, Jing; Cai, Lihui; Wang, Jiang; Cao, Yibin; Hao, Chongqing

2017-02-01

Electroencephalogram (EEG) signal evoked by acupuncture stimulation at "Zusanli" acupoint is analyzed to investigate the modulatory effect of manual acupuncture on the functional brain activity. Power spectral density of EEG signal is first calculated based on the autoregressive Burg method. It is shown that the EEG power is significantly increased during and after acupuncture in delta and theta bands, but decreased in alpha band. Furthermore, synchronization likelihood is used to estimate the nonlinear correlation between each pairwise EEG signals. By applying a threshold to resulting synchronization matrices, functional networks for each band are reconstructed and further quantitatively analyzed to study the impact of acupuncture on network structure. Graph theoretical analysis demonstrates that the functional connectivity of the brain undergoes obvious change under different conditions: pre-acupuncture, acupuncture, and post-acupuncture. The minimum path length is largely decreased and the clustering coefficient keeps increasing during and after acupuncture in delta and theta bands. It is indicated that acupuncture can significantly modulate the functional activity of the brain, and facilitate the information transmission within different brain areas. The obtained results may facilitate our understanding of the long-lasting effect of acupuncture on the brain function.

Monitoring alert and drowsy states by modeling EEG source nonstationarity

NASA Astrophysics Data System (ADS)

Hsu, Sheng-Hsiou; Jung, Tzyy-Ping

2017-10-01

Objective. As a human brain performs various cognitive functions within ever-changing environments, states of the brain characterized by recorded brain activities such as electroencephalogram (EEG) are inevitably nonstationary. The challenges of analyzing the nonstationary EEG signals include finding neurocognitive sources that underlie different brain states and using EEG data to quantitatively assess the state changes. Approach. This study hypothesizes that brain activities under different states, e.g. levels of alertness, can be modeled as distinct compositions of statistically independent sources using independent component analysis (ICA). This study presents a framework to quantitatively assess the EEG source nonstationarity and estimate levels of alertness. The framework was tested against EEG data collected from 10 subjects performing a sustained-attention task in a driving simulator. Main results. Empirical results illustrate that EEG signals under alert versus drowsy states, indexed by reaction speeds to driving challenges, can be characterized by distinct ICA models. By quantifying the goodness-of-fit of each ICA model to the EEG data using the model deviation index (MDI), we found that MDIs were significantly correlated with the reaction speeds (r  =  -0.390 with alertness models and r  =  0.449 with drowsiness models) and the opposite correlations indicated that the two models accounted for sources in the alert and drowsy states, respectively. Based on the observed source nonstationarity, this study also proposes an online framework using a subject-specific ICA model trained with an initial (alert) state to track the level of alertness. For classification of alert against drowsy states, the proposed online framework achieved an averaged area-under-curve of 0.745 and compared favorably with a classic power-based approach. Significance. This ICA-based framework provides a new way to study changes of brain states and can be applied to monitoring cognitive or mental states of human operators in attention-critical settings or in passive brain-computer interfaces.

Microsensors and wireless system for monitoring epilepsy

NASA Astrophysics Data System (ADS)

Whitchurch, Ashwin K.; Ashok, B. H.; Kumaar, Raman V.; Sarukesi, K.; Jose, K. A.; Varadan, Vijay K.

2003-07-01

Epilepsy is a form of brain disorder caused by abnormal discharges of neurons. The most common manifestations of epilepsy are seizures which could affect visual, aural and motor abilities of a person. Absence epilepsy is a form of epilepsy common mostly in children. The most common manifestations of absence epilepsy are staring and transient loss of responsiveness. Also, subtle motor activities may occur. Due to the subtle nature of these symptoms, episodes of absence epilepsy may often go unrecognized for long periods of time or be mistakenly attributed to attention deficit disorder or daydreaming. Spells of absence epilepsy may last about 10 seconds and occur hundreds of times each day. Patients have no recollections of the events occurred during those seizures and will resume normal activity without any postictal symptoms. The EEG during such episodes of Absence epilepsy shows intermittent activity of 3 Hz generalized spike and wave complexes. As EEG is the only way of detecting such symptoms, it is required to monitor the EEG of the patient for a long time, usually the whole day. This requires that the patient be connected to the EEG recorder all the time and thus remain only in the bed. So, effectively the EEG is being monitored only when the patient is stationary. The wireless monitoring system described in this paper aims at eliminating this constraint and enables the physician to monitor the EEG when the patient resumes his normal activities. This approach could even help the doctor identify possible triggers of absence epilepsy.

Olfactory Hallucinations without Clinical Motor Activity: A Comparison of Unirhinal with Birhinal Phantosmia

PubMed Central

Henkin, Robert I.; Potolicchio, Samuel J.; Levy, Lucien M.

2013-01-01

Olfactory hallucinations without subsequent myoclonic activity have not been well characterized or understood. Herein we describe, in a retrospective study, two major forms of olfactory hallucinations labeled phantosmias: one, unirhinal, the other, birhinal. To describe these disorders we performed several procedures to elucidate similarities and differences between these processes. From 1272, patients evaluated for taste and smell dysfunction at The Taste and Smell Clinic, Washington, DC with clinical history, neurological and otolaryngological examinations, evaluations of taste and smell function, EEG and neuroradiological studies 40 exhibited cyclic unirhinal phantosmia (CUP) usually without hyposmia whereas 88 exhibited non-cyclic birhinal phantosmia with associated symptomology (BPAS) with hyposmia. Patients with CUP developed phantosmia spontaneously or after laughing, coughing or shouting initially with spontaneous inhibition and subsequently with Valsalva maneuvers, sleep or nasal water inhalation; they had frequent EEG changes usually ipsilateral sharp waves. Patients with BPAS developed phantosmia secondary to several clinical events usually after hyposmia onset with few EEG changes; their phantosmia could not be initiated or inhibited by any physiological maneuver. CUP is uncommonly encountered and represents a newly defined clinical syndrome. BPAS is commonly encountered, has been observed previously but has not been clearly defined. Mechanisms responsible for phantosmia in each group were related to decreased gamma-aminobutyric acid (GABA) activity in specific brain regions. Treatment which activated brain GABA inhibited phantosmia in both groups. PMID:24961619

[Dextrals and sinistrals (right-handers and left-handers): specificity of interhemispheric brain asymmetry and EEG coherence parameters].

PubMed

Zhavoronkova, L A

2007-01-01

Data of literature about morphological, functional and biochemical specificity of the brain interhemispheric asymmetry of healthy right-handers and left-handers and about peculiarity of dynamics of cerebral pathology in patients with different individual asymmetry profiles are presented at the present article. Results of our investigation by using coherence parameters of electroencephalogram (EEG) in healthy right-handers and left-handers in state of rest, during functional tests and sleeping and in patients with different forms of the brain organic damage were analyzed too. EEG coherence analysis revealed the reciprocal changing of alpha-beta and theta-delta spectral bands in right-handers whilein left-handers synchronous changing of all EEG spectral bands were observed. Data about regional-frequent specificity of EEG coherence, peculiarity of EEG asymmetry in right-handers and left-handers, aslo about specificity of EEG spectral band genesis and point of view about a role of the brain regulator systems in forming of interhemispheric asymmetry in different functional states allowed to propose the conception about principle of interhermispheric brain asymmetry formation in left-handers and left-handers. Following this conception in dextrals elements of concurrent (summary-reciprocal) cooperation are predominant at the character of interhemispheric and cortical-subcortical interaction while in sinistrals a principle of concordance (supplementary) is preferable. These peculiarities the brain organization determine, from the first side, the quicker revovery of functions damaged after cranio-cerebral trauma in left-handers in comparison right-handers and from the other side - they determine the forming of the more expressed pathology in the remote terms after exposure the low dose of radiation.

[EEG markers of spontaneous recovery of vertical posture in patients with consequences of severe traumatic brain injury].

PubMed

Zhavoronkova, L A; Zharikova, A V; Maksakova, O A

2014-01-01

9 patients (mean age 23.6 +/- 3.15 y.o.) with severe traumatic brain injury (TBI) and impairment of vertical posture were included in complex clinical and EEG study during spontaneous recovery of vertical posture (VP). Patients were included in three different groups according to severity of deficit according to MPAI, FIM and MMSE scales. EEG data have been compared to those of 10 healthy volunteers (mean age 22.8 +/- 0.67 yo.). In patients with moderate brain impairment and fast recovery of VP (over 2 weeks) change of posture from sitting to standup has been accompanied by EEG-signs similar to those of healthy people. These included predominant increase of coherence in right hemisphere for majority of frequency bands, although in more complex conditions EEG of these patients showed pathological signs. In patients with more severe deficit spontaneous recovery of VP has been accompanied by "hyper-reactive" change of EEG for all frequency bands without local specificity. This finding didn't depend on side ofbrain impairment and could be considered as marker of positive dynamics of VP restoration. In patients with most severe brain impairment and deficit of functions VP didn't recover after 3 month of observation. EEG-investigation has revealed absence of reactive change of EEG during passive verticalisation. This finding can be used as marker of negative prognosis.

Brain Dynamics: Methodological Issues and Applications in Psychiatric and Neurologic Diseases

NASA Astrophysics Data System (ADS)

Pezard, Laurent

The human brain is a complex dynamical system generating the EEG signal. Numerical methods developed to study complex physical dynamics have been used to characterize EEG since the mid-eighties. This endeavor raised several issues related to the specificity of EEG. Firstly, theoretical and methodological studies should address the major differences between the dynamics of the human brain and physical systems. Secondly, this approach of EEG signal should prove to be relevant for dealing with physiological or clinical problems. A set of studies performed in our group is presented here within the context of these two problematic aspects. After the discussion of methodological drawbacks, we review numerical simulations related to the high dimension and spatial extension of brain dynamics. Experimental studies in neurologic and psychiatric disease are then presented. We conclude that if it is now clear that brain dynamics changes in relation with clinical situations, methodological problems remain largely unsolved.

Control of a visual keyboard using an electrocorticographic brain-computer interface.

PubMed

Krusienski, Dean J; Shih, Jerry J

2011-05-01

Brain-computer interfaces (BCIs) are devices that enable severely disabled people to communicate and interact with their environments using their brain waves. Most studies investigating BCI in humans have used scalp EEG as the source of electrical signals and focused on motor control of prostheses or computer cursors on a screen. The authors hypothesize that the use of brain signals obtained directly from the cortical surface will more effectively control a communication/spelling task compared to scalp EEG. A total of 6 patients with medically intractable epilepsy were tested for the ability to control a visual keyboard using electrocorticographic (ECOG) signals. ECOG data collected during a P300 visual task paradigm were preprocessed and used to train a linear classifier to subsequently predict the intended target letters. The classifier was able to predict the intended target character at or near 100% accuracy using fewer than 15 stimulation sequences in 5 of the 6 people tested. ECOG data from electrodes outside the language cortex contributed to the classifier and enabled participants to write words on a visual keyboard. This is a novel finding because previous invasive BCI research in humans used signals exclusively from the motor cortex to control a computer cursor or prosthetic device. These results demonstrate that ECOG signals from electrodes both overlying and outside the language cortex can reliably control a visual keyboard to generate language output without voice or limb movements.

Hardware enhance of brain computer interfaces

NASA Astrophysics Data System (ADS)

Wu, Jerry; Szu, Harold; Chen, Yuechen; Guo, Ran; Gu, Xixi

2015-05-01

The history of brain-computer interfaces (BCIs) starts with Hans Berger's discovery of the electrical activity of the human brain and the development of electroencephalography (EEG). Recent years, BCI researches are focused on Invasive, Partially invasive, and Non-invasive BCI. Furthermore, EEG can be also applied to telepathic communication which could provide the basis for brain-based communication using imagined speech. It is possible to use EEG signals to discriminate the vowels and consonants embedded in spoken and in imagined words and apply to military product. In this report, we begin with an example of using high density EEG with high electrode density and analysis the results by using BCIs. The BCIs in this work is enhanced by A field-programmable gate array (FPGA) board with optimized two dimension (2D) image Fast Fourier Transform (FFT) analysis.

Optimal use of EEG recordings to target active brain areas with transcranial electrical stimulation.

PubMed

Dmochowski, Jacek P; Koessler, Laurent; Norcia, Anthony M; Bikson, Marom; Parra, Lucas C

2017-08-15

To demonstrate causal relationships between brain and behavior, investigators would like to guide brain stimulation using measurements of neural activity. Particularly promising in this context are electroencephalography (EEG) and transcranial electrical stimulation (TES), as they are linked by a reciprocity principle which, despite being known for decades, has not led to a formalism for relating EEG recordings to optimal stimulation parameters. Here we derive a closed-form expression for the TES configuration that optimally stimulates (i.e., targets) the sources of recorded EEG, without making assumptions about source location or distribution. We also derive a duality between TES targeting and EEG source localization, and demonstrate that in cases where source localization fails, so does the proposed targeting. Numerical simulations with multiple head models confirm these theoretical predictions and quantify the achieved stimulation in terms of focality and intensity. We show that constraining the stimulation currents automatically selects optimal montages that involve only a few (4-7) electrodes, with only incremental loss in performance when targeting focal activations. The proposed technique allows brain scientists and clinicians to rationally target the sources of observed EEG and thus overcomes a major obstacle to the realization of individualized or closed-loop brain stimulation. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Optimal use of EEG recordings to target active brain areas with transcranial electrical stimulation

PubMed Central

Dmochowski, Jacek P.; Koessler, Laurent; Norcia, Anthony M.; Bikson, Marom; Parra, Lucas C.

2018-01-01

To demonstrate causal relationships between brain and behavior, investigators would like to guide brain stimulation using measurements of neural activity. Particularly promising in this context are electroencephalography (EEG) and transcranial electrical stimulation (TES), as they are linked by a reciprocity principle which, despite being known for decades, has not led to a formalism for relating EEG recordings to optimal stimulation parameters. Here we derive a closed-form expression for the TES configuration that optimally stimulates (i.e., targets) the sources of recorded EEG, without making assumptions about source location or distribution. We also derive a duality between TES targeting and EEG source localization, and demonstrate that in cases where source localization fails, so does the proposed targeting. Numerical simulations with multiple head models confirm these theoretical predictions and quantify the achieved stimulation in terms of focality and intensity. We show that constraining the stimulation currents automatically selects optimal montages that involve only a few (4–7) electrodes, with only incremental loss in performance when targeting focal activations. The proposed technique allows brain scientists and clinicians to rationally target the sources of observed EEG and thus overcomes a major obstacle to the realization of individualized or closed-loop brain stimulation. PMID:28578130

Quantitative Electroencephalography Analysis (qEEG) of Neuro-Electro-Adaptive Therapy 12™ [NEAT12] Up-Regulates Cortical Potentials in an Alcoholic during Protracted Abstinence: Putative Anti-Craving Implications

PubMed Central

Waite, Roger L; Oscar-Berman, Marlene; RBraverman, Eric; Barh, Debmalya; Blum, Kenneth

2015-01-01

Introduction Cranial electrotherapy stimulation (CES) is a noninvasive therapy that has been used for decades in the United States to treat anxiety, depression, and insomnia in the general population. The effectiveness of CES has been questioned by many and its use is considered controversial. In this study we are presenting data on one alcoholic patient using a newly engineered device we call Neuro-Electro-Adaptive Therapy 12™ [NEAT12]. This hybrid device utilizes TENS current characteristics yielding CES effects. This device has been found to primarily target the excitation of the Cingulate Gyrus region of the brain. Case presentation This is a 42 year old male who has been abstinent from alcohol for approximately two months. The data presented herein represents the pre to post qEEG differences of an alcoholic in protracted abstinence. This subject was evaluated both before and after using the NEAT-12 device. The pre to post comparisons suggest that the cortical potentials especially at the Cingulate Gyrus are up regulated after using the device. The absolute power changes obtained shows a decrease of more than 2 SD as noted in the delta wave spectrum. Also noted is an overall cortical increase in the alpha spectrum. The resting alert state of a neuro typical population is most prominently marked by a regulation of 7.5-11 Hz alpha throughout the cortex. The decreased in delta and theta suggests an up regulation of the prefrontal cortex and the anterior Cingulate Gyrus a site involved in substance use disorder (SUD). Conclusion A presence of dominant slow waves through the prefrontal cortex and the anterior Cingulate Gyrus is often associated with OCD, anxiety, impulsivity and cravings in addicted populations. It is conceivable that our initial finding of altered electrical activity of the brain using qEEG analysis suggests the NEAT-12 may induce a “normalization” of aberrant electrical activity of the cortical region of the brain known to occur during protracted abstinence of alcoholics. It may have utility as a putative anti-craving CES device and therefore warrants intensive investigation. PMID:25927012

Integration of EEG source imaging and fMRI during continuous viewing of natural movies.

PubMed

Whittingstall, Kevin; Bartels, Andreas; Singh, Vanessa; Kwon, Soyoung; Logothetis, Nikos K

2010-10-01

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are noninvasive neuroimaging tools which can be used to measure brain activity with excellent temporal and spatial resolution, respectively. By combining the neural and hemodynamic recordings from these modalities, we can gain better insight into how and where the brain processes complex stimuli, which may be especially useful in patients with different neural diseases. However, due to their vastly different spatial and temporal resolutions, the integration of EEG and fMRI recordings is not always straightforward. One fundamental obstacle has been that paradigms used for EEG experiments usually rely on event-related paradigms, while fMRI is not limited in this regard. Therefore, here we ask whether one can reliably localize stimulus-driven EEG activity using the continuously varying feature intensities occurring in natural movie stimuli presented over relatively long periods of time. Specifically, we asked whether stimulus-driven aspects in the EEG signal would be co-localized with the corresponding stimulus-driven BOLD signal during free viewing of a movie. Secondly, we wanted to integrate the EEG signal directly with the BOLD signal, by estimating the underlying impulse response function (IRF) that relates the BOLD signal to the underlying current density in the primary visual area (V1). We made sequential fMRI and 64-channel EEG recordings in seven subjects who passively watched 2-min-long segments of a James Bond movie. To analyze EEG data in this natural setting, we developed a method based on independent component analysis (ICA) to reject EEG artifacts due to blinks, subject movement, etc., in a way unbiased by human judgment. We then calculated the EEG source strength of this artifact-free data at each time point of the movie within the entire brain volume using low-resolution electromagnetic tomography (LORETA). This provided for every voxel in the brain (i.e., in 3D space) an estimate of the current density at every time point. We then carried out a correlation between the time series of visual contrast changes in the movie with that of EEG voxels. We found the most significant correlations in visual area V1, just as seen in previous fMRI studies (Bartels A, Zeki, S, Logothetis NK. Natural vision reveals regional specialization to local motion and to contrast-invariant, global flow in the human brain. Cereb Cortex 2008;18(3):705-717), but on the time scale of milliseconds rather than of seconds. To obtain an estimate of how the EEG signal relates to the BOLD signal, we calculated the IRF between the BOLD signal and the estimated current density in area V1. We found that this IRF was very similar to that observed using combined intracortical recordings and fMRI experiments in nonhuman primates. Taken together, these findings open a new approach to noninvasive mapping of the brain. It allows, firstly, the localization of feature-selective brain areas during natural viewing conditions with the temporal resolution of EEG. Secondly, it provides a tool to assess EEG/BOLD transfer functions during processing of more natural stimuli. This is especially useful in combined EEG/fMRI experiments, where one can now potentially study neural-hemodynamic relationships across the whole brain volume in a noninvasive manner. Copyright © 2010 Elsevier Inc. All rights reserved.

Multimodal 2D Brain Computer Interface.

PubMed

Almajidy, Rand K; Boudria, Yacine; Hofmann, Ulrich G; Besio, Walter; Mankodiya, Kunal

2015-08-01

In this work we used multimodal, non-invasive brain signal recording systems, namely Near Infrared Spectroscopy (NIRS), disc electrode electroencephalography (EEG) and tripolar concentric ring electrodes (TCRE) electroencephalography (tEEG). 7 healthy subjects participated in our experiments to control a 2-D Brain Computer Interface (BCI). Four motor imagery task were performed, imagery motion of the left hand, the right hand, both hands and both feet. The signal slope (SS) of the change in oxygenated hemoglobin concentration measured by NIRS was used for feature extraction while the power spectrum density (PSD) of both EEG and tEEG in the frequency band 8-30Hz was used for feature extraction. Linear Discriminant Analysis (LDA) was used to classify different combinations of the aforementioned features. The highest classification accuracy (85.2%) was achieved by using features from all the three brain signals recording modules. The improvement in classification accuracy was highly significant (p = 0.0033) when using the multimodal signals features as compared to pure EEG features.

An EEG blind source separation algorithm based on a weak exclusion principle.

PubMed

Lan Ma; Blu, Thierry; Wang, William S-Y

2016-08-01

The question of how to separate individual brain and non-brain signals, mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings, is a significant problem in contemporary neuroscience. This study proposes and evaluates a novel EEG Blind Source Separation (BSS) algorithm based on a weak exclusion principle (WEP). The chief point in which it differs from most previous EEG BSS algorithms is that the proposed algorithm is not based upon the hypothesis that the sources are statistically independent. Our first step was to investigate algorithm performance on simulated signals which have ground truth. The purpose of this simulation is to illustrate the proposed algorithm's efficacy. The results show that the proposed algorithm has good separation performance. Then, we used the proposed algorithm to separate real EEG signals from a memory study using a revised version of Sternberg Task. The results show that the proposed algorithm can effectively separate the non-brain and brain sources.

EEG Subspace Analysis and Classification Using Principal Angles for Brain-Computer Interfaces

NASA Astrophysics Data System (ADS)

Ashari, Rehab Bahaaddin

Brain-Computer Interfaces (BCIs) help paralyzed people who have lost some or all of their ability to communicate and control the outside environment from loss of voluntary muscle control. Most BCIs are based on the classification of multichannel electroencephalography (EEG) signals recorded from users as they respond to external stimuli or perform various mental activities. The classification process is fraught with difficulties caused by electrical noise, signal artifacts, and nonstationarity. One approach to reducing the effects of similar difficulties in other domains is the use of principal angles between subspaces, which has been applied mostly to video sequences. This dissertation studies and examines different ideas using principal angles and subspaces concepts. It introduces a novel mathematical approach for comparing sets of EEG signals for use in new BCI technology. The success of the presented results show that principal angles are also a useful approach to the classification of EEG signals that are recorded during a BCI typing application. In this application, the appearance of a subject's desired letter is detected by identifying a P300-wave within a one-second window of EEG following the flash of a letter. Smoothing the signals before using them is the only preprocessing step that was implemented in this study. The smoothing process based on minimizing the second derivative in time is implemented to increase the classification accuracy instead of using the bandpass filter that relies on assumptions on the frequency content of EEG. This study examines four different ways of removing outliers that are based on the principal angles and shows that the outlier removal methods did not help in the presented situations. One of the concepts that this dissertation focused on is the effect of the number of trials on the classification accuracies. The achievement of the good classification results by using a small number of trials starting from two trials only, should make this approach more appropriate for online BCI applications. In order to understand and test how EEG signals are different from one subject to another, different users are tested in this dissertation, some with motor impairments. Furthermore, the concept of transferring information between subjects is examined by training the approach on one subject and testing it on the other subject using the training subject's EEG subspaces to classify the testing subject's trials.

Automatic reference selection for quantitative EEG interpretation: identification of diffuse/localised activity and the active earlobe reference, iterative detection of the distribution of EEG rhythms.

PubMed

Wang, Bei; Wang, Xingyu; Ikeda, Akio; Nagamine, Takashi; Shibasaki, Hiroshi; Nakamura, Masatoshi

2014-01-01

EEG (Electroencephalograph) interpretation is important for the diagnosis of neurological disorders. The proper adjustment of the montage can highlight the EEG rhythm of interest and avoid false interpretation. The aim of this study was to develop an automatic reference selection method to identify a suitable reference. The results may contribute to the accurate inspection of the distribution of EEG rhythms for quantitative EEG interpretation. The method includes two pre-judgements and one iterative detection module. The diffuse case is initially identified by pre-judgement 1 when intermittent rhythmic waveforms occur over large areas along the scalp. The earlobe reference or averaged reference is adopted for the diffuse case due to the effect of the earlobe reference depending on pre-judgement 2. An iterative detection algorithm is developed for the localised case when the signal is distributed in a small area of the brain. The suitable averaged reference is finally determined based on the detected focal and distributed electrodes. The presented technique was applied to the pathological EEG recordings of nine patients. One example of the diffuse case is introduced by illustrating the results of the pre-judgements. The diffusely intermittent rhythmic slow wave is identified. The effect of active earlobe reference is analysed. Two examples of the localised case are presented, indicating the results of the iterative detection module. The focal and distributed electrodes are detected automatically during the repeating algorithm. The identification of diffuse and localised activity was satisfactory compared with the visual inspection. The EEG rhythm of interest can be highlighted using a suitable selected reference. The implementation of an automatic reference selection method is helpful to detect the distribution of an EEG rhythm, which can improve the accuracy of EEG interpretation during both visual inspection and automatic interpretation. Copyright © 2013 IPEM. Published by Elsevier Ltd. All rights reserved.

Estimation of the EEG power spectrum using MRI T(2) relaxation time in traumatic brain injury.

PubMed

Thatcher, R W; Biver, C; Gomez, J F; North, D; Curtin, R; Walker, R A; Salazar, A

2001-09-01

To study the relationship between magnetic resonance imaging (MRI) T(2) relaxation time and the power spectrum of the electroencephalogram (EEG) in long-term follow up of traumatic brain injury. Nineteen channel quantitative electroencephalograms or qEEG, tests of cognitive function and quantitative MRI T(2) relaxation times (qMRI) were measured in 18 mild to severe closed head injured outpatients 2 months to 4.6 years after injury and 11 normal controls. MRI T(2) and the Laplacian of T(2) were then correlated with the power spectrum of the scalp electrical potentials and current source densities of the qEEG. qEEG and qMRI T(2) were related by a frequency tuning with maxima in the alpha (8-12Hz) and the lower EEG frequencies (0.5-5Hz), which varied as a function of spatial location. The Laplacian of T(2) acted like a spatial-temporal "lens" by increasing the spatial-temporal resolution of correlation between 3-dimensional T(2) and the ear referenced alert but resting spontaneous qEEG. The severity of traumatic brain injury can be modeled by a linear transfer function that relates the molecular qMRI to qEEG resonant frequencies.

Real-time Adaptive EEG Source Separation using Online Recursive Independent Component Analysis

PubMed Central

Hsu, Sheng-Hsiou; Mullen, Tim; Jung, Tzyy-Ping; Cauwenberghs, Gert

2016-01-01

Independent Component Analysis (ICA) has been widely applied to electroencephalographic (EEG) biosignal processing and brain-computer interfaces. The practical use of ICA, however, is limited by its computational complexity, data requirements for convergence, and assumption of data stationarity, especially for high-density data. Here we study and validate an optimized online recursive ICA algorithm (ORICA) with online recursive least squares (RLS) whitening for blind source separation of high-density EEG data, which offers instantaneous incremental convergence upon presentation of new data. Empirical results of this study demonstrate the algorithm's: (a) suitability for accurate and efficient source identification in high-density (64-channel) realistically-simulated EEG data; (b) capability to detect and adapt to non-stationarity in 64-ch simulated EEG data; and (c) utility for rapidly extracting principal brain and artifact sources in real 61-channel EEG data recorded by a dry and wearable EEG system in a cognitive experiment. ORICA was implemented as functions in BCILAB and EEGLAB and was integrated in an open-source Real-time EEG Source-mapping Toolbox (REST), supporting applications in ICA-based online artifact rejection, feature extraction for real-time biosignal monitoring in clinical environments, and adaptable classifications in brain-computer interfaces. PMID:26685257

Analysis of the Influence of Complexity and Entropy of Odorant on Fractal Dynamics and Entropy of EEG Signal.

PubMed

Namazi, Hamidreza; Akrami, Amin; Nazeri, Sina; Kulish, Vladimir V

2016-01-01

An important challenge in brain research is to make out the relation between the features of olfactory stimuli and the electroencephalogram (EEG) signal. Yet, no one has discovered any relation between the structures of olfactory stimuli and the EEG signal. This study investigates the relation between the structures of EEG signal and the olfactory stimulus (odorant). We show that the complexity of the EEG signal is coupled with the molecular complexity of the odorant, where more structurally complex odorant causes less fractal EEG signal. Also, odorant having higher entropy causes the EEG signal to have lower approximate entropy. The method discussed here can be applied and investigated in case of patients with brain diseases as the rehabilitation purpose.

Analysis of the Influence of Complexity and Entropy of Odorant on Fractal Dynamics and Entropy of EEG Signal

PubMed Central

Akrami, Amin; Nazeri, Sina

2016-01-01

An important challenge in brain research is to make out the relation between the features of olfactory stimuli and the electroencephalogram (EEG) signal. Yet, no one has discovered any relation between the structures of olfactory stimuli and the EEG signal. This study investigates the relation between the structures of EEG signal and the olfactory stimulus (odorant). We show that the complexity of the EEG signal is coupled with the molecular complexity of the odorant, where more structurally complex odorant causes less fractal EEG signal. Also, odorant having higher entropy causes the EEG signal to have lower approximate entropy. The method discussed here can be applied and investigated in case of patients with brain diseases as the rehabilitation purpose. PMID:27699169

[Nootropics and antioxidants in the complex therapy of symptomatic posttraumatic epilepsy].

PubMed

Savenkov, A A; Badalian, O L; Avakian, G N

2013-01-01

To study the possibility of application of nootropics and antioxidants in the complex antiepileptic therapy, we examined 75 patients with symptomatic focal posttraumatic epilepsy. A statistically significant reduction in the number of epileptic seizures, improvement of cognitive function and quality of life of the patients as well as a decrease in the severity of depression and epileptic changes in the EEG were identified. The potentiation of antiepileptic activity of basic drugs, normalization of brain's electrical activity and reduction in EEG epileptiform activity, in particular coherent indicators of slow-wave activity, were noted after treatment with the antioxidant mexidol. A trend towards the improvement of neuropsychological performance and quality of life was observed. There was a lack of seizure aggravation typical of many nootropic drugs. Thus, phenotropil and mexidol can be recommended for complex treatment of symptomatic posttraumatic epilepsy.

The functional organization of human epileptic hippocampus

PubMed Central

Klimes, Petr; Duque, Juliano J.; Brinkmann, Ben; Van Gompel, Jamie; Stead, Matt; St. Louis, Erik K.; Halamek, Josef; Jurak, Pavel

2016-01-01

The function and connectivity of human brain is disrupted in epilepsy. We previously reported that the region of epileptic brain generating focal seizures, i.e., the seizure onset zone (SOZ), is functionally isolated from surrounding brain regions in focal neocortical epilepsy. The modulatory effect of behavioral state on the spatial and spectral scales over which the reduced functional connectivity occurs, however, is unclear. Here we use simultaneous sleep staging from scalp EEG with intracranial EEG recordings from medial temporal lobe to investigate how behavioral state modulates the spatial and spectral scales of local field potential synchrony in focal epileptic hippocampus. The local field spectral power and linear correlation between adjacent electrodes provide measures of neuronal population synchrony at different spatial scales, ∼1 and 10 mm, respectively. Our results show increased connectivity inside the SOZ and low connectivity between electrodes in SOZ and outside the SOZ. During slow-wave sleep, we observed decreased connectivity for ripple and fast ripple frequency bands within the SOZ at the 10 mm spatial scale, while the local synchrony remained high at the 1 mm spatial scale. Further study of these phenomena may prove useful for SOZ localization and help understand seizure generation, and the functional deficits seen in epileptic eloquent cortex. PMID:27030735

Hemispheric asymmetry of electroencephalography-based functional brain networks.

PubMed

Jalili, Mahdi

2014-11-12

Electroencephalography (EEG)-based functional brain networks have been investigated frequently in health and disease. It has been shown that a number of graph theory metrics are disrupted in brain disorders. EEG-based brain networks are often studied in the whole-brain framework, where all the nodes are grouped into a single network. In this study, we studied the brain networks in two hemispheres and assessed whether there are any hemispheric-specific patterns in the properties of the networks. To this end, resting state closed-eyes EEGs from 44 healthy individuals were processed and the network structures were extracted separately for each hemisphere. We examined neurophysiologically meaningful graph theory metrics: global and local efficiency measures. The global efficiency did not show any hemispheric asymmetry, whereas the local connectivity showed rightward asymmetry for a range of intermediate density values for the constructed networks. Furthermore, the age of the participants showed significant direct correlations with the global efficiency of the left hemisphere, but only in the right hemisphere, with local connectivity. These results suggest that only local connectivity of EEG-based functional networks is associated with brain hemispheres.

Mental Task Evaluation for Hybrid NIRS-EEG Brain-Computer Interfaces

PubMed Central

Gupta, Rishabh; Falk, Tiago H.

2017-01-01

Based on recent electroencephalography (EEG) and near-infrared spectroscopy (NIRS) studies that showed that tasks such as motor imagery and mental arithmetic induce specific neural response patterns, we propose a hybrid brain-computer interface (hBCI) paradigm in which EEG and NIRS data are fused to improve binary classification performance. We recorded simultaneous NIRS-EEG data from nine participants performing seven mental tasks (word generation, mental rotation, subtraction, singing and navigation, and motor and face imagery). Classifiers were trained for each possible pair of tasks using (1) EEG features alone, (2) NIRS features alone, and (3) EEG and NIRS features combined, to identify the best task pairs and assess the usefulness of a multimodal approach. The NIRS-EEG approach led to an average increase in peak kappa of 0.03 when using features extracted from one-second windows (equivalent to an increase of 1.5% in classification accuracy for balanced classes). The increase was much stronger (0.20, corresponding to an 10% accuracy increase) when focusing on time windows of high NIRS performance. The EEG and NIRS analyses further unveiled relevant brain regions and important feature types. This work provides a basis for future NIRS-EEG hBCI studies aiming to improve classification performance toward more efficient and flexible BCIs. PMID:29181021

EEG changes as heat stress reactions in rats irradiated by high intensity 35 GHz millimeter waves.

PubMed

Xie, Taorong; Pei, Jian; Cui, Yibin; Zhang, Jie; Qi, Hongxing; Chen, Shude; Qiao, Dengjiang

2011-06-01

As the application of millimeter waves for civilian and military use increases, the possibility of overexposure to millimeter waves will also increase. This paper attempts to evaluate stress reactions evoked by 35 GHz millimeter waves. The stress reactions in Sprague-Dawley (SD) rats were quantitatively studied by analyzing electroencephalogram (EEG) changes induced by overexposure to 35 GHz millimeter waves. The relative changes in average energy of the EEG and its wavelet decompositions were used for extracting the stress reaction indicators. Incident average power densities (IAPDs) of 35 GHz millimeter waves from 0.5 W cm(-2) to 7.5 W cm(-2) were employed to investigate the relation between irradiation dose and the stress reactions in the rats. Different stress reaction periods evoked by irradiation were quantitatively evaluated by EEG results. The results illustrate that stress reactions are more intense during the first part of the irradiation than during the later part. The skin temperature increase produced by millimeter wave irradiation is the principle reason for stress reactions and skin injuries. As expected, at the higher levels of irradiation, the reaction time decreases and the reaction intensity increases.

Effects of observation of hand movements reflected in a mirror on cortical activation in patients with stroke

PubMed Central

Chang, Moon-Young; Kim, Hwan-Hee; Kim, Kyeong-Mi; Oh, Jae-Seop; Jang, Chel; Yoon, Tae-Hyung

2017-01-01

[Purpose] The purpose of this study was to examine what changes occur in brain waves when patients with stroke receive mirror therapy intervention. [Subjects and Methods] The subjects of this study were 14 patients with stroke (6 females and 8 males). The subjects were assessed by measuring the alpha and beta waves of the EEG (QEEG-32 system CANS 3000). The mirror therapy intervention was delivered over the course of four weeks (a total of 20 sessions). [Results] Relative alpha power showed statistically significant differences in the F3, F4, O1, and O2 channels in the situation comparison and higher for hand observation than for mirror observation. Relative beta power showed statistically significant differences in the F3, F4, C3, and C4 channels. [Conclusion] This study analyzed activity of the brain in each area when patients with stroke observed movements reflected in a mirror, and future research on diverse tasks and stimuli to heighten activity of the brain should be carried out. PMID:28210035

Effects of observation of hand movements reflected in a mirror on cortical activation in patients with stroke.

PubMed

Chang, Moon-Young; Kim, Hwan-Hee; Kim, Kyeong-Mi; Oh, Jae-Seop; Jang, Chel; Yoon, Tae-Hyung

2017-01-01

[Purpose] The purpose of this study was to examine what changes occur in brain waves when patients with stroke receive mirror therapy intervention. [Subjects and Methods] The subjects of this study were 14 patients with stroke (6 females and 8 males). The subjects were assessed by measuring the alpha and beta waves of the EEG (QEEG-32 system CANS 3000). The mirror therapy intervention was delivered over the course of four weeks (a total of 20 sessions). [Results] Relative alpha power showed statistically significant differences in the F3, F4, O1, and O2 channels in the situation comparison and higher for hand observation than for mirror observation. Relative beta power showed statistically significant differences in the F3, F4, C3, and C4 channels. [Conclusion] This study analyzed activity of the brain in each area when patients with stroke observed movements reflected in a mirror, and future research on diverse tasks and stimuli to heighten activity of the brain should be carried out.

Electrophysiological correlates of the BOLD signal for EEG-informed fMRI

PubMed Central

Murta, Teresa; Leite, Marco; Carmichael, David W; Figueiredo, Patrícia; Lemieux, Louis

2015-01-01

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are important tools in cognitive and clinical neuroscience. Combined EEG–fMRI has been shown to help to characterise brain networks involved in epileptic activity, as well as in different sensory, motor and cognitive functions. A good understanding of the electrophysiological correlates of the blood oxygen level-dependent (BOLD) signal is necessary to interpret fMRI maps, particularly when obtained in combination with EEG. We review the current understanding of electrophysiological–haemodynamic correlates, during different types of brain activity. We start by describing the basic mechanisms underlying EEG and BOLD signals and proceed by reviewing EEG-informed fMRI studies using fMRI to map specific EEG phenomena over the entire brain (EEG–fMRI mapping), or exploring a range of EEG-derived quantities to determine which best explain colocalised BOLD fluctuations (local EEG–fMRI coupling). While reviewing studies of different forms of brain activity (epileptic and nonepileptic spontaneous activity; cognitive, sensory and motor functions), a significant attention is given to epilepsy because the investigation of its haemodynamic correlates is the most common application of EEG-informed fMRI. Our review is focused on EEG-informed fMRI, an asymmetric approach of data integration. We give special attention to the invasiveness of electrophysiological measurements and the simultaneity of multimodal acquisitions because these methodological aspects determine the nature of the conclusions that can be drawn from EEG-informed fMRI studies. We emphasise the advantages of, and need for, simultaneous intracranial EEG–fMRI studies in humans, which recently became available and hold great potential to improve our understanding of the electrophysiological correlates of BOLD fluctuations. PMID:25277370

Resting-state EEG coupling analysis of amnestic mild cognitive impairment with type 2 diabetes mellitus by using permutation conditional mutual information.

PubMed

Wen, Dong; Bian, Zhijie; Li, Qiuli; Wang, Lei; Lu, Chengbiao; Li, Xiaoli

2016-01-01

This study was meant to explore whether the coupling strength and direction of resting-state electroencephalogram (rsEEG) could be used as an indicator to distinguish the patients of type 2 diabetes mellitus (T2DM) with or without amnestic mild cognitive impairment (aMCI). Permutation conditional mutual information (PCMI) was used to calculate the coupling strength and direction of rsEEG signals between different brain areas of 19 aMCI and 20 normal control (NC) with T2DM on 7 frequency bands: Delta, Theta, Alpha1, Alpha2, Beta1, Beta2 and Gamma. The difference in coupling strength or direction of rsEEG between two groups was calculated. The correlation between coupling strength or direction of rsEEG and score of different neuropsychology scales were also calculated. We have demonstrated that PCMI can calculate effectively the coupling strength and directionality of EEG signals between different brain regions. The significant difference in coupling strength and directionality of EEG signals was found between the patients of aMCI and NC with T2DM on different brain regions. There also existed significant correlation between sex or age and coupling strength or coupling directionality of EEG signals between a few different brain regions from all subjects. The coupling strength or directionality of EEG signals calculated by PCMI are significantly different between aMCI and NC with T2DM. These results showed that the coupling strength or directionality of EEG signals calculated by PCMI might be used as a biomarker in distinguishing the aMCI from NC with T2DM. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Sleep and wakefulness in somnambulism: a spectral analysis study.

