Recognition of neural brain activity patterns correlated with complex motor activity

NASA Astrophysics Data System (ADS)

Kurkin, Semen; Musatov, Vyacheslav Yu.; Runnova, Anastasia E.; Grubov, Vadim V.; Efremova, Tatyana Yu.; Zhuravlev, Maxim O.

2018-04-01

In this paper, based on the apparatus of artificial neural networks, a technique for recognizing and classifying patterns corresponding to imaginary movements on electroencephalograms (EEGs) obtained from a group of untrained subjects was developed. The works on the selection of the optimal type, topology, training algorithms and neural network parameters were carried out from the point of view of the most accurate and fast recognition and classification of patterns on multi-channel EEGs associated with the imagination of movements. The influence of the number and choice of the analyzed channels of a multichannel EEG on the quality of recognition of imaginary movements was also studied, and optimal configurations of electrode arrangements were obtained. The effect of pre-processing of EEG signals is analyzed from the point of view of improving the accuracy of recognition of imaginary movements.

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.

Neurofeedback Training for BCI Control

NASA Astrophysics Data System (ADS)

Neuper, Christa; Pfurtscheller, Gert

Brain-computer interface (BCI) systems detect changes in brain signals that reflect human intention, then translate these signals to control monitors or external devices (for a comprehensive review, see [1]). BCIs typically measure electrical signals resulting from neural firing (i.e. neuronal action potentials, Electroencephalogram (ECoG), or Electroencephalogram (EEG)). Sophisticated pattern recognition and classification algorithms convert neural activity into the required control signals. BCI research has focused heavily on developing powerful signal processing and machine learning techniques to accurately classify neural activity [2-4].

Recognizing of stereotypic patterns in epileptic EEG using empirical modes and wavelets

NASA Astrophysics Data System (ADS)

Grubov, V. V.; Sitnikova, E.; Pavlov, A. N.; Koronovskii, A. A.; Hramov, A. E.

2017-11-01

Epileptic activity in the form of spike-wave discharges (SWD) appears in the electroencephalogram (EEG) during absence seizures. This paper evaluates two approaches for detecting stereotypic rhythmic activities in EEG, i.e., the continuous wavelet transform (CWT) and the empirical mode decomposition (EMD). The CWT is a well-known method of time-frequency analysis of EEG, whereas EMD is a relatively novel approach for extracting signal's waveforms. A new method for pattern recognition based on combination of CWT and EMD is proposed. It was found that this combined approach resulted to the sensitivity of 86.5% and specificity of 92.9% for sleep spindles and 97.6% and 93.2% for SWD, correspondingly. Considering strong within- and between-subjects variability of sleep spindles, the obtained efficiency in their detection was high in comparison with other methods based on CWT. It is concluded that the combination of a wavelet-based approach and empirical modes increases the quality of automatic detection of stereotypic patterns in rat's EEG.

Electroencephalogram and Heart Rate Measures of Working Memory at 5 and 10 Months of Age

ERIC Educational Resources Information Center

Cuevas, Kimberly; Bell, Martha Ann; Marcovitch, Stuart; Calkins, Susan D.

2012-01-01

We recorded electroencephalogram (EEG; 6-9 Hz) and heart rate (HR) from infants at 5 and 10 months of age during baseline and performance on the looking A-not-B task of infant working memory (WM). Longitudinal baseline-to-task comparisons revealed WM-related increases in EEG power (all electrodes) and EEG coherence (medial frontal-occipital…

The Dynamics of Visual Experience, an EEG Study of Subjective Pattern Formation

PubMed Central

Elliott, Mark A.; Twomey, Deirdre; Glennon, Mark

2012-01-01

Background Since the origin of psychological science a number of studies have reported visual pattern formation in the absence of either physiological stimulation or direct visual-spatial references. Subjective patterns range from simple phosphenes to complex patterns but are highly specific and reported reliably across studies. Methodology/Principal Findings Using independent-component analysis (ICA) we report a reduction in amplitude variance consistent with subjective-pattern formation in ventral posterior areas of the electroencephalogram (EEG). The EEG exhibits significantly increased power at delta/theta and gamma-frequencies (point and circle patterns) or a series of high-frequency harmonics of a delta oscillation (spiral patterns). Conclusions/Significance Subjective-pattern formation may be described in a way entirely consistent with identical pattern formation in fluids or granular flows. In this manner, we propose subjective-pattern structure to be represented within a spatio-temporal lattice of harmonic oscillations which bind topographically organized visual-neuronal assemblies by virtue of low frequency modulation. PMID:22292053

Multimodal neuroelectric interface development

NASA Technical Reports Server (NTRS)

Trejo, Leonard J.; Wheeler, Kevin R.; Jorgensen, Charles C.; Rosipal, Roman; Clanton, Sam T.; Matthews, Bryan; Hibbs, Andrew D.; Matthews, Robert; Krupka, Michael

2003-01-01

We are developing electromyographic and electroencephalographic methods, which draw control signals for human-computer interfaces from the human nervous system. We have made progress in four areas: 1) real-time pattern recognition algorithms for decoding sequences of forearm muscle activity associated with control gestures; 2) signal-processing strategies for computer interfaces using electroencephalogram (EEG) signals; 3) a flexible computation framework for neuroelectric interface research; and d) noncontact sensors, which measure electromyogram or EEG signals without resistive contact to the body.

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.

21 CFR 882.1855 - Electroencephalogram (EEG) telemetry system.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Electroencephalogram (EEG) telemetry system. 882.1855 Section 882.1855 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES NEUROLOGICAL DEVICES Neurological Diagnostic Devices § 882.1855...

21 CFR 882.1855 - Electroencephalogram (EEG) telemetry system.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Electroencephalogram (EEG) telemetry system. 882.1855 Section 882.1855 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES NEUROLOGICAL DEVICES Neurological Diagnostic Devices § 882.1855...

Helmet system broadcasts electroencephalograms of wearer

NASA Technical Reports Server (NTRS)

Westbrook, R. M.; Zuccaro, J. J.

1966-01-01

EEG monitoring system consisting of nonirritating sponge-type electrodes, amplifiers, and a battery-powered wireless transmitter, all mounted in the subjects helmet, obtains electroencephalograms /EEGs/ of pilots and astronauts performing tasks under stress. After a quick initial fitting, the helmet can be removed and replaced without adjustments.

21 CFR 882.1420 - Electroencephalogram (EEG) signal spectrum analyzer.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Electroencephalogram (EEG) signal spectrum analyzer. 882.1420 Section 882.1420 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES NEUROLOGICAL DEVICES Neurological Diagnostic Devices § 882...

21 CFR 882.1420 - Electroencephalogram (EEG) signal spectrum analyzer.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Electroencephalogram (EEG) signal spectrum analyzer. 882.1420 Section 882.1420 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES NEUROLOGICAL DEVICES Neurological Diagnostic Devices § 882...

Electro-encephalogram based brain-computer interface: improved performance by mental practice and concentration skills.

PubMed

Mahmoudi, Babak; Erfanian, Abbas

2006-11-01

Mental imagination is the essential part of the most EEG-based communication systems. Thus, the quality of mental rehearsal, the degree of imagined effort, and mind controllability should have a major effect on the performance of electro-encephalogram (EEG) based brain-computer interface (BCI). It is now well established that mental practice using motor imagery improves motor skills. The effects of mental practice on motor skill learning are the result of practice on central motor programming. According to this view, it seems logical that mental practice should modify the neuronal activity in the primary sensorimotor areas and consequently change the performance of EEG-based BCI. For developing a practical BCI system, recognizing the resting state with eyes opened and the imagined voluntary movement is important. For this purpose, the mind should be able to focus on a single goal for a period of time, without deviation to another context. In this work, we are going to examine the role of mental practice and concentration skills on the EEG control during imaginative hand movements. The results show that the mental practice and concentration can generally improve the classification accuracy of the EEG patterns. It is found that mental training has a significant effect on the classification accuracy over the primary motor cortex and frontal area.

Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion

PubMed Central

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. PMID:28558002

Patterns of Brain-Electrical Activity during Declarative Memory Performance in 10-Month-Old Infants

ERIC Educational Resources Information Center

Morasch, Katherine C.; Bell, Martha Ann

2009-01-01

This study of infant declarative memory concurrently examined brain-electrical activity and deferred imitation performance in 10-month-old infants. Continuous electroencephalogram (EEG) measures were collected throughout the activity-matched baseline, encoding (modeling) and retrieval (delayed test) phases of a within-subjects deferred imitation…

Behavioral Reactivity and Approach-Withdrawal Bias in Infancy

PubMed Central

Hane, Amie Ashley; Fox, Nathan A.; Henderson, Heather A.; Marshall, Peter J.

2008-01-01

Seven hundred and seventy nine infants were screened at 4 months of age for motor and emotional reactivity. At age 9 months, infants who showed extreme patterns of motor and negative (n = 75) or motor and positive (n = 73) reactivity and an unselected control group (n = 86) were administered the Laboratory Temperament Assessment Battery (Lab-TAB), and baseline electroencephalogram (EEG) data were collected. Negatively reactive infants showed significantly more avoidance than positively reactive infants and displayed a pattern of right frontal EEG asymmetry. Positively reactive infants exhibited significantly more approach behavior than controls and exhibited a pattern of left frontal asymmetry. Results support the notion that approach-withdrawal bias underlies reactivity in infancy. PMID:18793079

Probabilistic Common Spatial Patterns for Multichannel EEG Analysis

PubMed Central

Chen, Zhe; Gao, Xiaorong; Li, Yuanqing; Brown, Emery N.; Gao, Shangkai

2015-01-01

Common spatial patterns (CSP) is a well-known spatial filtering algorithm for multichannel electroencephalogram (EEG) analysis. In this paper, we cast the CSP algorithm in a probabilistic modeling setting. Specifically, probabilistic CSP (P-CSP) is proposed as a generic EEG spatio-temporal modeling framework that subsumes the CSP and regularized CSP algorithms. The proposed framework enables us to resolve the overfitting issue of CSP in a principled manner. We derive statistical inference algorithms that can alleviate the issue of local optima. In particular, an efficient algorithm based on eigendecomposition is developed for maximum a posteriori (MAP) estimation in the case of isotropic noise. For more general cases, a variational algorithm is developed for group-wise sparse Bayesian learning for the P-CSP model and for automatically determining the model size. The two proposed algorithms are validated on a simulated data set. Their practical efficacy is also demonstrated by successful applications to single-trial classifications of three motor imagery EEG data sets and by the spatio-temporal pattern analysis of one EEG data set recorded in a Stroop color naming task. PMID:26005228

Neural network classification of clinical neurophysiological data for acute care monitoring

NASA Technical Reports Server (NTRS)

Sgro, Joseph

1994-01-01

The purpose of neurophysiological monitoring of the 'acute care' patient is to allow the accurate recognition of changing or deteriorating neurological function as close to the moment of occurrence as possible, thus permitting immediate intervention. Results confirm that: (1) neural networks are able to accurately identify electroencephalogram (EEG) patterns and evoked potential (EP) wave components, and measuring EP waveform latencies and amplitudes; (2) neural networks are able to accurately detect EP and EEG recordings that have been contaminated by noise; (3) the best performance was obtained consistently with the back propagation network for EP and the HONN for EEG's; (4) neural network performed consistently better than other methods evaluated; and (5) neural network EEG and EP analyses are readily performed on multichannel data.

Automatic burst detection for the EEG of the preterm infant.

PubMed

Jennekens, Ward; Ruijs, Loes S; Lommen, Charlotte M L; Niemarkt, Hendrik J; Pasman, Jaco W; van Kranen-Mastenbroek, Vivianne H J M; Wijn, Pieter F F; van Pul, Carola; Andriessen, Peter

2011-10-01

To aid with prognosis and stratification of clinical treatment for preterm infants, a method for automated detection of bursts, interburst-intervals (IBIs) and continuous patterns in the electroencephalogram (EEG) is developed. Results are evaluated for preterm infants with normal neurological follow-up at 2 years. The detection algorithm (MATLAB®) for burst, IBI and continuous pattern is based on selection by amplitude, time span, number of channels and numbers of active electrodes. Annotations of two neurophysiologists were used to determine threshold values. The training set consisted of EEG recordings of four preterm infants with postmenstrual age (PMA, gestational age + postnatal age) of 29-34 weeks. Optimal threshold values were based on overall highest sensitivity. For evaluation, both observers verified detections in an independent dataset of four EEG recordings with comparable PMA. Algorithm performance was assessed by calculation of sensitivity and positive predictive value. The results of algorithm evaluation are as follows: sensitivity values of 90% ± 6%, 80% ± 9% and 97% ± 5% for burst, IBI and continuous patterns, respectively. Corresponding positive predictive values were 88% ± 8%, 96% ± 3% and 85% ± 15%, respectively. In conclusion, the algorithm showed high sensitivity and positive predictive values for bursts, IBIs and continuous patterns in preterm EEG. Computer-assisted analysis of EEG may allow objective and reproducible analysis for clinical treatment.

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.

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…

The effects of mild hypothermia on thiopental-induced electroencephalogram burst suppression.

PubMed

Kim, J H; Kim, S H; Yoo, S K; Kim, J Y; Nam, Y T

1998-07-01

Thiopental intravenous injections before temporary clipping and mild hypothermia have protective effects in the setting of cerebral ischemia, and are used clinically in some centers. However, it is not known whether mild hypothermia affects thiopental-induced electroencephalogram (EEG) burst suppression. In this study, the authors compared the onset and duration of EEG suppression by thiopental in normothermic (n=10) and mildly hypothermic (n=10) patients undergoing cerebral aneurysm surgery. Spectral analysis was used to compare the prethiopentonal continuous EEG patterns in normothermic and mild hypothermic patients. The patients' body temperatures were controlled by a circulating water mattress and intravenous fluids (normothermia = 36.4+/-0.1 degrees C, mild hypothermia = 33.3+/-0.1 degrees C). Immediately before temporary clipping, thiopental sodium (5 mg/kg) was administered intravenously. Onset time (the amount of time from thiopental injection to the first complete EEG suppression), duration of suppression (the amount of time from the first complete EEG suppression to recovery on continuous EEG from burst suppression), and maximum duration of isoelectric EEG (the longest time interval between two bursts during burst suppression) were measured. Onset time was shortened (25.8+/-1.4 versus 43.5+/-5.6 seconds), and duration of suppression (531.0+/-56.6 versus 165.0+/-16.9 seconds) and the maximum duration of isoelectric EEG (47.7+/-5.8 versus 22.8+/-2.0 seconds) were prolonged in the patients with mild hypothermia. In two normothermic patients, the standard dose of thiopental did not produce burst suppression, but only a mild decrease in spectral edge frequency. The authors concluded that the effects of mild hypothermia on thiopental-induced EEG suppression are not simply additive, but synergistic.

Cognitive workload modulation through degraded visual stimuli: a single-trial EEG study

NASA Astrophysics Data System (ADS)

Yu, K.; Prasad, I.; Mir, H.; Thakor, N.; Al-Nashash, H.