PubMed

Guilleminault, C; Poyares, D; Aftab, F A; Palombini, L; Abat, F

2001-08-01

The sleep structure and the dynamics of EEG slow-wave activity (SWA) were investigated in 12 young adults and age- and gender-matched controls. Polysomnography was performed in subjects with well-documented chronic sleepwalking and in matched controls. Blinded visual scoring was performed using the international criteria from the Rechtschaffen and Kales atlas [A manual of standardized technology, techniques and scoring systems for sleep stages of human subjects. Los Angeles: UCLA Brain Information Service, Brain Research Institute, 1968.] and by determining the presence of microarousals as defined in the American Sleep Disorders Association (ASDA) atlas [Sleep 15 (1992) 173.]. An evaluation of SWA overnight was performed on total nocturnal sleep to determine if a difference existed between groups of subjects, since sleepwalking usually originates with slow-wave sleep. Investigation of the delta power in successive nonoverlapping 4-second windows in the 32 seconds just prior to EMG activity associated with a confusional arousal was also conducted. One central EEG lead was used for all analyses. Somnambulistic individuals experienced more disturbed sleep than controls during the first NREM-REM sleep cycle. They had a higher number of ASDA arousals and presented lower peak of SWA during the first cycle that led to a lower SWA decline overnight. When the investigation focused on the short segment immediately preceding a confusional arousal, they presented an important increase in the relative power of low delta (0.75-2 Hz) just prior to the confusional arousal. Sleepwalkers undergo disturbed nocturnal sleep at the beginning of the night. The increased power of low delta just prior to the confusional arousal experienced may not be related to Stages 3-4 NREM sleep. We hypothesize that it may be translated as a cortical reaction to brain activation.

Modulatory effects of aromatherapy massage intervention on electroencephalogram, psychological assessments, salivary cortisol and plasma brain-derived neurotrophic factor.

PubMed

Wu, Jin-Ji; Cui, Yanji; Yang, Yoon-Sil; Kang, Moon-Seok; Jung, Sung-Cherl; Park, Hyeung Keun; Yeun, Hye-Young; Jang, Won Jung; Lee, Sunjoo; Kwak, Young Sook; Eun, Su-Yong

2014-06-01

Aromatherapy massage is commonly used for the stress management of healthy individuals, and also has been often employed as a therapeutic use for pain control and alleviating psychological distress, such as anxiety and depression, in oncological palliative care patients. However, the exact biological basis of aromatherapy massage is poorly understood. Therefore, we evaluated here the effects of aromatherapy massage interventions on multiple neurobiological indices such as quantitative psychological assessments, electroencephalogram (EEG) power spectrum pattern, salivary cortisol and plasma brain-derived neurotrophic factor (BDNF) levels. A control group without treatment (n = 12) and aromatherapy massage group (n = 13) were randomly recruited. They were all females whose children were diagnosed as attention deficit hyperactivity disorder and followed up in the Department of Psychiatry, Jeju National University Hospital. Participants were treated with aromatherapy massage for 40 min twice per week for 4 weeks (8 interventions). A 4-week-aromatherapy massage program significantly improved all psychological assessment scores in the Stat-Trait Anxiety Index, Beck Depression Inventory and Short Form of Psychosocial Well-being Index. Interestingly, plasma BDNF levels were significantly increased after a 4 week-aromatherapy massage program. Alpha-brain wave activities were significantly enhanced and delta wave activities were markedly reduced following the one-time aromatherapy massage treatment, as shown in the meditation and neurofeedback training. In addition, salivary cortisol levels were significantly reduced following the one-time aromatherapy massage treatment. These results suggest that aromatherapy massage could exert significant influences on multiple neurobiological indices such as EEG pattern, salivary cortisol and plasma BDNF levels as well as psychological assessments. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Source-based neurofeedback methods using EEG recordings: training altered brain activity in a functional brain source derived from blind source separation

PubMed Central

White, David J.; Congedo, Marco; Ciorciari, Joseph

2014-01-01

A developing literature explores the use of neurofeedback in the treatment of a range of clinical conditions, particularly ADHD and epilepsy, whilst neurofeedback also provides an experimental tool for studying the functional significance of endogenous brain activity. A critical component of any neurofeedback method is the underlying physiological signal which forms the basis for the feedback. While the past decade has seen the emergence of fMRI-based protocols training spatially confined BOLD activity, traditional neurofeedback has utilized a small number of electrode sites on the scalp. As scalp EEG at a given electrode site reflects a linear mixture of activity from multiple brain sources and artifacts, efforts to successfully acquire some level of control over the signal may be confounded by these extraneous sources. Further, in the event of successful training, these traditional neurofeedback methods are likely influencing multiple brain regions and processes. The present work describes the use of source-based signal processing methods in EEG neurofeedback. The feasibility and potential utility of such methods were explored in an experiment training increased theta oscillatory activity in a source derived from Blind Source Separation (BSS) of EEG data obtained during completion of a complex cognitive task (spatial navigation). Learned increases in theta activity were observed in two of the four participants to complete 20 sessions of neurofeedback targeting this individually defined functional brain source. Source-based EEG neurofeedback methods using BSS may offer important advantages over traditional neurofeedback, by targeting the desired physiological signal in a more functionally and spatially specific manner. Having provided preliminary evidence of the feasibility of these methods, future work may study a range of clinically and experimentally relevant brain processes where individual brain sources may be targeted by source-based EEG neurofeedback. PMID:25374520

Effects of Drawing on Alpha Activity: A Quantitative EEG Study with Implications for Art Therapy

ERIC Educational Resources Information Center

Belkofer, Christopher M.; Van Hecke, Amy Vaughan; Konopka, Lukasz M.

2014-01-01

Little empirical evidence exists as to how materials used in art therapy affect the brain and its neurobiological functioning. This pre/post within-groups study utilized the quantitative electroencephalogram (qEEG) to measure residual effects in the brain after 20 minutes of drawing. EEG recordings were conducted before and after participants (N =…

Distinctive time-lagged resting-state networks revealed by simultaneous EEG-fMRI.

PubMed

Feige, Bernd; Spiegelhalder, Kai; Kiemen, Andrea; Bosch, Oliver G; Tebartz van Elst, Ludger; Hennig, Jürgen; Seifritz, Erich; Riemann, Dieter

2017-01-15

Functional activation as evidenced by blood oxygen level-dependent (BOLD) functional MRI changes or event-related EEG is known to closely follow patterns of stimulation or self-paced action. Any lags are compatible with axonal conduction velocities and neural integration times. The important analysis of resting state networks is generally based on the assumption that these principles also hold for spontaneous fluctuations in brain activity. Previous observations using simultaneous EEG and fMRI indicate that slower processes, with delays in the seconds range, determine at least part of the relationship between spontaneous EEG and fMRI. To assess this relationship systematically, we used deconvolution analysis of EEG-fMRI during the resting state, assessing the relationship between EEG frequency bands and fMRI BOLD across the whole brain while allowing for time lags of up to 10.5s. Cluster analysis, identifying similar BOLD time courses in relation to EEG band power peaks, showed a clear segregation of functional subsystems of the brain. Our analysis shows that fMRI BOLD increases commonly precede EEG power increases by seconds. Most zero-lag correlations, on the other hand, were negative. This indicates two main distinct neuromodulatory mechanisms: an "idling" mechanism of simultaneous electric and metabolic network anticorrelation and a "regulatory" mechanism in which metabolic network activity precedes increased EEG power by some seconds. This has to be taken into consideration in further studies which address the causal and functional relationship of metabolic and electric brain activity patterns. Copyright © 2016 Elsevier Inc. All rights reserved.

A natural basis for efficient brain-actuated control

NASA Technical Reports Server (NTRS)

Makeig, S.; Enghoff, S.; Jung, T. P.; Sejnowski, T. J.

2000-01-01

The prospect of noninvasive brain-actuated control of computerized screen displays or locomotive devices is of interest to many and of crucial importance to a few 'locked-in' subjects who experience near total motor paralysis while retaining sensory and mental faculties. Currently several groups are attempting to achieve brain-actuated control of screen displays using operant conditioning of particular features of the spontaneous scalp electroencephalogram (EEG) including central mu-rhythms (9-12 Hz). A new EEG decomposition technique, independent component analysis (ICA), appears to be a foundation for new research in the design of systems for detection and operant control of endogenous EEG rhythms to achieve flexible EEG-based communication. ICA separates multichannel EEG data into spatially static and temporally independent components including separate components accounting for posterior alpha rhythms and central mu activities. We demonstrate using data from a visual selective attention task that ICA-derived mu-components can show much stronger spectral reactivity to motor events than activity measures for single scalp channels. ICA decompositions of spontaneous EEG would thus appear to form a natural basis for operant conditioning to achieve efficient and multidimensional brain-actuated control in motor-limited and locked-in subjects.

Statistical features of hypnagogic EEG measured by a new scoring system.

PubMed

Tanaka, H; Hayashi, M; Hori, T

1996-11-01

The purpose of this study was to examine the durations of individual occurrences of each of nine hypnagogic electroencephalographic (EEG) stages and the interchange relationship among these stages. Most of the alpha patterns (stages 1, 2, and 3), ripples (stage 5), and spindles (stage 9) tended to last > 2 minutes. On the other hand, histograms of the durations of time in EEG flattening (stage 4) and vertex sharp wave (stages 6, 7, and 8) patterns had peaks that lasted < 30 seconds. Analysis of the sequences of EEG stage changes demonstrated that shifts to adjacent stages were most common for all stages. A smooth change in EEG stage occurred in the downward or upward direction in the hypnagogic state. This was especially true for the first five stages. EEG stages with vertex sharp waves (stages 6, 7, and 8), however, showed less-smooth changes, with approximately 20% of all changes involving a jump of more than one stage. These results show that the basic EEG activities in the sleep onset period are the alpha, theta, and sleep spindles activities, whereas the activities of vertex sharp waves seem to have a secondary or enhancing role, instead of independent characteristics.

Hyperglycemia is associated with simultaneous alterations in electrical brain activity in youths with type 1 diabetes mellitus.

PubMed

Rachmiel, M; Cohen, M; Heymen, E; Lezinger, M; Inbar, D; Gilat, S; Bistritzer, T; Leshem, G; Kan-Dror, E; Lahat, E; Ekstein, D

2016-02-01

To assess the association between hyperglycemia and electrical brain activity in type 1 diabetes mellitus (T1DM). Nine youths with T1DM were monitored simultaneously and continuously by EEG and continuous glucose monitor system, for 40 h. EEG powers of 0.5-80 Hz frequency bands in all the different brain regions were analyzed according to interstitial glucose concentration (IGC) ranges of 4-11 mmol/l, 11-15.5 mmol/l and >15.5 mmol/l. Analysis of variance was used to examine the differences in EEG power of each frequency band between the subgroups of IGC. Analysis was performed separately during wakefulness and sleep, controlling for age, gender and HbA1c. Mean IGC was 11.49 ± 5.26 mmol/l in 1253 combined measurements. IGC>15.5 mmol/l compared to 4-11 mmol/l was associated during wakefulness with increased EEG power of low frequencies and with decreased EEG power of high frequencies. During sleep, it was associated with increased EEG power of low frequencies in all brain areas and of high frequencies in frontal and central areas. Asymptomatic transient hyperglycemia in youth with T1DM is associated with simultaneous alterations in electrical brain activity during wakefulness and sleep. The clinical implications of immediate electrical brain alterations under hyperglycemia need to be studied and may lead to adaptations of management. Copyright © 2015. Published by Elsevier Ireland Ltd.

Topographic Brain Mapping: A Window on Brain Function?

ERIC Educational Resources Information Center

Karniski, Walt M.

1989-01-01

The article reviews the method of topographic mapping of the brain's electrical activity. Multiple electroencephalogram (EEG) electrodes and computerized analysis of the EEG signal are used to generate maps of frequency and voltage (evoked potential). This relatively new technique holds promise in the evaluation of children with behavioral and…

Interictal Electroencephalography (EEG) Findings in Children with Epilepsy and Bilateral Brain Lesions on Magnetic Resonance Imaging (MRI).

PubMed

Zubcevic, Smail; Milos, Maja; Catibusic, Feriha; Uzicanin, Sajra; Krdzalic, Belma

2015-12-01

Neuroimaging procedures and electroencephalography (EEG) are basic parts of investigation of patients with epilepsies. The aim is to try to assess relationship between bilaterally localized brain lesions found in routine management of children with newly diagnosed epilepsy and their interictal EEG findings. Total amount of 68 patients filled criteria for inclusion in the study that was performed at Neuropediatrics Department, Pediatric Hospital, University Clinical Center Sarajevo, or its outpatient clinic. There were 33 girls (48,5%) and 35 boys (51,5%). Average age at diagnosis of epilepsy was 3,5 years. Both neurological and neuropsychological examination in the moment of making diagnosis of epilepsy was normal in 27 (39,7%) patients, and showed some kind of delay or other neurological finding in 41 (60,3%). Brain MRI showed lesions that can be related to antenatal or perinatal events in most of the patients (ventricular dilation in 30,9%, delayed myelination and post-hypoxic changes in 27,9%). More than half of patients (55,9%) showed bilateral interictal epileptiform discharges on their EEGs, and further 14,7% had other kinds of bilateral abnormalities. Frequency of bilateral epileptic discharges showed statistically significant predominance on level of p<0,05. Cross tabulation between specific types of bilateral brain MRI lesions and EEG finding did not reveal significant type of EEG for assessed brain lesions. We conclude that there exists relationship between bilaterally localized brain MRI lesions and interictal bilateral epileptiform or nonspecific EEG findings in children with newly diagnosed epilepsies. These data are suggesting that in cases when they do not correlate there is a need for further investigation of seizure etiology.

The EEG Split Alpha Peak: Phenomenological Origins and Methodological Aspects of Detection and Evaluation.

PubMed

Olejarczyk, Elzbieta; Bogucki, Piotr; Sobieszek, Aleksander

2017-01-01

Electroencephalographic (EEG) patterns were analyzed in a group of ambulatory patients who ranged in age and sex using spectral analysis as well as Directed Transfer Function, a method used to evaluate functional brain connectivity. We tested the impact of window size and choice of reference electrode on the identification of two or more peaks with close frequencies in the spectral power distribution, so called "split alpha." Together with the connectivity analysis, examination of spatiotemporal maps showing the distribution of amplitudes of EEG patterns allowed for better explanation of the mechanisms underlying the generation of split alpha peaks. It was demonstrated that the split alpha spectrum can be generated by two or more independent and interconnected alpha wave generators located in different regions of the cerebral cortex, but not necessarily in the occipital cortex. We also demonstrated the importance of appropriate reference electrode choice during signal recording. In addition, results obtained using the original data were compared with results obtained using re-referenced data, using average reference electrode and reference electrode standardization techniques.

Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis.

PubMed

Rashid, Nasir; Iqbal, Javaid; Javed, Amna; Tiwana, Mohsin I; Khan, Umar Shahbaz

2018-01-01

Brain Computer Interface (BCI) determines the intent of the user from a variety of electrophysiological signals. These signals, Slow Cortical Potentials, are recorded from scalp, and cortical neuronal activity is recorded by implanted electrodes. This paper is focused on design of an embedded system that is used to control the finger movements of an upper limb prosthesis using Electroencephalogram (EEG) signals. This is a follow-up of our previous research which explored the best method to classify three movements of fingers (thumb movement, index finger movement, and first movement). Two-stage logistic regression classifier exhibited the highest classification accuracy while Power Spectral Density (PSD) was used as a feature of the filtered signal. The EEG signal data set was recorded using a 14-channel electrode headset (a noninvasive BCI system) from right-handed, neurologically intact volunteers. Mu (commonly known as alpha waves) and Beta Rhythms (8-30 Hz) containing most of the movement data were retained through filtering using "Arduino Uno" microcontroller followed by 2-stage logistic regression to obtain a mean classification accuracy of 70%.

Effects of non-pharmacological pain treatments on brain states

PubMed Central

Jensen, Mark P.; Sherlin, Leslie H.; Askew, Robert L.; Fregni, Felipe; Witkop, Gregory; Gianas, Ann; Howe, Jon D.; Hakimian, Shahin

2013-01-01

Objective To (1) evaluate the effects of a single session of four non-pharmacological pain interventions, relative to a sham tDCS procedure, on pain and electroencephalogram- (EEG-) assessed brain oscillations, and (2) determine the extent to which procedure-related changes in pain intensity are associated with changes in brain oscillations. Methods 30 individuals with spinal cord injury and chronic pain were given an EEG and administered measures of pain before and after five procedures (hypnosis, meditation, transcranial direct current stimulation [tDCS], and neurofeedback) and a control sham tDCS procedure. Results Each procedure was associated with a different pattern of changes in brain activity, and all active procedures were significantly different from the control procedure in at least three bandwidths. Very weak and mostly non-significant associations were found between changes in EEG-assessed brain activity and pain. Conclusions Different non-pharmacological pain treatments have distinctive effects on brain oscillation patterns. However, changes in EEG-assessed brain oscillations are not significantly associated with changes in pain, and therefore such changes do not appear useful for explaining the benefits of these treatments. Significance The results provide new findings regarding the unique effects of four non-pharmacological treatments on pain and brain activity. PMID:23706958

Characterization of EEG patterns in brain-injured subjects and controls after a Snoezelen(®) intervention.

PubMed

Gómez, Carlos; Poza, Jesús; Gutiérrez, María T; Prada, Esther; Mendoza, Nuria; Hornero, Roberto

2016-11-01

The aim of this study was to assess the changes induced in electroencephalographic (EEG) activity by a Snoezelen(®) intervention on individuals with brain-injury and control subjects. EEG activity was recorded preceding and following a Snoezelen(®) session in 18 people with cerebral palsy (CP), 18 subjects who have sustained traumatic brain-injury (TBI) and 18 controls. EEG data were analyzed by means of spectral and nonlinear measures: median frequency (MF), individual alpha frequency (IAF), sample entropy (SampEn) and Lempel-Ziv complexity (LZC). Our results showed decreased values for MF, IAF, SampEn and LZC as a consequence of the therapy. The main changes between pre-stimulation and post-stimulation conditions were found in occipital and parietal brain areas. Additionally, these changes are more widespread in controls than in brain-injured subjects, which can be due to cognitive deficits in TBI and CP groups. Our findings support the notion that Snoezelen(®) therapy affects central nervous system, inducing a slowing of oscillatory activity, as well as a decrease of EEG complexity and irregularity. These alterations seem to be related with higher levels of relaxation of the participants. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A pathophysiologic approach for subacute encephalopathy with seizures in alcoholics (SESA) syndrome.

PubMed

Choi, Jun Yong; Kwon, Jiwon; Bae, Eun-Kee

2014-09-01

Subacute encephalopathy with seizures in alcoholics (SESA) syndrome is a unique disease entity characterized by typical clinical and electroencephalographic (EEG) features in the setting of chronic alcoholism. We present two patients with distinctive serial MRI and EEG findings which suggest a clue to the underlying pathophysiologic mechanisms of SESA syndrome. Two patients with chronic alcoholism and alcoholic liver cirrhosis presented with generalized seizures and confused mental status. Brain MRI demonstrated restricted diffusion, increased T2-weighted signal intensity, and hyperperfusion in the presumed seizure focus and nearby posterior regions of the cerebral hemispheres. EEG showed periodic lateralized epileptiform discharges which were prominent in the posterior regions of the cerebral hemispheres ipsilateral to the side of brain MRI abnormalities. Even after patients clinically improved, these brain abnormalities persisted with progressive atrophic changes on follow-up brain MRI. These patients had not only the distinguishing clinical and EEG features of SESA syndrome, but also showed novel brain MRI abnormalities. These changes on MRI displayed characteristics of seizure-related changes. The posterior dominance of abnormalities on MRI and EEG suggests that the pathophysiologic mechanisms of SESA syndrome may share those of posterior reversible encephalopathy syndrome. Copyright © 2014 Elsevier Ltd. All rights reserved.

Carrying the past to the future: Distinct brain networks underlie individual differences in human spatial working memory capacity.

PubMed

Liu, Siwei; Poh, Jia-Hou; Koh, Hui Li; Ng, Kwun Kei; Loke, Yng Miin; Lim, Joseph Kai Wei; Chong, Joanna Su Xian; Zhou, Juan

2018-08-01

Spatial working memory (SWM) relies on the interplay of anatomically separated and interconnected large-scale brain networks. EEG studies often observe load-associated sustained negative activity during SWM retention. Yet, whether and how such sustained negative activity in retention relates to network-specific functional activation/deactivation and relates to individual differences in SWM capacity remain to be elucidated. To cover these gaps, we recorded concurrent EEG-fMRI data in 70 healthy young adults during the Sternberg delayed-match-to-sample SWM task with three memory load levels. To a subset of participants (N = 28) that performed the task properly and had artefact-free fMRI and EEG data, we employed a novel temporo-spatial principal component analysis to derive load-dependent negative slow wave (NSW) from retention-related event-related potentials. The associations between NSW responses with SWM capacity were divergent in the higher (N = 14) and lower (N = 14) SWM capacity groups. Specifically, larger load-related increase in NSW amplitude was associated with greater SWM capacity for the higher capacity group but lower SWM capacity for the lower capacity group. Furthermore, for the higher capacity group, larger NSW amplitude was related to greater activation in bilateral parietal areas of the fronto-parietal network (FPN) and greater deactivation in medial frontal gyrus and posterior mid-cingulate cortex of the default mode network (DMN) during retention. In contrast, the lower capacity group did not show similar pattern. Instead, greater NSW was linked to higher deactivation in right posterior middle temporal gyrus. Our findings shed light on the possible differential EEG-informed neural network mechanism during memory maintenance underlying individual differences in SWM capacity. Copyright © 2018 Elsevier Inc. All rights reserved.

EEG microstates of wakefulness and NREM sleep.

PubMed

Brodbeck, Verena; Kuhn, Alena; von Wegner, Frederic; Morzelewski, Astrid; Tagliazucchi, Enzo; Borisov, Sergey; Michel, Christoph M; Laufs, Helmut

2012-09-01

EEG-microstates exploit spatio-temporal EEG features to characterize the spontaneous EEG as a sequence of a finite number of quasi-stable scalp potential field maps. So far, EEG-microstates have been studied mainly in wakeful rest and are thought to correspond to functionally relevant brain-states. Four typical microstate maps have been identified and labeled arbitrarily with the letters A, B, C and D. We addressed the question whether EEG-microstate features are altered in different stages of NREM sleep compared to wakefulness. 32-channel EEG of 32 subjects in relaxed wakefulness and NREM sleep was analyzed using a clustering algorithm, identifying the most dominant amplitude topography maps typical of each vigilance state. Fitting back these maps into the sleep-scored EEG resulted in a temporal sequence of maps for each sleep stage. All 32 subjects reached sleep stage N2, 19 also N3, for at least 1 min and 45 s. As in wakeful rest we found four microstate maps to be optimal in all NREM sleep stages. The wake maps were highly similar to those described in the literature for wakefulness. The sleep stage specific map topographies of N1 and N3 sleep showed a variable but overall relatively high degree of spatial correlation to the wake maps (Mean: N1 92%; N3 87%). The N2 maps were the least similar to wake (mean: 83%). Mean duration, total time covered, global explained variance and transition probabilities per subject, map and sleep stage were very similar in wake and N1. In wake, N1 and N3, microstate map C was most dominant w.r.t. global explained variance and temporal presence (ratio total time), whereas in N2 microstate map B was most prominent. In N3, the mean duration of all microstate maps increased significantly, expressed also as an increase in transition probabilities of all maps to themselves in N3. This duration increase was partly--but not entirely--explained by the occurrence of slow waves in the EEG. The persistence of exactly four main microstate classes in all NREM sleep stages might speak in favor of an in principle maintained large scale spatial brain organization from wakeful rest to NREM sleep. In N1 and N3 sleep, despite spectral EEG differences, the microstate maps and characteristics were surprisingly close to wakefulness. This supports the notion that EEG microstates might reflect a large scale resting state network architecture similar to preserved fMRI resting state connectivity. We speculate that the incisive functional alterations which can be observed during the transition to deep sleep might be driven by changes in the level and timing of activity within this architecture. Copyright © 2012 Elsevier Inc. All rights reserved.

The evolution of endovascular electroencephalography: historical perspective and future applications.

PubMed

Sefcik, Roberta K; Opie, Nicholas L; John, Sam E; Kellner, Christopher P; Mocco, J; Oxley, Thomas J

2016-05-01

Current standard practice requires an invasive approach to the recording of electroencephalography (EEG) for epilepsy surgery, deep brain stimulation (DBS), and brain-machine interfaces (BMIs). The development of endovascular techniques offers a minimally invasive route to recording EEG from deep brain structures. This historical perspective aims to describe the technical progress in endovascular EEG by reviewing the first endovascular recordings made using a wire electrode, which was followed by the development of nanowire and catheter recordings and, finally, the most recent progress in stent-electrode recordings. The technical progress in device technology over time and the development of the ability to record chronic intravenous EEG from electrode arrays is described. Future applications for the use of endovascular EEG in the preoperative and operative management of epilepsy surgery are then discussed, followed by the possibility of the technique's future application in minimally invasive operative approaches to DBS and BMI.

Automated network analysis to measure brain effective connectivity estimated from EEG data of patients with alcoholism.

PubMed

Bae, Youngoh; Yoo, Byeong Wook; Lee, Jung Chan; Kim, Hee Chan

2017-05-01

Detection and diagnosis based on extracting features and classification using electroencephalography (EEG) signals are being studied vigorously. A network analysis of time series EEG signal data is one of many techniques that could help study brain functions. In this study, we analyze EEG to diagnose alcoholism. We propose a novel methodology to estimate the differences in the status of the brain based on EEG data of normal subjects and data from alcoholics by computing many parameters stemming from effective network using Granger causality. Among many parameters, only ten parameters were chosen as final candidates. By the combination of ten graph-based parameters, our results demonstrate predictable differences between alcoholics and normal subjects. A support vector machine classifier with best performance had 90% accuracy with sensitivity of 95.3%, and specificity of 82.4% for differentiating between the two groups.

Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface

PubMed Central

Khan, M. Jawad; Hong, Melissa Jiyoun; Hong, Keum-Shik

2014-01-01

The hybrid brain-computer interface (BCI)'s multimodal technology enables precision brain-signal classification that can be used in the formulation of control commands. In the present study, an experimental hybrid near-infrared spectroscopy-electroencephalography (NIRS-EEG) technique was used to extract and decode four different types of brain signals. The NIRS setup was positioned over the prefrontal brain region, and the EEG over the left and right motor cortex regions. Twelve subjects participating in the experiment were shown four direction symbols, namely, “forward,” “backward,” “left,” and “right.” The control commands for forward and backward movement were estimated by performing arithmetic mental tasks related to oxy-hemoglobin (HbO) changes. The left and right directions commands were associated with right and left hand tapping, respectively. The high classification accuracies achieved showed that the four different control signals can be accurately estimated using the hybrid NIRS-EEG technology. PMID:24808844

Preservation of electroencephalographic organization in patients with impaired consciousness and imaging-based evidence of command-following.

PubMed

Forgacs, Peter B; Conte, Mary M; Fridman, Esteban A; Voss, Henning U; Victor, Jonathan D; Schiff, Nicholas D

2014-12-01

Standard clinical characterization of patients with disorders of consciousness (DOC) relies on observation of motor output and may therefore lead to the misdiagnosis of vegetative state or minimally conscious state in patients with preserved cognition. We used conventional electroencephalographic (EEG) measures to assess a cohort of DOC patients with and without functional magnetic resonance imaging (fMRI)-based evidence of command-following, and correlated the findings with standard clinical behavioral evaluation and brain metabolic activity. We enrolled 44 patients with severe brain injury. Behavioral diagnosis was established using standardized clinical assessments. Long-term EEG recordings were analyzed to determine wakeful background organization and presence of elements of sleep architecture. A subset of patients had fMRI testing of command-following using motor imagery paradigms (26 patients) and resting brain metabolism measurement using (18) fluorodeoxyglucose positron emission tomography (31 patients). All 4 patients with fMRI evidence of covert command-following consistently demonstrated well-organized EEG background during wakefulness, spindling activity during sleep, and relative preservation of cortical metabolic activity. In the entire cohort, EEG organization and overall brain metabolism showed no significant association with bedside behavioral testing, except in a few cases when EEG was severely abnormal. These findings suggest that conventional EEG is a simple strategy that complements behavioral and imaging characterization of DOC patients. Preservation of specific EEG features may be used to assess the likelihood of unrecognized cognitive abilities in severely brain-injured patients with very limited or no motor responses. © 2014 American Neurological Association.

Analysis of Brain Recurrence

NASA Astrophysics Data System (ADS)

Frilot, Clifton; Kim, Paul Y.; Carrubba, Simona; McCarty, David E.; Chesson, Andrew L.; Marino, Andrew A.

Analysis of Brain Recurrence (ABR) is a method for extracting physiologically significant information from the electroencephalogram (EEG), a non-stationary electrical output of the brain, the ultimate complex dynamical system. ABR permits quantification of temporal patterns in the EEG produced by the non-autonomous differential laws that govern brain metabolism. In the context of appropriate experimental and statistical designs, ABR is ideally suited to the task of interpreting the EEG. Present applications of ABR include discovery of a human magnetic sense, increased mechanistic understanding of neuronal membrane processes, diagnosis of degenerative neurological disease, detection of changes in brain metabolism caused by weak environmental electromagnetic fields, objective characterization of the quality of human sleep, and evaluation of sleep disorders. ABR has important beneficial implications for the development of clinical and experimental neuroscience.

Analysis of slow-wave activity and slow-wave oscillations prior to somnambulism.

PubMed

Jaar, Olivier; Pilon, Mathieu; Carrier, Julie; Montplaisir, Jacques; Zadra, Antonio

2010-11-01

STUDY OBJECTIVIES: several studies have investigated slow wave sleep EEG parameters, including slow-wave activity (SWA) in relation to somnambulism, but results have been both inconsistent and contradictory. The first goal of the present study was to conduct a quantitative analysis of sleepwalkers' sleep EEG by studying fluctuations in spectral power for delta (1-4 Hz) and slow delta (0.5-1 Hz) before the onset of somnambulistic episodes. A secondary aim was to detect slow-wave oscillations to examine changes in their amplitude and density prior to behavioral episodes. twenty-two adult sleepwalkers were investigated polysomnographically following 25 h of sleep deprivation. analysis of patients' sleep EEG over the 200 sec prior to the episodes' onset revealed that the episodes were not preceded by a gradual increase in spectral power for either delta or slow delta over frontal, central, or parietal leads. However, time course comparisons revealed significant changes in the density of slow-wave oscillations as well as in very slow oscillations with significant increases occurring during the final 20 sec immediately preceding episode onset. the specificity of these sleep EEG parameters for the occurrence and diagnosis of NREM parasomnias remains to be determined.

Cerebral perfusion abnormalities in therapy-resistant epilepsy in childhood: comparison between EEG, MRI and 99Tcm-ECD brain SPET.

PubMed

Vattimo, A; Burroni, L; Bertelli, P; Volterrani, D; Vella, A

1996-01-01

We performed 99Tcm-ethyl cysteinate dimer (ECD) interictal single photon emission tomography (SPET) in 26 children with severe therapy-resistant epilepsy. All the children underwent a detailed clinical examination, an electroencephalogram (EEG) investigation and brain magnetic resonance imaging (MRI). In 21 of the 26 children, SPET demonstrated brain blood flow abnormalities, in 13 cases in the same territories that showed EEG alterations. MRI showed structural lesions in 6 of the 26 children, while SPET imaging confirmed these abnormalities in only 5 children. The lesion not detected on SPET was shown to be 3 mm thick on MRI. Five symptomatic patients had normal SPET. In one of these patients, the EEG findings were normal and MRI revealed a small calcific nodule (4 mm thick); in the others, the EEG showed non-focal but diffuse abnormalities. These data confirm that brain SPET is sensitive in detecting and localizing hypoperfused areas that could be associated with epileptic foci in this group of patients, even when the MRI image is normal.

Classifying the Perceptual Interpretations of a Bistable Image Using EEG and Artificial Neural Networks

PubMed Central

Hramov, Alexander E.; Maksimenko, Vladimir A.; Pchelintseva, Svetlana V.; Runnova, Anastasiya E.; Grubov, Vadim V.; Musatov, Vyacheslav Yu.; Zhuravlev, Maksim O.; Koronovskii, Alexey A.; Pisarchik, Alexander N.

2017-01-01

In order to classify different human brain states related to visual perception of ambiguous images, we use an artificial neural network (ANN) to analyze multichannel EEG. The classifier built on the basis of a multilayer perceptron achieves up to 95% accuracy in classifying EEG patterns corresponding to two different interpretations of the Necker cube. The important feature of our classifier is that trained on one subject it can be used for the classification of EEG traces of other subjects. This result suggests the existence of common features in the EEG structure associated with distinct interpretations of bistable objects. We firmly believe that the significance of our results is not limited to visual perception of the Necker cube images; the proposed experimental approach and developed computational technique based on ANN can also be applied to study and classify different brain states using neurophysiological data recordings. This may give new directions for future research in the field of cognitive and pathological brain activity, and for the development of brain-computer interfaces. PMID:29255403

Resting and reactive frontal brain electrical activity (EEG) among a non-clinical sample of socially anxious adults: Does concurrent depressive mood matter?

PubMed Central

Beaton, Elliott A; Schmidt, Louis A; Ashbaugh, Andrea R; Santesso, Diane L; Antony, Martin M; McCabe, Randi E

2008-01-01

A number of studies have noted that the pattern of resting frontal brain electrical activity (EEG) is related to individual differences in affective style in healthy infants, children, and adults and some clinical populations when symptoms are reduced or in remission. We measured self-reported trait shyness and sociability, concurrent depressive mood, and frontal brain electrical activity (EEG) at rest and in anticipation of a speech task in a non-clinical sample of healthy young adults selected for high and low social anxiety. Although the patterns of resting and reactive frontal EEG asymmetry did not distinguish among individual differences in social anxiety, the pattern of resting frontal EEG asymmetry was related to trait shyness after controlling for concurrent depressive mood. Individuals who reported a higher degree of shyness were likely to exhibit greater relative right frontal EEG activity at rest. However, trait shyness was not related to frontal EEG asymmetry measured during the speech-preparation task, even after controlling for concurrent depressive mood. These findings replicate and extend prior work on resting frontal EEG asymmetry and individual differences in affective style in adults. Findings also highlight the importance of considering concurrent emotional states of participants when examining psychophysiological correlates of personality. PMID:18728822

Feature study of hysterical blindness EEG based on FastICA with combined-channel information.

PubMed

Qin, Xuying; Wang, Wei; Hu, Lintao; Wang, Xu; Yuan, Xiaojie

2015-01-01

An appropriate feature study of hysteria electroencephalograms (EEG) would provide new insights into neural mechanisms of the disease, and also make improvements in patient diagnosis and management. The objective of this paper is to provide an explanation for what causes a particular visual loss, by associating the features of hysterical blindness EEG with brain function. An idea for the novel feature extraction for hysterical blindness EEG, utilizing combined-channel information, was applied in this paper. After channels had been combined, the sliding-window-FastICA was applied to process the combined normal EEG and hysteria EEG, respectively. Kurtosis features were calculated from the processed signals. As the comparison feature, the power spectral density of normal and hysteria EEG were computed. According to the feature analysis results, a region of brain dysfunction was located at the occipital lobe, O1 and O2. Furthermore, new abnormality was found at the parietal lobe, C3, C4, P3, and P4, that provided us with a new perspective for understanding hysterical blindness. Indicated by the kurtosis results which were consistent with brain function and the clinical diagnosis, our method was found to be a useful tool to capture features in hysterical blindness EEG.

Analysis of the influence of memory content of auditory stimuli on the memory content of EEG signal

PubMed Central

Namazi, Hamidreza; Kulish, Vladimir V.

2016-01-01

One of the major challenges in brain research is to relate the structural features of the auditory stimulus to structural features of Electroencephalogram (EEG) signal. Memory content is an important feature of EEG signal and accordingly the brain. On the other hand, the memory content can also be considered in case of stimulus. Beside all works done on analysis of the effect of stimuli on human EEG and brain memory, no work discussed about the stimulus memory and also the relationship that may exist between the memory content of stimulus and the memory content of EEG signal. For this purpose we consider the Hurst exponent as the measure of memory. This study reveals the plasticity of human EEG signals in relation to the auditory stimuli. For the first time we demonstrated that the memory content of an EEG signal shifts towards the memory content of the auditory stimulus used. The results of this analysis showed that an auditory stimulus with higher memory content causes a larger increment in the memory content of an EEG signal. For the verification of this result, we benefit from approximate entropy as indicator of time series randomness. The capability, observed in this research, can be further investigated in relation to human memory. PMID:27528219

Analysis of the influence of memory content of auditory stimuli on the memory content of EEG signal.

PubMed

Namazi, Hamidreza; Khosrowabadi, Reza; Hussaini, Jamal; Habibi, Shaghayegh; Farid, Ali Akhavan; Kulish, Vladimir V

2016-08-30

One of the major challenges in brain research is to relate the structural features of the auditory stimulus to structural features of Electroencephalogram (EEG) signal. Memory content is an important feature of EEG signal and accordingly the brain. On the other hand, the memory content can also be considered in case of stimulus. Beside all works done on analysis of the effect of stimuli on human EEG and brain memory, no work discussed about the stimulus memory and also the relationship that may exist between the memory content of stimulus and the memory content of EEG signal. For this purpose we consider the Hurst exponent as the measure of memory. This study reveals the plasticity of human EEG signals in relation to the auditory stimuli. For the first time we demonstrated that the memory content of an EEG signal shifts towards the memory content of the auditory stimulus used. The results of this analysis showed that an auditory stimulus with higher memory content causes a larger increment in the memory content of an EEG signal. For the verification of this result, we benefit from approximate entropy as indicator of time series randomness. The capability, observed in this research, can be further investigated in relation to human memory.

Single-trial EEG-informed fMRI analysis of emotional decision problems in hot executive function.

PubMed

Guo, Qian; Zhou, Tiantong; Li, Wenjie; Dong, Li; Wang, Suhong; Zou, Ling

2017-07-01

Executive function refers to conscious control in psychological process which relates to thinking and action. Emotional decision is a part of hot executive function and contains emotion and logic elements. As a kind of important social adaptation ability, more and more attention has been paid in recent years. Gambling task can be well performed in the study of emotional decision. As fMRI researches focused on gambling task show not completely consistent brain activation regions, this study adopted EEG-fMRI fusion technology to reveal brain neural activity related with feedback stimuli. In this study, an EEG-informed fMRI analysis was applied to process simultaneous EEG-fMRI data. First, relative power-spectrum analysis and K-means clustering method were performed separately to extract EEG-fMRI features. Then, Generalized linear models were structured using fMRI data and using different EEG features as regressors. The results showed that in the win versus loss stimuli, the activated regions almost covered the caudate, the ventral striatum (VS), the orbital frontal cortex (OFC), and the cingulate. Wide activation areas associated with reward and punishment were revealed by the EEG-fMRI integration analysis than the conventional fMRI results, such as the posterior cingulate and the OFC. The VS and the medial prefrontal cortex (mPFC) were found when EEG power features were performed as regressors of GLM compared with results entering the amplitudes of feedback-related negativity (FRN) as regressors. Furthermore, the brain region activation intensity was the strongest when theta-band power was used as a regressor compared with the other two fusion results. The EEG-based fMRI analysis can more accurately depict the whole-brain activation map and analyze emotional decision problems.

A Comparison of Independent Event-Related Desynchronization Responses in Motor-Related Brain Areas to Movement Execution, Movement Imagery, and Movement Observation.

PubMed

Duann, Jeng-Ren; Chiou, Jin-Chern

2016-01-01

Electroencephalographic (EEG) event-related desynchronization (ERD) induced by movement imagery or by observing biological movements performed by someone else has recently been used extensively for brain-computer interface-based applications, such as applications used in stroke rehabilitation training and motor skill learning. However, the ERD responses induced by the movement imagery and observation might not be as reliable as the ERD responses induced by movement execution. Given that studies on the reliability of the EEG ERD responses induced by these activities are still lacking, here we conducted an EEG experiment with movement imagery, movement observation, and movement execution, performed multiple times each in a pseudorandomized order in the same experimental runs. Then, independent component analysis (ICA) was applied to the EEG data to find the common motor-related EEG source activity shared by the three motor tasks. Finally, conditional EEG ERD responses associated with the three movement conditions were computed and compared. Among the three motor conditions, the EEG ERD responses induced by motor execution revealed the alpha power suppression with highest strengths and longest durations. The ERD responses of the movement imagery and movement observation only partially resembled the ERD pattern of the movement execution condition, with slightly better detectability for the ERD responses associated with the movement imagery and faster ERD responses for movement observation. This may indicate different levels of involvement in the same motor-related brain circuits during different movement conditions. In addition, because the resulting conditional EEG ERD responses from the ICA preprocessing came with minimal contamination from the non-related and/or artifactual noisy components, this result can play a role of the reference for devising a brain-computer interface using the EEG ERD features of movement imagery or observation.

Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors

PubMed Central

2012-01-01

A brain-computer interface (BCI) is a communication system that can help users interact with the outside environment by translating brain signals into machine commands. The use of electroencephalographic (EEG) signals has become the most common approach for a BCI because of their usability and strong reliability. Many EEG-based BCI devices have been developed with traditional wet- or micro-electro-mechanical-system (MEMS)-type EEG sensors. However, those traditional sensors have uncomfortable disadvantage and require conductive gel and skin preparation on the part of the user. Therefore, acquiring the EEG signals in a comfortable and convenient manner is an important factor that should be incorporated into a novel BCI device. In the present study, a wearable, wireless and portable EEG-based BCI device with dry foam-based EEG sensors was developed and was demonstrated using a gaming control application. The dry EEG sensors operated without conductive gel; however, they were able to provide good conductivity and were able to acquire EEG signals effectively by adapting to irregular skin surfaces and by maintaining proper skin-sensor impedance on the forehead site. We have also demonstrated a real-time cognitive stage detection application of gaming control using the proposed portable device. The results of the present study indicate that using this portable EEG-based BCI device to conveniently and effectively control the outside world provides an approach for researching rehabilitation engineering. PMID:22284235

Hierarchical organization of brain functional networks during visual tasks.