2015-08-01

Objective. Our experiments explored the effect of visual stimuli degradation on cognitive workload. Approach. We investigated the subjective assessment, event-related potentials (ERPs) as well as electroencephalogram (EEG) as measures of cognitive workload. Main results. These experiments confirm that degradation of visual stimuli increases cognitive workload as assessed by subjective NASA task load index and confirmed by the observed P300 amplitude attenuation. Furthermore, the single-trial multi-level classification using features extracted from ERPs and EEG is found to be promising. Specifically, the adopted single-trial oscillatory EEG/ERP detection method achieved an average accuracy of 85% for discriminating 4 workload levels. Additionally, we found from the spatial patterns obtained from EEG signals that the frontal parts carry information that can be used for differentiating workload levels. Significance. Our results show that visual stimuli can modulate cognitive workload, and the modulation can be measured by the single trial EEG/ERP detection method.

Radiotelemetry recording of electroencephalogram in piglets during rest.

PubMed

Saito, Toshiyuki; Watanabe, Yasuko; Nemoto, Tetsu; Kasuya, Etsuko; Sakumoto, Ryosuke

2005-04-13

A wireless recording system was developed to study the electroencephalogram (EEG) in unrestrained, male Landrace piglets. Under general anesthesia, ball-tipped silver/silver chloride electrodes for EEG recording were implanted onto the dura matter of the parietal and frontal cortex of the piglets. A pair of miniature preamplifiers and transmitters was then mounted on the surface of the skull. To examine whether other bioelectrical activities interfere with the EEG measurements, an electrocardiogram (ECG) or electromyogram (EMG) of the neck was simultaneously recorded with the EEG. Next, wire electrodes for recording movement of the eyelid were implanted with EEG electrodes, and EEG and eyelid movements were simultaneously measured. Power spectral analysis using a Fast Fourier Transformation (FFT) algorithm indicates that EEG was successfully recorded in unrestrained piglets, at rest, during the daytime in the absence of interference from ECG, EMG or eyelid movements. These data indicate the feasibility of using our radiotelemetry system for measurement of EEG under these conditions.

Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN.

PubMed

Bascil, M Serdar; Tesneli, Ahmet Y; Temurtas, Feyzullah

2016-09-01

Brain computer interface (BCI) is a new communication way between man and machine. It identifies mental task patterns stored in electroencephalogram (EEG). So, it extracts brain electrical activities recorded by EEG and transforms them machine control commands. The main goal of BCI is to make available assistive environmental devices for paralyzed people such as computers and makes their life easier. This study deals with feature extraction and mental task pattern recognition on 2-D cursor control from EEG as offline analysis approach. The hemispherical power density changes are computed and compared on alpha-beta frequency bands with only mental imagination of cursor movements. First of all, power spectral density (PSD) features of EEG signals are extracted and high dimensional data reduced by principle component analysis (PCA) and independent component analysis (ICA) which are statistical algorithms. In the last stage, all features are classified with two types of support vector machine (SVM) which are linear and least squares (LS-SVM) and three different artificial neural network (ANN) structures which are learning vector quantization (LVQ), multilayer neural network (MLNN) and probabilistic neural network (PNN) and mental task patterns are successfully identified via k-fold cross validation technique.

Electroencephalogram (EEG) (For Parents)

MedlinePlus

... Most EEGs are done to diagnose and monitor seizure disorders. EEGs also can identify causes of other problems, ... are very safe. If your child has a seizure disorder, your doctor might want to stimulate and record ...

Detection of burst suppression patterns in EEG using recurrence rate.

PubMed

Liang, Zhenhu; Wang, Yinghua; Ren, Yongshao; Li, Duan; Voss, Logan; Sleigh, Jamie; Li, Xiaoli

2014-01-01

Burst suppression is a unique electroencephalogram (EEG) pattern commonly seen in cases of severely reduced brain activity such as overdose of general anesthesia. It is important to detect burst suppression reliably during the administration of anesthetic or sedative agents, especially for cerebral-protective treatments in various neurosurgical diseases. This study investigates recurrent plot (RP) analysis for the detection of the burst suppression pattern (BSP) in EEG. The RP analysis is applied to EEG data containing BSPs collected from 14 patients. Firstly we obtain the best selection of parameters for RP analysis. Then, the recurrence rate (RR), determinism (DET), and entropy (ENTR) are calculated. Then RR was selected as the best BSP index one-way analysis of variance (ANOVA) and multiple comparison tests. Finally, the performance of RR analysis is compared with spectral analysis, bispectral analysis, approximate entropy, and the nonlinear energy operator (NLEO). ANOVA and multiple comparison tests showed that the RR could detect BSP and that it was superior to other measures with the highest sensitivity of suppression detection (96.49%, P = 0.03). Tracking BSP patterns is essential for clinical monitoring in critically ill and anesthetized patients. The purposed RR may provide an effective burst suppression detector for developing new patient monitoring systems.

Epilepsy in fragile-X-syndrome mimicking panayiotopoulos syndrome: Description of three patients.

PubMed

Bonanni, Paolo; Casellato, Susanna; Fabbro, Franco; Negrin, Susanna

2017-10-01

Fragile-X-syndrome is the most common cause of inherited intellectual disability. Epilepsy is reported to occur in 10-20% of individuals with Fragile-X-syndrome. A frequent seizure/electroencephalogram (EEG) pattern resembles that of benign rolandic epilepsy. We describe the clinical features, EEG findings and evolution in three patients affected by Fragile-X-syndrome and epilepsy mimicking Panayiotopoulos syndrome. Age at seizure onset was between 4 and about 7 years. Seizures pattern comprised a constellation of autonomic symptoms with unilateral deviation of the eyes and ictal syncope. Duration of the seizures could be brief or lengthy. Interictal EEGs revealed functional multifocal abnormalities. The evolution was benign in all patients with seizures remission before the age of 14. This observation expands the spectrum of benign epileptic phenotypes present in Fragile-X-syndrome and may be quite helpful in guiding anticonvulsant management and counseling families as to expectations regarding seizure remission. © 2017 Wiley Periodicals, Inc.

Adaptive noise canceling of electrocardiogram artifacts in single channel electroencephalogram.

PubMed

Cho, Sung Pil; Song, Mi Hye; Park, Young Cheol; Choi, Ho Seon; Lee, Kyoung Joung

2007-01-01

A new method for estimating and eliminating electrocardiogram (ECG) artifacts from single channel scalp electroencephalogram (EEG) is proposed. The proposed method consists of emphasis of QRS complex from EEG using least squares acceleration (LSA) filter, generation of synchronized pulse with R-peak and ECG artifacts estimation and elimination using adaptive filter. The performance of the proposed method was evaluated using simulated and real EEG recordings, we found that the ECG artifacts were successfully estimated and eliminated in comparison with the conventional multi-channel techniques, which are independent component analysis (ICA) and ensemble average (EA) method. From this we can conclude that the proposed method is useful for the detecting and eliminating the ECG artifacts from single channel EEG and simple to use for ambulatory/portable EEG monitoring system.

Predictive value of EEG in postanoxic encephalopathy: A quantitative model-based approach.