PubMed

Zhuo, Zhao; Cai, Shi-Min; Fu, Zhong-Qian; Zhang, Jie

2011-09-01

The functional network of the brain is known to demonstrate modular structure over different hierarchical scales. In this paper, we systematically investigated the hierarchical modular organizations of the brain functional networks that are derived from the extent of phase synchronization among high-resolution EEG time series during a visual task. In particular, we compare the modular structure of the functional network from EEG channels with that of the anatomical parcellation of the brain cortex. Our results show that the modular architectures of brain functional networks correspond well to those from the anatomical structures over different levels of hierarchy. Most importantly, we find that the consistency between the modular structures of the functional network and the anatomical network becomes more pronounced in terms of vision, sensory, vision-temporal, motor cortices during the visual task, which implies that the strong modularity in these areas forms the functional basis for the visual task. The structure-function relationship further reveals that the phase synchronization of EEG time series in the same anatomical group is much stronger than that of EEG time series from different anatomical groups during the task and that the hierarchical organization of functional brain network may be a consequence of functional segmentation of the brain cortex.

Detecting large-scale networks in the human brain using high-density electroencephalography.

PubMed

Liu, Quanying; Farahibozorg, Seyedehrezvan; Porcaro, Camillo; Wenderoth, Nicole; Mantini, Dante

2017-09-01

High-density electroencephalography (hdEEG) is an emerging brain imaging technique that can be used to investigate fast dynamics of electrical activity in the healthy and the diseased human brain. Its applications are however currently limited by a number of methodological issues, among which the difficulty in obtaining accurate source localizations. In particular, these issues have so far prevented EEG studies from reporting brain networks similar to those previously detected by functional magnetic resonance imaging (fMRI). Here, we report for the first time a robust detection of brain networks from resting state (256-channel) hdEEG recordings. Specifically, we obtained 14 networks previously described in fMRI studies by means of realistic 12-layer head models and exact low-resolution brain electromagnetic tomography (eLORETA) source localization, together with independent component analysis (ICA) for functional connectivity analysis. Our analyses revealed three important methodological aspects. First, brain network reconstruction can be improved by performing source localization using the gray matter as source space, instead of the whole brain. Second, conducting EEG connectivity analyses in individual space rather than on concatenated datasets may be preferable, as it permits to incorporate realistic information on head modeling and electrode positioning. Third, the use of a wide frequency band leads to an unbiased and generally accurate reconstruction of several network maps, whereas filtering data in a narrow frequency band may enhance the detection of specific networks and penalize that of others. We hope that our methodological work will contribute to rise of hdEEG as a powerful tool for brain research. Hum Brain Mapp 38:4631-4643, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Preoperative EEG predicts memory and selective cognitive functions after temporal lobe surgery.

PubMed Central

Tuunainen, A; Nousiainen, U; Hurskainen, H; Leinonen, E; Pilke, A; Mervaala, E; Vapalahti, M; Partanen, J; Riekkinen, P

1995-01-01

Preoperative and postoperative cognitive and memory functions, psychiatric outcome, and EEGs were evaluated in 32 epileptic patients who underwent temporal lobe surgery. The presence and location of preoperative slow wave focus in routine EEG predicted memory functions of the non-resected side after surgery. Neuropsychological tests of the function of the frontal lobes also showed improvement. Moreover, psychiatric ratings showed that seizure free patients had significantly less affective symptoms postoperatively than those who were still exhibiting seizures. After temporal lobectomies, successful outcome in postoperative memory functions can be achieved in patients with unilateral slow wave activity in preoperative EEGs. This study suggests a new role for routine EEG in preoperative evaluation of patients with temporal lobe epilepsy. PMID:7608663

Validation of a low-cost EEG device for mood induction studies.

PubMed

Rodríguez, Alejandro; Rey, Beatriz; Alcañiz, Mariano

2013-01-01

New electroencephalography (EEG) devices, more portable and cheaper, are appearing on the market. Studying the reliability of these EEG devices for emotional studies would be interesting, as these devices could be more economical and compatible with Virtual Reality (VR) settings. Therefore, the aim in this work was to validate a low-cost EEG device (Emotiv Epoc) to monitor brain activity during a positive emotional induction procedure. Emotional pictures (IAPS) were used to induce a positive mood in sixteen participants. Changes in the brain activity of subjects were compared between positive induction and neutral conditions. Obtained results were in accordance with previous scientific literature regarding frontal EEG asymmetry, which supports the possibility of using this low-cost EEG device in future mood induction studies combined with VR.

Brain wave correlates of attentional states: Event related potentials and quantitative EEG analysis during performance of cognitive and perceptual tasks

NASA Technical Reports Server (NTRS)

Freeman, Frederick G.

1993-01-01

The increased use of automation in the cockpits of commercial planes has dramatically decreased the workload requirements of pilots, enabling them to function more efficiently and with a higher degree of safety. Unfortunately, advances in technology have led to an unexpected problem: the decreased demands on pilots have increased the probability of inducing 'hazardous states of awareness.' A hazardous state of awareness is defined as a decreased level of alertness or arousal which makes an individual less capable of reacting to unique or emergency types of situations. These states tend to be induced when an individual is not actively processing information. Under such conditions a person is likely to let his/her mind wander, either to internal states or to irrelevant external conditions. As a result, they are less capable of reacting quickly to emergency situations. Since emergencies are relatively rare, and since the high automated cockpit requires progressively decreasing levels of engagement, the probability of being seduced into a lowered state of awareness is increasing. This further decreases the readiness of the pilot to react to unique circumstances such as system failures. The HEM Lab at NASA-Langley Research Center has been studying how these states of awareness are induced and what the physiological correlates of these different states are. Specifically, they have been interested in studying electroencephalographic (EEG) measures of different states of alertness to determine if such states can be identified and, hopefully, avoided. The project worked on this summer involved analyzing the EEG and the event related potentials (ERP) data collected while subjects performed under two conditions. Each condition required subjects to perform a relatively boring vigilance task. The purpose of using these tasks was to induce a decreased state of awareness while still requiring the subject to process information. Each task involved identifying an infrequently presented target stimulus. In addition to the task requirements, irrelevant tones were presented in the background. Research has shown that even though these stimuli are not attended, ERP's to them can still be elicited. The amplitude of the ERP waves has been shown to change as a function of a person's level of alertness. ERP's were also collected and analyzed for the target stimuli for each task. Brain maps were produced based on the ERP voltages for the different stimuli. In addition to the ERP's, a quantitative EEG (QEEG) was performed on the data using a fast Fourier technique to produce a power spectral analysis of the EEG. This analysis was conducted on the continuous EEG while the subjects were performing the tasks. Finally, a QEEG was performed on periods during the task when subjects indicated that they were in an altered state of awareness. During the tasks, subjects were asked to indicate by pressing a button when they realized their level of task awareness had changed. EEG epochs were collected for times just before and just after subjects made this reponse. The purpose of this final analysis was to determine whether or not subjective indices of level of awareness could be correlated with different patterns of EEG.

A Single Session of rTMS Enhances Small-Worldness in Writer's Cramp: Evidence from Simultaneous EEG-fMRI Multi-Modal Brain Graph.

PubMed

Bharath, Rose D; Panda, Rajanikant; Reddam, Venkateswara Reddy; Bhaskar, M V; Gohel, Suril; Bhardwaj, Sujas; Prajapati, Arvind; Pal, Pramod Kumar

2017-01-01

Background and Purpose : Repetitive transcranial magnetic stimulation (rTMS) induces widespread changes in brain connectivity. As the network topology differences induced by a single session of rTMS are less known we undertook this study to ascertain whether the network alterations had a small-world morphology using multi-modal graph theory analysis of simultaneous EEG-fMRI. Method : Simultaneous EEG-fMRI was acquired in duplicate before (R1) and after (R2) a single session of rTMS in 14 patients with Writer's Cramp (WC). Whole brain neuronal and hemodynamic network connectivity were explored using the graph theory measures and clustering coefficient, path length and small-world index were calculated for EEG and resting state fMRI (rsfMRI). Multi-modal graph theory analysis was used to evaluate the correlation of EEG and fMRI clustering coefficients. Result : A single session of rTMS was found to increase the clustering coefficient and small-worldness significantly in both EEG and fMRI ( p < 0.05). Multi-modal graph theory analysis revealed significant modulations in the fronto-parietal regions immediately after rTMS. The rsfMRI revealed additional modulations in several deep brain regions including cerebellum, insula and medial frontal lobe. Conclusion : Multi-modal graph theory analysis of simultaneous EEG-fMRI can supplement motor physiology methods in understanding the neurobiology of rTMS in vivo . Coinciding evidence from EEG and rsfMRI reports small-world morphology for the acute phase network hyper-connectivity indicating changes ensuing low-frequency rTMS is probably not "noise".

Interhemispheric EEG differences in olfactory bulbectomized rats with different cognitive abilities and brain beta-amyloid levels.

PubMed

Bobkova, Natalia; Vorobyov, Vasily; Medvinskaya, Natalia; Aleksandrova, Irina; Nesterova, Inna

2008-09-26

Alterations in electroencephalogram (EEG) asymmetry and deficits in interhemispheric integration of information have been shown in patients with Alzheimer's disease (AD). However, no direct evidence of an association between EEG asymmetry, morphological markers in the brain, and cognition was found either in AD patients or in AD models. In this study we used rats with bilateral olfactory bulbectomy (OBX) as one of the AD models and measured their learning/memory abilities, brain beta-amyloid levels and EEG spectra in symmetrical frontal and occipital cortices. One year after OBX or sham-surgery, the rats were tested with the Morris water paradigm and assigned to three groups: sham-operated rats, SO, and OBX rats with virtually normal, OBX(+), or abnormal, OBX(-), learning (memory) abilities. In OBX vs. SO, the theta EEG activity was enhanced to a higher extent in the right frontal cortex and in the left occipital cortex. This produced significant interhemispheric differences in the frontal cortex of the OBX(-) rats and in the occipital cortex of both OBX groups. The beta1 EEG asymmetry in SO was attenuated in OBX(+) and completely eliminated in OBX(-). OBX produced highly significant beta2 EEG decline in the right frontal cortex, with OBX(-)>OBX(+) rank order of strength. The beta-amyloid level, examined by post-mortem immunological DOT-analysis in the cortex-hippocampus samples, was about six-fold higher in OBX(-) than in SO, but significantly less (enhanced by 82% vs. SO) in OBX(+) than in OBX(-). The involvement of the brain mediatory systems in the observed EEG asymmetry differences is discussed.

[Individual Types Reactivity of EEG Oscillations in Effective Heart Rhythm Biofeedback Parameters in Adolescents and Young People in the North].

PubMed

Krivonogova, E V; Poskotinova, L V; Demin, D B

2015-01-01

A single session of heart rate variability (HRV) biofeedback in apparently healthy young people and adolescents aged 14-17 years in order to increase vagal effects on heart rhythm and also electroencephalograms were carried out. Different variants of EEG spectral power during the successful HRV biofeedback session were identified. In the case of I variant of EEG activity the increase of power spectrum of alpha-, betal-, theta-components takes place in all parts of the brain. In the case of II variant of EEG activity the reduction of power spectrum of alpha-, betal-, theta-activity in all parts of the brain was observed. I and II variants of EEG activity cause more intensive regime of cortical-subcortical interactions. During the III variant of EEG activity the successful biofeedback is accompanied by increase of alpha activity in the central, front and anteriofrontal brain parts and so indicates the formation of thalamocortical relations of neural network in order to optimize the vegetal regulation of heart function. There was an increase in alpha- and beta1-activity in the parietal, central, frontal and temporal brain parts during the IV variant of EEG activity and so that it provides the relief of neural networks communication for information processing. As a result of V variance of EEG activity there was the increase of power spectrum of theta activity in the central and frontal parts of both cerebral hemispheres, so it was associated with the cortical-hippocampal interactions to achieve a successful biofeedback.

Behavioural, brain and cardiac responses to hypobaric hypoxia in broiler chickens.

PubMed

Martin, Jessica E; Christensen, Karen; Vizzier-Thaxton, Yvonne; Mitchell, Malcolm A; McKeegan, Dorothy E F

2016-09-01

A novel approach to pre-slaughter stunning of chickens has been developed in which birds are rendered unconscious by progressive hypobaric hypoxia. Termed Low Atmospheric Pressure Stunning (LAPS), this approach involves application of gradual decompression lasting 280s according to a prescribed curve. We examined responses to LAPS by recording behaviour, electroencephalogram (EEG) and electrocardiogram (ECG) in individual male chickens, and interpreted these with regard to the welfare impact of the process. We also examined the effect of two temperature adjusted pressure curves on these responses. Broiler chickens were exposed to LAPS in 30 triplets (16 and 14 triplets assigned to each pressure curve). In each triplet, one bird was instrumented for recording of EEG and ECG while the behaviour of all three birds was observed. Birds showed a consistent sequence of behaviours during LAPS (ataxia, loss of posture, clonic convulsions and motionless) which were observed in all birds. Leg paddling, tonic convulsions, slow wing flapping, mandibulation, head shaking, open bill breathing, deep inhalation, jumping and vocalisation were observed in a proportion of birds. Spectral analysis of EEG responses at 2s intervals throughout LAPS revealed progressive decreases in median frequency at the same time as corresponding progressive increases in total power, followed later by decreases in total power as all birds exhibited isoelectric EEG and died. There was a very pronounced increase in total power at 50-60s into the LAPS cycle, which corresponded to dominance of the signal by high amplitude slow waves, indicating loss of consciousness. Slow wave EEG was seen early in the LAPS process, before behavioural evidence of loss of consciousness such as ataxia and loss of posture, almost certainly due to the fact that it was completely dark in the LAPS chamber. ECG recordings showed a pronounced bradycardia (starting on average 49.6s into LAPS), often associated with arrhythmia, until around 60s into LAPS when heart rate levelled off. There was a good correlation between behavioural, EEG and cardiac measures in relation to loss of consciousness which collectively provide a loss of consciousness estimate of around 60s. There were some effects of temperature adjusted pressure curves on behavioural latencies and ECG responses, but in general responses were consistent and very similar to those reported in previous research on controlled atmosphere stunning with inert gases. The results suggest that the process is humane (slaughter without avoidable fear, anxiety, pain, suffering and distress). In particular, the maintenance of slow wave EEG patterns in the early part of LAPS (while birds are still conscious) is strongly suggestive that LAPS is non-aversive, since we would expect this to be interrupted by pain or discomfort. Copyright © 2016 Elsevier Inc. All rights reserved.

Practical Designs of Brain-Computer Interfaces Based on the Modulation of EEG Rhythms

NASA Astrophysics Data System (ADS)

Wang, Yijun; Gao, Xiaorong; Hong, Bo; Gao, Shangkai

A brain-computer interface (BCI) is a communication channel which does not depend on the brain's normal output pathways of peripheral nerves and muscles [1-3]. It supplies paralyzed patients with a new approach to communicate with the environment. Among various brain monitoring methods employed in current BCI research, electroencephalogram (EEG) is the main interest due to its advantages of low cost, convenient operation and non-invasiveness. In present-day EEG-based BCIs, the following signals have been paid much attention: visual evoked potential (VEP), sensorimotor mu/beta rhythms, P300 evoked potential, slow cortical potential (SCP), and movement-related cortical potential (MRCP). Details about these signals can be found in chapter "Brain Signals for Brain-Computer Interfaces". These systems offer some practical solutions (e.g., cursor movement and word processing) for patients with motor disabilities.

On the feasibility of concurrent human TMS-EEG-fMRI measurements

PubMed Central

Reithler, Joel; Schuhmann, Teresa; de Graaf, Tom; Uludağ, Kâmil; Goebel, Rainer; Sack, Alexander T.

2013-01-01

Simultaneously combining the complementary assets of EEG, functional MRI (fMRI), and transcranial magnetic stimulation (TMS) within one experimental session provides synergetic results, offering insights into brain function that go beyond the scope of each method when used in isolation. The steady increase of concurrent EEG-fMRI, TMS-EEG, and TMS-fMRI studies further underlines the added value of such multimodal imaging approaches. Whereas concurrent EEG-fMRI enables monitoring of brain-wide network dynamics with high temporal and spatial resolution, the combination with TMS provides insights in causal interactions within these networks. Thus the simultaneous use of all three methods would allow studying fast, spatially accurate, and distributed causal interactions in the perturbed system and its functional relevance for intact behavior. Concurrent EEG-fMRI, TMS-EEG, and TMS-fMRI experiments are already technically challenging, and the three-way combination of TMS-EEG-fMRI might yield additional difficulties in terms of hardware strain or signal quality. The present study explored the feasibility of concurrent TMS-EEG-fMRI studies by performing safety and quality assurance tests based on phantom and human data combining existing commercially available hardware. Results revealed that combined TMS-EEG-fMRI measurements were technically feasible, safe in terms of induced temperature changes, allowed functional MRI acquisition with comparable image quality as during concurrent EEG-fMRI or TMS-fMRI, and provided artifact-free EEG before and from 300 ms after TMS pulse application. Based on these empirical findings, we discuss the conceptual benefits of this novel complementary approach to investigate the working human brain and list a number of precautions and caveats to be heeded when setting up such multimodal imaging facilities with current hardware. PMID:23221407

Functional brain microstate predicts the outcome in a visuospatial working memory task.

PubMed

Muthukrishnan, Suriya-Prakash; Ahuja, Navdeep; Mehta, Nalin; Sharma, Ratna

2016-11-01

Humans have limited capacity of processing just up to 4 integrated items of information in the working memory. Thus, it is inevitable to commit more errors when challenged with high memory loads. However, the neural mechanisms that determine the accuracy of response at high memory loads still remain unclear. High temporal resolution of Electroencephalography (EEG) technique makes it the best tool to resolve the temporal dynamics of brain networks. EEG-defined microstate is the quasi-stable scalp electrical potential topography that represents the momentary functional state of brain. Thus, it has been possible to assess the information processing currently performed by the brain using EEG microstate analysis. We hypothesize that the EEG microstate preceding the trial could determine its outcome in a visuospatial working memory (VSWM) task. Twenty-four healthy participants performed a high memory load VSWM task, while their brain activity was recorded using EEG. Four microstate maps were found to represent the functional brain state prior to the trials in the VSWM task. One pre-trial microstate map was found to determine the accuracy of subsequent behavioural response. The intracranial generators of the pre-trial microstate map that determined the response accuracy were localized to the visuospatial processing areas at bilateral occipital, right temporal and limbic cortices. Our results imply that the behavioural outcome in a VSWM task could be determined by the intensity of activation of memory representations in the visuospatial processing brain regions prior to the trial. Copyright © 2016 Elsevier B.V. All rights reserved.

The inverse problem in electroencephalography using the bidomain model of electrical activity.

PubMed

Lopez Rincon, Alejandro; Shimoda, Shingo

2016-12-01

Acquiring information about the distribution of electrical sources in the brain from electroencephalography (EEG) data remains a significant challenge. An accurate solution would provide an understanding of the inner mechanisms of the electrical activity in the brain and information about damaged tissue. In this paper, we present a methodology for reconstructing brain electrical activity from EEG data by using the bidomain formulation. The bidomain model considers continuous active neural tissue coupled with a nonlinear cell model. Using this technique, we aim to find the brain sources that give rise to the scalp potential recorded by EEG measurements taking into account a non-static reconstruction. We simulate electrical sources in the brain volume and compare the reconstruction to the minimum norm estimates (MNEs) and low resolution electrical tomography (LORETA) results. Then, with the EEG dataset from the EEG Motor Movement/Imagery Database of the Physiobank, we identify the reaction to visual stimuli by calculating the time between stimulus presentation and the spike in electrical activity. Finally, we compare the activation in the brain with the registered activation using the LinkRbrain platform. Our methodology shows an improved reconstruction of the electrical activity and source localization in comparison with MNE and LORETA. For the Motor Movement/Imagery Database, the reconstruction is consistent with the expected position and time delay generated by the stimuli. Thus, this methodology is a suitable option for continuously reconstructing brain potentials. Copyright © 2016 The Author(s). Published by Elsevier B.V. All rights reserved.

Fuzzy topological digital space and their properties of flat electroencephalography in epilepsy disease

NASA Astrophysics Data System (ADS)

Muzafar Shah, Mazlina; Fatah Wahab, Abdul

2017-09-01

There are an abnormal electric activities or irregular interference in brain of epilepsy patient. Then a sensor will be put in patient’s scalp to measure and records all electric activities in brain. The result of the records known as Electroencephalography (EEG). The EEG has been transfer to flat EEG because it’s easier to analyze. In this study, the uncertainty in flat EEG data will be considered as fuzzy digital space. The purpose of this research is to show that the flat EEG is fuzzy topological digital space. Therefore, the main focus for this research is to introduce fuzzy topological digital space concepts with their properties such as neighbourhood, interior and closure by using fuzzy set digital concept and Chang’s fuzzy topology approach. The product fuzzy topology digital also will be shown. By introduce this concept, the data in flat EEG can considering having fuzzy topology digital properties and can identify the area in fuzzy digital space that has been affected by epilepsy seizure in epileptic patient’s brain.

A Review of EEG-Based Brain-Computer Interfaces as Access Pathways for Individuals with Severe Disabilities

ERIC Educational Resources Information Center

Moghimi, Saba; Kushki, Azadeh; Guerguerian, Anne Marie; Chau, Tom

2013-01-01

Electroencephalography (EEG) is a non-invasive method for measuring brain activity and is a strong candidate for brain-computer interface (BCI) development. While BCIs can be used as a means of communication for individuals with severe disabilities, the majority of existing studies have reported BCI evaluations by able-bodied individuals.…

Intelligence Community Forum

DTIC Science & Technology

2008-11-05

Description Operationally Feasible? EEG ms ms cm Measures electrical activity in the brain. Practical tool for applications - real time monitoring or...Cognitive Systems Device Development & Processing Methods Brain activity can be monitored in real-time in operational environments with EEG Brain...biological and cognitive findings about the user to customize the learning environment Neurofeedback • Present the user with real-time feedback

Modulation of critical brain dynamics using closed-loop neurofeedback stimulation.

PubMed

Zhigalov, Alexander; Kaplan, Alexander; Palva, J Matias

2016-08-01

EEG long-range temporal correlations (LRTCs) are a significant for both human cognition and brain disorders, but beyond suppression by sensory disruption, there are little means for influencing them non-invasively. We hypothesized that LRTCs could be controlled by engaging intrinsic neuroregulation through closed-loop neurofeedback stimulation. We used a closed-loop-stimulation paradigm where supra-threshold α-waves trigger visual flash stimuli while the subject performs the standard eyes-closed resting-state task. As a "sham" control condition, we applied similar stimulus sequences without the neurofeedback. Over three sessions, a significant difference in the LRTCs of α-band oscillations (U=89, p<0.028, Wilcoxon rank sum test) and their scalp topography (T=-2.92, p<0.010, T-test) emerged between the neurofeedback and sham conditions so that the LRTCs were stronger during neurofeedback than sham. No changes (F=0.16, p>0.69, ANOVA test) in the scalp topography of α-band power were observed in either condition. This study provides proof-of-concept for that EEG LRTCs, and hence critical brain dynamics, can be modulated with closed-loop stimulation in an automatic, involuntary fashion. We suggest that this modulation is mediated by an excitation-inhibition balance change achieved by the closed-loop neuroregulation. Automatic LRTC modulation opens novel avenues for both examining the functional roles of brain criticality in healthy subjects and for developing novel therapeutic approaches for brain disorders associated with abnormal LRTCs. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Valeriana wallichii root extract improves sleep quality and modulates brain monoamine level in rats.

PubMed

Sahu, Surajit; Ray, Koushik; Yogendra Kumar, M S; Gupta, Shilpa; Kauser, Hina; Kumar, Sanjeev; Mishra, Kshipra; Panjwani, Usha

2012-07-15

The present study was performed to investigate the effects of Valeriana wallichi (VW) aqueous root extract on sleep-wake profile and level of brain monoamines on Sprague-Dawley rats. Electrodes and transmitters were implanted to record EEG and EMG in freely moving condition and the changes were recorded telemetrically after oral administration of VW in the doses of 100, 200 and 300 mg/kg body weight. Sleep latency was decreased and duration of non-rapid eye movement (NREM) sleep was increased in a dose dependent manner. A significant decrease of sleep latency and duration of wakefulness were observed with VW at doses of 200 and 300 mg/kg. Duration of NREM sleep as well as duration of total sleep was increased significantly after treatment with VW at the doses of 200 and 300 mg/kg. VW also increased EEG slow wave activity during NREM sleep at the doses of 200 and 300 mg/kg. Level of norepinephrine (NE), dopamine (DA), dihydroxyphenylacetic acid (DOPAC), serotonin (5-HT) and hydroxy indole acetic acid (HIAA) were measured in frontal cortex and brain stem after VW treatment at the dose of 200mg/kg. NE and 5HT level were decreased significantly in both frontal cortex and brain stem. DA and HIAA level significantly decreased only in cortex. DOPAC level was not changed in any brain region studied. In conclusion it can be said that VW water extract has a sleep quality improving effect which may be dependent upon levels of monoamines in cortex and brainstem. Copyright © 2012 Elsevier GmbH. All rights reserved.

Analysis of Slow-Wave Activity and Slow-Wave Oscillations Prior to Somnambulism

PubMed Central

Jaar, Olivier; Pilon, Mathieu; Carrier, Julie; Montplaisir, Jacques; Zadra, Antonio

2010-01-01

Study Objectivies: Several studies have investigated slow wave sleep EEG parameters, including slow-wave activity (SWA) in relation to somnambulism, but results have been both inconsistent and contradictory. The first goal of the present study was to conduct a quantitative analysis of sleepwalkers' sleep EEG by studying fluctuations in spectral power for delta (1-4 Hz) and slow delta (0.5-1 Hz) before the onset of somnambulistic episodes. A secondary aim was to detect slow-wave oscillations to examine changes in their amplitude and density prior to behavioral episodes. Participants: Twenty-two adult sleepwalkers were investigated polysomnographically following 25 h of sleep deprivation. Results: Analysis of patients' sleep EEG over the 200 sec prior to the episodes' onset revealed that the episodes were not preceded by a gradual increase in spectral power for either delta or slow delta over frontal, central, or parietal leads. However, time course comparisons revealed significant changes in the density of slow-wave oscillations as well as in very slow oscillations with significant increases occurring during the final 20 sec immediately preceding episode onset. Conclusions: The specificity of these sleep EEG parameters for the occurrence and diagnosis of NREM parasomnias remains to be determined. Citation: Jaar O; Pilon M; Carrier J; Montplaisir J; Zadra A. Analysis of slow-wave activity and slow-wave oscillations prior to somnambulism. SLEEP 2010;33(11):1511-1516. PMID:21102993

Acquisition of electroencephalographic data in a large regional hospital - Bringing the brain waves to the computer.

NASA Technical Reports Server (NTRS)

Low, M. D.; Baker, M.; Ferguson, R.; Frost, J. D., Jr.

1972-01-01

This paper describes a complete electroencephalographic acquisition and transmission system, designed to meet the needs of a large hospital with multiple critical care patient monitoring units. The system provides rapid and prolonged access to a centralized recording and computing area from remote locations within the hospital complex, and from locations in other hospitals and other cities. The system includes quick-on electrode caps, amplifier units and cable transmission for access from within the hospital, and EEG digitization and telephone transmission for access from other hospitals or cities.

Post-acute stroke patients use brain-computer interface to activate electrical stimulation.

PubMed

Tan, H G; Kong, K H; Shee, C Y; Wang, C C; Guan, C T; Ang, W T

2010-01-01

Through certain mental actions, our electroencephalogram (EEG) can be regulated to operate a brain-computer interface (BCI), which translates the EEG patterns into commands that can be used to operate devices such as prostheses. This allows paralyzed persons to gain direct brain control of the paretic limb, which could open up many possibilities for rehabilitative and assistive applications. When using a BCI neuroprosthesis in stroke, one question that has surfaced is whether stroke patients are able to produce a sufficient change in EEG that can be used as a control signal to operate a prosthesis.

[Are subcortical signs in the EEG a reliable indication of brain stem displacement and impaction processes by intracranial space-occupying processes? A comparative computer tomography-electroencephalography study].

PubMed

Zettler, H; Järisch, M; Leonhard, T

1985-01-01

Within the scope of an elektroencephalographic-computertomographic comperative study carried out in 430 patients, the concurrence of secondary brain stem damage due to mass displacement and herniation processes and parroxysmal generalised slow activity in the EEG ("intermittant frontal delta rhythms", "projected discharges", "subcortical signs") in intracranial space-occupying processes were studied among others. The occurrence of the EEG pattern was independent of the presence of brain stem displacements in about 20 and 25 per cent, respectively, of the 152 patients with supratentorial space occupations. The absence of the characteristics on 80 per cent of the patients with clear CT criteria for a secondary brain stem impairment shows that it is not suitable as a warning sign of an imminent intracranial decompensation and that in particular from the non-occurrence in the EEG no contribution to the operative risk and to the choice of the time of the operation can be derived. A relation between the occurrence of paroxysmal slow activity and the acuity of the course of the disease or the degree of malignity of cerebral tumours could not be verified. Possible causes of the inconstant occurrence of this EEG pattern in brain stem alterations are discussed.

Quantitative electroencephalograms and neuro-optometry: a case study that explores changes in electrophysiology while wearing therapeutic eyeglasses

PubMed Central

Zelinsky, Deborah; Feinberg, Corey

2017-01-01

Abstract. The brain is equipped with a complex system for processing sensory information, including retinal circuitry comprising part of the central nervous system. Retinal stimulation can influence brain function via customized eyeglasses at both subcortical and cortical levels. We investigated cortical effects from wearing therapeutic eyeglasses, hypothesizing that they can create measureable changes in electroencephalogram (EEG) tracings. A Z-BellSM test was performed on a participant to select optimal lenses. An EEG measurement was recorded before and after the participant wore the eyeglasses. Equivalent quantitative electroencephalography (QEEG) analyses (statistical analysis on raw EEG recordings) were performed and compared with baseline findings. With glasses on, the participant’s readings were found to be closer to the normed database. The original objective of our investigation was met, and additional findings were revealed. The Z-bellSM test identified lenses to influence neurotypical brain activity, supporting the paradigm that eyeglasses can be utilized as a therapeutic intervention. Also, EEG analysis demonstrated that encephalographic techniques can be used to identify channels through which neuro-optomertric treatments work. This case study’s preliminary exploration illustrates the potential role of QEEG analysis and EEG-derived brain imaging in neuro-optometric research endeavors to affect brain function. PMID:28386574

Linking EEG signals, brain functions and mental operations: Advantages of the Laplacian transformation.

PubMed

Vidal, Franck; Burle, Boris; Spieser, Laure; Carbonnell, Laurence; Meckler, Cédric; Casini, Laurence; Hasbroucq, Thierry

2015-09-01

Electroencephalography (EEG) is a very popular technique for investigating brain functions and/or mental processes. To this aim, EEG activities must be interpreted in terms of brain and/or mental processes. EEG signals being a direct manifestation of neuronal activity it is often assumed that such interpretations are quite obvious or, at least, straightforward. However, they often rely on (explicit or even implicit) assumptions regarding the structures supposed to generate the EEG activities of interest. For these assumptions to be used appropriately, reliable links between EEG activities and the underlying brain structures must be established. Because of volume conduction effects and the mixture of activities they induce, these links are difficult to establish with scalp potential recordings. We present different examples showing how the Laplacian transformation, acting as an efficient source separation method, allowed to establish more reliable links between EEG activities and brain generators and, ultimately, with mental operations. The nature of those links depends on the depth of inferences that can vary from weak to strong. Along this continuum, we show that 1) while the effects of experimental manipulation can appear widely distributed with scalp potentials, Laplacian transformation allows to reveal several generators contributing (in different manners) to these modulations, 2) amplitude variations within the same set of generators can generate spurious differences in scalp potential topographies, often interpreted as reflecting different source configurations. In such a case, Laplacian transformation provides much more similar topographies, evidencing the same generator(s) set, and 3) using the LRP as an index of response activation most often produces ambiguous results, Laplacian-transformed response-locked ERPs obtained over motor areas allow resolving these ambiguities. Copyright © 2015 Elsevier B.V. All rights reserved.

Classification and evaluation of the pharmacodynamics of psychotropic drugs by single-lead pharmaco-EEG, EEG mapping and tomography (LORETA).

PubMed

Saletu, B; Anderer, P; Saletu-Zyhlarz, G M; Arnold, O; Pascual-Marqui, R D

2002-01-01

Utilizing computer-assisted quantitative analyses of human scalp-recorded electroencephalogram (EEG) in combination with certain statistical procedures (quantitative pharmaco-EEG) and mapping techniques (pharmaco-EEG mapping), it is possible to classify psychotropic substances and objectively evaluate their bioavailability at the target organ: the human brain. Specifically, one may determine at an early stage of drug development whether a drug is effective on the central nervous system (CNS) compared with placebo, what its clinical efficacy will be like, at which dosage it acts, when it acts and the equipotent dosages of different galenic formulations. Pharmaco-EEG profiles and maps of neuroleptics, antidepressants, tranquilizers, hypnotics, psychostimulants and nootropics/cognition-enhancing drugs will be described in this paper. Methodological problems, as well as the relationships between acute and chronic drug effects, alterations in normal subjects and patients, CNS effects, therapeutic efficacy and pharmacokinetic and pharmacodynamic data will be discussed. In recent times, imaging of drug effects on the regional brain electrical activity of healthy subjects by means of EEG tomography such as low-resolution electromagnetic tomography (LORETA) has been used for identifying brain areas predominantly involved in psychopharmacological action. This will be demonstrated for the representative drugs of the four main psychopharmacological classes, such as 3 mg haloperidol for neuroleptics, 20 mg citalopram for antidepressants, 2 mg lorazepam for tranquilizers and 20 mg methylphenidate for psychostimulants. LORETA demonstrates that these psychopharmacological classes affect brain structures differently.

Application of Tsallis Entropy to EEG: Quantifying the Presence of Burst Suppression After Asphyxial Cardiac Arrest in Rats

PubMed Central

Zhang, Dandan; Jia, Xiaofeng; Ding, Haiyan; Ye, Datian; Thakor, Nitish V.

2011-01-01

Burst suppression (BS) activity in EEG is clinically accepted as a marker of brain dysfunction or injury. Experimental studies in a rodent model of brain injury following asphyxial cardiac arrest (CA) show evidence of BS soon after resuscitation, appearing as a transitional recovery pattern between isoelectricity and continuous EEG. The EEG trends in such experiments suggest varying levels of uncertainty or randomness in the signals. To quantify the EEG data, Shannon entropy and Tsallis entropy (TsEn) are examined. More specifically, an entropy-based measure named TsEn area (TsEnA) is proposed to reveal the presence and the extent of development of BS following brain injury. The methodology of TsEnA and the selection of its parameter are elucidated in detail. To test the validity of this measure, 15 rats were subjected to 7 or 9 min of asphyxial CA. EEG recordings immediately after resuscitation from CA were investigated and characterized by TsEnA. The results show that TsEnA correlates well with the outcome assessed by evaluating the rodents after the experiments using a well-established neurological deficit score (Pearson correlation = 0.86, p ⪡ 0.01). This research shows that TsEnA reliably quantifies the complex dynamics in BS EEG, and may be useful as an experimental or clinical tool for objective estimation of the gravity of brain damage after CA. PMID:19695982

Double-blind, placebo-controlled, multiple-ascending-dose study on the pharmacodynamics of ABIO-08/01, a new CNS drug with potential anxiolytic activity. 2. EEG-tomography findings based on LORETA (low-resolution brain electromagnetic tomography).

PubMed

Saletu, Bernd; Anderer, Peter; Wolzt, Michael; Nosiska, Dorothea; Assandri, Alessandro; Noseda, Emanuele; Nannipieri, Fabrizio; Saletu-Zyhlarz, Gerda M

2009-01-01

Effects of ABIO-08/01, a new potentially anxiolytic isoxazoline, on regional electrical brain generators were investigated by 3-dimensional EEG tomography. In a double- blind, placebo-controlled, multiple-ascending-dose study, 16 healthy males (30.2 +/- 5.7 years) received 3 oral drug doses (10, 20, 40 mg) and placebo for 7 days (8-day wash-out) in a randomized non-balanced design for phase-1 studies. A 3-min vigilance-controlled (V) EEG, a 4-min resting (R) EEG with eyes closed, a 1-min eyes-open (EO) EEG and psychometric tests were performed 0, 1 and 6 h after taking the drug on days 1 and 5. Low-resolution brain electromagnetic tomography (LORETA) was computed from the spectrally analyzed EEG data, and differences between drug and placebo were displayed as statistical parametric maps. Data were registered to the Talairach-Tournoux Human Brain Atlas available as a digitized MRI. An overall omnibus significance test followed by a voxel-by-voxel t test demonstrated significant regional EEG changes after ABIO-08/01 versus placebo, dependent on recording condition, dose and time. While in the EO-EEG specifically the lowest dose of ABIO-08/01 induced pronounced sedative effects (delta/theta and beta increase) 1 h after acute and slightly less so after superimposed administration, in the 6th hour a decrease in alpha and beta activity signaled less sedative and more relaxant action. In the V-EEG these changes were less pronounced, in the R-EEG partly opposite. Hemisphere-specific changes were observed, suggesting increases in LORETA power over the left temporal, parietal, superior frontal regions and decreases over the right prefrontal, temporal pole and occipital regions. These LORETA changes are discussed in the light of neuroimaging findings on anxiety and anxiolytics. 2009 S. Karger AG, Basel.

Inhibition of growth hormone-releasing factor suppresses both sleep and growth hormone secretion in the rat.

PubMed

Obál, F; Payne, L; Kapás, L; Opp, M; Krueger, J M

1991-08-23

To study the possible involvement of hypothalamic growth hormone-releasing factor (GRF) in sleep regulation, a competitive GRF-antagonist, the peptide (N-Ac-Tyr1,D-Arg2)-GRF(1-29)-NH2, was intracerebroventricularly injected into rats (0.003, 0.3, and 14 nmol), and the EEG and brain temperature were recorded for 12 h during the light cycle of the day. Growth hormone (GH) concentrations were determined from plasma samples taken at 20-min intervals for 3 h after 14 nmol GRF-antagonist. The onset of non-rapid eye movement sleep (NREMS) was delayed in response to 0.3 and 14 nmol GRF-antagonist, the duration of NREMS was decreased for one or more hours and after 14 nmol EEG slow wave amplitudes were decreased during NREMS in postinjection hour 1. The high dose of GRF-antagonist also suppressed REMS for 4 h, inhibited GH secretion, and elicited a slight biphasic variation in brain temperature. These findings, together with previous observations indicating a sleep-promoting effect for GRF, support the hypothesis that hypothalamic GRF is involved in sleep regulation and might be responsible for the correlation between NREMS and GH secretion reported in various species.

[Study on effects of bioelectric parameters of rats in electromagnetic radiation of HV transmission line].

PubMed

Zhang, Anying; Pang, Xiaofeng; Yuan, Ping

2007-02-01

With the development of economy and coming of information era, the chance of exposure to electromagnetic fields with various frequencies has been increased for every human. The effects of electromagnetic radiattion on human being's health are versatile. To study the effects of bioelctronic parameters of rats in the electromagnetic radiations of HV transmission line, EEG, ECG and CMAP were measured in rats exposed to simulating high-voltage transmission line electromagnetic radiation for over one year. Brain tissues were studied by Fourier transform infrared spectroscopy. The results showed that no significant difference between exposed group and control group in EEG; however the FT-infrared spectra of brain tissues were different; the ECG of the exposed animals was considerably altered. Significant slowing of heart rate was observed in those rates exposed to EMFs; the latent period of CMAP in exposed group were not different compared with those of control group however there was a significant difference in wave amplitude of CMAP between the exposed group and control group. All results indicated that there must be some effects on bioelectric parameters of rats exposed to electromagnetic radiation of high-voltage transmission line for a long time.

Electroencephalography in the Diagnosis of Genetic Generalized Epilepsy Syndromes

PubMed Central

Seneviratne, Udaya; Cook, Mark J.; D’Souza, Wendyl Jude

2017-01-01

Genetic generalized epilepsy (GGE) consists of several syndromes diagnosed and classified on the basis of clinical features and electroencephalographic (EEG) abnormalities. The main EEG feature of GGE is bilateral, synchronous, symmetric, and generalized spike-wave complex. Other classic EEG abnormalities are polyspikes, epileptiform K-complexes and sleep spindles, polyspike-wave discharges, occipital intermittent rhythmic delta activity, eye-closure sensitivity, fixation-off sensitivity, and photoparoxysmal response. However, admixed with typical changes, atypical epileptiform discharges are also commonly seen in GGE. There are circadian variations of generalized epileptiform discharges. Sleep, sleep deprivation, hyperventilation, intermittent photic stimulation, eye closure, and fixation-off are often used as activation techniques to increase the diagnostic yield of EEG recordings. Reflex seizure-related EEG abnormalities can be elicited by the use of triggers such as cognitive tasks and pattern stimulation during the EEG recording in selected patients. Distinct electrographic abnormalities to help classification can be identified among different electroclinical syndromes. PMID:28993753

Exercise benefits for the aging brain depend on the accompanying cognitive load: insights from sleep electroencephalogram.

PubMed

Horne, Jim

2013-11-01

Although exercise clearly offsets aging effects on the body, its benefits for the aging brain are likely to depend on the extent that physical activity (especially locomotion) facilitates multisensory encounters, curiosity, and interactions with novel environments; this is especially true for exploratory activity, which occupies much of wakefulness for most mammals in the wild. Cognition is inseparable from physical activity, with both interlinked to promote neuroplasticity and more successful brain aging. In these respects and for humans, exercising in a static, featureless, artificially lit indoor setting contrasts with exploratory outdoor walking within a novel environment during daylight. However, little is known about the comparative benefits for the aging brain of longer-term daily regimens of this latter nature including the role of sleep, to the extent that sleep enhances neuroplasticity as shown in short-term laboratory studies. More discerning analyses of sleep electroencephalogram (EEG) slow-wave activity especially 0.5-2-Hz activity would provide greater insights into use-dependent recovery processes during longer-term tracking of these regimens and complement slower changing waking neuropsychologic and resting functional magnetic resonance imaging (fMRI) measures, including those of the brain's default mode network. Although the limited research only points to ephemeral small sleep EEG effects of pure exercise, more enduring effects seem apparent when physical activity incorporates cognitive challenges. In terms of "use it or lose it," curiosity-driven "getting out and about," encountering, interacting with, and enjoying novel situations may well provide the brain with its real exercise, further reflected in changes to the dynamics of sleep. Copyright © 2013 Elsevier B.V. All rights reserved.

Estimating the mutual information of an EEG-based Brain-Computer Interface.

PubMed

Schlögl, A; Neuper, C; Pfurtscheller, G

2002-01-01

An EEG-based Brain-Computer Interface (BCI) could be used as an additional communication channel between human thoughts and the environment. The efficacy of such a BCI depends mainly on the transmitted information rate. Shannon's communication theory was used to quantify the information rate of BCI data. For this purpose, experimental EEG data from four BCI experiments was analyzed off-line. Subjects imaginated left and right hand movements during EEG recording from the sensorimotor area. Adaptive autoregressive (AAR) parameters were used as features of single trial EEG and classified with linear discriminant analysis. The intra-trial variation as well as the inter-trial variability, the signal-to-noise ratio, the entropy of information, and the information rate were estimated. The entropy difference was used as a measure of the separability of two classes of EEG patterns.

Robust electroencephalogram phase estimation with applications in brain-computer interface systems.

PubMed

Seraj, Esmaeil; Sameni, Reza

2017-03-01

In this study, a robust method is developed for frequency-specific electroencephalogram (EEG) phase extraction using the analytic representation of the EEG. Based on recent theoretical findings in this area, it is shown that some of the phase variations-previously associated to the brain response-are systematic side-effects of the methods used for EEG phase calculation, especially during low analytical amplitude segments of the EEG. With this insight, the proposed method generates randomized ensembles of the EEG phase using minor perturbations in the zero-pole loci of narrow-band filters, followed by phase estimation using the signal's analytical form and ensemble averaging over the randomized ensembles to obtain a robust EEG phase and frequency. This Monte Carlo estimation method is shown to be very robust to noise and minor changes of the filter parameters and reduces the effect of fake EEG phase jumps, which do not have a cerebral origin. As proof of concept, the proposed method is used for extracting EEG phase features for a brain computer interface (BCI) application. The results show significant improvement in classification rates using rather simple phase-related features and a standard K-nearest neighbors and random forest classifiers, over a standard BCI dataset. The average performance was improved between 4-7% (in absence of additive noise) and 8-12% (in presence of additive noise). The significance of these improvements was statistically confirmed by a paired sample t-test, with 0.01 and 0.03 p-values, respectively. The proposed method for EEG phase calculation is very generic and may be applied to other EEG phase-based studies.

Hybrid ICA-Regression: Automatic Identification and Removal of Ocular Artifacts from Electroencephalographic Signals.

PubMed

Mannan, Malik M Naeem; Jeong, Myung Y; Kamran, Muhammad A

2016-01-01

Electroencephalography (EEG) is a portable brain-imaging technique with the advantage of high-temporal resolution that can be used to record electrical activity of the brain. However, it is difficult to analyze EEG signals due to the contamination of ocular artifacts, and which potentially results in misleading conclusions. Also, it is a proven fact that the contamination of ocular artifacts cause to reduce the classification accuracy of a brain-computer interface (BCI). It is therefore very important to remove/reduce these artifacts before the analysis of EEG signals for applications like BCI. In this paper, a hybrid framework that combines independent component analysis (ICA), regression and high-order statistics has been proposed to identify and eliminate artifactual activities from EEG data. We used simulated, experimental and standard EEG signals to evaluate and analyze the effectiveness of the proposed method. Results demonstrate that the proposed method can effectively remove ocular artifacts as well as it can preserve the neuronal signals present in EEG data. A comparison with four methods from literature namely ICA, regression analysis, wavelet-ICA (wICA), and regression-ICA (REGICA) confirms the significantly enhanced performance and effectiveness of the proposed method for removal of ocular activities from EEG, in terms of lower mean square error and mean absolute error values and higher mutual information between reconstructed and original EEG.

Hybrid ICA—Regression: Automatic Identification and Removal of Ocular Artifacts from Electroencephalographic Signals

PubMed Central

Mannan, Malik M. Naeem; Jeong, Myung Y.; Kamran, Muhammad A.

2016-01-01

Electroencephalography (EEG) is a portable brain-imaging technique with the advantage of high-temporal resolution that can be used to record electrical activity of the brain. However, it is difficult to analyze EEG signals due to the contamination of ocular artifacts, and which potentially results in misleading conclusions. Also, it is a proven fact that the contamination of ocular artifacts cause to reduce the classification accuracy of a brain-computer interface (BCI). It is therefore very important to remove/reduce these artifacts before the analysis of EEG signals for applications like BCI. In this paper, a hybrid framework that combines independent component analysis (ICA), regression and high-order statistics has been proposed to identify and eliminate artifactual activities from EEG data. We used simulated, experimental and standard EEG signals to evaluate and analyze the effectiveness of the proposed method. Results demonstrate that the proposed method can effectively remove ocular artifacts as well as it can preserve the neuronal signals present in EEG data. A comparison with four methods from literature namely ICA, regression analysis, wavelet-ICA (wICA), and regression-ICA (REGICA) confirms the significantly enhanced performance and effectiveness of the proposed method for removal of ocular activities from EEG, in terms of lower mean square error and mean absolute error values and higher mutual information between reconstructed and original EEG. PMID:27199714

Cortex-based inter-subject analysis of iEEG and fMRI data sets: application to sustained task-related BOLD and gamma responses.

PubMed

Esposito, Fabrizio; Singer, Neomi; Podlipsky, Ilana; Fried, Itzhak; Hendler, Talma; Goebel, Rainer

2013-02-01

Linking regional metabolic changes with fluctuations in the local electromagnetic fields directly on the surface of the human cerebral cortex is of tremendous importance for a better understanding of detailed brain processes. Functional magnetic resonance imaging (fMRI) and intra-cranial electro-encephalography (iEEG) measure two technically unrelated but spatially and temporally complementary sets of functional descriptions of human brain activity. In order to allow fine-grained spatio-temporal human brain mapping at the population-level, an effective comparative framework for the cortex-based inter-subject analysis of iEEG and fMRI data sets is needed. We combined fMRI and iEEG recordings of the same patients with epilepsy during alternated intervals of passive movie viewing and music listening to explore the degree of local spatial correspondence and temporal coupling between blood oxygen level dependent (BOLD) fMRI changes and iEEG spectral power modulations across the cortical surface after cortex-based inter-subject alignment. To this purpose, we applied a simple model of the iEEG activity spread around each electrode location and the cortex-based inter-subject alignment procedure to transform discrete iEEG measurements into cortically distributed group patterns by establishing a fine anatomic correspondence of many iEEG cortical sites across multiple subjects. Our results demonstrate the feasibility of a multi-modal inter-subject cortex-based distributed analysis for combining iEEG and fMRI data sets acquired from multiple subjects with the same experimental paradigm but with different iEEG electrode coverage. The proposed iEEG-fMRI framework allows for improved group statistics in a common anatomical space and preserves the dynamic link between the temporal features of the two modalities. Copyright © 2012 Elsevier Inc. All rights reserved.

Seizures and electroencephalography findings in 61 patients with fetal alcohol spectrum disorders.

PubMed

Boronat, S; Vicente, M; Lainez, E; Sánchez-Montañez, A; Vázquez, E; Mangado, L; Martínez-Ribot, L; Del Campo, M

2017-01-01

Fetal alcohol spectrum disorders (FASD) cause neurodevelopmental abnormalities. However, publications about epilepsy and electroencephalographic features are scarce. In this study, we prospectively performed electroencephalography (EEG) and brain magnetic resonance (MR) imaging in 61 patients with diagnosis of FASD. One patient had multiple febrile seizures with normal EEGs. Fourteen children showed EEG anomalies, including slow background activity and interictal epileptiform discharges, focal and/or generalized, and 3 of them had epilepsy. In one patient, seizures were first detected during the EEG recording and one case had an encephalopathy with electrical status epilepticus during slow sleep (ESES). Focal interictal discharges in our patients did not imply the presence of underlying visible focal brain lesions in the neuroimaging studies, such as cortical dysplasia or polymicrogyria. However, they had nonspecific brain MR abnormalities, including corpus callosum hypoplasia, vermis hypoplasia or cavum septum pellucidum. The latter was significantly more frequent in the group with EEG abnormal findings (p < 0.01). Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Multi-channel linear descriptors for event-related EEG collected in brain computer interface.

PubMed

Pei, Xiao-mei; Zheng, Chong-xun; Xu, Jin; Bin, Guang-yu; Wang, Hong-wu

2006-03-01

By three multi-channel linear descriptors, i.e. spatial complexity (omega), field power (sigma) and frequency of field changes (phi), event-related EEG data within 8-30 Hz were investigated during imagination of left or right hand movement. Studies on the event-related EEG data indicate that a two-channel version of omega, sigma and phi could reflect the antagonistic ERD/ERS patterns over contralateral and ipsilateral areas and also characterize different phases of the changing brain states in the event-related paradigm. Based on the selective two-channel linear descriptors, the left and right hand motor imagery tasks are classified to obtain satisfactory results, which testify the validity of the three linear descriptors omega, sigma and phi for characterizing event-related EEG. The preliminary results show that omega, sigma together with phi have good separability for left and right hand motor imagery tasks, which could be considered for classification of two classes of EEG patterns in the application of brain computer interfaces.

Cortical connectivity modulation during sleep onset: A study via graph theory on EEG data.

PubMed

Vecchio, Fabrizio; Miraglia, Francesca; Gorgoni, Maurizio; Ferrara, Michele; Iberite, Francesco; Bramanti, Placido; De Gennaro, Luigi; Rossini, Paolo Maria

2017-11-01

Sleep onset is characterized by a specific and orchestrated pattern of frequency and topographical EEG changes. Conventional power analyses of electroencephalographic (EEG) and computational assessments of network dynamics have described an earlier synchronization of the centrofrontal areas rhythms and a spread of synchronizing signals from associative prefrontal to posterior areas. Here, we assess how "small world" characteristics of the brain networks, as reflected in the EEG rhythms, are modified in the wakefulness-sleep transition comparing the pre- and post-sleep onset epochs. The results show that sleep onset is characterized by a less ordered brain network (as reflected by the higher value of small world) in the sigma band for the frontal lobes indicating stronger connectivity, and a more ordered brain network in the low frequency delta and theta bands indicating disconnection on the remaining brain areas. Our results depict the timing and topography of the specific mechanisms for the maintenance of functional connectivity of frontal brain regions at the sleep onset, also providing a possible explanation for the prevalence of the frontal-to-posterior information flow directionality previously observed after sleep onset. Hum Brain Mapp 38:5456-5464, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Interictal Epileptiform Discharges (IEDs) classification in EEG data of epilepsy patients

NASA Astrophysics Data System (ADS)

Puspita, J. W.; Soemarno, G.; Jaya, A. I.; Soewono, E.

2017-12-01

Interictal Epileptiform Dischargers (IEDs), which consists of spike waves and sharp waves, in human electroencephalogram (EEG) are characteristic signatures of epilepsy. Spike waves are characterized by a pointed peak with a duration of 20-70 ms, while sharp waves has a duration of 70-200 ms. The purpose of the study was to classify spike wave and sharp wave of EEG data of epilepsy patients using Backpropagation Neural Network. The proposed method consists of two main stages: feature extraction stage and classification stage. In the feature extraction stage, we use frequency, amplitude and statistical feature, such as mean, standard deviation, and median, of each wave. The frequency values of the IEDs are very sensitive to the selection of the wave baseline. The selected baseline must contain all data of rising and falling slopes of the IEDs. Thus, we have a feature that is able to represent the type of IEDs, appropriately. The results show that the proposed method achieves the best classification results with the recognition rate of 93.75 % for binary sigmoid activation function and learning rate of 0.1.

Non-thermal continuous and modulated electromagnetic radiation fields effects on sleep EEG of rats☆

PubMed Central

Mohammed, Haitham S.; Fahmy, Heba M.; Radwan, Nasr M.; Elsayed, Anwar A.

2012-01-01

In the present study, the alteration in the sleep EEG in rats due to chronic exposure to low-level non-thermal electromagnetic radiation was investigated. Two types of radiation fields were used; 900 MHz unmodulated wave and 900 MHz modulated at 8 and 16 Hz waves. Animals has exposed to radiation fields for 1 month (1 h/day). EEG power spectral analyses of exposed and control animals during slow wave sleep (SWS) and rapid eye movement sleep (REM sleep) revealed that the REM sleep is more susceptible to modulated radiofrequency radiation fields (RFR) than the SWS. The latency of REM sleep increased due to radiation exposure indicating a change in the ultradian rhythm of normal sleep cycles. The cumulative and irreversible effect of radiation exposure was proposed and the interaction of the extremely low frequency radiation with the similar EEG frequencies was suggested. PMID:25685416

EEG functional connectivity, axon delays and white matter disease.

PubMed

Nunez, Paul L; Srinivasan, Ramesh; Fields, R Douglas

2015-01-01

Both structural and functional brain connectivities are closely linked to white matter disease. We discuss several such links of potential interest to neurologists, neurosurgeons, radiologists, and non-clinical neuroscientists. Treatment of brains as genuine complex systems suggests major emphasis on the multi-scale nature of brain connectivity and dynamic behavior. Cross-scale interactions of local, regional, and global networks are apparently responsible for much of EEG's oscillatory behaviors. Finite axon propagation speed, often assumed to be infinite in local network models, is central to our conceptual framework. Myelin controls axon speed, and the synchrony of impulse traffic between distant cortical regions appears to be critical for optimal mental performance and learning. Several experiments suggest that axon conduction speed is plastic, thereby altering the regional and global white matter connections that facilitate binding of remote local networks. Combined EEG and high resolution EEG can provide distinct multi-scale estimates of functional connectivity in both healthy and diseased brains with measures like frequency and phase spectra, covariance, and coherence. White matter disease may profoundly disrupt normal EEG coherence patterns, but currently these kinds of studies are rare in scientific labs and essentially missing from clinical environments. Copyright © 2014 International Federation of Clinical Neurophysiology. All rights reserved.

A quantitative evaluation of dry-sensor electroencephalography

NASA Astrophysics Data System (ADS)

Uy, E. Timothy

Neurologists, neuroscientists, and experimental psychologists study electrical activity within the brain by recording voltage fluctuations at the scalp. This is electroencephalography (EEG). In conventional or "wet" EEG, scalp abrasion and use of electrolytic paste are required to insure good electrical connection between sensor and skin. Repeated abrasion quickly becomes irritating to subjects, severely limiting the number and frequency of sessions. Several groups have produced "dry" EEG sensors that do not require abrasion or conductive paste. These, in addition to sidestepping the issue of abrasion, promise to reduce setup time from about 30 minutes with a technician to less than 30 seconds without one. The availability of such an instrument would (1) reduce the cost of brain-related medical care, (2) lower the barrier of entry on brain experimentation, and (3) allow individual subjects to contribute substantially more data without fear of abrasion or fatigue. Accuracy of the EEG is paramount in the medical diagnosis of epilepsy, in experimental psychology and in the burgeoning field of brain-computer interface. Without a sufficiently accurate measurement, the advantages of dry sensors remain a moot point. However, even after nearly a decade, demonstrations of dry EEG accuracy with respect to wet have been limited to visual comparison of short snippets of spontaneous EEG, averaged event-related potentials or plots of power spectrum. In this dissertation, I propose a detailed methodology based on single-trial EEG classification for comparing dry EEG sensors to their wet counterparts. Applied to a set of commercially fabricated dry sensors, this work reveals that dry sensors can perform as well their wet counterparts with careful screening and attention to the bandwidth of interest.

Measure Projection Analysis: A Probabilistic Approach to EEG Source Comparison and Multi-Subject Inference

PubMed Central

Bigdely-Shamlo, Nima; Mullen, Tim; Kreutz-Delgado, Kenneth; Makeig, Scott

2013-01-01

A crucial question for the analysis of multi-subject and/or multi-session electroencephalographic (EEG) data is how to combine information across multiple recordings from different subjects and/or sessions, each associated with its own set of source processes and scalp projections. Here we introduce a novel statistical method for characterizing the spatial consistency of EEG dynamics across a set of data records. Measure Projection Analysis (MPA) first finds voxels in a common template brain space at which a given dynamic measure is consistent across nearby source locations, then computes local-mean EEG measure values for this voxel subspace using a statistical model of source localization error and between-subject anatomical variation. Finally, clustering the mean measure voxel values in this locally consistent brain subspace finds brain spatial domains exhibiting distinguishable measure features and provides 3-D maps plus statistical significance estimates for each EEG measure of interest. Applied to sufficient high-quality data, the scalp projections of many maximally independent component (IC) processes contributing to recorded high-density EEG data closely match the projection of a single equivalent dipole located in or near brain cortex. We demonstrate the application of MPA to a multi-subject EEG study decomposed using independent component analysis (ICA), compare the results to k-means IC clustering in EEGLAB (sccn.ucsd.edu/eeglab), and use surrogate data to test MPA robustness. A Measure Projection Toolbox (MPT) plug-in for EEGLAB is available for download (sccn.ucsd.edu/wiki/MPT). Together, MPA and ICA allow use of EEG as a 3-D cortical imaging modality with near-cm scale spatial resolution. PMID:23370059

Hybrid EEG-fNIRS-Based Eight-Command Decoding for BCI: Application to Quadcopter Control.

PubMed

Khan, Muhammad Jawad; Hong, Keum-Shik

2017-01-01

In this paper, a hybrid electroencephalography-functional near-infrared spectroscopy (EEG-fNIRS) scheme to decode eight active brain commands from the frontal brain region for brain-computer interface is presented. A total of eight commands are decoded by fNIRS, as positioned on the prefrontal cortex, and by EEG, around the frontal, parietal, and visual cortices. Mental arithmetic, mental counting, mental rotation, and word formation tasks are decoded with fNIRS, in which the selected features for classification and command generation are the peak, minimum, and mean ΔHbO values within a 2-s moving window. In the case of EEG, two eyeblinks, three eyeblinks, and eye movement in the up/down and left/right directions are used for four-command generation. The features in this case are the number of peaks and the mean of the EEG signal during 1 s window. We tested the generated commands on a quadcopter in an open space. An average accuracy of 75.6% was achieved with fNIRS for four-command decoding and 86% with EEG for another four-command decoding. The testing results show the possibility of controlling a quadcopter online and in real-time using eight commands from the prefrontal and frontal cortices via the proposed hybrid EEG-fNIRS interface.

Neural correlates of brain state in chronic ischemia and stroke: combined resting state electroencephalogram and transcranial Doppler ultrasonographic study.

PubMed

Martynova, Olga V; Portnova, Galina V; Gladun, Ksenya V

2017-02-08

Clinical neurology is constantly searching for reliable indices of ischemic brain damage to prevent a possible development of stroke. We suggest that resting state electroencephalogram (rsEEG) with respect to other clinical data may provide important information about the severity of ischemia. We carried out correlation analysis of rsEEG, data of transcranial Doppler ultrasonography of head vessels, and clinical assessment scores collected from healthy volunteers and four groups of patients with mild chronic microvascular ischemia (CMI-1), moderate CMI (CMI-2), severe atrophy of the cerebral hemisphere, ischemic stroke in the left middle cerebral artery stroke, and ischemic stroke in the right middle cerebral artery stroke. Using independent component analysis and k-mean clustering of EEG data, we observed prominent changes in rsEEG reflected in specific distributions of spectral peaks in all groups of patients. We found a significant correlation of EEG spectral distribution and the blood flow velocity in coronal arteries, which was also affected by the severity of ischemia and the localization of stroke. Moreover, EEG spectral distribution was more indicative of early stages of ischemia than the blood flow velocity. Our data support the hypothesis that rsEEG may reflect altered neural activity caused by ischemic brain damage.

Detecting Large-Scale Brain Networks Using EEG: Impact of Electrode Density, Head Modeling and Source Localization

PubMed Central

Liu, Quanying; Ganzetti, Marco; Wenderoth, Nicole; Mantini, Dante

2018-01-01

Resting state networks (RSNs) in the human brain were recently detected using high-density electroencephalography (hdEEG). This was done by using an advanced analysis workflow to estimate neural signals in the cortex and to assess functional connectivity (FC) between distant cortical regions. FC analyses were conducted either using temporal (tICA) or spatial independent component analysis (sICA). Notably, EEG-RSNs obtained with sICA were very similar to RSNs retrieved with sICA from functional magnetic resonance imaging data. It still remains to be clarified, however, what technological aspects of hdEEG acquisition and analysis primarily influence this correspondence. Here we examined to what extent the detection of EEG-RSN maps by sICA depends on the electrode density, the accuracy of the head model, and the source localization algorithm employed. Our analyses revealed that the collection of EEG data using a high-density montage is crucial for RSN detection by sICA, but also the use of appropriate methods for head modeling and source localization have a substantial effect on RSN reconstruction. Overall, our results confirm the potential of hdEEG for mapping the functional architecture of the human brain, and highlight at the same time the interplay between acquisition technology and innovative solutions in data analysis. PMID:29551969

Characterization of ictal slow waves in epileptic spasms.

PubMed

Honda, Ryoko; Saito, Yoshiaki; Okumura, Akihisa; Abe, Shinpei; Saito, Takashi; Nakagawa, Eiji; Sugai, Kenji; Sasaki, Masayuki

2015-12-01

We characterized the clinico-neurophysiological features of epileptic spasms, particularly focusing on high-voltage slow waves during ictal EEG. We studied 22 patients with epileptic spasms recorded during digital video-scalp EEG, including five individuals who still had persistent spasms after callosotomy. We analysed the duration, amplitude, latency to onset of electromyographic bursts, and distribution of the highest positive and negative peaks of slow waves in 352 spasms. High-voltage positive slow waves preceded the identifiable muscle contractions of spasms. The mean duration of these positive waves was 569±228 m, and the mean latency to electromyographic onset was 182±127 m. These parameters varied markedly even within a patient. The highest peak of the positive component was distributed in variable regions, which was not consistent with the location of lesions on MRI. The peak of the negative component following the positivity was distributed in the neighbouring or opposite areas of the positive peak distribution. No changes were evident in the pre- or post-surgical distributions of the positive peak, or in the interhemispheric delay between both hemispheres, in individuals with callosotomy. Our data imply that ictal positive slow waves are the most common EEG changes during spasms associated with a massive motor component. Plausible explanations for these widespread positive slow waves include the notion that EEG changes possibly reflect involvement of both cortical and subcortical structures.

Characterization of EEG signals revealing covert cognition in the injured brain.

PubMed

Curley, William H; Forgacs, Peter B; Voss, Henning U; Conte, Mary M; Schiff, Nicholas D

2018-05-01

See Boly and Laureys (doi:10.1093/brain/awy080) for a scientific commentary on this article.Patients with severe brain injury are difficult to assess and frequently subject to misdiagnosis. 'Cognitive motor dissociation' is a term used to describe a subset of such patients with preserved cognition as detected with neuroimaging methods but not evident in behavioural assessments. Unlike the locked-in state, cognitive motor dissociation after severe brain injury is prominently marked by concomitant injuries across the cerebrum in addition to limited or no motoric function. In the present study, we sought to characterize the EEG signals used as indicators of cognition in patients with disorders of consciousness and examine their reliability for potential future use to re-establish communication. We compared EEG-based assessments to the results of using similar methods with functional MRI. Using power spectral density analysis to detect EEG evidence of task performance (Two Group Test, P ≤ 0.05, with false discovery rate correction), we found evidence of the capacity to follow commands in 21 of 28 patients with severe brain injury and all 15 healthy individuals studied. We found substantial variability in the temporal and spatial characteristics of significant EEG signals among the patients in contrast to only modest variation in these domains across healthy controls; the majority of healthy controls showed suppression of either 8-12 Hz 'alpha' or 13-40 Hz 'beta' power during task performance, or both. Nine of the 21 patients with EEG evidence of command-following also demonstrated functional MRI evidence of command-following. Nine of the patients with command-following capacity demonstrated by EEG showed no behavioural evidence of a communication channel as detected by a standardized behavioural assessment, the Coma Recovery Scale - Revised. We further examined the potential contributions of fluctuations in arousal that appeared to co-vary with some patients' ability to reliably generate EEG signals in response to command. Five of nine patients with statistically indeterminate responses to one task tested showed a positive response after accounting for variations in overall background state (as visualized in the qualitative shape of the power spectrum) and grouping of trial runs with similar background state characteristics. Our findings reveal signal variations of EEG responses in patients with severe brain injuries and provide insight into the underlying physiology of cognitive motor dissociation. These results can help guide future efforts aimed at re-establishment of communication in such patients who will need customization for brain-computer interfaces.

Using EEG/MEG Data of Cognitive Processes in Brain-Computer Interfaces

NASA Astrophysics Data System (ADS)

Gutiérrez, David

2008-08-01

Brain-computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using electroencephalographic (EEG) and, more recently, magnetoencephalographic (MEG) measurements of the brain function. Most of the current implementations of BCIs rely on EEG/MEG data of motor activities as such neural processes are well characterized, while the use of data related to cognitive activities has been neglected due to its intrinsic complexity. However, cognitive data usually has larger amplitude, lasts longer and, in some cases, cognitive brain signals are easier to control at will than motor signals. This paper briefy reviews the use of EEG/MEG data of cognitive processes in the implementation of BCIs. Specifically, this paper reviews some of the neuromechanisms, signal features, and processing methods involved. This paper also refers to some of the author's work in the area of detection and classifcation of cognitive signals for BCIs using variability enhancement, parametric modeling, and spatial fltering, as well as recent developments in BCI performance evaluation.

Discovering the Neural Nature of Moral Cognition? Empirical, Theoretical, and Practical Challenges in Bioethical Research with Electroencephalography (EEG).

PubMed

Wagner, Nils-Frederic; Chaves, Pedro; Wolff, Annemarie

2017-06-01

In this article we critically review the neural mechanisms of moral cognition that have recently been studied via electroencephalography (EEG). Such studies promise to shed new light on traditional moral questions by helping us to understand how effective moral cognition is embodied in the brain. It has been argued that conflicting normative ethical theories require different cognitive features and can, accordingly, in a broadly conceived naturalistic attempt, be associated with different brain processes that are rooted in different brain networks and regions. This potentially morally relevant brain activity has been empirically investigated through EEG-based studies on moral cognition. From neuroscientific evidence gathered in these studies, a variety of normative conclusions have been drawn and bioethical applications have been suggested. We discuss methodological and theoretical merits and demerits of the attempt to use EEG techniques in a morally significant way, point to legal challenges and policy implications, indicate the potential to reveal biomarkers of psychopathological conditions, and consider issues that might inform future bioethical work.

Using EEG/MEG Data of Cognitive Processes in Brain-Computer Interfaces

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gutierrez, David

2008-08-11

Brain-computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using electroencephalographic (EEG) and, more recently, magnetoencephalographic (MEG) measurements of the brain function. Most of the current implementations of BCIs rely on EEG/MEG data of motor activities as such neural processes are well characterized, while the use of data related to cognitive activities has been neglected due to its intrinsic complexity. However, cognitive data usually has larger amplitude, lasts longer and, in some cases, cognitive brain signals are easier to control at will than motor signals. This paper briefy reviews the use of EEG/MEGmore » data of cognitive processes in the implementation of BCIs. Specifically, this paper reviews some of the neuromechanisms, signal features, and processing methods involved. This paper also refers to some of the author's work in the area of detection and classifcation of cognitive signals for BCIs using variability enhancement, parametric modeling, and spatial fltering, as well as recent developments in BCI performance evaluation.« less

Far-red to near infrared emission and scattering spectroscopy for biomedical applications

NASA Astrophysics Data System (ADS)

Zhang, Gang

2001-06-01

The thesis investigates the far-red and near infrared (NIR) spectral region from biomedical tissue samples for monitoring the state of tissues. The NIR emission wing intensity is weak in comparison to the emission in the visible spectral region. The wing emission from biomedical samples has revealed meaningful information about the state of the tissues. A model is presented to explain the shape of the spectral wing based on a continuum of energy levels. The wing can be used to classify different kinds of tissues; especially it can be used to differentiate cancer part from normal human breast tissues. The research work of the far-red emission from thermal damaged tissue samples shows that the emission intensity in this spectral region is proportional to the extent of the thermal damage of the tissue. Near infrared spectral absorption method is used to investigate blood hemodynamics (perfusion and oxygenation) in brain during sleep-wake transition. The result of the research demonstrates that the continuous wave (CW) type near infrared spectroscopy (NIRS) device can be used to investigate brain blood perfusion and oxygenation with a similar precision with frequency domain (FD) type device. The human subject sleep and wake transition, has been monitored by CW type NIRS instrument with traditional electroencephalograph (EEG) method. Parallel change in oxy-Hb and deoxy-Hb is a discrete event that occurs in the transition from both sleep to wakefulness and wakefulness to sleep. These hemodynamic switches are generally about few seconds delayed from the human decided transition point between sleep and wake on the polygraph EEG recording paper. The combination of NIRS and EEG methods monitor the brain activity, gives more information about the brain activity. The sleep apnea investigation was associated with recurrent apneas, insufficient nasal continuous positive airway pressure (CPAP) and the different response of the peripheral and central compartments to breathing events. The different results with finger pulse oximetry and NIRS suggest that optical monitoring of the brain may have advantages that may help clarify the morbidity of obstructive sleep apnea (OSA) Syndrome.

Microstates in resting-state EEG: current status and future directions.

PubMed

Khanna, Arjun; Pascual-Leone, Alvaro; Michel, Christoph M; Farzan, Faranak

2015-02-01

Electroencephalography (EEG) is a powerful method of studying the electrophysiology of the brain with high temporal resolution. Several analytical approaches to extract information from the EEG signal have been proposed. One method, termed microstate analysis, considers the multichannel EEG recording as a series of quasi-stable "microstates" that are each characterized by a unique topography of electric potentials over the entire channel array. Because this technique simultaneously considers signals recorded from all areas of the cortex, it is capable of assessing the function of large-scale brain networks whose disruption is associated with several neuropsychiatric disorders. In this review, we first introduce the method of EEG microstate analysis. We then review studies that have discovered significant changes in the resting-state microstate series in a variety of neuropsychiatric disorders and behavioral states. We discuss the potential utility of this method in detecting neurophysiological impairments in disease and monitoring neurophysiological changes in response to an intervention. Finally, we discuss how the resting-state microstate series may reflect rapid switching among neural networks while the brain is at rest, which could represent activity of resting-state networks described by other neuroimaging modalities. We conclude by commenting on the current and future status of microstate analysis, and suggest that EEG microstates represent a promising neurophysiological tool for understanding and assessing brain network dynamics on a millisecond timescale in health and disease. Copyright © 2014 Elsevier Ltd. All rights reserved.

Microstates in Resting-State EEG: Current Status and Future Directions

PubMed Central

Khanna, Arjun; Pascual-Leone, Alvaro; Michel, Christoph M.; Farzan, Faranak

2015-01-01

Electroencephalography (EEG) is a powerful method of studying the electrophysiology of the brain with high temporal resolution. Several analytical approaches to extract information from the EEG signal have been proposed. One method, termed microstate analysis, considers the multichannel EEG recording as a series of quasi-stable “microstates” that are each characterized by a unique topography of electric potentials over the entire channel array. Because this technique simultaneously considers signals recorded from all areas of the cortex, it is capable of assessing the function of large-scale brain networks whose disruption is associated with several neuropsychiatric disorders. In this review, we first introduce the method of EEG microstate analysis. We then review studies that have discovered significant changes in the resting-state microstate series in a variety of neuropsychiatric disorders and behavioral states. We discuss the potential utility of this method in detecting neurophysiological impairments in disease and monitoring neurophysiological changes in response to an intervention. Finally, we discuss how the resting-state microstate series may reflect rapid switching among neural networks while the brain is at rest, which could represent activity of resting-state networks described by other neuroimaging modalities. We conclude by commenting on the current and future status of microstate analysis, and suggest that EEG microstates represent a promising neurophysiological tool for understanding and assessing brain network dynamics on a millisecond timescale in health and disease. PMID:25526823

Electrophysiological CNS-processes related to associative learning in humans.

PubMed

Christoffersen, Gert R J; Schachtman, Todd R

2016-01-01

The neurophysiology of human associative memory has been studied with electroencephalographic techniques since the 1930s. This research has revealed that different types of electrophysiological processes in the human brain can be modified by conditioning: sensory evoked potentials, sensory induced gamma-band activity, periods of frequency-specific waves (alpha and beta waves, the sensorimotor rhythm and the mu-rhythm) and slow cortical potentials. Conditioning of these processes has been studied in experiments that either use operant conditioning or repeated contingent pairings of conditioned and unconditioned stimuli (classical conditioning). In operant conditioning, the appearance of a specific brain process is paired with an external stimulus (neurofeedback) and the feedback enables subjects to obtain varying degrees of control of the CNS-process. Such acquired self-regulation of brain activity has found practical uses for instance in the amelioration of epileptic seizures, Autism Spectrum Disorders (ASD) and Attention Deficit Hyperactivity Disorder (ADHD). It has also provided communicative means of assistance for tetraplegic patients through the use of brain computer interfaces. Both extra and intracortically recorded signals have been coupled with contingent external feedback. It is the aim for this review to summarize essential results on all types of electromagnetic brain processes that have been modified by classical or operant conditioning. The results are organized according to type of conditioned EEG-process, type of conditioning, and sensory modalities of the conditioning stimuli. Copyright © 2015 Elsevier B.V. All rights reserved.

Quantitative complexity analysis in multi-channel intracranial EEG recordings form epilepsy brains

PubMed Central

Liu, Chang-Chia; Pardalos, Panos M.; Chaovalitwongse, W. Art; Shiau, Deng-Shan; Ghacibeh, Georges; Suharitdamrong, Wichai; Sackellares, J. Chris

2008-01-01

Epilepsy is a brain disorder characterized clinically by temporary but recurrent disturbances of brain function that may or may not be associated with destruction or loss of consciousness and abnormal behavior. Human brain is composed of more than 10 to the power 10 neurons, each of which receives electrical impulses known as action potentials from others neurons via synapses and sends electrical impulses via a sing output line to a similar (the axon) number of neurons. When neuronal networks are active, they produced a change in voltage potential, which can be captured by an electroencephalogram (EEG). The EEG recordings represent the time series that match up to neurological activity as a function of time. By analyzing the EEG recordings, we sought to evaluate the degree of underlining dynamical complexity prior to progression of seizure onset. Through the utilization of the dynamical measurements, it is possible to classify the state of the brain according to the underlying dynamical properties of EEG recordings. The results from two patients with temporal lobe epilepsy (TLE), the degree of complexity start converging to lower value prior to the epileptic seizures was observed from epileptic regions as well as non-epileptic regions. The dynamical measurements appear to reflect the changes of EEG’s dynamical structure. We suggest that the nonlinear dynamical analysis can provide a useful information for detecting relative changes in brain dynamics, which cannot be detected by conventional linear analysis. PMID:19079790

Neural network detects the effects of p-CPA pre-treatment on brain electrophysiology in a rat model of focal brain injury.

PubMed

Sinha, Rakesh Kumar; Aggarwal, Yogender

2009-04-01

To examine the performance of Artificial Neural Network (ANN) in evaluation of the effects of pretreatment of para-Chlorophenylalanine (p-CPA), a serotonin blocker, in experimental brain injury. Continuous 4 h digital electroencephalogram (EEG) recordings from male Charles Foster rats and its power spectrum analysis by using fast Fourier transform (FFT) were performed in two experimental (i) drug untreated injury group; (ii) p-CPA pretreated injury group as well as a control group. The EEG power spectrum data were tested by ANN containing 60 nodes in input layer, weighted from the digital values of power spectrum from 0 to 30 Hz, 18 nodes in hidden layer and an output node. The effects of injury and of the drug pretreatment were confirmed with the help of calculation of edematous swelling in the brain. The changes in EEG spectral patterns were compared with the ANN and the accuracy was determined in terms of percent (%). Overall performance of the network was found the best in control group (97.9%) in comparison to p-CPA untreated injury group (96.3%) and p-CPA pretreated injury group (71.9%). The decrease in accuracy in p-CPA pretreated injury group of subjects have occurred due to increase in misclassified patterns due to faster recovery in brain cortical potentials. EEG spectrum analysis with ANN was found successful in identifying the changes due to brain swelling as well as the effect of pretreatment of p-CPA in focal brain injury condition. Thus, the training and testing of ANN with EEG power spectra can be used as an effective diagnostic tool for early prediction and monitoring of brain injury as well as the effects of drugs in this condition.

Exploiting neurovascular coupling: a Bayesian sequential Monte Carlo approach applied to simulated EEG fNIRS data

NASA Astrophysics Data System (ADS)

Croce, Pierpaolo; Zappasodi, Filippo; Merla, Arcangelo; Chiarelli, Antonio Maria

2017-08-01

Objective. Electrical and hemodynamic brain activity are linked through the neurovascular coupling process and they can be simultaneously measured through integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Thanks to the lack of electro-optical interference, the two procedures can be easily combined and, whereas EEG provides electrophysiological information, fNIRS can provide measurements of two hemodynamic variables, such as oxygenated and deoxygenated hemoglobin. A Bayesian sequential Monte Carlo approach (particle filter, PF) was applied to simulated recordings of electrical and neurovascular mediated hemodynamic activity, and the advantages of a unified framework were shown. Approach. Multiple neural activities and hemodynamic responses were simulated in the primary motor cortex of a subject brain. EEG and fNIRS recordings were obtained by means of forward models of volume conduction and light propagation through the head. A state space model of combined EEG and fNIRS data was built and its dynamic evolution was estimated through a Bayesian sequential Monte Carlo approach (PF). Main results. We showed the feasibility of the procedure and the improvements in both electrical and hemodynamic brain activity reconstruction when using the PF on combined EEG and fNIRS measurements. Significance. The investigated procedure allows one to combine the information provided by the two methodologies, and, by taking advantage of a physical model of the coupling between electrical and hemodynamic response, to obtain a better estimate of brain activity evolution. Despite the high computational demand, application of such an approach to in vivo recordings could fully exploit the advantages of this combined brain imaging technology.

Reversing pathologically increased EEG power by acoustic coordinated reset neuromodulation

PubMed Central

Adamchic, Ilya; Toth, Timea; Hauptmann, Christian; Tass, Peter Alexander

2014-01-01

Acoustic Coordinated Reset (CR) neuromodulation is a patterned stimulation with tones adjusted to the patient's dominant tinnitus frequency, which aims at desynchronizing pathological neuronal synchronization. In a recent proof-of-concept study, CR therapy, delivered 4–6 h/day more than 12 weeks, induced a significant clinical improvement along with a significant long-lasting decrease of pathological oscillatory power in the low frequency as well as γ band and an increase of the α power in a network of tinnitus-related brain areas. As yet, it remains unclear whether CR shifts the brain activity toward physiological levels or whether it induces clinically beneficial, but nonetheless abnormal electroencephalographic (EEG) patterns, for example excessively decreased δ and/or γ. Here, we compared the patients' spontaneous EEG data at baseline as well as after 12 weeks of CR therapy with the spontaneous EEG of healthy controls by means of Brain Electrical Source Analysis source montage and standardized low-resolution brain electromagnetic tomography techniques. The relationship between changes in EEG power and clinical scores was investigated using a partial least squares approach. In this way, we show that acoustic CR neuromodulation leads to a normalization of the oscillatory power in the tinnitus-related network of brain areas, most prominently in temporal regions. A positive association was found between the changes in tinnitus severity and the normalization of δ and γ power in the temporal, parietal, and cingulate cortical regions. Our findings demonstrate a widespread CR-induced normalization of EEG power, significantly associated with a reduction of tinnitus severity. PMID:23907785

myBrain: a novel EEG embedded system for epilepsy monitoring.