PubMed

Efthymiou, Evdokia; Renzel, Roland; Baumann, Christian R; Poryazova, Rositsa; Imbach, Lukas L

2017-10-01

The majority of comatose patients after cardiac arrest do not regain consciousness due to severe postanoxic encephalopathy. Early and accurate outcome prediction is therefore essential in determining further therapeutic interventions. The electroencephalogram is a standardized and commonly available tool used to estimate prognosis in postanoxic patients. The identification of pathological EEG patterns with poor prognosis relies however primarily on visual EEG scoring by experts. We introduced a model-based approach of EEG analysis (state space model) that allows for an objective and quantitative description of spectral EEG variability. We retrospectively analyzed standard EEG recordings in 83 comatose patients after cardiac arrest between 2005 and 2013 in the intensive care unit of the University Hospital Zürich. Neurological outcome was assessed one month after cardiac arrest using the Cerebral Performance Category. For a dynamic and quantitative EEG analysis, we implemented a model-based approach (state space analysis) to quantify EEG background variability independent from visual scoring of EEG epochs. Spectral variability was compared between groups and correlated with clinical outcome parameters and visual EEG patterns. Quantitative assessment of spectral EEG variability (state space velocity) revealed significant differences between patients with poor and good outcome after cardiac arrest: Lower mean velocity in temporal electrodes (T4 and T5) was significantly associated with poor prognostic outcome (p<0.005) and correlated with independently identified visual EEG patterns such as generalized periodic discharges (p<0.02). Receiver operating characteristic (ROC) analysis confirmed the predictive value of lower state space velocity for poor clinical outcome after cardiac arrest (AUC 80.8, 70% sensitivity, 15% false positive rate). Model-based quantitative EEG analysis (state space analysis) provides a novel, complementary marker for prognosis in postanoxic encephalopathy. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Frequency analysis of electroencephalogram recorded from a bottlenose dolphin (Tursiops truncatus) with a novel method during transportation by truck

PubMed Central

Tamura, Shinichi; Okada, Yasunori; Morimoto, Shigeru; Ohta, Mitsuaki; Uchida, Naoyuki

2010-01-01

In order to obtain information regarding the correlation between an electroencephalogram (EEG) and the state of a dolphin, we developed a noninvasive recording method of EEG of a bottlenose dolphin (Tursiops truncatus) and an extraction method of true-EEG (EEG) from recorded-EEG (R-EEG) based on a human EEG recording method, and then carried out frequency analysis during transportation by truck. The frequency detected in the EEG of dolphin during apparent awakening was divided conveniently into three bands (5–15, 15–25, and 25–40 Hz) based on spectrum profiles. Analyses of the relationship between power ratio and movement of the dolphin revealed that the power ratio of dolphin in a situation when it was being quiet was evenly distributed among the three bands. These results suggested that the EEG of a dolphin could be detected accurately by this method, and that the frequency analysis of the detected EEG seemed to provide useful information for understanding the central nerve activity of these animals. PMID:20429047

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.

Investigating Patterns for Self-Induced Emotion Recognition from EEG Signals.

PubMed

Zhuang, Ning; Zeng, Ying; Yang, Kai; Zhang, Chi; Tong, Li; Yan, Bin

2018-03-12

Most current approaches to emotion recognition are based on neural signals elicited by affective materials such as images, sounds and videos. However, the application of neural patterns in the recognition of self-induced emotions remains uninvestigated. In this study we inferred the patterns and neural signatures of self-induced emotions from electroencephalogram (EEG) signals. The EEG signals of 30 participants were recorded while they watched 18 Chinese movie clips which were intended to elicit six discrete emotions, including joy, neutrality, sadness, disgust, anger and fear. After watching each movie clip the participants were asked to self-induce emotions by recalling a specific scene from each movie. We analyzed the important features, electrode distribution and average neural patterns of different self-induced emotions. Results demonstrated that features related to high-frequency rhythm of EEG signals from electrodes distributed in the bilateral temporal, prefrontal and occipital lobes have outstanding performance in the discrimination of emotions. Moreover, the six discrete categories of self-induced emotion exhibit specific neural patterns and brain topography distributions. We achieved an average accuracy of 87.36% in the discrimination of positive from negative self-induced emotions and 54.52% in the classification of emotions into six discrete categories. Our research will help promote the development of comprehensive endogenous emotion recognition methods.

Investigating Patterns for Self-Induced Emotion Recognition from EEG Signals

PubMed Central

Zeng, Ying; Yang, Kai; Tong, Li; Yan, Bin

2018-01-01

Most current approaches to emotion recognition are based on neural signals elicited by affective materials such as images, sounds and videos. However, the application of neural patterns in the recognition of self-induced emotions remains uninvestigated. In this study we inferred the patterns and neural signatures of self-induced emotions from electroencephalogram (EEG) signals. The EEG signals of 30 participants were recorded while they watched 18 Chinese movie clips which were intended to elicit six discrete emotions, including joy, neutrality, sadness, disgust, anger and fear. After watching each movie clip the participants were asked to self-induce emotions by recalling a specific scene from each movie. We analyzed the important features, electrode distribution and average neural patterns of different self-induced emotions. Results demonstrated that features related to high-frequency rhythm of EEG signals from electrodes distributed in the bilateral temporal, prefrontal and occipital lobes have outstanding performance in the discrimination of emotions. Moreover, the six discrete categories of self-induced emotion exhibit specific neural patterns and brain topography distributions. We achieved an average accuracy of 87.36% in the discrimination of positive from negative self-induced emotions and 54.52% in the classification of emotions into six discrete categories. Our research will help promote the development of comprehensive endogenous emotion recognition methods. PMID:29534515

Electroencephalogram and Alzheimer's Disease: Clinical and Research Approaches

PubMed Central

Tsolaki, Anthoula; Kazis, Dimitrios; Kompatsiaris, Ioannis; Kosmidou, Vasiliki; Tsolaki, Magda

2014-01-01

Alzheimer's disease (AD) is a neurodegenerative disorder that is characterized by cognitive deficits, problems in activities of daily living, and behavioral disturbances. Electroencephalogram (EEG) has been demonstrated as a reliable tool in dementia research and diagnosis. The application of EEG in AD has a wide range of interest. EEG contributes to the differential diagnosis and the prognosis of the disease progression. Additionally such recordings can add important information related to the drug effectiveness. This review is prepared to form a knowledge platform for the project entitled “Cognitive Signal Processing Lab,” which is in progress in Information Technology Institute in Thessaloniki. The team tried to focus on the main research fields of AD via EEG and recent published studies. PMID:24868482

Frontal-temporal synchronization of EEG signals quantified by order patterns cross recurrence analysis during propofol anesthesia.

PubMed

Shalbaf, Reza; Behnam, Hamid; Sleigh, Jamie W; Steyn-Ross, D Alistair; Steyn-Ross, Moira L

2015-05-01

Characterizing brain dynamics during anesthesia is a main current challenge in anesthesia study. Several single channel electroencephalogram (EEG)-based commercial monitors like the Bispectral index (BIS) have suggested to examine EEG signal. But, the BIS index has obtained numerous critiques. In this study, we evaluate the concentration-dependent effect of the propofol on long-range frontal-temporal synchronization of EEG signals collected from eight subjects during a controlled induction and recovery design. We used order patterns cross recurrence plot and provide an index named order pattern laminarity (OPL) to assess changes in neuronal synchronization as the mechanism forming the foundation of conscious perception. The prediction probability of 0.9 and 0.84 for OPL and BIS specified that the OPL index correlated more strongly with effect-site propofol concentration. Also, our new index makes faster reaction to transients in EEG recordings based on pharmacokinetic and pharmacodynamic model parameters and demonstrates less variability at the point of loss of consciousness (standard deviation of 0.04 for OPL compared with 0.09 for BIS index). The result show that the OPL index can estimate anesthetic state of patient more efficiently than the BIS index in lightly sedated state with more tolerant of artifacts.

Chaos based encryption system for encrypting electroencephalogram signals.

PubMed

Lin, Chin-Feng; Shih, Shun-Han; Zhu, Jin-De

2014-05-01

In the paper, we use the Microsoft Visual Studio Development Kit and C# programming language to implement a chaos-based electroencephalogram (EEG) encryption system involving three encryption levels. A chaos logic map, initial value, and bifurcation parameter for the map were used to generate Level I chaos-based EEG encryption bit streams. Two encryption-level parameters were added to these elements to generate Level II chaos-based EEG encryption bit streams. An additional chaotic map and chaotic address index assignment process was used to implement the Level III chaos-based EEG encryption system. Eight 16-channel EEG Vue signals were tested using the encryption system. The encryption was the most rapid and robust in the Level III system. The test yielded superior encryption results, and when the correct deciphering parameter was applied, the EEG signals were completely recovered. However, an input parameter error (e.g., a 0.00001 % initial point error) causes chaotic encryption bit streams, preventing the recovery of 16-channel EEG Vue signals.

Real-time segmentation of burst suppression patterns in critical care EEG monitoring

PubMed Central

Westover, M. Brandon; Shafi, Mouhsin M.; Ching, ShiNung; Chemali, Jessica J.; Purdon, Patrick L.; Cash, Sydney S.; Brown, Emery N.

2014-01-01

Objective Develop a real-time algorithm to automatically discriminate suppressions from non-suppressions (bursts) in electroencephalograms of critically ill adult patients. Methods A real-time method for segmenting adult ICU EEG data into bursts and suppressions is presented based on thresholding local voltage variance. Results are validated against manual segmentations by two experienced human electroencephalographers. We compare inter-rater agreement between manual EEG segmentations by experts with inter-rater agreement between human vs automatic segmentations, and investigate the robustness of segmentation quality to variations in algorithm parameter settings. We further compare the results of using these segmentations as input for calculating the burst suppression probability (BSP), a continuous measure of depth-of-suppression. Results Automated segmentation was comparable to manual segmentation, i.e. algorithm-vs-human agreement was comparable to human-vs-human agreement, as judged by comparing raw EEG segmentations or the derived BSP signals. Results were robust to modest variations in algorithm parameter settings. Conclusions Our automated method satisfactorily segments burst suppression data across a wide range adult ICU EEG patterns. Performance is comparable to or exceeds that of manual segmentation by human electroencephalographers. Significance Automated segmentation of burst suppression EEG patterns is an essential component of quantitative brain activity monitoring in critically ill and anesthetized adults. The segmentations produced by our algorithm provide a basis for accurate tracking of suppression depth. PMID:23891828

Real-time segmentation of burst suppression patterns in critical care EEG monitoring.

PubMed

Brandon Westover, M; Shafi, Mouhsin M; Ching, Shinung; Chemali, Jessica J; Purdon, Patrick L; Cash, Sydney S; Brown, Emery N

2013-09-30

Develop a real-time algorithm to automatically discriminate suppressions from non-suppressions (bursts) in electroencephalograms of critically ill adult patients. A real-time method for segmenting adult ICU EEG data into bursts and suppressions is presented based on thresholding local voltage variance. Results are validated against manual segmentations by two experienced human electroencephalographers. We compare inter-rater agreement between manual EEG segmentations by experts with inter-rater agreement between human vs automatic segmentations, and investigate the robustness of segmentation quality to variations in algorithm parameter settings. We further compare the results of using these segmentations as input for calculating the burst suppression probability (BSP), a continuous measure of depth-of-suppression. Automated segmentation was comparable to manual segmentation, i.e. algorithm-vs-human agreement was comparable to human-vs-human agreement, as judged by comparing raw EEG segmentations or the derived BSP signals. Results were robust to modest variations in algorithm parameter settings. Our automated method satisfactorily segments burst suppression data across a wide range adult ICU EEG patterns. Performance is comparable to or exceeds that of manual segmentation by human electroencephalographers. Automated segmentation of burst suppression EEG patterns is an essential component of quantitative brain activity monitoring in critically ill and anesthetized adults. The segmentations produced by our algorithm provide a basis for accurate tracking of suppression depth. Copyright © 2013 Elsevier B.V. All rights reserved.

Spatial-temporal-spectral EEG patterns of BOLD functional network connectivity dynamics

NASA Astrophysics Data System (ADS)

Lamoš, Martin; Mareček, Radek; Slavíček, Tomáš; Mikl, Michal; Rektor, Ivan; Jan, Jiří

2018-06-01

Objective. Growing interest in the examination of large-scale brain network functional connectivity dynamics is accompanied by an effort to find the electrophysiological correlates. The commonly used constraints applied to spatial and spectral domains during electroencephalogram (EEG) data analysis may leave part of the neural activity unrecognized. We propose an approach that blindly reveals multimodal EEG spectral patterns that are related to the dynamics of the BOLD functional network connectivity. Approach. The blind decomposition of EEG spectrogram by parallel factor analysis has been shown to be a useful technique for uncovering patterns of neural activity. The simultaneously acquired BOLD fMRI data were decomposed by independent component analysis. Dynamic functional connectivity was computed on the component’s time series using a sliding window correlation, and between-network connectivity states were then defined based on the values of the correlation coefficients. ANOVA tests were performed to assess the relationships between the dynamics of between-network connectivity states and the fluctuations of EEG spectral patterns. Main results. We found three patterns related to the dynamics of between-network connectivity states. The first pattern has dominant peaks in the alpha, beta, and gamma bands and is related to the dynamics between the auditory, sensorimotor, and attentional networks. The second pattern, with dominant peaks in the theta and low alpha bands, is related to the visual and default mode network. The third pattern, also with peaks in the theta and low alpha bands, is related to the auditory and frontal network. Significance. Our previous findings revealed a relationship between EEG spectral pattern fluctuations and the hemodynamics of large-scale brain networks. In this study, we suggest that the relationship also exists at the level of functional connectivity dynamics among large-scale brain networks when no standard spatial and spectral constraints are applied on the EEG data.

High-voltage electroencephalogram spindles in rats, aging and 5-HT2 antagonism.

PubMed

Moyanova, S; Kortenska, L; Kirov, R

1998-03-09

We examined the effects of serotonin-2 (5-hydroxytryptamine-2, 5-HT2) receptor antagonists on the so-called high-voltage spindles (HVS, electroencephalographic patterns, characterized by large amplitude rhythmic waves mainly in the alpha band), recorded from the frontal cortex of young, middle-aged and old freely-moving rats during waking immobility. The study was based on the assumption that the effects of 5-HT2 receptor antagonists on the HVS activity depend on the age of rats, because there is evidence for an age-related decrease in the 5-HT2 binding sites density. Four parameters of the electroencephalogram (EEG) were used to characterize the HVS activity: the square root-transformed EEG peak power in the alpha band, the frequency corresponding to this peak (both measured from the EEG power spectra using the fast Fourier transform), the HVS mean duration, and the HVS incidence (both measured from the EEG records). The EEG parameters were analyzed after i.p. administration of three 5-HT2 receptor antagonists: ketanserin, ritanserin and cyproheptadine. In young rats, the three drugs increased the alpha power, but did not change the alpha peak-corresponding frequency. Ketanserin and ritanserin did not change the HVS mean duration and HVS incidence, while cyproheptadine increased both these parameters in young rats. In middle-aged and old untreated rats, the HVS activity was significantly increased. The three 5-HT2 antagonists did not change the HVS activity in aged rats, which could be due to age-related suppression of the 5-HT2 receptor functions. Copyright 1998 Elsevier Science B.V.

Detection of pseudosinusoidal epileptic seizure segments in the neonatal EEG by cascading a rule-based algorithm with a neural network.

PubMed

Karayiannis, Nicolaos B; Mukherjee, Amit; Glover, John R; Ktonas, Periklis Y; Frost, James D; Hrachovy, Richard A; Mizrahi, Eli M

2006-04-01

This paper presents an approach to detect epileptic seizure segments in the neonatal electroencephalogram (EEG) by characterizing the spectral features of the EEG waveform using a rule-based algorithm cascaded with a neural network. A rule-based algorithm screens out short segments of pseudosinusoidal EEG patterns as epileptic based on features in the power spectrum. The output of the rule-based algorithm is used to train and compare the performance of conventional feedforward neural networks and quantum neural networks. The results indicate that the trained neural networks, cascaded with the rule-based algorithm, improved the performance of the rule-based algorithm acting by itself. The evaluation of the proposed cascaded scheme for the detection of pseudosinusoidal seizure segments reveals its potential as a building block of the automated seizure detection system under development.