PubMed

Pinho, Francisco; Cerqueira, João; Correia, José; Sousa, Nuno; Dias, Nuno

2017-10-01

The World Health Organisation has pointed that a successful health care delivery, requires effective medical devices as tools for prevention, diagnosis, treatment and rehabilitation. Several studies have concluded that longer monitoring periods and outpatient settings might increase diagnosis accuracy and success rate of treatment selection. The long-term monitoring of epileptic patients through electroencephalography (EEG) has been considered a powerful tool to improve the diagnosis, disease classification, and treatment of patients with such condition. This work presents the development of a wireless and wearable EEG acquisition platform suitable for both long-term and short-term monitoring in inpatient and outpatient settings. The developed platform features 32 passive dry electrodes, analogue-to-digital signal conversion with 24-bit resolution and a variable sampling frequency from 250 Hz to 1000 Hz per channel, embedded in a stand-alone module. A computer-on-module embedded system runs a Linux ® operating system that rules the interface between two software frameworks, which interact to satisfy the real-time constraints of signal acquisition as well as parallel recording, processing and wireless data transmission. A textile structure was developed to accommodate all components. Platform performance was evaluated in terms of hardware, software and signal quality. The electrodes were characterised through electrochemical impedance spectroscopy and the operating system performance running an epileptic discrimination algorithm was evaluated. Signal quality was thoroughly assessed in two different approaches: playback of EEG reference signals and benchmarking with a clinical-grade EEG system in alpha-wave replacement and steady-state visual evoked potential paradigms. The proposed platform seems to efficiently monitor epileptic patients in both inpatient and outpatient settings and paves the way to new ambulatory clinical regimens as well as non-clinical EEG applications.

Review of Sparse Representation-Based Classification Methods on EEG Signal Processing for Epilepsy Detection, Brain-Computer Interface and Cognitive Impairment

PubMed Central

Wen, Dong; Jia, Peilei; Lian, Qiusheng; Zhou, Yanhong; Lu, Chengbiao

2016-01-01

At present, the sparse representation-based classification (SRC) has become an important approach in electroencephalograph (EEG) signal analysis, by which the data is sparsely represented on the basis of a fixed dictionary or learned dictionary and classified based on the reconstruction criteria. SRC methods have been used to analyze the EEG signals of epilepsy, cognitive impairment and brain computer interface (BCI), which made rapid progress including the improvement in computational accuracy, efficiency and robustness. However, these methods have deficiencies in real-time performance, generalization ability and the dependence of labeled sample in the analysis of the EEG signals. This mini review described the advantages and disadvantages of the SRC methods in the EEG signal analysis with the expectation that these methods can provide the better tools for analyzing EEG signals. PMID:27458376

Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy.

PubMed

Mishra, Vikas; Gautier, Nicole M; Glasscock, Edward

2018-01-29

In epilepsy, seizures can evoke cardiac rhythm disturbances such as heart rate changes, conduction blocks, asystoles, and arrhythmias, which can potentially increase risk of sudden unexpected death in epilepsy (SUDEP). Electroencephalography (EEG) and electrocardiography (ECG) are widely used clinical diagnostic tools to monitor for abnormal brain and cardiac rhythms in patients. Here, a technique to simultaneously record video, EEG, and ECG in mice to measure behavior, brain, and cardiac activities, respectively, is described. The technique described herein utilizes a tethered (i.e., wired) recording configuration in which the implanted electrode on the head of the mouse is hard-wired to the recording equipment. Compared to wireless telemetry recording systems, the tethered arrangement possesses several technical advantages such as a greater possible number of channels for recording EEG or other biopotentials; lower electrode costs; and greater frequency bandwidth (i.e., sampling rate) of recordings. The basics of this technique can also be easily modified to accommodate recording other biosignals, such as electromyography (EMG) or plethysmography for assessment of muscle and respiratory activity, respectively. In addition to describing how to perform the EEG-ECG recordings, we also detail methods to quantify the resulting data for seizures, EEG spectral power, cardiac function, and heart rate variability, which we demonstrate in an example experiment using a mouse with epilepsy due to Kcna1 gene deletion. Video-EEG-ECG monitoring in mouse models of epilepsy or other neurological disease provides a powerful tool to identify dysfunction at the level of the brain, heart, or brain-heart interactions.

Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis

PubMed Central

Javed, Amna; Tiwana, Mohsin I.; Khan, Umar Shahbaz

2018-01-01

Brain Computer Interface (BCI) determines the intent of the user from a variety of electrophysiological signals. These signals, Slow Cortical Potentials, are recorded from scalp, and cortical neuronal activity is recorded by implanted electrodes. This paper is focused on design of an embedded system that is used to control the finger movements of an upper limb prosthesis using Electroencephalogram (EEG) signals. This is a follow-up of our previous research which explored the best method to classify three movements of fingers (thumb movement, index finger movement, and first movement). Two-stage logistic regression classifier exhibited the highest classification accuracy while Power Spectral Density (PSD) was used as a feature of the filtered signal. The EEG signal data set was recorded using a 14-channel electrode headset (a noninvasive BCI system) from right-handed, neurologically intact volunteers. Mu (commonly known as alpha waves) and Beta Rhythms (8–30 Hz) containing most of the movement data were retained through filtering using “Arduino Uno” microcontroller followed by 2-stage logistic regression to obtain a mean classification accuracy of 70%. PMID:29888252

Heart-Brain Interactions in the MR Environment: Characterization of the Ballistocardiogram in EEG Signals Collected During Simultaneous fMRI.

PubMed

Marino, Marco; Liu, Quanying; Del Castello, Mariangela; Corsi, Cristiana; Wenderoth, Nicole; Mantini, Dante

2018-05-01

The ballistocardiographic (BCG) artifact is linked to cardiac activity and occurs in electroencephalographic (EEG) recordings acquired inside the magnetic resonance (MR) environment. Its variability in terms of amplitude, waveform shape and spatial distribution over subject's scalp makes its attenuation a challenging task. In this study, we aimed to provide a detailed characterization of the BCG properties, including its temporal dependency on cardiac events and its spatio-temporal dynamics. To this end, we used high-density EEG data acquired during simultaneous functional MR imaging in six healthy volunteers. First, we investigated the relationship between cardiac activity and BCG occurrences in the EEG recordings. We observed large variability in the delay between ECG and subsequent BCG events (ECG-BCG delay) across subjects and non-negligible epoch-by-epoch variations at the single subject level. The inspection of spatial-temporal variations revealed a prominent non-stationarity of the BCG signal. We identified five main BCG waves, which were common across subjects. Principal component analysis revealed two spatially distinct patterns to explain most of the variance (85% in total). These components are possibly related to head rotation and pulse-driven scalp expansion, respectively. Our results may inspire the development of novel, more effective methods for the removal of the BCG, capable of isolating and attenuating artifact occurrences while preserving true neuronal activity.

Ion channels in EEG: isolating channel dysfunction in NMDA receptor antibody encephalitis.

PubMed

Symmonds, Mkael; Moran, Catherine H; Leite, M Isabel; Buckley, Camilla; Irani, Sarosh R; Stephan, Klaas Enno; Friston, Karl J; Moran, Rosalyn J

2018-06-01

See Roberts and Breakspear (doi:10.1093/brain/awy136) for a scientific commentary on this article.Neurological and psychiatric practice frequently lack diagnostic probes that can assess mechanisms of neuronal communication non-invasively in humans. In N-methyl-d-aspartate (NMDA) receptor antibody encephalitis, functional molecular assays are particularly important given the presence of NMDA antibodies in healthy populations, the multifarious symptomology and the lack of radiological signs. Recent advances in biophysical modelling techniques suggest that inferring cellular-level properties of neural circuits from macroscopic measures of brain activity is possible. Here, we estimated receptor function from EEG in patients with NMDA receptor antibody encephalitis (n = 29) as well as from encephalopathic and neurological patient controls (n = 36). We show that the autoimmune patients exhibit distinct fronto-parietal network changes from which ion channel estimates can be obtained using a microcircuit model. Specifically, a dynamic causal model of EEG data applied to spontaneous brain responses identifies a selective deficit in signalling at NMDA receptors in patients with NMDA receptor antibody encephalitis but not at other ionotropic receptors. Moreover, though these changes are observed across brain regions, these effects predominate at the NMDA receptors of excitatory neurons rather than at inhibitory interneurons. Given that EEG is a ubiquitously available clinical method, our findings suggest a unique re-purposing of EEG data as an assay of brain network dysfunction at the molecular level.

Classifying Different Emotional States by Means of EEG-Based Functional Connectivity Patterns

PubMed Central

Lee, You-Yun; Hsieh, Shulan

2014-01-01

This study aimed to classify different emotional states by means of EEG-based functional connectivity patterns. Forty young participants viewed film clips that evoked the following emotional states: neutral, positive, or negative. Three connectivity indices, including correlation, coherence, and phase synchronization, were used to estimate brain functional connectivity in EEG signals. Following each film clip, participants were asked to report on their subjective affect. The results indicated that the EEG-based functional connectivity change was significantly different among emotional states. Furthermore, the connectivity pattern was detected by pattern classification analysis using Quadratic Discriminant Analysis. The results indicated that the classification rate was better than chance. We conclude that estimating EEG-based functional connectivity provides a useful tool for studying the relationship between brain activity and emotional states. PMID:24743695

Creutzfeldt-Jakob Disease-Like Periodic Sharp Wave Complexes in Voltage-Gated Potassium Channel-Complex Antibodies Encephalitis: A Case Report.

PubMed

Savard, Martin; Irani, Sarosh R; Guillemette, Annie; Gosselin-Lefebvre, Stéphanie; Geschwind, Michael; Jansen, Gerard H; Gould, Peter V; Laforce, Robert

2016-02-01

Voltage-gated potassium channel-complex antibodies (VGKC-cAbs) encephalitis, a treatable autoantibody encephalopathy, has been previously reported to clinically mimic sporadic Creutzfeldt-Jakob disease. Among available clinical clues to distinguish them, periodic sharp wave complexes, a typical finding in sporadic Creutzfeldt-Jakob disease, have never been reported in association with VGKC-cAbs encephalitis. A 76-year-old man was transferred to a tertiary neurology center with a clinical history of 6-month weight loss, cognitive disturbance, and nonspecific generalized weakness. He had two seizures the month before transfer and then evolved to severe encephalopathy, requiring mechanical ventilation. Periodic sharp wave complexes every 1 to 2 seconds over slowed background were found on EEG, and MRI showed cerebellar and bifrontal cortical T2/FLAIR/DWI hypersignal without restricted diffusion on ADC mapping. Pancorporal positron emission tomography scan was negative. An immunotherapy trial did not improve the patient condition. Therefore, he died after life support withdrawal. Brain autopsy revealed mononuclear neocortex infiltrate without significant spongiosis, and the anti-VGKC test showed a seropositivity of 336 pmol/L (normal, 0-31), 3 month after the patient deceased. This is the first reported case of VGKC-cAbs encephalitis associated with periodic sharp wave complexes on EEG, which further confuse the differential diagnosis with sporadic Creutzfeldt-Jakob disease. However, the cortical DWI hypersignal without restriction seems to remain a way to discriminate these two entities appropriately, when present. These clues are of paramount importance because VGKC-cAbs encephalitis is a treatable disease.

Evaluation of blue light exposure to beta brainwaves on simulated night driving

NASA Astrophysics Data System (ADS)

Purawijaya, Dandri Aly; Fitri, Lulu Lusianti; Suprijanto

2015-09-01

Numbers of night driving accident in Indonesia since 2010 are exponentially rising each year with total of loss more than 50 billion rupiah. One of the causes that contribute to night driving accident is drowsiness. Drowsiness is affected by circadian rhythm resulted from the difference of blue light quality and quantity between night and day. Blue light may effect on human physiology through non-visual pathway by suppressing melatonin hormone suppression that influence drowsiness. Meanwhile, the production of hormones and other activities in brain generate bioelectrical activity such as brainwaves and can be recorded using Electroencephalograph (EEG). Therefore, this research objective is to evaluate the effect of blue light exposure to beta brainwave emergence during night driving simulation to a driver. This research was conducted to 4 male subjects who are able to drive and have a legitimate car driving license. The driving simulator was done using SCANIA Truck Driving Simulator on freeform driving mode in dark environment. Subjects drove for total 32 minutes. The data collections were taken in 2 days with 16 minutes for each day. The 16 minutes were divided again into 8 minutes adaptation in dark and 8 minutes for driving either in blue light exposure or in total darkness. While driving the simulation, subjects' brainwaves were recorded using EEG EMOTIV 14 Channels, exposed by LED monochromatic blue light with 160 Lux from source and angle 45o and sat 1 m in front of the screen. Channels used on this research were for visual (O1; O2), cognition (F3; F4; P7; P8), and motor (FC5; FC6). EEG brainwave result was filtered with EEGLab to obtain beta waves at 13 - 30 Hz frequencies. Results showed that beta waves response to blue light varied for each subject. Blue light exposure either increased or decreased beta waves in 2 minutes pattern and maintaining beta waves on cognition and motor area in 3 out of 4 subjects. Meanwhile, blue light exposure did not maintain and induce beta waves fluctuation on visual area of another 2 subjects. The conclusion of this research is that blue light exposure affected the pattern of beta waves on frontal, parietal, premotor cortex and visual lobes.

Bradycardia from flash stimulation.

PubMed

Einspenner, Michael; Brunet, Donald G; Boissé Lomax, Lysa; Spiller, Allison E

2015-12-01

This case study documents a patient who experienced bradycardia brought on by flash stimulation during a routine outpatient EEG recording. The patient had known photosensitive seizures in the past. During this routine EEG, the patient's heart rate dropped to about 12 beats per minute with the EEG displaying slow-delta-frequency waves with no epileptiform spikes or sharp waves. During immediate follow-up, in our emergency department, the patient had a brief asystolic event, followed by bradycardia. Cardiology examinations were normal. We propose that this response was a photic-triggered reflex vasovagal reaction.

Cerebral mechanisms underlying the effects of music during a fatiguing isometric ankle-dorsiflexion task.

PubMed

Bigliassi, Marcelo; Karageorghis, Costas I; Nowicky, Alexander V; Orgs, Guido; Wright, Michael J

2016-10-01

The brain mechanisms by which music-related interventions ameliorate fatigue-related symptoms during the execution of fatiguing motor tasks are hitherto under-researched. The objective of the present study was to investigate the effects of music on brain electrical activity and psychophysiological measures during the execution of an isometric fatiguing ankle-dorsiflexion task performed until the point of volitional exhaustion. Nineteen healthy participants performed two fatigue tests at 40% of maximal voluntary contraction while listening to music or in silence. Electrical activity in the brain was assessed by use of a 64-channel EEG. The results indicated that music downregulated theta waves in the frontal, central, and parietal regions of the brain during exercise. Music also induced a partial attentional switching from associative thoughts to task-unrelated factors (dissociative thoughts) during exercise, which led to improvements in task performance. Moreover, participants experienced a more positive affective state while performing the isometric task under the influence of music. © 2016 Society for Psychophysiological Research.

Functional brain networks in Alzheimer's disease: EEG analysis based on limited penetrable visibility graph and phase space method

NASA Astrophysics Data System (ADS)

Wang, Jiang; Yang, Chen; Wang, Ruofan; Yu, Haitao; Cao, Yibin; Liu, Jing

2016-10-01

In this paper, EEG series are applied to construct functional connections with the correlation between different regions in order to investigate the nonlinear characteristic and the cognitive function of the brain with Alzheimer's disease (AD). First, limited penetrable visibility graph (LPVG) and phase space method map single EEG series into networks, and investigate the underlying chaotic system dynamics of AD brain. Topological properties of the networks are extracted, such as average path length and clustering coefficient. It is found that the network topology of AD in several local brain regions are different from that of the control group with no statistically significant difference existing all over the brain. Furthermore, in order to detect the abnormality of AD brain as a whole, functional connections among different brain regions are reconstructed based on similarity of clustering coefficient sequence (CCSS) of EEG series in the four frequency bands (delta, theta, alpha, and beta), which exhibit obvious small-world properties. Graph analysis demonstrates that for both methodologies, the functional connections between regions of AD brain decrease, particularly in the alpha frequency band. AD causes the graph index complexity of the functional network decreased, the small-world properties weakened, and the vulnerability increased. The obtained results show that the brain functional network constructed by LPVG and phase space method might be more effective to distinguish AD from the normal control than the analysis of single series, which is helpful for revealing the underlying pathological mechanism of the disease.

Clinical review: Continuous and simplified electroencephalography to monitor brain recovery after cardiac arrest

PubMed Central

2013-01-01

There has been a dramatic change in hospital care of cardiac arrest survivors in recent years, including the use of target temperature management (hypothermia). Clinical signs of recovery or deterioration, which previously could be observed, are now concealed by sedation, analgesia, and muscle paralysis. Seizures are common after cardiac arrest, but few centers can offer high-quality electroencephalography (EEG) monitoring around the clock. This is due primarily to its complexity and lack of resources but also to uncertainty regarding the clinical value of monitoring EEG and of treating post-ischemic electrographic seizures. Thanks to technical advances in recent years, EEG monitoring has become more available. Large amounts of EEG data can be linked within a hospital or between neighboring hospitals for expert opinion. Continuous EEG (cEEG) monitoring provides dynamic information and can be used to assess the evolution of EEG patterns and to detect seizures. cEEG can be made more simple by reducing the number of electrodes and by adding trend analysis to the original EEG curves. In our version of simplified cEEG, we combine a reduced montage, displaying two channels of the original EEG, with amplitude-integrated EEG trend curves (aEEG). This is a convenient method to monitor cerebral function in comatose patients after cardiac arrest but has yet to be validated against the gold standard, a multichannel cEEG. We recently proposed a simplified system for interpreting EEG rhythms after cardiac arrest, defining four major EEG patterns. In this topical review, we will discuss cEEG to monitor brain function after cardiac arrest in general and how a simplified cEEG, with a reduced number of electrodes and trend analysis, may facilitate and improve care. PMID:23876221

Triheptanoin for glucose transporter type I deficiency (G1D): Modulation of human ictogenesis, cerebral metabolic rate and cognitive indices by a food supplement

PubMed Central

Pascual, Juan M.; Liu, Peiying; Mao, Deng; Kelly, Dorothy; Hernandez, Ana; Sheng, Min; Good, Levi B.; Ma, Qian; Marin-Valencia, Isaac; Zhang, Xuchen; Park, Jason Y.; Hynan, Linda S.; Stavinoha, Peter; Roe, Charles R.; Lu, Hanzhang

2015-01-01

Objective G1D is commonly associated with electrographic spike-wave and - less-noticeably – with absence seizures. The G1D syndrome has long been attributed to energy (i.e., ATP-synthetic) failure, as have experimental, toxic-rodent epilepsies to impaired brain metabolism and tricarboxylic acid (TCA) cycle intermediate depletion. Indeed, a (seldom-acknowledged) function of glucose and other substrates is the generation of brain TCAs via carbon-donor reactions collectively named anaplerosis. However, TCAs are preserved in murine G1D. This renders inferences about energy failure premature and suggests a different hypothesis, also grounded on our findings, that consumption of alternate TCA precursors is stimulated, potentially detracting from other functions. Second, common ketogenic diets can ameliorate G1D seizures, but lead to a therapeutically-counterintuitive reduction in blood glucose available to the brain, and they can prove ineffective in 1/3 of cases. While developing G1D treatments, all of this motivated us to: a) uphold (rather than attenuate) the residual brain glucose flux that all G1D patients possess; and b) stimulate the TCA cycle, including anaplerosis. Therefore, we tested the medium-chain triglyceride triheptanoin, a widely-used medical food supplement that can fulfill both of these metabolic roles. The rationale is that ketone bodies derived from ketogenic diets are not anaplerotic, in contrast with triheptanoin metabolites, as we have shown in the G1D mouse brain. Design We supplemented the regular diet of a case series of G1D patients with food-grade triheptanoin. First we confirmed that, despite their frequent electroencephalographic (EEG) presence as spike-waves, most seizures are rarely visible, such that perceptions by patients or others are inadequate for treatment evaluation. Thus, we used EEG, quantitative neuropsychological, blood analytical, and MRI cerebral metabolic rate measurements as main outcomes. Setting Academic and unsponsored. Patients Fourteen G1D children and adults not receiving a ketogenic diet. Results Eleven patients tolerated triheptanoin without significant adverse effects. Spike-waves decreased robustly in all patients who manifested them. Additionally, neuropsychological performance and cerebral metabolic rate increased in most patients. Conclusions Triheptanoin can favorably influence cardinal aspects of neural function in G1D. Additionally, our outcome measures offer a framework for the evaluation of therapies for G1D and other encephalopathies associated with impaired intermediary metabolism. PMID:25110966

The Track of Brain Activity during the Observation of TV Commercials with the High-Resolution EEG Technology

PubMed Central

Astolfi, Laura; Vecchiato, Giovanni; De Vico Fallani, Fabrizio; Salinari, Serenella; Cincotti, Febo; Aloise, Fabio; Mattia, Donatella; Marciani, Maria Grazia; Bianchi, Luigi; Soranzo, Ramon; Babiloni, Fabio

2009-01-01

We estimate cortical activity in normal subjects during the observation of TV commercials inserted within a movie by using high-resolution EEG techniques. The brain activity was evaluated in both time and frequency domains by solving the associate inverse problem of EEG with the use of realistic head models. In particular, we recover statistically significant information about cortical areas engaged by particular scenes inserted within the TV commercial proposed with respect to the brain activity estimated while watching a documentary. Results obtained in the population investigated suggest that the statistically significant brain activity during the observation of the TV commercial was mainly concentrated in frontoparietal cortical areas, roughly coincident with the Brodmann areas 8, 9, and 7, in the analyzed population. PMID:19584910

Analyze the dynamic features of rat EEG using wavelet entropy.

PubMed

Feng, Zhouyan; Chen, Hang

2005-01-01

Wavelet entropy (WE), a new method of complexity measure for non-stationary signals, was used to investigate the dynamic features of rat EEGs under three vigilance states. The EEGs of the freely moving rats were recorded with implanted electrodes and were decomposed into four components of delta, theta, alpha and beta by using multi-resolution wavelet transform. Then, the wavelet entropy curves were calculated as a function of time. The results showed that there were significant differences among the average WEs of EEGs recorded under the vigilance states of waking, slow wave sleep (SWS) and rapid eye movement (REM) sleep. The changes of WE had different relationships with the four power components under different states. Moreover, there was evident rhythm in EEG WEs of SWS sleep for most experimental rats, which indicated a reciprocal relationship between slow waves and sleep spindles in the micro-states of SWS sleep. Therefore, WE can be used not only to distinguish the long-term changes in EEG complexity, but also to reveal the short-term changes in EEG micro-state.

Time course of EEG slow-wave activity in pre-school children with sleep disordered breathing: a possible mechanism for daytime deficits?

PubMed

Biggs, Sarah N; Walter, Lisa M; Nisbet, Lauren C; Jackman, Angela R; Anderson, Vicki; Nixon, Gillian M; Davey, Margot J; Trinder, John; Hoffmann, Robert; Armitage, Roseanne; Horne, Rosemary S C

2012-09-01

Daytime deficits in children with sleep disordered breathing (SDB) are theorized to result from hypoxic insult to the developing brain or fragmented sleep. Yet, these do not explain why deficits occur in primary snorers (PS). The time course of slow wave EEG activity (SWA), a proxy of homeostatic regulation and cortical maturation, may provide insight. Clinical and control subjects (N=175: mean age 4.3±0.9 y: 61% male) participated in overnight polysomnography (PSG). Standard sleep scoring and power spectral analyses were conducted on EEG (C4/A1; 0.5-<3.9Hz). Univariate ANOVA's evaluated group differences in sleep stages and respiratory parameters. Repeated-measures ANCOVA evaluated group differences in the time course of SWA. Four groups were classified: controls (OAHI ≤ 1 event/h; no clinical history); PS (OAHI ≤ 1 event/h; clinical history); mild OSA (OAHI=1-5 events/h); and moderate to severe OSA (MS OSA: OAHI>5 events/h). Group differences were found in the percentage of time spent in NREM Stages 1 and 4 (p<0.001) and in the time course of SWA. PS and Mild OSA children had higher SWA in the first NREM period than controls (p<0.05). All SDB groups had higher SWA in the fourth NREM period (p<0.01). These results suggest enhanced sleep pressure but impaired restorative sleep function in pre-school children with SDB, providing new insights into the possible mechanism for daytime deficits observed in all severities of SDB. Copyright © 2012 Elsevier B.V. All rights reserved.

Neuroelectrical Decomposition of Spontaneous Brain Activity Measured with Functional Magnetic Resonance Imaging

PubMed Central

Liu, Zhongming; de Zwart, Jacco A.; Chang, Catie; Duan, Qi; van Gelderen, Peter; Duyn, Jeff H.

2014-01-01

Spontaneous activity in the human brain occurs in complex spatiotemporal patterns that may reflect functionally specialized neural networks. Here, we propose a subspace analysis method to elucidate large-scale networks by the joint analysis of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data. The new approach is based on the notion that the neuroelectrical activity underlying the fMRI signal may have EEG spectral features that report on regional neuronal dynamics and interregional interactions. Applying this approach to resting healthy adults, we indeed found characteristic spectral signatures in the EEG correlates of spontaneous fMRI signals at individual brain regions as well as the temporal synchronization among widely distributed regions. These spectral signatures not only allowed us to parcel the brain into clusters that resembled the brain's established functional subdivision, but also offered important clues for disentangling the involvement of individual regions in fMRI network activity. PMID:23796947

Analysis of bioelectric records and fabrication of phototype sleep analysis equipment

NASA Technical Reports Server (NTRS)

Kellaway, P.

1972-01-01

A computer-analysis technique was used to evaluate the changes in the waking EEGs of 5 normal subjects which occurred during the oral administration of flurazepam hydrochloride (Dalmane). While the subjects were receiving the drug, there was an increase in the amount of beta (14-38 c/sec) activity in fronto-central EEG leads in all 5 subjects. This increase in beta activity was characterized by a highly consistent increase in the number of waves that occurred during an EEG recording interval of fixed duration and by a less consistent increase in average wave amplitude. There was no detectable change in mean EEG wavelength (frequency) within the beta frequency range. The EEG patterns reverted to their baseline condition during 2-3 weeks after withdrawal of the drug. Analysis of the alpha, theta and delta components of the EEG indicated no changes during or following administration of the drug. This study clearly illustrates the usefulness of specific computer-analysis techniques in the characterization and quantification of sleep-promoting drugs upon the EEG of the normal young adults in the waking state. Two preamplifiers and 150 EEG monitoring caps with electrodes were delivered to MSC.

Brain-computer interfaces for EEG neurofeedback: peculiarities and solutions.

PubMed

Huster, René J; Mokom, Zacharais N; Enriquez-Geppert, Stefanie; Herrmann, Christoph S

2014-01-01

Neurofeedback training procedures designed to alter a person's brain activity have been in use for nearly four decades now and represent one of the earliest applications of brain-computer interfaces (BCI). The majority of studies using neurofeedback technology relies on recordings of the electroencephalogram (EEG) and applies neurofeedback in clinical contexts, exploring its potential as treatment for psychopathological syndromes. This clinical focus significantly affects the technology behind neurofeedback BCIs. For example, in contrast to other BCI applications, neurofeedback BCIs usually rely on EEG-derived features with only a minimum of additional processing steps being employed. Here, we highlight the peculiarities of EEG-based neurofeedback BCIs and consider their relevance for software implementations. Having reviewed already existing packages for the implementation of BCIs, we introduce our own solution which specifically considers the relevance of multi-subject handling for experimental and clinical trials, for example by implementing ready-to-use solutions for pseudo-/sham-neurofeedback. © 2013.

Comparison of Quantitative Characteristics of Early Post-resuscitation EEG Between Asphyxial and Ventricular Fibrillation Cardiac Arrest in Rats.

PubMed

Chen, Bihua; Chen, Gang; Dai, Chenxi; Wang, Pei; Zhang, Lei; Huang, Yuanyuan; Li, Yongqin

2018-04-01

Quantitative electroencephalogram (EEG) analysis has shown promising results in studying brain injury and functional recovery after cardiac arrest (CA). However, whether the quantitative characteristics of EEG, as potential indicators of neurological prognosis, are influenced by CA causes is unknown. The purpose of this study was designed to compare the quantitative characteristics of early post-resuscitation EEG between asphyxial CA (ACA) and ventricular fibrillation CA (VFCA) in rats. Thirty-two Sprague-Dawley rats of both sexes were randomized into either ACA or VFCA group. Cardiopulmonary resuscitation was initiated after 5-min untreated CA. Characteristics of early post-resuscitation EEG were compared, and the relationships between quantitative EEG features and neurological outcomes were investigated. Compared with VFCA, serum level of S100B, neurological deficit score and brain histopathologic damage score were dramatically higher in the ACA group. Quantitative measures of EEG, including onset time of EEG burst, time to normal trace, burst suppression ratio, and information quantity, were significantly lower for CA caused by asphyxia and correlated with the 96-h neurological outcome and survival. Characteristics of earlier post-resuscitation EEG differed between cardiac and respiratory causes. Quantitative measures of EEG not only predicted neurological outcome and survival, but also have the potential to stratify CA with different causes.

Human brain distinctiveness based on EEG spectral coherence connectivity.

PubMed

Rocca, D La; Campisi, P; Vegso, B; Cserti, P; Kozmann, G; Babiloni, F; Fallani, F De Vico

2014-09-01

The use of EEG biometrics, for the purpose of automatic people recognition, has received increasing attention in the recent years. Most of the current analyses rely on the extraction of features characterizing the activity of single brain regions, like power spectrum estimation, thus neglecting possible temporal dependencies between the generated EEG signals. However, important physiological information can be extracted from the way different brain regions are functionally coupled. In this study, we propose a novel approach that fuses spectral coherence-based connectivity between different brain regions as a possibly viable biometric feature. The proposed approach is tested on a large dataset of subjects (N = 108) during eyes-closed (EC) and eyes-open (EO) resting state conditions. The obtained recognition performance shows that using brain connectivity leads to higher distinctiveness with respect to power-spectrum measurements, in both the experimental conditions. Notably, a 100% recognition accuracy is obtained in EC and EO when integrating functional connectivity between regions in the frontal lobe, while a lower 97.5% is obtained in EC (96.26% in EO) when fusing power spectrum information from parieto-occipital (centro-parietal in EO) regions. Taken together, these results suggest that the functional connectivity patterns represent effective features for improving EEG-based biometric systems.

[The Influence of the Functioning of Brain Regulatory Systems onto the Voluntary Regulation of Cognitive Performance in Children. Report 2. Neuropsychological and Electrophysiological Assessment of Brain Regulatory Functions in Children Aged 10-12 with Learning Difficulties].

PubMed

Semenova, O A; Machinskaya, R I

2015-01-01

A total number of 172 children aged 10-12 were electrophysiologically and neuropsychologically assessed in order to analyze the influence of the functioning of brain regulatory systems onto the voluntary regulation of cognitive performance during the preteen years. EEG patterns associated with the nonoptimal functioning of brain regulatory systems, particularly fronto-thalamic, limbic and fronto-striatal structures were significantly more often observed in children with learning and behavioral difficulties, as compared to the control group. Neuropsychological assessment showed that the nonoptimal functioning of different brain regulatory systems specifically affect the voluntary regulation of cognitive performance. Children with EEG patterns of fronto-thalamic nonoptimal functioning demonstrated poor voluntary regulation such as impulsiveness and difficulties in continuing the same algorithms. Children with EEG patterns of limbic nonoptimal functioning showed a less pronounced executive dysfunction manifested only in poor switching between program units within a task. Children with EEG patterns of fronto-striatal nonoptimal functioning struggled with such executive dysfunctions as motor and tactile perseverations and emotional-motivational deviations such as poor motivation and communicative skills.

Phase Synchronization in Electroencephalographic Recordings Prognosticates Outcome in Paediatric Coma

PubMed Central

Nenadovic, Vera; Perez Velazquez, Jose Luis; Hutchison, James Saunders

2014-01-01

Brain injury from trauma, cardiac arrest or stroke is the most important cause of death and acquired disability in the paediatric population. Due to the lifetime impact of brain injury, there is a need for methods to stratify patient risk and ultimately predict outcome. Early prognosis is fundamental to the implementation of interventions to improve recovery, but no clinical model as yet exists. Healthy physiology is associated with a relative high variability of physiologic signals in organ systems. This was first evaluated in heart rate variability research. Brain variability can be quantified through electroencephalographic (EEG) phase synchrony. We hypothesised that variability in brain signals from EEG recordings would correlate with patient outcome after brain injury. Lower variability in EEG phase synchronization, would be associated with poor patient prognosis. A retrospective study, spanning 10 years (2000–2010) analysed the scalp EEGs of children aged 1 month to 17 years in coma (Glasgow Coma Scale, GCS, <8) admitted to the paediatric critical care unit (PCCU) following brain injury from TBI, cardiac arrest or stroke. Phase synchrony of the EEGs was evaluated using the Hilbert transform and the variability of the phase synchrony calculated. Outcome was evaluated using the 6 point Paediatric Performance Category Score (PCPC) based on chart review at the time of hospital discharge. Outcome was dichotomized to good outcome (PCPC score 1 to 3) and poor outcome (PCPC score 4 to 6). Children who had a poor outcome following brain injury secondary to cardiac arrest, TBI or stroke, had a higher magnitude of synchrony (R index), a lower spatial complexity of the synchrony patterns and a lower temporal variability of the synchrony index values at 15 Hz when compared to those patients with a good outcome. PMID:24752289

Scalp-recorded slow EEG responses generated in response to hemodynamic changes in the human brain.

PubMed

Vanhatalo, S; Tallgren, P; Becker, C; Holmes, M D; Miller, J W; Kaila, K; Voipio, J

2003-09-01

To study whether hemodynamic changes in human brain generate scalp-EEG responses. Direct current EEG (DC-EEG) was recorded from 12 subjects during 5 non-invasive manipulations that affect intracranial hemodynamics by different mechanisms: bilateral jugular vein compression (JVC), head-up tilt (HUT), head-down tilt (HDT), Valsalva maneuver (VM), and Mueller maneuver (MM). DC shifts were compared to changes in cerebral blood volume (CBV) measured by near-infrared spectroscopy (NIRS). DC shifts were observed during all manipulations with highest amplitudes (up to 250 microV) at the midline electrodes, and the most pronounced changes (up to 15 microV/cm) in the DC voltage gradient around vertex. In spite of inter-individual variation in both amplitude and polarity, the DC shifts were consistent and reproducible for each subject and they showed a clear temporal correlation with changes in CBV. Our results indicate that hemodynamic changes in human brain are associated with marked DC shifts that cannot be accounted for by intracortical neuronal or glial currents. Instead, the data are consistent with a non-neuronal generator mechanism that is associated with the blood-brain barrier. These findings have direct implications for mechanistic interpretation of slow EEG responses in various experimental paradigms.

[Sex differences of spatial-temporal organization of biopotentials of the brain in adults and child 5-6 years old].

PubMed

Panasevich, E A; Tsitseroshin, M N

2011-01-01

Research of topical features of spatial structure of EEG distant relationships has been performed with correlation and coherent analyses of EEG for 26 children of 5-6 years old (12 boys and 14 girls) in comparison to the data at 33 adult subjects (15 men and 18 women). Men have much higher level of EEG intrahemispherical relations of posttemporal and frontal regions of the left hemisphere whereas women have the higher level prevalence of interhemispheric interactions, especially of bilateral-symmetrical arials of both hemispheres. Preschoolers have another character of sex differences in the system organization of inter-regional interactions of brain biopotentials than adults. In particularly the girls have exceeding of EEG distant relations in the same zones of left hemispheres, where at men such relations have exceeding in comparison with woman. The obtained data shows that the pronounced sexual dimorphism of inter-regional interactions of cortical biopotentials at adults and at children is formed, first of all, owing to of EEG distant relations topology differing in males and females subject. Investigation sex differences of spatial-temporal organization of biopotentials of the brain in children can promote forming of more hole and deep understanding of role of sex factor in development of human brain system activity.

Brain Dynamics in Predicting Driving Fatigue Using a Recurrent Self-Evolving Fuzzy Neural Network.

PubMed

Liu, Yu-Ting; Lin, Yang-Yin; Wu, Shang-Lin; Chuang, Chun-Hsiang; Lin, Chin-Teng

2016-02-01

This paper proposes a generalized prediction system called a recurrent self-evolving fuzzy neural network (RSEFNN) that employs an on-line gradient descent learning rule to address the electroencephalography (EEG) regression problem in brain dynamics for driving fatigue. The cognitive states of drivers significantly affect driving safety; in particular, fatigue driving, or drowsy driving, endangers both the individual and the public. For this reason, the development of brain-computer interfaces (BCIs) that can identify drowsy driving states is a crucial and urgent topic of study. Many EEG-based BCIs have been developed as artificial auxiliary systems for use in various practical applications because of the benefits of measuring EEG signals. In the literature, the efficacy of EEG-based BCIs in recognition tasks has been limited by low resolutions. The system proposed in this paper represents the first attempt to use the recurrent fuzzy neural network (RFNN) architecture to increase adaptability in realistic EEG applications to overcome this bottleneck. This paper further analyzes brain dynamics in a simulated car driving task in a virtual-reality environment. The proposed RSEFNN model is evaluated using the generalized cross-subject approach, and the results indicate that the RSEFNN is superior to competing models regardless of the use of recurrent or nonrecurrent structures.

Analysis of connectivity in NeuCube spiking neural network models trained on EEG data for the understanding of functional changes in the brain: A case study on opiate dependence treatment.

PubMed

Capecci, Elisa; Kasabov, Nikola; Wang, Grace Y

2015-08-01

The paper presents a methodology for the analysis of functional changes in brain activity across different conditions and different groups of subjects. This analysis is based on the recently proposed NeuCube spiking neural network (SNN) framework and more specifically on the analysis of the connectivity of a NeuCube model trained with electroencephalography (EEG) data. The case study data used to illustrate this method is EEG data collected from three groups-subjects with opiate addiction, patients undertaking methadone maintenance treatment, and non-drug users/healthy control group. The proposed method classifies more accurately the EEG data than traditional statistical and artificial intelligence (AI) methods and can be used to predict response to treatment and dose-related drug effect. But more importantly, the method can be used to compare functional brain activities of different subjects and the changes of these activities as a result of treatment, which is a step towards a better understanding of both the EEG data and the brain processes that generated it. The method can also be used for a wide range of applications, such as a better understanding of disease progression or aging. Copyright © 2015 Elsevier Ltd. All rights reserved.

[Features of brain biopotentials' spatial organization in adolescents].

PubMed

Kruchinina, O V; Gal'perina, E I; Shepoval'nikov, A N

2014-01-01

Adolescence is characterized by an intensive formation of inter-regional cortical fields interaction, in this period significantly reorganized the activities of deep brain structures and cortical-subcortical interaction are enhanced. Our objectives were to evaluate the nature of changes in the spatial organization of brain bioelectric potentials with age and characteristics of such an organization in adolescents. For this purpose, EEG studies have been conducted in 230 subjects of both sexes aged 4 to 35 years. We estimated interdependent changes of biopotentials correlations fluctuations in 20-lead EEG, using the integral index Vol. Analyzed age-related changes of EEG correlations in rest condition and during verbal activity (Russian and English texts audition). Verbal tasks were sued in subjects over 8 years. It was found that the spatial synchronization of the EEG both in background and verbal activity increases with age, but after 20 years the rate of change is significantly reduced. In adolescence (12-17 years old), sex differences appear between the degree of EEG coherence processes occurring in the left and right hemispheres in subjects performing verbal tasks. In males 12 to 14 years nonlinear changes in overall correlation (indicators VOL) was observed, whereas in females of this age systemic reorganization of the brain interrelations occurs more smoothly, ahead of 1.5-2 years.

Epileptic encephalopathy with continuous spike-waves during sleep: the need for transition from childhood to adulthood medical care appears to be related to etiology.

PubMed

de Saint-Martin, Anne; Rudolf, Gabrielle; Seegmuller, Caroline; Valenti-Hirsch, Maria Paola; Hirsch, Edouard

2014-08-01

Epileptic encephalopathy with continuous diffuse spike-waves during slow-wave sleep (ECSWS) presents clinically with infrequent nocturnal focal seizures, atypical absences related to secondary bilateral synchrony, negative myoclonia, and atonic and rare generalized tonic-clonic seizures. The unique electroencephalography (EEG) pattern found in ECSWS consists of continuous, diffuse, bilateral spike-waves during slow-wave sleep. Despite the eventual disappearance of clinical seizures and EEG abnormalities by adolescence, the prognosis is guarded in most cases because of neuropsychological and behavioral deficits. ECSWS has a heterogeneous etiology (genetic, structural, and unknown). Because epilepsy and electroencephalography (EEG) abnormalities in epileptic encephalopathy with continuous diffuse spike-waves during slow-wave sleep (ECSWS) are self-limited and age related, the need for ongoing medical care and transition to adult care might be questioned. For adolescents in whom etiology remains unknown (possibly genetic) and who experience the disappearance of seizures and EEG abnormalities, there is rarely need for long-term neurologic follow-up, because often a relatively normal cognitive and social evolution follows. However, the majority of patients with structural and possibly "genetic syndromic" etiologies will have persistent cognitive deficits and will need suitable socioeducative care. Therefore, the transition process in ECSWS will depend mainly on etiology and its related features (epileptic active phase duration, and cognitive and behavioral evolution) and revolve around neuropsychological and social support rather than medical and pharmacologic follow-up. Wiley Periodicals, Inc. © 2014 International League Against Epilepsy.