Chronic alcohol abuse and the acute sedative and neurophysiologic effects of midazolam.

PubMed

Bauer, L O; Gross, J B; Meyer, R E; Greenblatt, D J

1997-10-01

The aim of the present investigation was to examine benzodiazepine sensitivity in abstinent alcoholics. For this purpose, two escalating doses of the benzodiazepine midazolam were i.v. administered to nine alcohol-dependent patients after 2-3 weeks of abstinence and 12 healthy, non-alcoholic volunteers. A variety of dependent measures were examined, including the power spectrum of the resting electroencephalogram (EEG) and evoked EEG responses, saccadic eye movements, self-reported sedation, and vigilance task performance. Analyses revealed a significant association between plasma midazolam levels and changes in EEG beta power, pattern shift visual evoked potential amplitude, heart rate, and saccade amplitude and velocity. The patient and control groups differed significantly in the onset latencies of their saccadic eye movements, and marginally in EEG beta power, both before and after midazolam. However, no differences were detected between the groups in the dose of midazolam required to produce sedation or in midazolam's neurophysiological effects.

Entropy as an indicator of cerebral perfusion in patients with increased intracranial pressure.

PubMed

Khan, James; Mariappan, Ramamani; Venkatraghavan, Lashmi

2014-07-01

Changes in electroencephalogram (EEG) patterns correlate well with changes in cerebral perfusion pressure (CPP) and hence entropy and bispectral index values may also correlate with CPP. To highlight the potential application of entropy, an EEG-based anesthetic depth monitor, on indicating cerebral perfusion in patients with increased intracranial pressure (ICP), we report two cases of emergency neurosurgical procedure in patients with raised ICP where anesthesia was titrated to entropy values and the entropy values suddenly increased after cranial decompression, reflecting the increase in CPP. Maintaining systemic blood pressure in order to maintain the CPP is the anesthetic goal while managing patients with raised ICP. EEG-based anesthetic depth monitors may hold valuable information on guiding anesthetic management in patients with decreased CPP for better neurological outcome.

On analysis of electroencephalogram by multiresolution-based energetic approach

NASA Astrophysics Data System (ADS)

Sevindir, Hulya Kodal; Yazici, Cuneyt; Siddiqi, A. H.; Aslan, Zafer

2013-10-01

Epilepsy is a common brain disorder where the normal neuronal activity gets affected. Electroencephalography (EEG) is the recording of electrical activity along the scalp produced by the firing of neurons within the brain. The main application of EEG is in the case of epilepsy. On a standard EEG some abnormalities indicate epileptic activity. EEG signals like many biomedical signals are highly non-stationary by their nature. For the investigation of biomedical signals, in particular EEG signals, wavelet analysis have found prominent position in the study for their ability to analyze such signals. Wavelet transform is capable of separating the signal energy among different frequency scales and a good compromise between temporal and frequency resolution is obtained. The present study is an attempt for better understanding of the mechanism causing the epileptic disorder and accurate prediction of occurrence of seizures. In the present paper following Magosso's work [12], we identify typical patterns of energy redistribution before and during the seizure using multiresolution wavelet analysis on Kocaeli University's Medical School's data.

Processed electroencephalogram during donation after cardiac death.

PubMed

Auyong, David B; Klein, Stephen M; Gan, Tong J; Roche, Anthony M; Olson, Daiwai; Habib, Ashraf S

2010-05-01

We present a case series of increased bispectral index values during donation after cardiac death (DCD). During the DCD process, a patient was monitored with processed electroencephalogram (EEG), which showed considerable changes traditionally associated with lighter planes of anesthesia immediately after withdrawal of care. Subsequently, to validate the findings of this case, processed EEG was recorded during 2 other cases in which care was withdrawn without the use of hypnotic or anesthetic drugs. We found that changes in processed EEG immediately after withdrawal of care were not only reproducible, but can happen in the absence of changes in major electromyographic or electrocardiographic artifact. It is well documented that processed EEG is prone to artifacts. However, in the setting of DCD, these changes in processed EEG deserve some consideration. If these changes are not due to artifact, dosing of hypnotic or anesthetic drugs might be warranted. Use of these drugs during DCD based primarily on processed EEG values has never been addressed.

EEG (Electroencephalogram)

MedlinePlus

... in diagnosing brain disorders, especially epilepsy or another seizure disorder. An EEG might also be helpful for diagnosing ... Sometimes seizures are intentionally triggered in people with epilepsy during the test, but appropriate medical care is ...

Regularized Filters for L1-Norm-Based Common Spatial Patterns.

PubMed

Wang, Haixian; Li, Xiaomeng

2016-02-01

The l1 -norm-based common spatial patterns (CSP-L1) approach is a recently developed technique for optimizing spatial filters in the field of electroencephalogram (EEG)-based brain computer interfaces. The l1 -norm-based expression of dispersion in CSP-L1 alleviates the negative impact of outliers. In this paper, we further improve the robustness of CSP-L1 by taking into account noise which does not necessarily have as large a deviation as with outliers. The noise modelling is formulated by using the waveform length of the EEG time course. With the noise modelling, we then regularize the objective function of CSP-L1, in which the l1-norm is used in two folds: one is the dispersion and the other is the waveform length. An iterative algorithm is designed to resolve the optimization problem of the regularized objective function. A toy illustration and the experiments of classification on real EEG data sets show the effectiveness of the proposed method.

Hypoglycemia-Associated EEG Changes in Prepubertal Children With Type 1 Diabetes.

PubMed

Hansen, Grith Lærkholm; Foli-Andersen, Pia; Fredheim, Siri; Juhl, Claus; Remvig, Line Sofie; Rose, Martin H; Rosenzweig, Ivana; Beniczky, Sándor; Olsen, Birthe; Pilgaard, Kasper; Johannesen, Jesper

2016-11-01

The purpose of this study was to explore the possible difference in the electroencephalogram (EEG) pattern between euglycemia and hypoglycemia in children with type 1 diabetes (T1D) during daytime and during sleep. The aim is to develop a hypoglycemia alarm based on continuous EEG measurement and real-time signal processing. Eight T1D patients aged 6-12 years were included. A hyperinsulinemic hypoglycemic clamp was performed to induce hypoglycemia both during daytime and during sleep. Continuous EEG monitoring was performed. For each patient, quantitative EEG (qEEG) measures were calculated. A within-patient analysis was conducted comparing hypoglycemia versus euglycemia changes in the qEEG. The nonparametric Wilcoxon signed rank test was performed. A real-time analyzing algorithm developed for adults was applied. The qEEG showed significant differences in specific bands comparing hypoglycemia to euglycemia both during daytime and during sleep. In daytime the EEG-based algorithm identified hypoglycemia in all children on average at a blood glucose (BG) level of 2.5 ± 0.5 mmol/l and 18.4 (ranging from 0 to 55) minutes prior to blood glucose nadir. During sleep the nighttime algorithm did not perform. We found significant differences in the qEEG in euglycemia and hypoglycemia both during daytime and during sleep. The algorithm developed for adults detected hypoglycemia in all children during daytime. The algorithm had too many false alarms during the night because it was more sensitive to deep sleep EEG patterns than hypoglycemia-related EEG changes. An algorithm for nighttime EEG is needed for accurate detection of nocturnal hypoglycemic episodes in children. This study indicates that a hypoglycemia alarm may be developed using real-time continuous EEG monitoring. © 2016 Diabetes Technology Society.

EEG power during waking and NREM sleep in primary insomnia.

PubMed

Wu, You Meme; Pietrone, Regina; Cashmere, J David; Begley, Amy; Miewald, Jean M; Germain, Anne; Buysse, Daniel J

2013-10-15

Pathophysiological models of insomnia invoke the concept of 24-hour hyperarousal, which could lead to symptoms and physiological findings during waking and sleep. We hypothesized that this arousal could be seen in the waking electroencephalogram (EEG) of individuals with primary insomnia (PI), and that waking EEG power would correlate with non-REM (NREM) EEG. Subjects included 50 PI and 32 good sleeper controls (GSC). Five minutes of eyes closed waking EEG were collected at subjects' usual bedtimes, followed by polysomnography (PSG) at habitual sleep times. An automated algorithm and visual editing were used to remove artifacts from waking and sleep EEGs, followed by power spectral analysis to estimate power from 0.5-32 Hz. We did not find significant differences in waking or NREM EEG spectral power of PI and GSC. Significant correlations between waking and NREM sleep power were observed across all frequency bands in the PI group and in most frequency bands in the GSC group. The absence of significant differences between groups in waking or NREM EEG power suggests that our sample was not characterized by a high degree of cortical arousal. The consistent correlations between waking and NREM EEG power suggest that, in samples with elevated NREM EEG beta activity, waking EEG power may show a similar pattern.

Electroencephalography findings in patients presenting to the ED for evaluation of seizures.

PubMed

Kadambi, Pooja; Hart, Kimberly W; Adeoye, Opeolu M; Lindsell, Christopher J; Knight, William A

2015-01-01

Status epilepticus is a life-threatening, time-sensitive emergency. Acquiring an electroencephalogram (EEG) in the emergency department (ED) could impact therapeutic and disposition decisions for patients with suspected status epilepticus. The objective of this study is to estimate the proportion of EEGs diagnostic for seizures in patients presenting to an ED with a complaint of seizures. This retrospective chart review included adults presenting to the ED of an urban, academic, tertiary care hospital with suspected seizures or status epilepticus, who received an EEG within 24 hours of hospital admission. Data abstraction was performed by a single, trained, nonblinded abstractor. Seizures were defined as an epileptologist's diagnosis of either seizures or status epilepticus on EEG. The proportion of patients with seizures is given with confidence interval95 (CI95). Of 120 included patients, 67 (56%) had a history of epilepsy. Mean age was 52 years (SD, 16), 58% were White, and 61% were male. Within 24 hours, 3% had an EEG diagnostic for seizures. Electroencephalogram was obtained in the ED in 32 (27%) of 120 (CI95, 19%-35%), and 2 (6%) of 32 (CI95, 1%-19%) had seizures. Electroencephalogram was performed inpatient for 88 (73%) of 120 (CI95, 65%-81%), and 2 (2%) of 88 (CI95, 0.5%-7.1%) had seizures. Only 3% of ED patients with suspected seizures or status epilepticus had EEG confirmation of seizures within 24 hours. Early EEG acquisition in the ED may identify a group of patients amenable to ED observation and subsequent discharge from the hospital. Copyright © 2014 Elsevier Inc. All rights reserved.

Use of EEG in critically ill children and neonates in the United States of America.

PubMed

Gaínza-Lein, Marina; Sánchez Fernández, Iván; Loddenkemper, Tobias

2017-06-01

The objective of the study was to estimate the proportion of patients who receive an electroencephalogram (EEG) among five common indications for EEG monitoring in the intensive care unit: traumatic brain injury (TBI), extracorporeal membrane oxygenation (ECMO), cardiac arrest, cardiac surgery and hypoxic-ischemic encephalopathy (HIE). We performed a retrospective cross-sectional descriptive study utilizing the Kids' Inpatient Database (KID) for the years 2010-2012. The KID is the largest pediatric inpatient database in the USA and it is based on discharge reports created by hospitals for billing purposes. We evaluated the use of electroencephalogram (EEG) or video-electroencephalogram in critically ill children who were mechanically ventilated. The KID database had a population of approximately 6,000,000 pediatric admissions. Among 22,127 admissions of critically ill children who had mechanical ventilation, 1504 (6.8%) admissions had ECMO, 9201 (41.6%) TBI, 4068 (18.4%) HIE, 2774 (12.5%) cardiac arrest, and 4580 (20.7%) cardiac surgery. All five conditions had a higher proportion of males, with the highest (69.8%) in the TBI group. The mortality rates ranged from 7.02 to 39.9% (lowest in cardiac surgery and highest in ECMO). The estimated use of EEG was 1.6% in cardiac surgery, 4.1% in TBI, 7.2% in ECMO, 8.2% in cardiac arrest, and 12.1% in HIE, with an overall use of 5.8%. Among common indications for EEG monitoring in critically ill children and neonates, the estimated proportion of patients actually having an EEG is low.

Structure constrained semi-nonnegative matrix factorization for EEG-based motor imagery classification.

PubMed

Lu, Na; Li, Tengfei; Pan, Jinjin; Ren, Xiaodong; Feng, Zuren; Miao, Hongyu

2015-05-01

Electroencephalogram (EEG) provides a non-invasive approach to measure the electrical activities of brain neurons and has long been employed for the development of brain-computer interface (BCI). For this purpose, various patterns/features of EEG data need to be extracted and associated with specific events like cue-paced motor imagery. However, this is a challenging task since EEG data are usually non-stationary time series with a low signal-to-noise ratio. In this study, we propose a novel method, called structure constrained semi-nonnegative matrix factorization (SCS-NMF), to extract the key patterns of EEG data in time domain by imposing the mean envelopes of event-related potentials (ERPs) as constraints on the semi-NMF procedure. The proposed method is applicable to general EEG time series, and the extracted temporal features by SCS-NMF can also be combined with other features in frequency domain to improve the performance of motor imagery classification. Real data experiments have been performed using the SCS-NMF approach for motor imagery classification, and the results clearly suggest the superiority of the proposed method. Comparison experiments have also been conducted. The compared methods include ICA, PCA, Semi-NMF, Wavelets, EMD and CSP, which further verified the effectivity of SCS-NMF. The SCS-NMF method could obtain better or competitive performance over the state of the art methods, which provides a novel solution for brain pattern analysis from the perspective of structure constraint. Copyright © 2015 Elsevier Ltd. All rights reserved.

[Use of quantitative electroencephalogram in patients with septic shock].

PubMed

Ma, Yujie; Ouyang, Bin; Guan, Xiangdong

2016-01-19

To observe the quantitative electroencephalogram (qEEG) characteristics of the patients with septic shock in intensive care unit (ICU), and to find the early presence and severity of septic-associated encephalopathy (SAE) in these patients. During November 2014 to August 2015, 26 cases with septic shock were included from the ICU of the First Affiliated Hospital, Sun Yat-sen University.During the same period, 14 healthy volunteers were included as control. The brain function instrument was used to monitor the patients by the bed, placing leads as the internationally used 10-20 system, bipolar longitudinal F3-P3, F4-P4 four channels, and then consecutive clips of 5 minutes was chosen, using the average value of the clips, the amplitude integrated electroencephalogram (aEEG), relative frequency band energy, spectrum entropy, relative alpha ariability to carry out statistical analysis.And the qEEG features of septic shock patients with different Glasgow coma scale (GCS) levels were also analyzed. (1) 96% of the patients with septic shock had EEG abnormalities.Alpha frequency band energy, alpha ariability, aEEG amplitude, spectrum entropy decreased significantly (P<0.05=, while the delta frequency band energy significantly increased (P<0.05=. (2) aEEG amplitude decline appeared in 34% of patients with septic shock, and within the septic shock groups, amplitude decreased significantly (P<0.05= in patients with GCS under five. Patients with septic shock tends to have diffuse inhibition in EEG, and the inhibition degree can reflect cerebral lesion degree; changes of EEG frequency as early warning indicators of brain damage are sensitive, and the decline of amplitude often indicates critical injury.