[Changes in Spatial Organization of Cortical Rhythm Vibrations in Children uner the Influence of Music].

PubMed

Shepovalnikov, A N; Egorov, M V

2015-01-01

Changes is systemic brain activity under influence of classical music (minor and major music) were studied at two groups of healthy children aged 5-6 years (n = 53). In 25 of studied children the Luscher test showed increased level of anxiety which significantly decreased after music therapy sessions. Bioelectrical cortical activity registered from 20 unipolar leads was subjected to correlation, coherence and factor analysis. Also the dynamics of the power spectrum for each of the EEG was studied. According to EEG all children after listening to both minor and major tones showed reorganization of brain rhythm structure accompanied by a decrease in the level of coherence and correlation of EEG; also was found significant and almost universal decrease in the EEG power spectrum. Registered EEG changes under the influence of classical music seems to reflect a decrease in excess of "internal tension" and weakening degree of "stiffness" to ensure the activity of cerebral structures responsible for mechanisms of "basic integration" which maintain constant readiness of brain to rapid and complete inclusion in action.

Exploring differences between left and right hand motor imagery via spatio-temporal EEG microstate.

PubMed

Liu, Weifeng; Liu, Xiaoming; Dai, Ruomeng; Tang, Xiaoying

2017-12-01

EEG-based motor imagery is very useful in brain-computer interface. How to identify the imaging movement is still being researched. Electroencephalography (EEG) microstates reflect the spatial configuration of quasi-stable electrical potential topographies. Different microstates represent different brain functions. In this paper, microstate method was used to process the EEG-based motor imagery to obtain microstate. The single-trial EEG microstate sequences differences between two motor imagery tasks - imagination of left and right hand movement were investigated. The microstate parameters - duration, time coverage and occurrence per second as well as the transition probability of the microstate sequences were obtained with spatio-temporal microstate analysis. The results were shown significant differences (P < 0.05) with paired t-test between the two tasks. Then these microstate parameters were used as features and a linear support vector machine (SVM) was utilized to classify the two tasks with mean accuracy 89.17%, superior performance compared to the other methods. These indicate that the microstate can be a promising feature to improve the performance of the brain-computer interface classification.

A New Statistical Model of Electroencephalogram Noise Spectra for Real-Time Brain-Computer Interfaces.

PubMed

Paris, Alan; Atia, George K; Vosoughi, Azadeh; Berman, Stephen A

2017-08-01

A characteristic of neurological signal processing is high levels of noise from subcellular ion channels up to whole-brain processes. In this paper, we propose a new model of electroencephalogram (EEG) background periodograms, based on a family of functions which we call generalized van der Ziel-McWhorter (GVZM) power spectral densities (PSDs). To the best of our knowledge, the GVZM PSD function is the only EEG noise model that has relatively few parameters, matches recorded EEG PSD's with high accuracy from 0 to over 30 Hz, and has approximately 1/f θ behavior in the midfrequencies without infinities. We validate this model using three approaches. First, we show how GVZM PSDs can arise in a population of ion channels at maximum entropy equilibrium. Second, we present a class of mixed autoregressive models, which simulate brain background noise and whose periodograms are asymptotic to the GVZM PSD. Third, we present two real-time estimation algorithms for steady-state visual evoked potential (SSVEP) frequencies, and analyze their performance statistically. In pairwise comparisons, the GVZM-based algorithms showed statistically significant accuracy improvement over two well-known and widely used SSVEP estimators. The GVZM noise model can be a useful and reliable technique for EEG signal processing. Understanding EEG noise is essential for EEG-based neurology and applications such as real-time brain-computer interfaces, which must make accurate control decisions from very short data epochs. The GVZM approach represents a successful new paradigm for understanding and managing this neurological noise.

EEGNET: An Open Source Tool for Analyzing and Visualizing M/EEG Connectome.

PubMed

Hassan, Mahmoud; Shamas, Mohamad; Khalil, Mohamad; El Falou, Wassim; Wendling, Fabrice

2015-01-01

The brain is a large-scale complex network often referred to as the "connectome". Exploring the dynamic behavior of the connectome is a challenging issue as both excellent time and space resolution is required. In this context Magneto/Electroencephalography (M/EEG) are effective neuroimaging techniques allowing for analysis of the dynamics of functional brain networks at scalp level and/or at reconstructed sources. However, a tool that can cover all the processing steps of identifying brain networks from M/EEG data is still missing. In this paper, we report a novel software package, called EEGNET, running under MATLAB (Math works, inc), and allowing for analysis and visualization of functional brain networks from M/EEG recordings. EEGNET is developed to analyze networks either at the level of scalp electrodes or at the level of reconstructed cortical sources. It includes i) Basic steps in preprocessing M/EEG signals, ii) the solution of the inverse problem to localize / reconstruct the cortical sources, iii) the computation of functional connectivity among signals collected at surface electrodes or/and time courses of reconstructed sources and iv) the computation of the network measures based on graph theory analysis. EEGNET is the unique tool that combines the M/EEG functional connectivity analysis and the computation of network measures derived from the graph theory. The first version of EEGNET is easy to use, flexible and user friendly. EEGNET is an open source tool and can be freely downloaded from this webpage: https://sites.google.com/site/eegnetworks/.

EEGNET: An Open Source Tool for Analyzing and Visualizing M/EEG Connectome

PubMed Central

Hassan, Mahmoud; Shamas, Mohamad; Khalil, Mohamad; El Falou, Wassim; Wendling, Fabrice

2015-01-01

The brain is a large-scale complex network often referred to as the “connectome”. Exploring the dynamic behavior of the connectome is a challenging issue as both excellent time and space resolution is required. In this context Magneto/Electroencephalography (M/EEG) are effective neuroimaging techniques allowing for analysis of the dynamics of functional brain networks at scalp level and/or at reconstructed sources. However, a tool that can cover all the processing steps of identifying brain networks from M/EEG data is still missing. In this paper, we report a novel software package, called EEGNET, running under MATLAB (Math works, inc), and allowing for analysis and visualization of functional brain networks from M/EEG recordings. EEGNET is developed to analyze networks either at the level of scalp electrodes or at the level of reconstructed cortical sources. It includes i) Basic steps in preprocessing M/EEG signals, ii) the solution of the inverse problem to localize / reconstruct the cortical sources, iii) the computation of functional connectivity among signals collected at surface electrodes or/and time courses of reconstructed sources and iv) the computation of the network measures based on graph theory analysis. EEGNET is the unique tool that combines the M/EEG functional connectivity analysis and the computation of network measures derived from the graph theory. The first version of EEGNET is easy to use, flexible and user friendly. EEGNET is an open source tool and can be freely downloaded from this webpage: https://sites.google.com/site/eegnetworks/. PMID:26379232

Activation of the serotonergic system by pedaling exercise changes anterior cingulate cortex activity and improves negative emotion.

PubMed

Ohmatsu, Satoko; Nakano, Hideki; Tominaga, Takanori; Terakawa, Yuzo; Murata, Takaho; Morioka, Shu

2014-08-15

Pedaling exercise (PE) of moderate intensity has been shown to ease anxiety and discomfort; however, little is known of the changes that occur in brain activities and in the serotonergic (5-HT) system after PE. Therefore, this study was conducted for the following reasons: (1) to localize the changes in the brain activities induced by PE using a distributed source localization algorithm, (2) to examine the changes in frontal asymmetry, as used in the Davidson model, with electroencephalography (EEG) activity, and (3) to examine the effect of PE on the 5-HT system. A 32-channel EEG was used to record before and after PE. Profile of Mood States tests indicated that there was a significant decrease in tension-anxiety and a significant increase in vigor after PE. A standardized low-resolution brain electromagnetic tomography analysis showed a significant decrease in brain activities after PE in the alpha-2 band (10-12.5 Hz) in the anterior cingulate cortex (ACC). Moreover, a significant increase in frontal EEG asymmetry was observed after PE in the alpha-1 band (7.5-10 Hz). Urine 5-HT levels significantly increased after PE. Urine 5-HT levels positively correlated with the degree of frontal EEG asymmetry in the alpha-1 band and negatively correlated with brain activity in ACC. Our results suggested that PE activates the 5-HT system and consequently induces increases in frontal EEG asymmetry in the alpha-1 band and reductions of brain activity in the alpha-2 band in the ACC region. Copyright © 2014 Elsevier B.V. All rights reserved.

Characterization of dynamic changes of current source localization based on spatiotemporal fMRI constrained EEG source imaging

NASA Astrophysics Data System (ADS)

Nguyen, Thinh; Potter, Thomas; Grossman, Robert; Zhang, Yingchun

2018-06-01

Objective. Neuroimaging has been employed as a promising approach to advance our understanding of brain networks in both basic and clinical neuroscience. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) represent two neuroimaging modalities with complementary features; EEG has high temporal resolution and low spatial resolution while fMRI has high spatial resolution and low temporal resolution. Multimodal EEG inverse methods have attempted to capitalize on these properties but have been subjected to localization error. The dynamic brain transition network (DBTN) approach, a spatiotemporal fMRI constrained EEG source imaging method, has recently been developed to address these issues by solving the EEG inverse problem in a Bayesian framework, utilizing fMRI priors in a spatial and temporal variant manner. This paper presents a computer simulation study to provide a detailed characterization of the spatial and temporal accuracy of the DBTN method. Approach. Synthetic EEG data were generated in a series of computer simulations, designed to represent realistic and complex brain activity at superficial and deep sources with highly dynamical activity time-courses. The source reconstruction performance of the DBTN method was tested against the fMRI-constrained minimum norm estimates algorithm (fMRIMNE). The performances of the two inverse methods were evaluated both in terms of spatial and temporal accuracy. Main results. In comparison with the commonly used fMRIMNE method, results showed that the DBTN method produces results with increased spatial and temporal accuracy. The DBTN method also demonstrated the capability to reduce crosstalk in the reconstructed cortical time-course(s) induced by neighboring regions, mitigate depth bias and improve overall localization accuracy. Significance. The improved spatiotemporal accuracy of the reconstruction allows for an improved characterization of complex neural activity. This improvement can be extended to any subsequent brain connectivity analyses used to construct the associated dynamic brain networks.

Scale-free brain quartet: artistic filtering of multi-channel brainwave music.

PubMed

Wu, Dan; Li, Chaoyi; Yao, Dezhong

2013-01-01

To listen to the brain activities as a piece of music, we proposed the scale-free brainwave music (SFBM) technology, which translated scalp EEGs into music notes according to the power law of both EEG and music. In the present study, the methodology was extended for deriving a quartet from multi-channel EEGs with artistic beat and tonality filtering. EEG data from multiple electrodes were first translated into MIDI sequences by SFBM, respectively. Then, these sequences were processed by a beat filter which adjusted the duration of notes in terms of the characteristic frequency. And the sequences were further filtered from atonal to tonal according to a key defined by the analysis of the original music pieces. Resting EEGs with eyes closed and open of 40 subjects were utilized for music generation. The results revealed that the scale-free exponents of the music before and after filtering were different: the filtered music showed larger variety between the eyes-closed (EC) and eyes-open (EO) conditions, and the pitch scale exponents of the filtered music were closer to 1 and thus it was more approximate to the classical music. Furthermore, the tempo of the filtered music with eyes closed was significantly slower than that with eyes open. With the original materials obtained from multi-channel EEGs, and a little creative filtering following the composition process of a potential artist, the resulted brainwave quartet opened a new window to look into the brain in an audible musical way. In fact, as the artistic beat and tonal filters were derived from the brainwaves, the filtered music maintained the essential properties of the brain activities in a more musical style. It might harmonically distinguish the different states of the brain activities, and therefore it provided a method to analyze EEGs from a relaxed audio perspective.

Scale-Free Brain Quartet: Artistic Filtering of Multi-Channel Brainwave Music

PubMed Central

Wu, Dan; Li, Chaoyi; Yao, Dezhong

2013-01-01

To listen to the brain activities as a piece of music, we proposed the scale-free brainwave music (SFBM) technology, which translated scalp EEGs into music notes according to the power law of both EEG and music. In the present study, the methodology was extended for deriving a quartet from multi-channel EEGs with artistic beat and tonality filtering. EEG data from multiple electrodes were first translated into MIDI sequences by SFBM, respectively. Then, these sequences were processed by a beat filter which adjusted the duration of notes in terms of the characteristic frequency. And the sequences were further filtered from atonal to tonal according to a key defined by the analysis of the original music pieces. Resting EEGs with eyes closed and open of 40 subjects were utilized for music generation. The results revealed that the scale-free exponents of the music before and after filtering were different: the filtered music showed larger variety between the eyes-closed (EC) and eyes-open (EO) conditions, and the pitch scale exponents of the filtered music were closer to 1 and thus it was more approximate to the classical music. Furthermore, the tempo of the filtered music with eyes closed was significantly slower than that with eyes open. With the original materials obtained from multi-channel EEGs, and a little creative filtering following the composition process of a potential artist, the resulted brainwave quartet opened a new window to look into the brain in an audible musical way. In fact, as the artistic beat and tonal filters were derived from the brainwaves, the filtered music maintained the essential properties of the brain activities in a more musical style. It might harmonically distinguish the different states of the brain activities, and therefore it provided a method to analyze EEGs from a relaxed audio perspective. PMID:23717527

Prognostic value of EEG in different etiological types of coma.

PubMed

Khaburzania, M; Beridze, M

2013-06-01

Study aimed at evaluation of prognostic value of standard EEG in different etiology of coma and the influence of etiological factor on the EEG patterns and coma outcome. Totally 175 coma patients were investigated. Patients were evaluated by Glasgow Coma Scale (GCS), clinically and by 16 channel electroencephalography. Auditory evoked potentials studied by EEG -regime for evoked potentials in patients with vegetative state (VS). Patients divided in 8 groups according to coma etiology. All patients were studied for photoreaction, brainstem reflexes, localization of sound and pain, length of coma state and outcome. Brain injury visualized by conventional CT. Outcome defined as death, VS, recovery with disability and without disability. Disability was rated by Disability Rating Scale (DRS). Recovered patients assessed by Mini Mental State Examination (MMSE) scale. Statistics performed by SPSS-11.0. From 175 coma patients 55 patients died, 23 patients found in VS, 97 patients recovered with and without disability. In all etiological groups of coma the background EEG patterns were established. Correspondence analysis of all investigated factors revealed that sound localization had the significant association with EEG delta and theta rhythms and with recovery from coma state (Chi-sqr. =31.10493; p= 0.000001). Among 23 VS patients 9 patients had the signs of MCS and showed the long latency waves (p300) after binaural stimulation. The high amplitude theta frequencies in frontal and temporal lobes significantly correlated with prolongation of latency of cognitive evoked potentials (r=+0.47; p<0.01). Etiological factor had the significant effect on EEG patterns' association with coma outcome only in hemorrhagic and traumatic coma (chi-sqr.=12.95; p<0.005; chi-sqr.=7.92; p<0.03 respectively). Significant correlations established between the delta and theta EEG patterns and coma outcome. Low amplitude decreased power delta and theta frequencies correlated with SND in survived coma patients (r=+0.21; p<0.001; r=+0.27; p<0.001 respectively). Standard EEG is the useful tool for elucidation of coma patients with a high probability to recover as well as those patients, who are at high risk of SND in case of recovery from coma state.

Comparison between psycho-acoustics and physio-acoustic measurement to determine optimum reverberation time of pentatonic angklung music concert hall

NASA Astrophysics Data System (ADS)

Sudarsono, Anugrah S.; Merthayasa, I. G. N.; Suprijanto

2015-09-01

This research tried to compare psycho-acoustics and Physio-acoustic measurement to find the optimum reverberation time of soundfield from angklung music. Psycho-acoustic measurement was conducted using a paired comparison method and Physio-acoustic measurement was conducted with EEG Measurement on T3, T4, FP1, and FP2 measurement points. EEG measurement was conducted with 5 persons. Pentatonic angklung music was used as a stimulus with reverberation time variation. The variation was between 0.8 s - 1.6 s with 0.2 s step. EEG signal was analysed using a Power Spectral Density method on Alpha Wave, High Alpha Wave, and Theta Wave. Psycho-acoustic measurement on 50 persons showed that reverberation time preference of pentatonic angklung music was 1.2 second. The result was similar to Theta Wave measurement on FP2 measurement point. High Alpha wave on T4 measurement gave different results, but had similar patterns with psycho-acoustic measurement

EEG topography and tomography (LORETA) in the classification and evaluation of the pharmacodynamics of psychotropic drugs.

PubMed

Saletu, Bernd; Anderer, Peter; Saletu-Zyhlarz, Gerda M

2006-04-01

By multi-lead computer-assisted quantitative analyses of human scalp-recorded electroencephalogram (QEEG) in combination with certain statistical procedures (quantitative pharmaco-EEG) and mapping techniques (pharmaco-EEG mapping or topography), it is possible to classify psychotropic substances and objectively evaluate their bioavailability at the target organ, the human brain. Specifically, one may determine at an early stage of drug development whether a drug is effective on the central nervous system (CNS) compared with placebo, what its clinical efficacy will be like, at which dosage it acts, when it acts and the equipotent dosages of different galenic formulations. Pharmaco-EEG maps of neuroleptics, antidepressants, tranquilizers, hypnotics, psychostimulants and nootropics/cognition-enhancing drugs will be described. Methodological problems, as well as the relationships between acute and chronic drug effects, alterations in normal subjects and patients, CNS effects and therapeutic efficacy will be discussed. Imaging of drug effects on the regional brain electrical activity of healthy subjects by means of EEG tomography such as low-resolution electromagnetic tomography (LORETA) has been used for identifying brain areas predominantly involved in psychopharmacological action. This will be shown for the representative drugs of the four main psychopharmacological classes, such as 3 mg haloperidol for neuroleptics, 20 mg citalopram for antidepressants, 2 mg lorazepam for tranquilizers and 20 mg methylphenidate for psychostimulants. LORETA demonstrates that these psychopharmacological classes affect brain structures differently. By considering these differences between psychotropic drugs and placebo in normal subjects, as well as between mental disorder patients and normal controls, it may be possible to choose the optimum drug for a specific patient according to a key-lock principle, since the drug should normalize the deviant brain function. Thus, pharmaco-EEG topography and tomography are valuable methods in human neuropsychopharmacology, clinical psychiatry and neurology.

Evaluation of an automated spike-and-wave complex detection algorithm in the EEG from a rat model of absence epilepsy.

PubMed

Bauquier, Sebastien H; Lai, Alan; Jiang, Jonathan L; Sui, Yi; Cook, Mark J

2015-10-01

The aim of this prospective blinded study was to evaluate an automated algorithm for spike-and-wave discharge (SWD) detection applied to EEGs from genetic absence epilepsy rats from Strasbourg (GAERS). Five GAERS underwent four sessions of 20-min EEG recording. Each EEG was manually analyzed for SWDs longer than one second by two investigators and automatically using an algorithm developed in MATLAB®. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for the manual (reference) versus the automatic (test) methods. The results showed that the algorithm had specificity, sensitivity, PPV and NPV >94%, comparable to published methods that are based on analyzing EEG changes in the frequency domain. This provides a good alternative as a method designed to mimic human manual marking in the time domain.

Relationship between brain function (aEEG) and brain structure (MRI) and their predictive value for neurodevelopmental outcome of preterm infants.

PubMed

Hüning, Britta; Storbeck, Tobias; Bruns, Nora; Dransfeld, Frauke; Hobrecht, Julia; Karpienski, Julia; Sirin, Selma; Schweiger, Bernd; Weiss, Christel; Felderhoff-Müser, Ursula; Müller, Hanna

2018-05-22

To improve the prediction of neurodevelopmental outcome in very preterm infants, this study used the combination of amplitude-integrated electroencephalography (aEEG) within the first 72 h of life and cranial magnetic resonance imaging (MRI) at term equivalent age. A single-center cohort of 38 infants born before 32 weeks of gestation was subjected to both investigations. Structural measurements were performed on MRI. Multiple regression analysis was used to identify independent factors including functional and structural brain measurements associated with outcome at a corrected age of 24 months. aEEG parameters significantly correlated with MRI measurements. Reduced deep gray matter volume was associated with low Burdjalov Score on day 3 (p < 0.0001) and day 1-3 (p = 0.0012). The biparietal width and the transcerebellar diameter were related to Burdjalov Score on day 1 (p = 0.0111; p = 0.0002). The final multiple regression analysis revealed independent predictors of neurodevelopmental outcome: intraventricular hemorrhage (p = 0.0060) and interhemispheric distance (p = 0.0052) for mental developmental index; Burdjalov Score day 1 (p = 0.0201) and interhemispheric distance (p = 0.0142) for psychomotor developmental index. Functional aEEG parameters were associated with altered brain maturation on MRI. The combination of aEEG and MRI contributes to the prediction of outcome at 24 months. What is Known: • Prematurity remains a risk factor for impaired neurodevelopment. • aEEG is used to measure brain activity in preterm infants and cranial MRI is performed to identify structural gray and white matter abnormalities with impact on neurodevelopmental outcome. What is New: • aEEG parameters observed within the first 72 h of life were associated with altered deep gray matter volumes, biparietal width, and transcerebellar diameter at term equivalent age. • The combination of aEEG and MRI contributes to the prediction of neurodevelopmental outcome at 2 years of corrected age in very preterm infants.

Mobile Phone Chips Reduce Increases in EEG Brain Activity Induced by Mobile Phone-Emitted Electromagnetic Fields.

PubMed

Henz, Diana; Schöllhorn, Wolfgang I; Poeggeler, Burkhard

2018-01-01

Recent neurophysiological studies indicate that exposure to electromagnetic fields (EMFs) generated by mobile phone radiation can exert effects on brain activity. One technical solution to reduce effects of EMFs in mobile phone use is provided in mobile phone chips that are applied to mobile phones or attached to their surfaces. To date, there are no systematical studies on the effects of mobile phone chip application on brain activity and the underlying neural mechanisms. The present study investigated whether mobile phone chips that are applied to mobile phones reduce effects of EMFs emitted by mobile phone radiation on electroencephalographic (EEG) brain activity in a laboratory study. Thirty participants volunteered in the present study. Experimental conditions (mobile phone chip, placebo chip, no chip) were set up in a randomized within-subjects design. Spontaneous EEG was recorded before and after mobile phone exposure for two 2-min sequences at resting conditions. During mobile phone exposure, spontaneous EEG was recorded for 30 min during resting conditions, and 5 min during performance of an attention test (d2-R). Results showed increased activity in the theta, alpha, beta and gamma bands during EMF exposure in the placebo and no chip conditions. Application of the mobile phone chip reduced effects of EMFs on EEG brain activity and attentional performance significantly. Attentional performance level was maintained regarding number of edited characters. Further, a dipole analysis revealed different underlying activation patterns in the chip condition compared to the placebo chip and no chip conditions. Finally, a correlational analysis for the EEG frequency bands and electromagnetic high-frequency (HF) emission showed significant correlations in the placebo chip and no chip condition for the theta, alpha, beta, and gamma bands. In the chip condition, a significant correlation of HF with the theta and alpha bands, but not with the beta and gamma bands was shown. We hypothesize that a reduction of EEG beta and gamma activation constitutes the key neural mechanism in mobile phone chip use that supports the brain to a degree in maintaining its natural activity and performance level during mobile phone use.

Mobile Phone Chips Reduce Increases in EEG Brain Activity Induced by Mobile Phone-Emitted Electromagnetic Fields

PubMed Central

Henz, Diana; Schöllhorn, Wolfgang I.; Poeggeler, Burkhard

2018-01-01

Recent neurophysiological studies indicate that exposure to electromagnetic fields (EMFs) generated by mobile phone radiation can exert effects on brain activity. One technical solution to reduce effects of EMFs in mobile phone use is provided in mobile phone chips that are applied to mobile phones or attached to their surfaces. To date, there are no systematical studies on the effects of mobile phone chip application on brain activity and the underlying neural mechanisms. The present study investigated whether mobile phone chips that are applied to mobile phones reduce effects of EMFs emitted by mobile phone radiation on electroencephalographic (EEG) brain activity in a laboratory study. Thirty participants volunteered in the present study. Experimental conditions (mobile phone chip, placebo chip, no chip) were set up in a randomized within-subjects design. Spontaneous EEG was recorded before and after mobile phone exposure for two 2-min sequences at resting conditions. During mobile phone exposure, spontaneous EEG was recorded for 30 min during resting conditions, and 5 min during performance of an attention test (d2-R). Results showed increased activity in the theta, alpha, beta and gamma bands during EMF exposure in the placebo and no chip conditions. Application of the mobile phone chip reduced effects of EMFs on EEG brain activity and attentional performance significantly. Attentional performance level was maintained regarding number of edited characters. Further, a dipole analysis revealed different underlying activation patterns in the chip condition compared to the placebo chip and no chip conditions. Finally, a correlational analysis for the EEG frequency bands and electromagnetic high-frequency (HF) emission showed significant correlations in the placebo chip and no chip condition for the theta, alpha, beta, and gamma bands. In the chip condition, a significant correlation of HF with the theta and alpha bands, but not with the beta and gamma bands was shown. We hypothesize that a reduction of EEG beta and gamma activation constitutes the key neural mechanism in mobile phone chip use that supports the brain to a degree in maintaining its natural activity and performance level during mobile phone use. PMID:29670503

Estimation of effective brain connectivity with dual Kalman filter and EEG source localization methods.

PubMed

Rajabioun, Mehdi; Nasrabadi, Ali Motie; Shamsollahi, Mohammad Bagher

2017-09-01

Effective connectivity is one of the most important considerations in brain functional mapping via EEG. It demonstrates the effects of a particular active brain region on others. In this paper, a new method is proposed which is based on dual Kalman filter. In this method, firstly by using a brain active localization method (standardized low resolution brain electromagnetic tomography) and applying it to EEG signal, active regions are extracted, and appropriate time model (multivariate autoregressive model) is fitted to extracted brain active sources for evaluating the activity and time dependence between sources. Then, dual Kalman filter is used to estimate model parameters or effective connectivity between active regions. The advantage of this method is the estimation of different brain parts activity simultaneously with the calculation of effective connectivity between active regions. By combining dual Kalman filter with brain source localization methods, in addition to the connectivity estimation between parts, source activity is updated during the time. The proposed method performance has been evaluated firstly by applying it to simulated EEG signals with interacting connectivity simulation between active parts. Noisy simulated signals with different signal to noise ratios are used for evaluating method sensitivity to noise and comparing proposed method performance with other methods. Then the method is applied to real signals and the estimation error during a sweeping window is calculated. By comparing proposed method results in different simulation (simulated and real signals), proposed method gives acceptable results with least mean square error in noisy or real conditions.

Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion.

PubMed

Zafar, Raheel; Dass, Sarat C; Malik, Aamir Saeed

2017-01-01

Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain-computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method.

Study of heart-brain interactions through EEG, ECG, and emotions

NASA Astrophysics Data System (ADS)

Ramasamy, Mouli; Varadan, Vijay K.

2017-04-01

Neurocardiology is the exploration of neurophysiological, neurological and neuroanatomical facets of neuroscience's influence in cardiology. The paraphernalia of emotions on the heart and brain are premeditated because of the interaction between the central and peripheral nervous system. This is an investigative attempt to study emotion based neurocardiology and the factors that influence this phenomenon. The factors include: interaction between sleep EEG (electroencephalogram) and ECG (electrocardiogram), relationship between emotion and music, psychophysiological coherence between the heart and brain, emotion recognition techniques, and biofeedback mechanisms. Emotions contribute vitally to the mundane life and are quintessential to a numerous biological and everyday-functional modality of a human being. Emotions are best represented through EEG signals, and to a certain extent, can be observed through ECG and body temperature. Confluence of medical and engineering science has enabled the monitoring and discrimination of emotions influenced by happiness, anxiety, distress, excitement and several other factors that influence the thinking patterns and the electrical activity of the brain. Similarly, HRV (Heart Rate Variability) widely investigated for its provision and discerning characteristics towards EEG and the perception in neurocardiology.

Brain-heart linear and nonlinear dynamics during visual emotional elicitation in healthy subjects.

PubMed

Valenza, G; Greco, A; Gentili, C; Lanata, A; Toschi, N; Barbieri, R; Sebastiani, L; Menicucci, D; Gemignani, A; Scilingo, E P

2016-08-01

This study investigates brain-heart dynamics during visual emotional elicitation in healthy subjects through linear and nonlinear coupling measures of EEG spectrogram and instantaneous heart rate estimates. To this extent, affective pictures including different combinations of arousal and valence levels, gathered from the International Affective Picture System, were administered to twenty-two healthy subjects. Time-varying maps of cortical activation were obtained through EEG spectral analysis, whereas the associated instantaneous heartbeat dynamics was estimated using inhomogeneous point-process linear models. Brain-Heart linear and nonlinear coupling was estimated through the Maximal Information Coefficient (MIC), considering EEG time-varying spectra and point-process estimates defined in the time and frequency domains. As a proof of concept, we here show preliminary results considering EEG oscillations in the θ band (4-8 Hz). This band, indeed, is known in the literature to be involved in emotional processes. MIC highlighted significant arousal-dependent changes, mediated by the prefrontal cortex interplay especially occurring at intermediate arousing levels. Furthermore, lower and higher arousing elicitations were associated to not significant brain-heart coupling changes in response to pleasant/unpleasant elicitations.

Experienced Mindfulness Meditators Exhibit Higher Parietal-Occipital EEG Gamma Activity during NREM Sleep

PubMed Central

Ferrarelli, Fabio; Smith, Richard; Dentico, Daniela; Riedner, Brady A.; Zennig, Corinna; Benca, Ruth M.; Lutz, Antoine; Davidson, Richard J.; Tononi, Giulio

2013-01-01

Over the past several years meditation practice has gained increasing attention as a non-pharmacological intervention to provide health related benefits, from promoting general wellness to alleviating the symptoms of a variety of medical conditions. However, the effects of meditation training on brain activity still need to be fully characterized. Sleep provides a unique approach to explore the meditation-related plastic changes in brain function. In this study we performed sleep high-density electroencephalographic (hdEEG) recordings in long-term meditators (LTM) of Buddhist meditation practices (approximately 8700 mean hours of life practice) and meditation naive individuals. We found that LTM had increased parietal-occipital EEG gamma power during NREM sleep. This increase was specific for the gamma range (25–40 Hz), was not related to the level of spontaneous arousal during NREM and was positively correlated with the length of lifetime daily meditation practice. Altogether, these findings indicate that meditation practice produces measurable changes in spontaneous brain activity, and suggest that EEG gamma activity during sleep represents a sensitive measure of the long-lasting, plastic effects of meditative training on brain function. PMID:24015304

Real-time EEG-based detection of fatigue driving danger for accident prediction.

PubMed

Wang, Hong; Zhang, Chi; Shi, Tianwei; Wang, Fuwang; Ma, Shujun

2015-03-01

This paper proposes a real-time electroencephalogram (EEG)-based detection method of the potential danger during fatigue driving. To determine driver fatigue in real time, wavelet entropy with a sliding window and pulse coupled neural network (PCNN) were used to process the EEG signals in the visual area (the main information input route). To detect the fatigue danger, the neural mechanism of driver fatigue was analyzed. The functional brain networks were employed to track the fatigue impact on processing capacity of brain. The results show the overall functional connectivity of the subjects is weakened after long time driving tasks. The regularity is summarized as the fatigue convergence phenomenon. Based on the fatigue convergence phenomenon, we combined both the input and global synchronizations of brain together to calculate the residual amount of the information processing capacity of brain to obtain the dangerous points in real time. Finally, the danger detection system of the driver fatigue based on the neural mechanism was validated using accident EEG. The time distributions of the output danger points of the system have a good agreement with those of the real accident points.

Neurofeedback training aimed to improve focused attention and alertness in children with ADHD: a study of relative power of EEG rhythms using custom-made software application.

PubMed

Hillard, Brent; El-Baz, Ayman S; Sears, Lonnie; Tasman, Allan; Sokhadze, Estate M

2013-07-01

Neurofeedback is a nonpharmacological treatment for attention-deficit hyperactivity disorder (ADHD). We propose that operant conditioning of electroencephalogram (EEG) in neurofeedback training aimed to mitigate inattention and low arousal in ADHD, will be accompanied by changes in EEG bands' relative power. Patients were 18 children diagnosed with ADHD. The neurofeedback protocol ("Focus/Alertness" by Peak Achievement Trainer) has a focused attention and alertness training mode. The neurofeedback protocol provides one for Focus and one for Alertness. This does not allow for collecting information regarding changes in specific EEG bands (delta, theta, alpha, low and high beta, and gamma) power within the 2 to 45 Hz range. Quantitative EEG analysis was completed on each of twelve 25-minute-long sessions using a custom-made MatLab application to determine the relative power of each of the aforementioned EEG bands throughout each session, and from the first session to the last session. Additional statistical analysis determined significant changes in relative power within sessions (from minute 1 to minute 25) and between sessions (from session 1 to session 12). Analysis was of relative power of theta, alpha, low and high beta, theta/alpha, theta/beta, and theta/low beta and theta/high beta ratios. Additional secondary measures of patients' post-neurofeedback outcomes were assessed, using an audiovisual selective attention test (IVA + Plus) and behavioral evaluation scores from the Aberrant Behavior Checklist. Analysis of data computed in the MatLab application, determined that theta/low beta and theta/alpha ratios decreased significantly from session 1 to session 12, and from minute 1 to minute 25 within sessions. The findings regarding EEG changes resulting from brain wave self-regulation training, along with behavioral evaluations, will help elucidate neural mechanisms of neurofeedback aimed to improve focused attention and alertness in ADHD.

A Within-subjects Experimental Protocol to Assess the Effects of Social Input on Infant EEG.

PubMed

St John, Ashley M; Kao, Katie; Chita-Tegmark, Meia; Liederman, Jacqueline; Grieve, Philip G; Tarullo, Amanda R

2017-05-03

Despite the importance of social interactions for infant brain development, little research has assessed functional neural activation while infants socially interact. Electroencephalography (EEG) power is an advantageous technique to assess infant functional neural activation. However, many studies record infant EEG only during one baseline condition. This protocol describes a paradigm that is designed to comprehensively assess infant EEG activity in both social and nonsocial contexts as well as tease apart how different types of social inputs differentially relate to infant EEG. The within-subjects paradigm includes four controlled conditions. In the nonsocial condition, infants view objects on computer screens. The joint attention condition involves an experimenter directing the infant's attention to pictures. The joint attention condition includes three types of social input: language, face-to-face interaction, and the presence of joint attention. Differences in infant EEG between the nonsocial and joint attention conditions could be due to any of these three types of input. Therefore, two additional conditions (one with language input while the experimenter is hidden behind a screen and one with face-to-face interaction) were included to assess the driving contextual factors in patterns of infant neural activation. Representative results demonstrate that infant EEG power varied by condition, both overall and differentially by brain region, supporting the functional nature of infant EEG power. This technique is advantageous in that it includes conditions that are clearly social or nonsocial and allows for examination of how specific types of social input relate to EEG power. This paradigm can be used to assess how individual differences in age, affect, socioeconomic status, and parent-infant interaction quality relate to the development of the social brain. Based on the demonstrated functional nature of infant EEG power, future studies should consider the role of EEG recording context and design conditions that are clearly social or nonsocial.

Insights into sleep's role for insight: Studies with the number reduction task

PubMed Central

Verleger, Rolf; Rose, Michael; Wagner, Ullrich; Yordanova, Juliana; Kolev, Vasil

2013-01-01

In recent years, vibrant research has developed on “consolidation” during sleep: To what extent are newly experienced impressions reprocessed or even restructured during sleep? We used the number reduction task (NRT) to study if and how sleep does not only reiterate new experiences but may even lead to new insights. In the NRT, covert regularities may speed responses. This implicit acquisition of regularities may become explicitly conscious at some point, leading to a qualitative change in behavior which reflects this insight. By applying the NRT at two consecutive sessions separated by an interval, we investigated the role of sleep in this interval for attaining insight at the second session. In the first study, a night of sleep was shown to triple the number of participants attaining insight above the base rate of about 20%. In the second study, this hard core of 20% discoverers differed from other participants in their task-related EEG potentials from the very beginning already. In the third study, the additional role of sleep was specified as an effect of the deep-sleep phase of slow-wave sleep on participants who had implicitly acquired the covert regularity before sleep. It was in these participants that a specific increase of EEG during slow-wave sleep in the 10-12 Hz band was obtained. These results support the view that neuronal memory reprocessing during slow-wave sleep restructures task-related representations in the brain, and that such restructuring promotes the gain of explicit knowledge. PMID:24605175

Coherence and phase synchrony analyses of EEG signals in Mild Cognitive Impairment (MCI): A study of functional brain connectivity

NASA Astrophysics Data System (ADS)

Handayani, Nita; Haryanto, Freddy; Khotimah, Siti Nurul; Arif, Idam; Taruno, Warsito Purwo

2018-03-01

This paper presents an EEG study for coherence and phase synchrony in mild cognitive impairment (MCI) subjects. MCI is characterized by cognitive decline, which is an early stage of Alzheimer's disease (AD). AD is a neurodegenerative disorder with symptoms such as memory loss and cognitive impairment. EEG coherence is a statistical measure of correlation between signals from electrodes spatially separated on the scalp. The magnitude of phase synchrony is expressed in the phase locking value (PLV), a statistical measure of neuronal connectivity in the human brain. Brain signals were recorded using an Emotiv Epoc 14-channel wireless EEG at a sampling frequency of 128 Hz. In this study, we used 22 elderly subjects consisted of 10 MCI subjects and 12 healthy subjects as control group. The coherence between each electrode pair was measured for all frequency bands (delta, theta, alpha and beta). In the MCI subjects, the value of coherence and phase synchrony was generally lower than in the healthy subjects especially in the beta frequency. A decline of intrahemisphere coherence in the MCI subjects occurred in the left temporo-parietal-occipital region. The pattern of decline in MCI coherence is associated with decreased cholinergic connectivity along the path that connects the temporal, occipital, and parietal areas of the brain to the frontal area of the brain. EEG coherence and phase synchrony are able to distinguish persons who suffer AD in the early stages from healthy elderly subjects.

Brain Oscillations in Sport: Toward EEG Biomarkers of Performance.

PubMed

Cheron, Guy; Petit, Géraldine; Cheron, Julian; Leroy, Axelle; Cebolla, Anita; Cevallos, Carlos; Petieau, Mathieu; Hoellinger, Thomas; Zarka, David; Clarinval, Anne-Marie; Dan, Bernard

2016-01-01

Brain dynamics is at the basis of top performance accomplishment in sports. The search for neural biomarkers of performance remains a challenge in movement science and sport psychology. The non-invasive nature of high-density electroencephalography (EEG) recording has made it a most promising avenue for providing quantitative feedback to practitioners and coaches. Here, we review the current relevance of the main types of EEG oscillations in order to trace a perspective for future practical applications of EEG and event-related potentials (ERP) in sport. In this context, the hypotheses of unified brain rhythms and continuity between wake and sleep states should provide a functional template for EEG biomarkers in sport. The oscillations in the thalamo-cortical and hippocampal circuitry including the physiology of the place cells and the grid cells provide a frame of reference for the analysis of delta, theta, beta, alpha (incl.mu), and gamma oscillations recorded in the space field of human performance. Based on recent neuronal models facilitating the distinction between the different dynamic regimes (selective gating and binding) in these different oscillations we suggest an integrated approach articulating together the classical biomechanical factors (3D movements and EMG) and the high-density EEG and ERP signals to allow finer mathematical analysis to optimize sport performance, such as microstates, coherency/directionality analysis and neural generators.

Brain Oscillations in Sport: Toward EEG Biomarkers of Performance

PubMed Central

Cheron, Guy; Petit, Géraldine; Cheron, Julian; Leroy, Axelle; Cebolla, Anita; Cevallos, Carlos; Petieau, Mathieu; Hoellinger, Thomas; Zarka, David; Clarinval, Anne-Marie; Dan, Bernard

2016-01-01

Brain dynamics is at the basis of top performance accomplishment in sports. The search for neural biomarkers of performance remains a challenge in movement science and sport psychology. The non-invasive nature of high-density electroencephalography (EEG) recording has made it a most promising avenue for providing quantitative feedback to practitioners and coaches. Here, we review the current relevance of the main types of EEG oscillations in order to trace a perspective for future practical applications of EEG and event-related potentials (ERP) in sport. In this context, the hypotheses of unified brain rhythms and continuity between wake and sleep states should provide a functional template for EEG biomarkers in sport. The oscillations in the thalamo-cortical and hippocampal circuitry including the physiology of the place cells and the grid cells provide a frame of reference for the analysis of delta, theta, beta, alpha (incl.mu), and gamma oscillations recorded in the space field of human performance. Based on recent neuronal models facilitating the distinction between the different dynamic regimes (selective gating and binding) in these different oscillations we suggest an integrated approach articulating together the classical biomechanical factors (3D movements and EMG) and the high-density EEG and ERP signals to allow finer mathematical analysis to optimize sport performance, such as microstates, coherency/directionality analysis and neural generators. PMID:26955362

Variable outcome for epilepsy after neonatal hypoglycaemia.

PubMed

Fong, Choong Yi; Harvey, A Simon

2014-11-01

To evaluate the electroclinical features of epilepsy secondary to neonatal hypoglycaemia. This was a retrospective study of children who had seizures beyond infancy after neonatal hypoglycaemia treated at The Royal Children's Hospital, Melbourne between 1996 and 2012. Patients with perinatal asphyxia were excluded. Clinical details were obtained from medical records. Digital electroencephalography (EEG) and brain magnetic resonance imaging (MRI) were reviewed. Eleven patients met the inclusion criteria (six males, five females; mean age 10y 5mo, range 4-18y at the time of review). Age at seizure onset ranged from 4 months to 5 years. Seizures were focal occipital in nine and generalized tonic in two patients. MRI showed gliosis with or without cortical atrophy in the occipital lobe with or without parietal lobe in all. Predominant EEG findings were stereotyped occipital sharp-slow discharges in five, polymorphic occipital spike-wave or paroxysmal fast activity in three, and generalized slow spike-wave and fast activity in two. Seizures were infrequent or remitted in six of the nine children with focal occipital seizures, and frequent and refractory in both children with generalized seizures. Despite the common antecedent and bilateral occipital lobe injury, the seizure manifestations and course of epilepsy after neonatal hypoglycaemia were variable, with mild occipital, refractory occipital, and symptomatic generalized epilepsy recognized. © 2014 Mac Keith Press.

The impact of blackcurrant juice on attention, mood and brain wave spectral activity in young healthy volunteers.

PubMed

Watson, A W; Okello, E J; Brooker, H J; Lester, S; McDougall, G J; Wesnes, K A

2018-01-17

There is a growing body of evidence from randomized controlled trials which indicates that consumption of berries has a positive effect upon the cognitive function of healthy adults. It has been recommended that studies combining cognitive and physiological measures be undertaken in order to strengthen the evidence base for the putative effects of flavonoid consumption on cognitive outcomes. This pilot study utilized a randomized, double-blind and placebo controlled crossover design to assess the influence of the acute administration of anthocyanin-rich blackcurrant juice, standardized at 500 mg of polyphenols, on mood and attention. Additionally, this trial used electroencephalography (EEG) to assess if any changes in cognitive performance are associated with changes in localized prefrontal cortex neuronal activity in nine healthy young adults. Outcomes from the pilot EEG data highlight an anxiolytic effect of the consumption of a single serve blackcurrant juice, as indexed by a suppression of α spectral power, and an increase in the slow wave δ and θ spectral powers. There was also an indication of greater alertness and lower fatigue, as indexed by an increase in β power and suppression of α spectral power. Outcomes from the CogTrack™ system indicated a small acute increase in reaction times during the digit vigilance task.

Different Effects of Sleep Deprivation and Torpor on EEG Slow-Wave Characteristics in Djungarian Hamsters

PubMed Central

Palchykova, S.; Achermann, P.; Tobler, I.; Deboer, T.

2017-01-01

Abstract It has been shown previously in Djungarian hamsters that the initial electroencephalography (EEG) slow-wave activity (power in the 0.5–4.0 Hz band; SWA) in non-rapid eye movement (NREM) sleep following an episode of daily torpor is consistently enhanced, similar to the SWA increase after sleep deprivation (SD). However, it is unknown whether the network mechanisms underlying the SWA increase after torpor and SD are similar. EEG slow waves recorded in the neocortex during sleep reflect synchronized transitions between periods of activity and silence among large neuronal populations. We therefore set out to investigate characteristics of individual cortical EEG slow waves recorded during NREM sleep after 4 h SD and during sleep after emergence from an episode of daily torpor in adult male Djungarian hamsters. We found that during the first hour after both SD and torpor, the SWA increase was associated with an increase in slow-wave incidence and amplitude. However, the slopes of single slow waves during NREM sleep were steeper in the first hour after SD but not after torpor, and, in contrast to sleep after SD, the magnitude of change in slopes after torpor was unrelated to the changes in SWA. Furthermore, slow-wave slopes decreased progressively within the first 2 h after SD, while a progressive increase in slow-wave slopes was apparent during the first 2 h after torpor. The data suggest that prolonged waking and torpor have different effects on cortical network activity underlying slow-wave characteristics, while resulting in a similar homeostatic sleep response of SWA. We suggest that sleep plays an important role in network homeostasis after both waking and torpor, consistent with a recovery function for both states. PMID:28168294

Perturbation of Brain Oscillations after Ischemic Stroke: A Potential Biomarker for Post-Stroke Function and Therapy

PubMed Central

Rabiller, Gratianne; He, Ji-Wei; Nishijima, Yasuo; Wong, Aaron; Liu, Jialing

2015-01-01

Brain waves resonate from the generators of electrical current and propagate across brain regions with oscillation frequencies ranging from 0.05 to 500 Hz. The commonly observed oscillatory waves recorded by an electroencephalogram (EEG) in normal adult humans can be grouped into five main categories according to the frequency and amplitude, namely δ (1–4 Hz, 20–200 μV), θ (4–8 Hz, 10 μV), α (8–12 Hz, 20–200 μV), β (12–30 Hz, 5–10 μV), and γ (30–80 Hz, low amplitude). Emerging evidence from experimental and human studies suggests that groups of function and behavior seem to be specifically associated with the presence of each oscillation band, although the complex relationship between oscillation frequency and function, as well as the interaction between brain oscillations, are far from clear. Changes of brain oscillation patterns have long been implicated in the diseases of the central nervous system including ischemic stroke, in which the reduction of cerebral blood flow as well as the progression of tissue damage have direct spatiotemporal effects on the power of several oscillatory bands and their interactions. This review summarizes the current knowledge in behavior and function associated with each brain oscillation, and also in the specific changes in brain electrical activities that correspond to the molecular events and functional alterations observed after experimental and human stroke. We provide the basis of the generations of brain oscillations and potential cellular and molecular mechanisms underlying stroke-induced perturbation. We will also discuss the implications of using brain oscillation patterns as biomarkers for the prediction of stroke outcome and therapeutic efficacy. PMID:26516838

High-resolution EEG (HR-EEG) and magnetoencephalography (MEG).

PubMed

Gavaret, M; Maillard, L; Jung, J

2015-03-01

High-resolution EEG (HR-EEG) and magnetoencephalography (MEG) allow the recording of spontaneous or evoked electromagnetic brain activity with excellent temporal resolution. Data must be recorded with high temporal resolution (sampling rate) and high spatial resolution (number of channels). Data analyses are based on several steps with selection of electromagnetic signals, elaboration of a head model and use of algorithms in order to solve the inverse problem. Due to considerable technical advances in spatial resolution, these tools now represent real methods of ElectroMagnetic Source Imaging. HR-EEG and MEG constitute non-invasive and complementary examinations, characterized by distinct sensitivities according to the location and orientation of intracerebral generators. In the presurgical assessment of drug-resistant partial epilepsies, HR-EEG and MEG can characterize and localize interictal activities and thus the irritative zone. HR-EEG and MEG often yield significant additional data that are complementary to other presurgical investigations and particularly relevant in MRI-negative cases. Currently, the determination of the epileptogenic zone and functional brain mapping remain rather less well-validated indications. In France, in 2014, HR-EEG is now part of standard clinical investigation of epilepsy, while MEG remains a research technique. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Recovering TMS-evoked EEG responses masked by muscle artifacts.

PubMed

Mutanen, Tuomas P; Kukkonen, Matleena; Nieminen, Jaakko O; Stenroos, Matti; Sarvas, Jukka; Ilmoniemi, Risto J

2016-10-01

Combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) often suffers from large muscle artifacts. Muscle artifacts can be removed using signal-space projection (SSP), but this can make the visual interpretation of the remaining EEG data difficult. We suggest to use an additional step after SSP that we call source-informed reconstruction (SIR). SSP-SIR improves substantially the signal quality of artifactual TMS-EEG data, causing minimal distortion in the neuronal signal components. In the SSP-SIR approach, we first project out the muscle artifact using SSP. Utilizing an anatomical model and the remaining signal, we estimate an equivalent source distribution in the brain. Finally, we map the obtained source estimate onto the original signal space, again using anatomical information. This approach restores the neuronal signals in the sensor space and interpolates EEG traces onto the completely rejected channels. The introduced algorithm efficiently suppresses TMS-related muscle artifacts in EEG while retaining well the neuronal EEG topographies and signals. With the presented method, we can remove muscle artifacts from TMS-EEG data and recover the underlying brain responses without compromising the readability of the signals of interest. Copyright © 2016 Elsevier Inc. All rights reserved.

Resting State EEG in Children With Learning Disabilities: An Independent Component Analysis Approach.

PubMed

Jäncke, Lutz; Alahmadi, Nsreen

2016-01-01

In this study, the neurophysiological underpinnings of learning disabilities (LD) in children are examined using resting state EEG. We were particularly interested in the neurophysiological differences between children with learning disabilities not otherwise specified (LD-NOS), learning disabilities with verbal disabilities (LD-Verbal), and healthy control (HC) children. We applied 2 different approaches to examine the differences between the different groups. First, we calculated theta/beta and theta/alpha ratios in order to quantify the relationship between slow and fast EEG oscillations. Second, we used a recently developed method for analyzing spectral EEG, namely the group independent component analysis (gICA) model. Using these measures, we identified substantial differences between LD and HC children and between LD-NOS and LD-Verbal children in terms of their spectral EEG profiles. We obtained the following findings: (a) theta/beta and theta/alpha ratios were substantially larger in LD than in HC children, with no difference between LD-NOS and LD-Verbal children; (b) there was substantial slowing of EEG oscillations, especially for gICs located in frontal scalp positions, with LD-NOS children demonstrating the strongest slowing; (c) the estimated intracortical sources of these gICs were mostly located in brain areas involved in the control of executive functions, attention, planning, and language; and (d) the LD-Verbal children demonstrated substantial differences in EEG oscillations compared with LD-NOS children, and these differences were localized in language-related brain areas. The general pattern of atypical neurophysiological activation found in LD children suggests that they suffer from neurophysiological dysfunction in brain areas involved with the control of attention, executive functions, planning, and language functions. LD-Verbal children also demonstrate atypical activation, especially in language-related brain areas. These atypical neurophysiological activation patterns might provide a helpful guide for rehabilitation strategies to treat the deficiencies in these children with LD. © EEG and Clinical Neuroscience Society (ECNS) 2015.

Real-time monitoring of human blood-brain barrier disruption

PubMed Central

Kiviniemi, Vesa; Korhonen, Vesa; Kortelainen, Jukka; Rytky, Seppo; Keinänen, Tuija; Tuovinen, Timo; Isokangas, Matti; Sonkajärvi, Eila; Siniluoto, Topi; Nikkinen, Juha; Alahuhta, Seppo; Tervonen, Osmo; Turpeenniemi-Hujanen, Taina; Myllylä, Teemu; Kuittinen, Outi; Voipio, Juha

2017-01-01

Chemotherapy aided by opening of the blood-brain barrier with intra-arterial infusion of hyperosmolar mannitol improves the outcome in primary central nervous system lymphoma. Proper opening of the blood-brain barrier is crucial for the treatment, yet there are no means available for its real-time monitoring. The intact blood-brain barrier maintains a mV-level electrical potential difference between blood and brain tissue, giving rise to a measurable electrical signal at the scalp. Therefore, we used direct-current electroencephalography (DC-EEG) to characterize the spatiotemporal behavior of scalp-recorded slow electrical signals during blood-brain barrier opening. Nine anesthetized patients receiving chemotherapy were monitored continuously during 47 blood-brain barrier openings induced by carotid or vertebral artery mannitol infusion. Left or right carotid artery mannitol infusion generated a strongly lateralized DC-EEG response that began with a 2 min negative shift of up to 2000 μV followed by a positive shift lasting up to 20 min above the infused carotid artery territory, whereas contralateral responses were of opposite polarity. Vertebral artery mannitol infusion gave rise to a minimally lateralized and more uniformly distributed slow negative response with a posterior-frontal gradient. Simultaneously performed near-infrared spectroscopy detected a multiphasic response beginning with mannitol-bolus induced dilution of blood and ending in a prolonged increase in the oxy/deoxyhemoglobin ratio. The pronounced DC-EEG shifts are readily accounted for by opening and sealing of the blood-brain barrier. These data show that DC-EEG is a promising real-time monitoring tool for blood-brain barrier disruption augmented drug delivery. PMID:28319185

Temporal lobe deficits in murderers: EEG findings undetected by PET.

PubMed

Gatzke-Kopp, L M; Raine, A; Buchsbaum, M; LaCasse, L

2001-01-01

This study evaluates electroencephalography (EEG) and positron emission tomography (PET) in the same subjects. Fourteen murderers were assessed by using both PET (while they were performing the continuous performance task) and EEG during a resting state. EEG revealed significant increases in slow-wave activity in the temporal, but not frontal, lobe in murderers, in contrast to prior PET findings that showed reduced prefrontal, but not temporal, glucose metabolism. Results suggest that resting EEG shows empirical utility distinct from PET activation findings.

Encephalolexianalyzer

DOEpatents

Altschuler, E.L.; Dowla, F.U.

1998-11-24

The encephalolexianalyzer uses digital signal processing techniques on electroencephalograph (EEG) brain waves to determine whether or not someone is thinking about moving, e.g., tapping their fingers, or, alternatively, whether someone is actually moving, e.g., tapping their fingers, or at rest, i.e., not moving and not thinking of moving. The mu waves measured by a pair of electrodes placed over the motor cortex are signal processed to determine the power spectrum. At rest, the peak value of the power spectrum in the 8-13 Hz range is high, while when moving or thinking of moving, the peak value of the power spectrum in the 8-13 Hz range is low. This measured change in signal power spectrum is used to produce a control signal. The encephalolexianalyzer can be used to communicate either directly using Morse code, or via a cursor controlling a remote control; the encephalolexianalyzer can also be used to control other devices. The encephalolexianalyzer will be of great benefit to people with various handicaps and disabilities, and also has enormous commercial potential, as well as being an invaluable tool for studying the brain. 14 figs.

Encephalolexianalyzer

DOEpatents

Altschuler, Eric L.; Dowla, Farid U.

1998-01-01

The encephalolexianalyzer uses digital signal processing techniques on electroencephalograph (EEG) brain waves to determine whether or not someone is thinking about moving, e.g., tapping their fingers, or, alternatively, whether someone is actually moving, e.g., tapping their fingers, or at rest, i.e., not moving and not thinking of moving. The mu waves measured by a pair of electrodes placed over the motor cortex are signal processed to determine the power spectrum. At rest, the peak value of the power spectrum in the 8-13 Hz range is high, while when moving or thinking of moving, the peak value of the power spectrum in the 8-13 Hz range is low. This measured change in signal power spectrum is used to produce a control signal. The encephalolexianalyzer can be used to communicate either directly using Morse code, or via a cursor controlling a remote control; the encephalolexianalyzer can also be used to control other devices. The encephalolexianalyzer will be of great benefit to people with various handicaps and disabilities, and also has enormous commercial potential, as well as being an invaluable tool for studying the brain.

Significant improvement in one-dimensional cursor control using Laplacian electroencephalography over electroencephalography

NASA Astrophysics Data System (ADS)

Boudria, Yacine; Feltane, Amal; Besio, Walter

2014-06-01

Objective. Brain-computer interfaces (BCIs) based on electroencephalography (EEG) have been shown to accurately detect mental activities, but the acquisition of high levels of control require extensive user training. Furthermore, EEG has low signal-to-noise ratio and low spatial resolution. The objective of the present study was to compare the accuracy between two types of BCIs during the first recording session. EEG and tripolar concentric ring electrode (TCRE) EEG (tEEG) brain signals were recorded and used to control one-dimensional cursor movements. Approach. Eight human subjects were asked to imagine either ‘left’ or ‘right’ hand movement during one recording session to control the computer cursor using TCRE and disc electrodes. Main results. The obtained results show a significant improvement in accuracies using TCREs (44%-100%) compared to disc electrodes (30%-86%). Significance. This study developed the first tEEG-based BCI system for real-time one-dimensional cursor movements and showed high accuracies with little training.

Nonlinear dimensionality reduction of electroencephalogram (EEG) for Brain Computer interfaces.

PubMed

Teli, Mohammad Nayeem; Anderson, Charles

2009-01-01

Patterns in electroencephalogram (EEG) signals are analyzed for a Brain Computer Interface (BCI). An important aspect of this analysis is the work on transformations of high dimensional EEG data to low dimensional spaces in which we can classify the data according to mental tasks being performed. In this research we investigate how a Neural Network (NN) in an auto-encoder with bottleneck configuration can find such a transformation. We implemented two approximate second-order methods to optimize the weights of these networks, because the more common first-order methods are very slow to converge for networks like these with more than three layers of computational units. The resulting non-linear projections of time embedded EEG signals show interesting separations that are related to tasks. The bottleneck networks do indeed discover nonlinear transformations to low-dimensional spaces that capture much of the information present in EEG signals. However, the resulting low-dimensional representations do not improve classification rates beyond what is possible using Quadratic Discriminant Analysis (QDA) on the original time-lagged EEG.

EEG-based emotion recognition in music listening.

PubMed

Lin, Yuan-Pin; Wang, Chi-Hong; Jung, Tzyy-Ping; Wu, Tien-Lin; Jeng, Shyh-Kang; Duann, Jeng-Ren; Chen, Jyh-Horng

2010-07-01

Ongoing brain activity can be recorded as electroencephalograph (EEG) to discover the links between emotional states and brain activity. This study applied machine-learning algorithms to categorize EEG dynamics according to subject self-reported emotional states during music listening. A framework was proposed to optimize EEG-based emotion recognition by systematically 1) seeking emotion-specific EEG features and 2) exploring the efficacy of the classifiers. Support vector machine was employed to classify four emotional states (joy, anger, sadness, and pleasure) and obtained an averaged classification accuracy of 82.29% +/- 3.06% across 26 subjects. Further, this study identified 30 subject-independent features that were most relevant to emotional processing across subjects and explored the feasibility of using fewer electrodes to characterize the EEG dynamics during music listening. The identified features were primarily derived from electrodes placed near the frontal and the parietal lobes, consistent with many of the findings in the literature. This study might lead to a practical system for noninvasive assessment of the emotional states in practical or clinical applications.

SSVEP recognition using common feature analysis in brain-computer interface.

PubMed

Zhang, Yu; Zhou, Guoxu; Jin, Jing; Wang, Xingyu; Cichocki, Andrzej

2015-04-15

Canonical correlation analysis (CCA) has been successfully applied to steady-state visual evoked potential (SSVEP) recognition for brain-computer interface (BCI) application. Although the CCA method outperforms the traditional power spectral density analysis through multi-channel detection, it requires additionally pre-constructed reference signals of sine-cosine waves. It is likely to encounter overfitting in using a short time window since the reference signals include no features from training data. We consider that a group of electroencephalogram (EEG) data trials recorded at a certain stimulus frequency on a same subject should share some common features that may bear the real SSVEP characteristics. This study therefore proposes a common feature analysis (CFA)-based method to exploit the latent common features as natural reference signals in using correlation analysis for SSVEP recognition. Good performance of the CFA method for SSVEP recognition is validated with EEG data recorded from ten healthy subjects, in contrast to CCA and a multiway extension of CCA (MCCA). Experimental results indicate that the CFA method significantly outperformed the CCA and the MCCA methods for SSVEP recognition in using a short time window (i.e., less than 1s). The superiority of the proposed CFA method suggests it is promising for the development of a real-time SSVEP-based BCI. Copyright © 2014 Elsevier B.V. All rights reserved.

Application of Independent Component Analysis for the Data Mining of Simultaneous EEG-fMRI: Preliminary Experience on Sleep Onset

PubMed Central

Lee, Jong-Hwan; Oh, Sungsuk; Jolesz, Ferenc A.; Park, Hyunwook; Yoo, Seung-Schik

2010-01-01

The simultaneous acquisition of electroencephalogram (EEG) and functional MRI (fMRI) signals is potentially advantageous because of the superior resolution that is achieved in both the temporal and spatial domains, respectively. However, ballistocardiographic artifacts along with the ocular artifacts are a major obstacle for the detection of the EEG signatures of interest. Since the sources corresponding to these artifacts are independent from those producing the EEG signatures, we applied the Infomax-based independent component analysis (ICA) technique to separate the EEG signatures from the artifacts. The isolated EEG signatures were further utilized to model the canonical hemodynamic response functions (HRFs). Subsequently, the brain areas from which these EEG signatures originated were identified as locales of activation patterns from the analysis of fMRI data. Upon the identification and subsequent evaluation of brain areas generating interictal epileptic discharge (IED) spikes from an epileptic subject, the presented method was successfully applied to detect the theta- and alpha-rhythms that are sleep onset related EEG signatures along with the subsequent neural circuitries from a sleep deprived volunteer. These results suggest that the ICA technique may be useful for the preprocessing of simultaneous EEG-fMRI acquisitions, especially when a reference paradigm is unavailable. PMID:19922343

Application of independent component analysis for the data mining of simultaneous Eeg-fMRI: preliminary experience on sleep onset.

PubMed

Lee, Jong-Hwan; Oh, Sungsuk; Jolesz, Ferenc A; Park, Hyunwook; Yoo, Seung-Schik

2009-01-01

The simultaneous acquisition of electroencephalogram (EEG) and functional MRI (fMRI) signals is potentially advantageous because of the superior resolution that is achieved in both the temporal and spatial domains, respectively. However, ballistocardiographic artifacts along with ocular artifacts are a major obstacle for the detection of the EEG signatures of interest. Since the sources corresponding to these artifacts are independent from those producing the EEG signatures, we applied the Infomax-based independent component analysis (ICA) technique to separate the EEG signatures from the artifacts. The isolated EEG signatures were further utilized to model the canonical hemodynamic response functions (HRFs). Subsequently, the brain areas from which these EEG signatures originated were identified as locales of activation patterns from the analysis of fMRI data. Upon the identification and subsequent evaluation of brain areas generating interictal epileptic discharge (IED) spikes from an epileptic subject, the presented method was successfully applied to detect the theta and alpha rhythms that are sleep onset-related EEG signatures along with the subsequent neural circuitries from a sleep-deprived volunteer. These results suggest that the ICA technique may be useful for the preprocessing of simultaneous EEG-fMRI acquisitions, especially when a reference paradigm is unavailable.

Pulse Wave Amplitude Drops during Sleep are Reliable Surrogate Markers of Changes in Cortical Activity

PubMed Central

Delessert, Alexandre; Espa, Fabrice; Rossetti, Andrea; Lavigne, Gilles; Tafti, Mehdi; Heinzer, Raphael

2010-01-01

Background: During sleep, sudden drops in pulse wave amplitude (PWA) measured by pulse oximetry are commonly associated with simultaneous arousals and are thought to result from autonomic vasoconstriction. In the present study, we determine whether PWA drops were associated with changes in cortical activity as determined by EEG spectral analysis. Methods: A 20% decrease in PWA was chosen as a minimum for a drop. A total of 1085 PWA drops from 10 consecutive sleep recordings were analyzed. EEG spectral analysis was performed over 5 consecutive epochs of 5 seconds: 2 before, 1 during, and 2 after the PWA drop. EEG spectral analysis was performed over delta, theta, alpha, sigma, and beta frequency bands. Within each frequency band, power density was compared across the five 5-sec epochs. Presence or absence of visually scored EEG arousals were adjudicated by an investigator blinded to the PWA signal and considered associated with PWA drop if concomitant. Results: A significant increase in EEG power density in all EEG frequency bands was found during PWA drops (P < 0.001) compared to before and after drop. Even in the absence of visually scored arousals, PWA drops were associated with a significant increase in EEG power density (P < 0.001) in most frequency bands. Conclusions: Drops in PWA are associated with a significant increase in EEG power density, suggesting that these events can be used as a surrogate for changes in cortical activity during sleep. This approach may prove of value in scoring respiratory events on limited-channel (type III) portable monitors. Citation: Delessert A; Espa F; Rossetti A; Lavigne G; Tafti M; Heinzer R. Pulse wave amplitude drops during sleep are reliable surrogate markers of changes in cortical activity. SLEEP 2010;33(12):1687-1692. PMID:21120131

Visual perception affected by motivation and alertness controlled by a noninvasive brain-computer interface.

PubMed

Maksimenko, Vladimir A; Runnova, Anastasia E; Zhuravlev, Maksim O; Makarov, Vladimir V; Nedayvozov, Vladimir; Grubov, Vadim V; Pchelintceva, Svetlana V; Hramov, Alexander E; Pisarchik, Alexander N

2017-01-01

The influence of motivation and alertness on brain activity associated with visual perception was studied experimentally using the Necker cube, which ambiguity was controlled by the contrast of its ribs. The wavelet analysis of recorded multichannel electroencephalograms (EEG) allowed us to distinguish two different scenarios while the brain processed the ambiguous stimulus. The first scenario is characterized by a particular destruction of alpha rhythm (8-12 Hz) with a simultaneous increase in beta-wave activity (20-30 Hz), whereas in the second scenario, the beta rhythm is not well pronounced while the alpha-wave energy remains unchanged. The experiments were carried out with a group of financially motivated subjects and another group of unpaid volunteers. It was found that the first scenario occurred mainly in the motivated group. This can be explained by the increased alertness of the motivated subjects. The prevalence of the first scenario was also observed in a group of subjects to whom images with higher ambiguity were presented. We believe that the revealed scenarios can occur not only during the perception of bistable images, but also in other perceptual tasks requiring decision making. The obtained results may have important applications for monitoring and controlling human alertness in situations which need substantial attention. On the base of the obtained results we built a brain-computer interface to estimate and control the degree of alertness in real time.

Application and Evaluation of Independent Component Analysis Methods to Generalized Seizure Disorder Activities Exhibited in the Brain.

PubMed

George, S Thomas; Balakrishnan, R; Johnson, J Stanly; Jayakumar, J

2017-07-01

EEG records the spontaneous electrical activity of the brain using multiple electrodes placed on the scalp, and it provides a wealth of information related to the functions of brain. Nevertheless, the signals from the electrodes cannot be directly applied to a diagnostic tool like brain mapping as they undergo a "mixing" process because of the volume conduction effect in the scalp. A pervasive problem in neuroscience is determining which regions of the brain are active, given voltage measurements at the scalp. Because of which, there has been a surge of interest among the biosignal processing community to investigate the process of mixing and unmixing to identify the underlying active sources. According to the assumptions of independent component analysis (ICA) algorithms, the resultant mixture obtained from the scalp can be closely approximated by a linear combination of the "actual" EEG signals emanating from the underlying sources of electrical activity in the brain. As a consequence, using these well-known ICA techniques in preprocessing of the EEG signals prior to clinical applications could result in development of diagnostic tool like quantitative EEG which in turn can assist the neurologists to gain noninvasive access to patient-specific cortical activity, which helps in treating neuropathologies like seizure disorders. The popular and proven ICA schemes mentioned in various literature and applications were selected (which includes Infomax, JADE, and SOBI) and applied on generalized seizure disorder samples using EEGLAB toolbox in MATLAB environment to see their usefulness in source separations; and they were validated by the expert neurologist for clinical relevance in terms of pathologies on brain functionalities. The performance of Infomax method was found to be superior when compared with other ICA schemes applied on EEG and it has been established based on the validations carried by expert neurologist for generalized seizure and its clinical correlation. The results are encouraging for furthering the studies in the direction of developing useful brain mapping tools using ICA methods.

A review of classification algorithms for EEG-based brain-computer interfaces.

PubMed

Lotte, F; Congedo, M; Lécuyer, A; Lamarche, F; Arnaldi, B

2007-06-01

In this paper we review classification algorithms used to design brain-computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI.

EEG Delta Band as a Marker of Brain Damage in Aphasic Patients after Recovery of Language

ERIC Educational Resources Information Center

Spironelli, Chiara; Angrilli, Alessandro

2009-01-01

In this study spectral delta percentage was used to assess both brain dysfunction/inhibition and functional linguistic impairment during different phases of word processing. To this aim, EEG delta amplitude was measured in 17 chronic non-fluent aphasic patients while engaged in three linguistic tasks: Orthographic, Phonological and Semantic.…

The human brain pacemaker: Synchronized infra-slow neurovascular coupling in patients undergoing non-pulsatile cardiopulmonary bypass.

PubMed

Zanatta, Paolo; Toffolo, Gianna Maria; Sartori, Elisa; Bet, Anna; Baldanzi, Fabrizio; Agarwal, Nivedita; Golanov, Eugene

2013-05-15

In non-pulsatile cardiopulmonary bypass surgery, middle cerebral artery blood flow velocity (BFV) is characterized by infra-slow oscillations of approximately 0.06Hz, which are paralleled by changes in total EEG power variability (EEG-PV), measured in 2s intervals. Since the origin of these BFV oscillations is not known, we explored their possible causative relationships with oscillations in EEG-PV at around 0.06Hz. We monitored 28 patients undergoing non-pulsatile cardiopulmonary bypass using transcranial Doppler sonography and scalp electroencephalography at two levels of anesthesia, deep (prevalence of burst suppression rhythm) and moderate (prevalence of theta rhythm). Under deep anesthesia, the EEG bursts suppression pattern was highly correlative with BFV oscillations. Hence, a detailed quantitative picture of the coupling between electrical brain activity and BFV was derived, both in deep and moderate anesthesia, via linear and non linear processing of EEG-PV and BFV signals, resorting to widely used measures of signal coupling such as frequency of oscillations, coherence, Granger causality and cross-approximate entropy. Results strongly suggest the existence of coupling between EEG-PV and BFV. In moderate anesthesia EEG-PV mean dominant frequency is similar to frequency of BFV oscillations (0.065±0.010Hz vs 0.045±0.019Hz); coherence between the two signals was significant in about 55% of subjects, and the Granger causality suggested an EEG-PV→BFV causal effect direction. The strength of the coupling increased with deepening anesthesia, as EEG-PV oscillations mean dominant frequency virtually coincided with the BFV peak frequency (0.062±0.017Hz vs 0.060±0.024Hz), and coherence became significant in a larger number (65%) of subjects. Cross-approximate entropy decreased significantly from moderate to deep anesthesia, indicating a higher level of synchrony between the two signals. Presence of a subcortical brain pacemaker that drives vascular infra-slow oscillations in the brain is proposed. These findings allow to suggest an original hypothesis explaining the mechanism underlying infra-slow neurovascular coupling. Copyright © 2013 Elsevier Inc. All rights reserved.

Correlations between the Signal Complexity of Cerebral and Cardiac Electrical Activity: A Multiscale Entropy Analysis

PubMed Central

Lin, Pei-Feng; Lo, Men-Tzung; Tsao, Jenho; Chang, Yi-Chung; Lin, Chen; Ho, Yi-Lwun

2014-01-01

The heart begins to beat before the brain is formed. Whether conventional hierarchical central commands sent by the brain to the heart alone explain all the interplay between these two organs should be reconsidered. Here, we demonstrate correlations between the signal complexity of brain and cardiac activity. Eighty-seven geriatric outpatients with healthy hearts and varied cognitive abilities each provided a 24-hour electrocardiography (ECG) and a 19-channel eye-closed routine electroencephalography (EEG). Multiscale entropy (MSE) analysis was applied to three epochs (resting-awake state, photic stimulation of fast frequencies (fast-PS), and photic stimulation of slow frequencies (slow-PS)) of EEG in the 1–58 Hz frequency range, and three RR interval (RRI) time series (awake-state, sleep and that concomitant with the EEG) for each subject. The low-to-high frequency power (LF/HF) ratio of RRI was calculated to represent sympatho-vagal balance. With statistics after Bonferroni corrections, we found that: (a) the summed MSE value on coarse scales of the awake RRI (scales 11–20, RRI-MSE-coarse) were inversely correlated with the summed MSE value on coarse scales of the resting-awake EEG (scales 6–20, EEG-MSE-coarse) at Fp2, C4, T6 and T4; (b) the awake RRI-MSE-coarse was inversely correlated with the fast-PS EEG-MSE-coarse at O1, O2 and C4; (c) the sleep RRI-MSE-coarse was inversely correlated with the slow-PS EEG-MSE-coarse at Fp2; (d) the RRI-MSE-coarse and LF/HF ratio of the awake RRI were correlated positively to each other; (e) the EEG-MSE-coarse at F8 was proportional to the cognitive test score; (f) the results conform to the cholinergic hypothesis which states that cognitive impairment causes reduction in vagal cardiac modulation; (g) fast-PS significantly lowered the EEG-MSE-coarse globally. Whether these heart-brain correlations could be fully explained by the central autonomic network is unknown and needs further exploration. PMID:24498375

Neural Entrainment to Auditory Imagery of Rhythms.

PubMed

Okawa, Haruki; Suefusa, Kaori; Tanaka, Toshihisa

2017-01-01

A method of reconstructing perceived or imagined music by analyzing brain activity has not yet been established. As a first step toward developing such a method, we aimed to reconstruct the imagery of rhythm, which is one element of music. It has been reported that a periodic electroencephalogram (EEG) response is elicited while a human imagines a binary or ternary meter on a musical beat. However, it is not clear whether or not brain activity synchronizes with fully imagined beat and meter without auditory stimuli. To investigate neural entrainment to imagined rhythm during auditory imagery of beat and meter, we recorded EEG while nine participants (eight males and one female) imagined three types of rhythm without auditory stimuli but with visual timing, and then we analyzed the amplitude spectra of the EEG. We also recorded EEG while the participants only gazed at the visual timing as a control condition to confirm the visual effect. Furthermore, we derived features of the EEG using canonical correlation analysis (CCA) and conducted an experiment to individually classify the three types of imagined rhythm from the EEG. The results showed that classification accuracies exceeded the chance level in all participants. These results suggest that auditory imagery of meter elicits a periodic EEG response that changes at the imagined beat and meter frequency even in the fully imagined conditions. This study represents the first step toward the realization of a method for reconstructing the imagined music from brain activity.

Techniques for chronic monitoring of brain activity in freely moving sheep using wireless EEG recording.

PubMed

Perentos, N; Nicol, A U; Martins, A Q; Stewart, J E; Taylor, P; Morton, A J

2017-03-01

Large mammals with complex central nervous systems offer new possibilities for translational research into basic brain function. Techniques for monitoring brain activity in large mammals, however, are not as well developed as they are in rodents. We have developed a method for chronic monitoring of electroencephalographic (EEG) activity in unrestrained sheep. We describe the methods for behavioural training prior to implantation, surgical procedures for implantation, a protocol for reliable anaesthesia and recovery, methods for EEG data collection, as well as data pertaining to suitability and longevity of different types of electrodes. Sheep tolerated all procedures well, and surgical complications were minimal. Electrode types used included epidural and subdural screws, intracortical needles and subdural disk electrodes, with the latter producing the best and most reliable results. The implants yielded longitudinal EEG data of consistent quality for periods of at least a year, and in some cases up to 2 years. This is the first detailed methodology to be described for chronic brain function monitoring in freely moving unrestrained sheep. The developed method will be particularly useful in chronic investigations of brain activity during normal behaviour that can include sleep, learning and memory. As well, within the context of disease, the method can be used to monitor brain pathology or the progress of therapeutic trials in transgenic or natural disease models in sheep. Copyright © 2016 Elsevier B.V. All rights reserved.

Altered Brain Microstate Dynamics in Adolescents with Narcolepsy

PubMed Central

Drissi, Natasha M.; Szakács, Attila; Witt, Suzanne T.; Wretman, Anna; Ulander, Martin; Ståhlbrandt, Henriettae; Darin, Niklas; Hallböök, Tove; Landtblom, Anne-Marie; Engström, Maria

2016-01-01

Narcolepsy is a chronic sleep disorder caused by a loss of hypocretin-1 producing neurons in the hypothalamus. Previous neuroimaging studies have investigated brain function in narcolepsy during rest using positron emission tomography (PET) and single photon emission computed tomography (SPECT). In addition to hypothalamic and thalamic dysfunction they showed aberrant prefrontal perfusion and glucose metabolism in narcolepsy. Given these findings in brain structure and metabolism in narcolepsy, we anticipated that changes in functional magnetic resonance imaging (fMRI) resting state network (RSN) dynamics might also be apparent in patients with narcolepsy. The objective of this study was to investigate and describe brain microstate activity in adolescents with narcolepsy and correlate these to RSNs using simultaneous fMRI and electroencephalography (EEG). Sixteen adolescents (ages 13–20) with a confirmed diagnosis of narcolepsy were recruited and compared to age-matched healthy controls. Simultaneous EEG and fMRI data were collected during 10 min of wakeful rest. EEG data were analyzed for microstates, which are discrete epochs of stable global brain states obtained from topographical EEG analysis. Functional MRI data were analyzed for RSNs. Data showed that narcolepsy patients were less likely than controls to spend time in a microstate which we found to be related to the default mode network and may suggest a disruption of this network that is disease specific. We concluded that adolescents with narcolepsy have altered resting state brain dynamics. PMID:27536225

Electric Field Encephalography as a tool for functional brain research: a modeling study.

PubMed

Petrov, Yury; Sridhar, Srinivas

2013-01-01

We introduce the notion of Electric Field Encephalography (EFEG) based on measuring electric fields of the brain and demonstrate, using computer modeling, that given the appropriate electric field sensors this technique may have significant advantages over the current EEG technique. Unlike EEG, EFEG can be used to measure brain activity in a contactless and reference-free manner at significant distances from the head surface. Principal component analysis using simulated cortical sources demonstrated that electric field sensors positioned 3 cm away from the scalp and characterized by the same signal-to-noise ratio as EEG sensors provided the same number of uncorrelated signals as scalp EEG. When positioned on the scalp, EFEG sensors provided 2-3 times more uncorrelated signals. This significant increase in the number of uncorrelated signals can be used for more accurate assessment of brain states for non-invasive brain-computer interfaces and neurofeedback applications. It also may lead to major improvements in source localization precision. Source localization simulations for the spherical and Boundary Element Method (BEM) head models demonstrated that the localization errors are reduced two-fold when using electric fields instead of electric potentials. We have identified several techniques that could be adapted for the measurement of the electric field vector required for EFEG and anticipate that this study will stimulate new experimental approaches to utilize this new tool for functional brain research.

Automatic EEG artifact removal: a weighted support vector machine approach with error correction.

PubMed

Shao, Shi-Yun; Shen, Kai-Quan; Ong, Chong Jin; Wilder-Smith, Einar P V; Li, Xiao-Ping

2009-02-01

An automatic electroencephalogram (EEG) artifact removal method is presented in this paper. Compared to past methods, it has two unique features: 1) a weighted version of support vector machine formulation that handles the inherent unbalanced nature of component classification and 2) the ability to accommodate structural information typically found in component classification. The advantages of the proposed method are demonstrated on real-life EEG recordings with comparisons made to several benchmark methods. Results show that the proposed method is preferable to the other methods in the context of artifact removal by achieving a better tradeoff between removing artifacts and preserving inherent brain activities. Qualitative evaluation of the reconstructed EEG epochs also demonstrates that after artifact removal inherent brain activities are largely preserved.

Entropy changes in brain function.

PubMed

Rosso, Osvaldo A

2007-04-01

The traditional way of analyzing brain electrical activity, on the basis of electroencephalography (EEG) records, relies mainly on visual inspection and years of training. Although it is quite useful, of course, one has to acknowledge its subjective nature that hardly allows for a systematic protocol. In the present work quantifiers based on information theory and wavelet transform are reviewed. The "relative wavelet energy" provides information about the relative energy associated with different frequency bands present in the EEG and their corresponding degree of importance. The "normalized total wavelet entropy" carries information about the degree of order-disorder associated with a multi-frequency signal response. Their application in the analysis and quantification of short duration EEG signals (event-related potentials) and epileptic EEG records are summarized.

Nonlinear dynamical systems effects of homeopathic remedies on multiscale entropy and correlation dimension of slow wave sleep EEG in young adults with histories of coffee-induced insomnia.

PubMed

Bell, Iris R; Howerter, Amy; Jackson, Nicholas; Aickin, Mikel; Bootzin, Richard R; Brooks, Audrey J

2012-07-01

Investigators of homeopathy have proposed that nonlinear dynamical systems (NDS) and complex systems science offer conceptual and analytic tools for evaluating homeopathic remedy effects. Previous animal studies demonstrate that homeopathic medicines alter delta electroencephalographic (EEG) slow wave sleep. The present study extended findings of remedy-related sleep stage alterations in human subjects by testing the feasibility of using two different NDS analytic approaches to assess remedy effects on human slow wave sleep EEG. Subjects (N=54) were young adult male and female college students with a history of coffee-related insomnia who participated in a larger 4-week study of the polysomnographic effects of homeopathic medicines on home-based all-night sleep recordings. Subjects took one bedtime dose of a homeopathic remedy (Coffea cruda or Nux vomica 30c). We computed multiscale entropy (MSE) and the correlation dimension (Mekler-D2) for stages 3 and 4 slow wave sleep EEG sampled in artifact-free 2-min segments during the first two rapid-eye-movement (REM) cycles for remedy and post-remedy nights, controlling for placebo and post-placebo night effects. MSE results indicate significant, remedy-specific directional effects, especially later in the night (REM cycle 2) (CC: remedy night increases and post-remedy night decreases in MSE at multiple sites for both stages 3 and 4 in both REM cycles; NV: remedy night decreases and post-remedy night increases, mainly in stage 3 REM cycle 2 MSE). D2 analyses yielded more sporadic and inconsistent findings. Homeopathic medicines Coffea cruda and Nux vomica in 30c potencies alter short-term nonlinear dynamic parameters of slow wave sleep EEG in healthy young adults. MSE may provide a more sensitive NDS analytic method than D2 for evaluating homeopathic remedy effects on human sleep EEG patterns. Copyright © 2012 The Faculty of Homeopathy. Published by Elsevier Ltd. All rights reserved.

Nonlinear Dynamical Systems Effects of Homeopathic Remedies on Multiscale Entropy and Correlation Dimension of Slow Wave Sleep EEG in Young Adults with Histories of Coffee-Induced Insomnia

PubMed Central

Bell, Iris R.; Howerter, Amy; Jackson, Nicholas; Aickin, Mikel; Bootzin, Richard R.; Brooks, Audrey J.

2012-01-01

Background Investigators of homeopathy have proposed that nonlinear dynamical systems (NDS) and complex systems science offer conceptual and analytic tools for evaluating homeopathic remedy effects. Previous animal studies demonstrate that homeopathic medicines alter delta electroencephalographic (EEG) slow wave sleep. The present study extended findings of remedy-related sleep stage alterations in human subjects by testing the feasibility of using two different NDS analytic approaches to assess remedy effects on human slow wave sleep EEG. Methods Subjects (N=54) were young adult male and female college students with a history of coffee-related insomnia who participated in a larger 4-week study of the polysomnographic effects of homeopathic medicines on home-based all-night sleep recordings. Subjects took one bedtime dose of a homeopathic remedy (Coffea cruda or Nux vomica 30c). We computed multiscale entropy (MSE) and the correlation dimension (Mekler-D2) for stage 3 and 4 slow wave sleep EEG sampled in artifact-free 2-minute segments during the first two rapid-eye-movement (REM) cycles for remedy and post-remedy nights, controlling for placebo and post-placebo night effects. Results MSE results indicate significant, remedy-specific directional effects, especially later in the night (REM cycle 2) (CC: remedy night increases and post-remedy night decreases in MSE at multiple sites for both stages 3 and 4 in both REM cycles; NV: remedy night decreases and post-remedy night increases, mainly in stage 3 REM cycle 2 MSE). D2 analyses yielded more sporadic and inconsistent findings. Conclusions Homeopathic medicines Coffea cruda and Nux vomica in 30c potencies alter short-term nonlinear dynamic parameters of slow wave sleep EEG in healthy young adults. MSE may provide a more sensitive NDS analytic method than D2 for evaluating homeopathic remedy effects on human sleep EEG patterns. PMID:22818237

A method for detecting nonlinear determinism in normal and epileptic brain EEG signals.

PubMed

Meghdadi, Amir H; Fazel-Rezai, Reza; Aghakhani, Yahya

2007-01-01

A robust method of detecting determinism for short time series is proposed and applied to both healthy and epileptic EEG signals. The method provides a robust measure of determinism through characterizing the trajectories of the signal components which are obtained through singular value decomposition. Robustness of the method is shown by calculating proposed index of determinism at different levels of white and colored noise added to a simulated chaotic signal. The method is shown to be able to detect determinism at considerably high levels of additive noise. The method is then applied to both intracranial and scalp EEG recordings collected in different data sets for healthy and epileptic brain signals. The results show that for all of the studied EEG data sets there is enough evidence of determinism. The determinism is more significant for intracranial EEG recordings particularly during seizure activity.

Low Activity Microstates During Sleep.

PubMed

Miyawaki, Hiroyuki; Billeh, Yazan N; Diba, Kamran

2017-06-01

To better understand the distinct activity patterns of the brain during sleep, we observed and investigated periods of diminished oscillatory and population spiking activity lasting for seconds during non-rapid eye movement (non-REM) sleep, which we call "LOW" activity sleep. We analyzed spiking and local field potential (LFP) activity of hippocampal CA1 region alongside neocortical electroencephalogram (EEG) and electromyogram (EMG) in 19 sessions from four male Long-Evans rats (260-360 g) during natural wake/sleep across the 24-hr cycle as well as data from other brain regions obtained from http://crcns.org.1,2. LOW states lasted longer than OFF/DOWN states and were distinguished by a subset of "LOW-active" cells. LOW activity sleep was preceded and followed by increased sharp-wave ripple activity. We also observed decreased slow-wave activity and sleep spindles in the hippocampal LFP and neocortical EEG upon LOW onset, with a partial rebound immediately after LOW. LOW states demonstrated activity patterns consistent with sleep but frequently transitioned into microarousals and showed EMG and LFP differences from small-amplitude irregular activity during quiet waking. Their likelihood decreased within individual non-REM epochs yet increased over the course of sleep. By analyzing data from the entorhinal cortex of rats,1 as well as the hippocampus, the medial prefrontal cortex, the postsubiculum, and the anterior thalamus of mice,2 obtained from http://crcns.org, we confirmed that LOW states corresponded to markedly diminished activity simultaneously in all of these regions. We propose that LOW states are an important microstate within non-REM sleep that provide respite from high-activity sleep and may serve a restorative function. © Sleep Research Society 2017. Published by Oxford University Press [on behalf of the Sleep Research Society].

Estimating the Integrated Information Measure Phi from High-Density Electroencephalography during States of Consciousness in Humans

PubMed Central

Kim, Hyoungkyu; Hudetz, Anthony G.; Lee, Joseph; Mashour, George A.; Lee, UnCheol; Avidan, Michael S.

2018-01-01

The integrated information theory (IIT) proposes a quantitative measure, denoted as Φ, of the amount of integrated information in a physical system, which is postulated to have an identity relationship with consciousness. IIT predicts that the value of Φ estimated from brain activities represents the level of consciousness across phylogeny and functional states. Practical limitations, such as the explosive computational demands required to estimate Φ for real systems, have hindered its application to the brain and raised questions about the utility of IIT in general. To achieve practical relevance for studying the human brain, it will be beneficial to establish the reliable estimation of Φ from multichannel electroencephalogram (EEG) and define the relationship of Φ to EEG properties conventionally used to define states of consciousness. In this study, we introduce a practical method to estimate Φ from high-density (128-channel) EEG and determine the contribution of each channel to Φ. We examine the correlation of power, frequency, functional connectivity, and modularity of EEG with regional Φ in various states of consciousness as modulated by diverse anesthetics. We find that our approximation of Φ alone is insufficient to discriminate certain states of anesthesia. However, a multi-dimensional parameter space extended by four parameters related to Φ and EEG connectivity is able to differentiate all states of consciousness. The association of Φ with EEG connectivity during clinically defined anesthetic states represents a new practical approach to the application of IIT, which may be used to characterize various physiological (sleep), pharmacological (anesthesia), and pathological (coma) states of consciousness in the human brain. PMID:29503611

Estimating the Integrated Information Measure Phi from High-Density Electroencephalography during States of Consciousness in Humans.

PubMed

Kim, Hyoungkyu; Hudetz, Anthony G; Lee, Joseph; Mashour, George A; Lee, UnCheol

2018-01-01

The integrated information theory (IIT) proposes a quantitative measure, denoted as Φ, of the amount of integrated information in a physical system, which is postulated to have an identity relationship with consciousness. IIT predicts that the value of Φ estimated from brain activities represents the level of consciousness across phylogeny and functional states. Practical limitations, such as the explosive computational demands required to estimate Φ for real systems, have hindered its application to the brain and raised questions about the utility of IIT in general. To achieve practical relevance for studying the human brain, it will be beneficial to establish the reliable estimation of Φ from multichannel electroencephalogram (EEG) and define the relationship of Φ to EEG properties conventionally used to define states of consciousness. In this study, we introduce a practical method to estimate Φ from high-density (128-channel) EEG and determine the contribution of each channel to Φ. We examine the correlation of power, frequency, functional connectivity, and modularity of EEG with regional Φ in various states of consciousness as modulated by diverse anesthetics. We find that our approximation of Φ alone is insufficient to discriminate certain states of anesthesia. However, a multi-dimensional parameter space extended by four parameters related to Φ and EEG connectivity is able to differentiate all states of consciousness. The association of Φ with EEG connectivity during clinically defined anesthetic states represents a new practical approach to the application of IIT, which may be used to characterize various physiological (sleep), pharmacological (anesthesia), and pathological (coma) states of consciousness in the human brain.

Automatic interpretation and writing report of the adult waking electroencephalogram.

PubMed

Shibasaki, Hiroshi; Nakamura, Masatoshi; Sugi, Takenao; Nishida, Shigeto; Nagamine, Takashi; Ikeda, Akio

2014-06-01

Automatic interpretation of the EEG has so far been faced with significant difficulties because of a large amount of spatial as well as temporal information contained in the EEG, continuous fluctuation of the background activity depending on changes in the subject's vigilance and attention level, the occurrence of paroxysmal activities such as spikes and spike-and-slow-waves, contamination of the EEG with a variety of artefacts and the use of different recording electrodes and montages. Therefore, previous attempts of automatic EEG interpretation have been focussed only on a specific EEG feature such as paroxysmal abnormalities, delta waves, sleep stages and artefact detection. As a result of a long-standing cooperation between clinical neurophysiologists and system engineers, we report for the first time on a comprehensive, computer-assisted, automatic interpretation of the adult waking EEG. This system analyses the background activity, intermittent abnormalities, artefacts and the level of vigilance and attention of the subject, and automatically presents its report in written form. Besides, it also detects paroxysmal abnormalities and evaluates the effects of intermittent photic stimulation and hyperventilation on the EEG. This system of automatic EEG interpretation was formed by adopting the strategy that the qualified EEGers employ for the systematic visual inspection. This system can be used as a supplementary tool for the EEGer's visual inspection, and for educating EEG trainees and EEG technicians. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Progress in EEG-Based Brain Robot Interaction Systems

PubMed Central

Li, Mengfan; Niu, Linwei; Xian, Bin; Zeng, Ming; Chen, Genshe

2017-01-01

The most popular noninvasive Brain Robot Interaction (BRI) technology uses the electroencephalogram- (EEG-) based Brain Computer Interface (BCI), to serve as an additional communication channel, for robot control via brainwaves. This technology is promising for elderly or disabled patient assistance with daily life. The key issue of a BRI system is to identify human mental activities, by decoding brainwaves, acquired with an EEG device. Compared with other BCI applications, such as word speller, the development of these applications may be more challenging since control of robot systems via brainwaves must consider surrounding environment feedback in real-time, robot mechanical kinematics, and dynamics, as well as robot control architecture and behavior. This article reviews the major techniques needed for developing BRI systems. In this review article, we first briefly introduce the background and development of mind-controlled robot technologies. Second, we discuss the EEG-based brain signal models with respect to generating principles, evoking mechanisms, and experimental paradigms. Subsequently, we review in detail commonly used methods for decoding brain signals, namely, preprocessing, feature extraction, and feature classification, and summarize several typical application examples. Next, we describe a few BRI applications, including wheelchairs, manipulators, drones, and humanoid robots with respect to synchronous and asynchronous BCI-based techniques. Finally, we address some existing problems and challenges with future BRI techniques. PMID:28484488

Electroencephalographic field influence on calcium momentum waves.

PubMed

Ingber, Lester; Pappalepore, Marco; Stesiak, Ronald R

2014-02-21

Macroscopic electroencephalographic (EEG) fields can be an explicit top-down neocortical mechanism that directly drives bottom-up processes that describe memory, attention, and other neuronal processes. The top-down mechanism considered is macrocolumnar EEG firings in neocortex, as described by a statistical mechanics of neocortical interactions (SMNI), developed as a magnetic vector potential A. The bottom-up process considered is Ca(2+) waves prominent in synaptic and extracellular processes that are considered to greatly influence neuronal firings. Here, the complimentary effects are considered, i.e., the influence of A on Ca(2+) momentum, p. The canonical momentum of a charged particle in an electromagnetic field, Π=p+qA (SI units), is calculated, where the charge of Ca(2+) is q=-2e, e is the magnitude of the charge of an electron. Calculations demonstrate that macroscopic EEG A can be quite influential on the momentum p of Ca(2+) ions, in both classical and quantum mechanics. Molecular scales of Ca(2+) wave dynamics are coupled with A fields developed at macroscopic regional scales measured by coherent neuronal firing activity measured by scalp EEG. The project has three main aspects: fitting A models to EEG data as reported here, building tripartite models to develop A models, and studying long coherence times of Ca(2+) waves in the presence of A due to coherent neuronal firings measured by scalp EEG. The SMNI model supports a mechanism wherein the p+qA interaction at tripartite synapses, via a dynamic centering mechanism (DCM) to control background synaptic activity, acts to maintain short-term memory (STM) during states of selective attention. © 2013 Published by Elsevier Ltd. All rights reserved.

Deep learning for hybrid EEG-fNIRS brain-computer interface: application to motor imagery classification.

PubMed

Chiarelli, Antonio Maria; Croce, Pierpaolo; Merla, Arcangelo; Zappasodi, Filippo

2018-06-01

Brain-computer interface (BCI) refers to procedures that link the central nervous system to a device. BCI was historically performed using electroencephalography (EEG). In the last years, encouraging results were obtained by combining EEG with other neuroimaging technologies, such as functional near infrared spectroscopy (fNIRS). A crucial step of BCI is brain state classification from recorded signal features. Deep artificial neural networks (DNNs) recently reached unprecedented complex classification outcomes. These performances were achieved through increased computational power, efficient learning algorithms, valuable activation functions, and restricted or back-fed neurons connections. By expecting significant overall BCI performances, we investigated the capabilities of combining EEG and fNIRS recordings with state-of-the-art deep learning procedures. We performed a guided left and right hand motor imagery task on 15 subjects with a fixed classification response time of 1 s and overall experiment length of 10 min. Left versus right classification accuracy of a DNN in the multi-modal recording modality was estimated and it was compared to standalone EEG and fNIRS and other classifiers. At a group level we obtained significant increase in performance when considering multi-modal recordings and DNN classifier with synergistic effect. BCI performances can be significantly improved by employing multi-modal recordings that provide electrical and hemodynamic brain activity information, in combination with advanced non-linear deep learning classification procedures.

Rhythmic EEG patterns in extremely preterm infants: Classification and association with brain injury and outcome.

PubMed

Weeke, Lauren C; van Ooijen, Inge M; Groenendaal, Floris; van Huffelen, Alexander C; van Haastert, Ingrid C; van Stam, Carolien; Benders, Manon J; Toet, Mona C; Hellström-Westas, Lena; de Vries, Linda S

2017-12-01

Classify rhythmic EEG patterns in extremely preterm infants and relate these to brain injury and outcome. Retrospective analysis of 77 infants born <28 weeks gestational age (GA) who had a 2-channel EEG during the first 72 h after birth. Patterns detected by the BrainZ seizure detection algorithm were categorized: ictal discharges, periodic epileptiform discharges (PEDs) and other waveforms. Brain injury was assessed with sequential cranial ultrasound (cUS) and MRI at term-equivalent age. Neurodevelopmental outcome was assessed with the BSITD-III (2 years) and WPPSI-III-NL (5 years). Rhythmic patterns were observed in 62.3% (ictal 1.3%, PEDs 44%, other waveforms 86.3%) with multiple patterns in 36.4%. Ictal discharges were only observed in one and excluded from further analyses. The EEG location of the other waveforms (p<0.05), but not PEDs (p=0.238), was significantly associated with head position. No relation was found between the median total duration of each pattern and injury on cUS and MRI or cognition at 2 and 5 years. Clear ictal discharges are rare in extremely preterm infants. PEDs are common but their significance is unclear. Rhythmic waveforms related to head position are likely artefacts. Rhythmic EEG patterns may have a different significance in extremely preterm infants. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Wavelet entropy: a new tool for analysis of short duration brain electrical signals.

PubMed

Rosso, O A; Blanco, S; Yordanova, J; Kolev, V; Figliola, A; Schürmann, M; Başar, E

2001-01-30

Since traditional electrical brain signal analysis is mostly qualitative, the development of new quantitative methods is crucial for restricting the subjectivity in the study of brain signals. These methods are particularly fruitful when they are strongly correlated with intuitive physical concepts that allow a better understanding of brain dynamics. Here, new method based on orthogonal discrete wavelet transform (ODWT) is applied. It takes as a basic element the ODWT of the EEG signal, and defines the relative wavelet energy, the wavelet entropy (WE) and the relative wavelet entropy (RWE). The relative wavelet energy provides information about the relative energy associated with different frequency bands present in the EEG and their corresponding degree of importance. The WE carries information about the degree of order/disorder associated with a multi-frequency signal response, and the RWE measures the degree of similarity between different segments of the signal. In addition, the time evolution of the WE is calculated to give information about the dynamics in the EEG records. Within this framework, the major objective of the present work was to characterize in a quantitative way functional dynamics of order/disorder microstates in short duration EEG signals. For that aim, spontaneous EEG signals under different physiological conditions were analyzed. Further, specific quantifiers were derived to characterize how stimulus affects electrical events in terms of frequency synchronization (tuning) in the event related potentials.

EEG phase reset due to auditory attention: an inverse time-scale approach.

PubMed

Low, Yin Fen; Strauss, Daniel J

2009-08-01

We propose a novel tool to evaluate the electroencephalograph (EEG) phase reset due to auditory attention by utilizing an inverse analysis of the instantaneous phase for the first time. EEGs were acquired through auditory attention experiments with a maximum entropy stimulation paradigm. We examined single sweeps of auditory late response (ALR) with the complex continuous wavelet transform. The phase in the frequency band that is associated with auditory attention (6-10 Hz, termed as theta-alpha border) was reset to the mean phase of the averaged EEGs. The inverse transform was applied to reconstruct the phase-modified signal. We found significant enhancement of the N100 wave in the reconstructed signal. Analysis of the phase noise shows the effects of phase jittering on the generation of the N100 wave implying that a preferred phase is necessary to generate the event-related potential (ERP). Power spectrum analysis shows a remarkable increase of evoked power but little change of total power after stabilizing the phase of EEGs. Furthermore, by resetting the phase only at the theta border of no attention data to the mean phase of attention data yields a result that resembles attention data. These results show strong connections between EEGs and ERP, in particular, we suggest that the presentation of an auditory stimulus triggers the phase reset process at the theta-alpha border which leads to the emergence of the N100 wave. It is concluded that our study reinforces other studies on the importance of the EEG in ERP genesis.

[Electroencephalographic effects of chlorphenesin carbamate, a new central muscle relaxant, in rabbits (author's transl)].

PubMed

Watanabe, S; Araki, H; Kawasaki, H; Ueki, S

1977-05-01

Electroencephalographic (EEG) effects of chlorphenesin carbamate were investigated in rabbits with chronic electrode implants, and compared with those of chlormezanone and methocarbamol. Chlorphenesin carbamate (50 mg/kg i.v., 100 mg/kg i.d.) induced a drowsy pattern of spontaneous EEG consisting of high voltage slow waves in the cortex and amygdala, and desynchronization of hippocampal theta waves. Chlormezanone also elicited similar EEG changes but such were much more potent than chlorphenesin carbamate. Methocarbamol showed no effect on spontaneous EEG. Chlorphenesin carbamate caused sedation in this period and muscle relaxation was more potent than that of chlormezanone. The EEG arousal response to auditory stimulation and to electric stimulation of the posterior hypothalamus, centromedian thalamus and mesencephalic reticular formation was slightly depressed by chlorphenesin carbamate. Chlorphenesin carbamate, as with chlormezanone, markedly depressed the limbic afterdischarges elicited by hippocampal stimulation. These EEG effects of chlorphenesin carbamate were qualitatively similar to but much weaker than those of chlormezanone, whereas the muscle relaxant effect of chlorphenesin carbamate was more potent than that of chlormezanone.

Acute effect of carbamazepine on corticothalamic 5-9-Hz and thalamocortical spindle (10-16-Hz) oscillations in the rat.

PubMed

Zheng, Thomas W; O'Brien, Terence J; Kulikova, Sofya P; Reid, Christopher A; Morris, Margaret J; Pinault, Didier

2014-03-01

A major side effect of carbamazepine (CBZ), a drug used to treat neurological and neuropsychiatric disorders, is drowsiness, a state characterized by increased slow-wave oscillations with the emergence of sleep spindles in the electroencephalogram (EEG). We conducted cortical EEG and thalamic cellular recordings in freely moving or lightly anesthetized rats to explore the impact of CBZ within the intact corticothalamic (CT)-thalamocortical (TC) network, more specifically on CT 5-9-Hz and TC spindle (10-16-Hz) oscillations. Two to three successive 5-9-Hz waves were followed by a spindle in the cortical EEG. A single systemic injection of CBZ (20 mg/kg) induced a significant increase in the power of EEG 5-9-Hz oscillations and spindles. Intracellular recordings of glutamatergic TC neurons revealed 5-9-Hz depolarizing wave-hyperpolarizing wave sequences prolonged by robust, rhythmic spindle-frequency hyperpolarizing waves. This hybrid sequence occurred during a slow hyperpolarizing trough, and was at least 10 times more frequent under the CBZ condition than under the control condition. The hyperpolarizing waves reversed at approximately -70 mV, and became depolarizing when recorded with KCl-filled intracellular micropipettes, indicating that they were GABAA receptor-mediated potentials. In neurons of the GABAergic thalamic reticular nucleus, the principal source of TC GABAergic inputs, CBZ augmented both the number and the duration of sequences of rhythmic spindle-frequency bursts of action potentials. This indicates that these GABAergic neurons are responsible for the generation of at least the spindle-frequency hyperpolarizing waves in TC neurons. In conclusion, CBZ potentiates GABAA receptor-mediated TC spindle oscillations. Furthermore, we propose that CT 5-9-Hz waves can trigger TC spindles. © 2013 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Hypoglycemia-Induced Changes in the Electroencephalogram

PubMed Central

Blaabjerg, Lykke; Juhl, Claus B.

2016-01-01

Hypoglycemia is defined by an abnormally low blood glucose level. The condition develops when rates of glucose entry into the systematic circulation are reduced relative to the glucose uptake by the tissues. A cardinal manifestation of hypoglycemia arises from inadequate supply of glucose to the brain, where glucose is the primary metabolic fuel. The brain is one of the first organs to be affected by hypoglycemia. Shortage of glucose in the brain, or neuroglycopenia, results in a gradual loss of cognitive functions causing slower reaction time, blurred speech, loss of consciousness, seizures, and ultimately death, as the hypoglycemia progresses. The electrical activity in the brain represents the metabolic state of the brain cells and can be measured by electroencephalography (EEG). An association between hypoglycemia and changes in the EEG has been demonstrated, although blood glucose levels alone do not seem to predict neuroglycopenia. This review provides an overview of the current literature regarding changes in the EEG during episodes of low blood glucose. PMID:27464753

Effects of partial sleep deprivation on slow waves during non-rapid eye movement sleep: A high density EEG investigation.

PubMed

Plante, David T; Goldstein, Michael R; Cook, Jesse D; Smith, Richard; Riedner, Brady A; Rumble, Meredith E; Jelenchick, Lauren; Roth, Andrea; Tononi, Giulio; Benca, Ruth M; Peterson, Michael J

2016-02-01

Changes in slow waves during non-rapid eye movement (NREM) sleep in response to acute total sleep deprivation are well-established measures of sleep homeostasis. This investigation utilized high-density electroencephalography (hdEEG) to examine topographic changes in slow waves during repeated partial sleep deprivation. Twenty-four participants underwent a 6-day sleep restriction protocol. Spectral and period-amplitude analyses of sleep hdEEG data were used to examine changes in slow wave energy, count, amplitude, and slope relative to baseline. Changes in slow wave energy were dependent on the quantity of NREM sleep utilized for analysis, with widespread increases during sleep restriction and recovery when comparing data from the first portion of the sleep period, but restricted to recovery sleep if the entire sleep episode was considered. Period-amplitude analysis was less dependent on the quantity of NREM sleep utilized, and demonstrated topographic changes in the count, amplitude, and distribution of slow waves, with frontal increases in slow wave amplitude, numbers of high-amplitude waves, and amplitude/slopes of low amplitude waves resulting from partial sleep deprivation. Topographic changes in slow waves occur across the course of partial sleep restriction and recovery. These results demonstrate a homeostatic response to partial sleep loss in humans. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Evaluation of a Low-cost and Low-noise Active Dry Electrode for Long-term Biopotential Recording

PubMed Central

Pourahmad, Ali; Mahnam, Amin

2016-01-01

Wet Ag/AgCl electrodes, although very popular in clinical diagnosis, are not appropriate for expanding applications of wearable biopotential recording systems which are used repetitively and for a long time. Here, the development of a low-cost and low-noise active dry electrode is presented. The performance of the new electrodes was assessed for recording electrocardiogram (ECG) and electroencephalogram (EEG) in comparison with that of typical gel-based electrodes in a series of long-term recording experiments. The ECG signal recorded by these electrodes was well comparable with usual Ag/AgCl electrodes with a correlation up to 99.5% and mean power line noise below 6.0 μVRMS. The active electrodes were also used to measure alpha wave and steady state visual evoked potential by recording EEG. The recorded signals were comparable in quality with signals recorded by standard gel electrodes, suggesting that the designed electrodes can be employed in EEG-based rehabilitation systems and brain-computer interface applications. The mean power line noise in EEG signals recorded by the active electrodes (1.3 μVRMS) was statistically lower than when conventional gold cup electrodes were used (2.0 μVRMS) with a significant level of 0.05, and the new electrodes appeared to be more resistant to the electromagnetic interferences. These results suggest that the developed low-cost electrodes can be used to develop wearable monitoring systems for long-term biopotential recording. PMID:28028495

Complexity of EEG-signal in Time Domain - Possible Biomedical Application

NASA Astrophysics Data System (ADS)

Klonowski, Wlodzimierz; Olejarczyk, Elzbieta; Stepien, Robert

2002-07-01

Human brain is a highly complex nonlinear system. So it is not surprising that in analysis of EEG-signal, which represents overall activity of the brain, the methods of Nonlinear Dynamics (or Chaos Theory as it is commonly called) can be used. Even if the signal is not chaotic these methods are a motivating tool to explore changes in brain activity due to different functional activation states, e.g. different sleep stages, or to applied therapy, e.g. exposure to chemical agents (drugs) and physical factors (light, magnetic field). The methods supplied by Nonlinear Dynamics reveal signal characteristics that are not revealed by linear methods like FFT. Better understanding of principles that govern dynamics and complexity of EEG-signal can help to find `the signatures' of different physiological and pathological states of human brain, quantitative characteristics that may find applications in medical diagnostics.

Brain computer interface for operating a robot

NASA Astrophysics Data System (ADS)

Nisar, Humaira; Balasubramaniam, Hari Chand; Malik, Aamir Saeed

2013-10-01

A Brain-Computer Interface (BCI) is a hardware/software based system that translates the Electroencephalogram (EEG) signals produced by the brain activity to control computers and other external devices. In this paper, we will present a non-invasive BCI system that reads the EEG signals from a trained brain activity using a neuro-signal acquisition headset and translates it into computer readable form; to control the motion of a robot. The robot performs the actions that are instructed to it in real time. We have used the cognitive states like Push, Pull to control the motion of the robot. The sensitivity and specificity of the system is above 90 percent. Subjective results show a mixed trend of the difficulty level of the training activities. The quantitative EEG data analysis complements the subjective results. This technology may become very useful for the rehabilitation of disabled and elderly people.

Using EEG to Study Cognitive Development: Issues and Practices

ERIC Educational Resources Information Center

Bell, Martha Ann; Cuevas, Kimberly

2012-01-01

Developmental research is enhanced by use of multiple methodologies for examining psychological processes. The electroencephalogram (EEG) is an efficient and relatively inexpensive method for the study of developmental changes in brain-behavior relations. In this review, we highlight some of the challenges for using EEG in cognitive development…

ERP evidence of preserved early memory function in term infants with neonatal encephalopathy following therapeutic hypothermia.

PubMed

Pfister, Katie M; Zhang, Lei; Miller, Neely C; Hultgren, Solveig; Boys, Chris J; Georgieff, Michael K

2016-12-01

Neonatal encephalopathy (NE) carries high risk for neurodevelopmental impairments. Therapeutic hypothermia (TH) reduces this risk, particularly for moderate encephalopathy (ME). Nevertheless, these infants often have subtle functional deficits, including abnormal memory function. Detection of deficits at the earliest possible time-point would allow for intervention during a period of maximal brain plasticity. Recognition memory function in 22 infants with NE treated with TH was compared to 23 healthy controls using event-related potentials (ERPs) at 2 wk of age. ERPs were recorded to mother's voice alternating with a stranger's voice to assess attentional responses (P2), novelty detection (slow wave), and discrimination between familiar and novel (difference wave). Development was tested at 12 mo using the Bayley Scales of Infant Development, Third Edition (BSID-III). The NE group showed similar ERP components and BSID-III scores to controls. However, infants with NE showed discrimination at midline leads (P = 0.01), whereas controls showed discrimination in the left hemisphere (P = 0.05). Normal MRI (P = 0.05) and seizure-free electroencephalogram (EEG) (P = 0.04) correlated positively with outcomes. Infants with NE have preserved recognition memory function after TH. The spatially different recognition memory processing after early brain injury may represent compensatory changes in the brain circuitry and reflect a benefit of TH.

Brain gene expression during REM sleep depends on prior waking experience.

PubMed

Ribeiro, S; Goyal, V; Mello, C V; Pavlides, C

1999-01-01

In most mammalian species studied, two distinct and successive phases of sleep, slow wave (SW), and rapid eye movement (REM), can be recognized on the basis of their EEG profiles and associated behaviors. Both phases have been implicated in the offline sensorimotor processing of daytime events, but the molecular mechanisms remain elusive. We studied brain expression of the plasticity-associated immediate-early gene (IEG) zif-268 during SW and REM sleep in rats exposed to rich sensorimotor experience in the preceding waking period. Whereas nonexposed controls show generalized zif-268 down-regulation during SW and REM sleep, zif-268 is upregulated during REM sleep in the cerebral cortex and the hippocampus of exposed animals. We suggest that this phenomenon represents a window of increased neuronal plasticity during REM sleep that follows enriched waking experience.

A Pharmacokinetics-Neural Mass Model (PK-NMM) for the Simulation of EEG Activity during Propofol Anesthesia

PubMed Central

Liang, Zhenhu; Duan, Xuejing; Su, Cui; Voss, Logan; Sleigh, Jamie; Li, Xiaoli

2015-01-01

Modeling the effects of anesthetic drugs on brain activity is very helpful in understanding anesthesia mechanisms. The aim of this study was to set up a combined model to relate actual drug levels to EEG dynamics and behavioral states during propofol-induced anesthesia. We proposed a new combined theoretical model based on a pharmacokinetics (PK) model and a neural mass model (NMM), which we termed PK-NMM—with the aim of simulating electroencephalogram (EEG) activity during propofol-induced general anesthesia. The PK model was used to derive propofol effect-site drug concentrations (C eff) based on the actual drug infusion regimen. The NMM model took C eff as the control parameter to produce simulated EEG-like (sEEG) data. For comparison, we used real prefrontal EEG (rEEG) data of nine volunteers undergoing propofol anesthesia from a previous experiment. To see how well the sEEG could describe the dynamic changes of neural activity during anesthesia, the rEEG data and the sEEG data were compared with respect to: power-frequency plots; nonlinear exponent (permutation entropy (PE)); and bispectral SynchFastSlow (SFS) parameters. We found that the PK-NMM model was able to reproduce anesthesia EEG-like signals based on the estimated drug concentration and patients’ condition. The frequency spectrum indicated that the frequency power peak of the sEEG moved towards the low frequency band as anesthesia deepened. Different anesthetic states could be differentiated by the PE index. The correlation coefficient of PE was 0.80±0.13 (mean±standard deviation) between rEEG and sEEG for all subjects. Additionally, SFS could track the depth of anesthesia and the SFS of rEEG and sEEG were highly correlated with a correlation coefficient of 0.77±0.13. The PK-NMM model could simulate EEG activity and might be a useful tool for understanding the action of propofol on brain activity. PMID:26720495

The Causal Inference of Cortical Neural Networks during Music Improvisations

PubMed Central

Wan, Xiaogeng; Crüts, Björn; Jensen, Henrik Jeldtoft

2014-01-01

We present an EEG study of two music improvisation experiments. Professional musicians with high level of improvisation skills were asked to perform music either according to notes (composed music) or in improvisation. Each piece of music was performed in two different modes: strict mode and “let-go” mode. Synchronized EEG data was measured from both musicians and listeners. We used one of the most reliable causality measures: conditional Mutual Information from Mixed Embedding (MIME), to analyze directed correlations between different EEG channels, which was combined with network theory to construct both intra-brain and cross-brain networks. Differences were identified in intra-brain neural networks between composed music and improvisation and between strict mode and “let-go” mode. Particular brain regions such as frontal, parietal and temporal regions were found to play a key role in differentiating the brain activities between different playing conditions. By comparing the level of degree centralities in intra-brain neural networks, we found a difference between the response of musicians and the listeners when comparing the different playing conditions. PMID:25489852

The causal inference of cortical neural networks during music improvisations.

PubMed

Wan, Xiaogeng; Crüts, Björn; Jensen, Henrik Jeldtoft

2014-01-01

We present an EEG study of two music improvisation experiments. Professional musicians with high level of improvisation skills were asked to perform music either according to notes (composed music) or in improvisation. Each piece of music was performed in two different modes: strict mode and "let-go" mode. Synchronized EEG data was measured from both musicians and listeners. We used one of the most reliable causality measures: conditional Mutual Information from Mixed Embedding (MIME), to analyze directed correlations between different EEG channels, which was combined with network theory to construct both intra-brain and cross-brain networks. Differences were identified in intra-brain neural networks between composed music and improvisation and between strict mode and "let-go" mode. Particular brain regions such as frontal, parietal and temporal regions were found to play a key role in differentiating the brain activities between different playing conditions. By comparing the level of degree centralities in intra-brain neural networks, we found a difference between the response of musicians and the listeners when comparing the different playing conditions.

Comparative analysis of brain EEG signals generated from the right and left hand while writing

NASA Astrophysics Data System (ADS)

Sardesai, Neha; Jamali Mahabadi, S. E.; Meng, Qinglei; Choa, Fow-Sen

2016-05-01

This paper provides a comparative analysis of right handed people and left handed people when they write with both their hands. Two left handed and one right handed subject were asked to write their respective names on a paper using both, their left and right handed, and their brain signals were measured using EEG. Similarly, they were asked to perform simple mathematical calculations using both their hand. The data collected from the EEG from writing with both hands is compared. It is observed that though it is expected that the right brain only would contribute to left handed writing and vice versa, it is not so. When a right handed person writes with his/her left hand, the initial instinct is to go for writing with the right hand. Hence, both parts of the brain are active when a subject writes with the other hand. However, when the activity is repeated, the brain learns to expect to write with the other hand as the activity is repeated and then only the expected part of the brain is active.

Substance P and the neurokinin-1 receptor regulate electroencephalogram non-rapid eye movement sleep slow-wave activity locally

PubMed Central

Zielinski, Mark R.; Karpova, Svetlana A.; Yang, Xiaomei; Gerashchenko, Dmitry

2014-01-01

The neuropeptide substance P is an excitatory neurotransmitter produced by various cells including neurons and microglia that is involved in regulating inflammation and cerebral blood flow—functions that affect sleep and slow-wave activity (SWA). Substance P is the major ligand for the neurokinin-1 receptor (NK-1R), which is found throughout the brain including the cortex. The NK-1R is found on sleep-active cortical neurons expressing neuronal nitric oxide synthase whose activity is associated with SWA. We determined the effects of local cortical administration of a NK-1R agonist (substance P-fragment 1, 7) and a NK-1R antagonist (CP96345) on sleep and SWA in mice. The NK-1R agonist significantly enhanced SWA for several hours when applied locally to the cortex of the ipsilateral hemisphere as the electroencephalogram (EEG) electrode but not after application to the contralateral hemisphere when compared to saline vehicle control injections. In addition, a significant compensatory reduction in SWA was found after the NK-1R agonist-induced enhancements in SWA. Conversely, injections of the NK-1R antagonist into the cortex of the ipsilateral hemisphere of the EEG electrode attenuated SWA compared to vehicle injections but this effect was not found after injections of the NK-1R antagonist into contralateral hemisphere as the EEG electrode. Non-rapid eye movement sleep and rapid eye movement sleep duration responses after NK-1R agonist and antagonist injections were not significantly different from the responses to the vehicle. Our findings indicate that the substance P and the NK-1R are involved in regulating SWA locally. PMID:25301750

Topographic mapping of electroencephalography coherence in hypnagogic state.

PubMed

Tanaka, H; Hayashi, M; Hori, T

1998-04-01

The present study examined the topographic characteristics of hypnagogic electroencephalography (EEG), using topographic mapping of EEG power and coherence corresponding to nine EEG stages (Hori's hypnagogic EEG stages). EEG stages 1 and 2, the EEG stages 3-8, and the EEG stage 9 each correspond with standard sleep stage W, 1 and 2, respectively. The dominant topographic components of delta and theta activities increased clearly from the vertex sharp-wave stage (the EEG stages 6 and 7) in the anterior-central areas. The dominant topographic component of alpha 3 activities increased clearly from the EEG stage 9 in the anterior-central areas. The dominant topographic component of sigma activities increased clearly from the EEG stage 8 in the central-parietal area. These results suggested basic sleep process might start before the onset of sleep stage 2 or of the manually scored spindles.

Optimizing Performance Through Sleep-Wake Homeostasis: Integrating Physiological and Neurobehavioral Data via Ambulatory Acquisition in Laboratory and Field Environments

DTIC Science & Technology

2009-04-18

intake and sophisticated signal processing of electroencephalographic (EEG), electrooculographic ( EOG ), electrocardiographic (ECG), and...electroencephalographic (EEG), electrooculographic ( EOG ), electrocardiographic (ECG), and electromyographic (EMG) physiological signals . It also has markedly...ambulatory physiological acquisition and quantitative signal processing; (2) Brain Amp MR Plus 32 and BrainVision Recorder Professional Software Package for

Guidelines and Best Practices for Electrophysiological Data Collection, Analysis and Reporting in Autism

ERIC Educational Resources Information Center

Webb, Sara Jane; Bernier, Raphael; Henderson, Heather A.; Johnson, Mark H.; Jones, Emily J. H.; Lerner, Matthew D.; McPartland, James C.; Nelson, Charles A.; Rojas, Donald C.; Townsend, Jeanne; Westerfield, Marissa

2015-01-01

The EEG reflects the activation of large populations of neurons that act in synchrony and propagate to the scalp surface. This activity reflects both the brain's background electrical activity and when the brain is being challenged by a task. Despite strong theoretical and methodological arguments for the use of EEG in understanding the…

First and Second Language in the Brain: Neuronal Correlates of Language Processing and Spelling Strategies

ERIC Educational Resources Information Center

Weber, Patricia; Kozel, Nadja; Purgstaller, Christian; Kargl, Reinhard; Schwab, Daniela; Fink, Andreas

2013-01-01

This study explores oscillatory brain activity by means of event-related synchronization and desynchronization (%ERS/ERD) of EEG activity during the use of phonological and orthographic-morphological spelling strategies in L2 (English) and L1 (German) in native German speaking children. EEG was recorded while 33 children worked on a task requiring…

Surface EEG Shows that Functional Segregation via Phase Coupling Contributes to the Neural Substrate of Mental Calculations

ERIC Educational Resources Information Center

Dimitriadis, Stavros I.; Kanatsouli, Kassiani; Laskaris, Nikolaos A.; Tsirka, Vasso; Vourkas, Michael; Micheloyannis, Sifis

2012-01-01

Multichannel EEG traces from healthy subjects are used to investigate the brain's self-organisation tendencies during two different mental arithmetic tasks. By making a comparison with a control-state in the form of a classification problem, we can detect and quantify the changes in coordinated brain activity in terms of functional connectivity.…

Linear and Nonlinear Analysis of Brain Dynamics in Children with Cerebral Palsy

ERIC Educational Resources Information Center

Sajedi, Firoozeh; Ahmadlou, Mehran; Vameghi, Roshanak; Gharib, Masoud; Hemmati, Sahel

2013-01-01

This study was carried out to determine linear and nonlinear changes of brain dynamics and their relationships with the motor dysfunctions in CP children. For this purpose power of EEG frequency bands (as a linear analysis) and EEG fractality (as a nonlinear analysis) were computed in eyes-closed resting state and statistically compared between 26…