Evaluation of a Piezoelectric System as an Alternative to Electroencephalogram/ Electromyogram Recordings in Mouse Sleep Studies

PubMed Central

Mang, Géraldine M.; Nicod, Jérôme; Emmenegger, Yann; Donohue, Kevin D.; O'Hara, Bruce F.; Franken, Paul

2014-01-01

Study Objectives: Traditionally, sleep studies in mammals are performed using electroencephalogram/electromyogram (EEG/EMG) recordings to determine sleep-wake state. In laboratory animals, this requires surgery and recovery time and causes discomfort to the animal. In this study, we evaluated the performance of an alternative, noninvasive approach utilizing piezoelectric films to determine sleep and wakefulness in mice by simultaneous EEG/EMG recordings. The piezoelectric films detect the animal's movements with high sensitivity and the regularity of the piezo output signal, related to the regular breathing movements characteristic of sleep, serves to automatically determine sleep. Although the system is commercially available (Signal Solutions LLC, Lexington, KY), this is the first statistical validation of various aspects of sleep. Design: EEG/EMG and piezo signals were recorded simultaneously during 48 h. Setting: Mouse sleep laboratory. Participants: Nine male and nine female CFW outbred mice. Interventions: EEG/EMG surgery. Measurements and Results: The results showed a high correspondence between EEG/EMG-determined and piezo-determined total sleep time and the distribution of sleep over a 48-h baseline recording with 18 mice. Moreover, the piezo system was capable of assessing sleep quality (i.e., sleep consolidation) and interesting observations at transitions to and from rapid eye movement sleep were made that could be exploited in the future to also distinguish the two sleep states. Conclusions: The piezo system proved to be a reliable alternative to electroencephalogram/electromyogram recording in the mouse and will be useful for first-pass, large-scale sleep screens for genetic or pharmacological studies. Citation: Mang GM, Nicod J, Emmenegger Y, Donohue KD, O'Hara BF, Franken P. Evaluation of a piezoelectric system as an alternative to electroencephalogram/electromyogram recordings in mouse sleep studies. SLEEP 2014;37(8):1383-1392. PMID:25083019

Evaluation of a piezoelectric system as an alternative to electroencephalogram/ electromyogram recordings in mouse sleep studies.

PubMed

Mang, Géraldine M; Nicod, Jérôme; Emmenegger, Yann; Donohue, Kevin D; O'Hara, Bruce F; Franken, Paul

2014-08-01

Traditionally, sleep studies in mammals are performed using electroencephalogram/electromyogram (EEG/EMG) recordings to determine sleep-wake state. In laboratory animals, this requires surgery and recovery time and causes discomfort to the animal. In this study, we evaluated the performance of an alternative, noninvasive approach utilizing piezoelectric films to determine sleep and wakefulness in mice by simultaneous EEG/EMG recordings. The piezoelectric films detect the animal's movements with high sensitivity and the regularity of the piezo output signal, related to the regular breathing movements characteristic of sleep, serves to automatically determine sleep. Although the system is commercially available (Signal Solutions LLC, Lexington, KY), this is the first statistical validation of various aspects of sleep. EEG/EMG and piezo signals were recorded simultaneously during 48 h. Mouse sleep laboratory. Nine male and nine female CFW outbred mice. EEG/EMG surgery. The results showed a high correspondence between EEG/EMG-determined and piezo-determined total sleep time and the distribution of sleep over a 48-h baseline recording with 18 mice. Moreover, the piezo system was capable of assessing sleep quality (i.e., sleep consolidation) and interesting observations at transitions to and from rapid eye movement sleep were made that could be exploited in the future to also distinguish the two sleep states. The piezo system proved to be a reliable alternative to electroencephalogram/electromyogram recording in the mouse and will be useful for first-pass, large-scale sleep screens for genetic or pharmacological studies. Mang GM, Nicod J, Emmenegger Y, Donohue KD, O'Hara BF, Franken P. Evaluation of a piezoelectric system as an alternative to electroencephalogram/electromyogram recordings in mouse sleep studies.

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…

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.

Emotion Recognition from Single-Trial EEG Based on Kernel Fisher's Emotion Pattern and Imbalanced Quasiconformal Kernel Support Vector Machine

PubMed Central

Liu, Yi-Hung; Wu, Chien-Te; Cheng, Wei-Teng; Hsiao, Yu-Tsung; Chen, Po-Ming; Teng, Jyh-Tong

2014-01-01

Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher's discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher's emotion pattern (KFEP), and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM) serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS) are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68%) and arousal (84.79%) among all testing methods. PMID:25061837

Emotion recognition from single-trial EEG based on kernel Fisher's emotion pattern and imbalanced quasiconformal kernel support vector machine.

PubMed

Liu, Yi-Hung; Wu, Chien-Te; Cheng, Wei-Teng; Hsiao, Yu-Tsung; Chen, Po-Ming; Teng, Jyh-Tong

2014-07-24

Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher's discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher's emotion pattern (KFEP), and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM) serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS) are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68%) and arousal (84.79%) among all testing methods.

Individually adapted imagery improves brain-computer interface performance in end-users with disability.

PubMed

Scherer, Reinhold; Faller, Josef; Friedrich, Elisabeth V C; Opisso, Eloy; Costa, Ursula; Kübler, Andrea; Müller-Putz, Gernot R

2015-01-01

Brain-computer interfaces (BCIs) translate oscillatory electroencephalogram (EEG) patterns into action. Different mental activities modulate spontaneous EEG rhythms in various ways. Non-stationarity and inherent variability of EEG signals, however, make reliable recognition of modulated EEG patterns challenging. Able-bodied individuals who use a BCI for the first time achieve - on average - binary classification performance of about 75%. Performance in users with central nervous system (CNS) tissue damage is typically lower. User training generally enhances reliability of EEG pattern generation and thus also robustness of pattern recognition. In this study, we investigated the impact of mental tasks on binary classification performance in BCI users with central nervous system (CNS) tissue damage such as persons with stroke or spinal cord injury (SCI). Motor imagery (MI), that is the kinesthetic imagination of movement (e.g. squeezing a rubber ball with the right hand), is the "gold standard" and mainly used to modulate EEG patterns. Based on our recent results in able-bodied users, we hypothesized that pair-wise combination of "brain-teaser" (e.g. mental subtraction and mental word association) and "dynamic imagery" (e.g. hand and feet MI) tasks significantly increases classification performance of induced EEG patterns in the selected end-user group. Within-day (How stable is the classification within a day?) and between-day (How well does a model trained on day one perform on unseen data of day two?) analysis of variability of mental task pair classification in nine individuals confirmed the hypothesis. We found that the use of the classical MI task pair hand vs. feed leads to significantly lower classification accuracy - in average up to 15% less - in most users with stroke or SCI. User-specific selection of task pairs was again essential to enhance performance. We expect that the gained evidence will significantly contribute to make imagery-based BCI technology become accessible to a larger population of users including individuals with special needs due to CNS damage.

Individually Adapted Imagery Improves Brain-Computer Interface Performance in End-Users with Disability

PubMed Central

Scherer, Reinhold; Faller, Josef; Friedrich, Elisabeth V. C.; Opisso, Eloy; Costa, Ursula; Kübler, Andrea; Müller-Putz, Gernot R.

2015-01-01

Brain-computer interfaces (BCIs) translate oscillatory electroencephalogram (EEG) patterns into action. Different mental activities modulate spontaneous EEG rhythms in various ways. Non-stationarity and inherent variability of EEG signals, however, make reliable recognition of modulated EEG patterns challenging. Able-bodied individuals who use a BCI for the first time achieve - on average - binary classification performance of about 75%. Performance in users with central nervous system (CNS) tissue damage is typically lower. User training generally enhances reliability of EEG pattern generation and thus also robustness of pattern recognition. In this study, we investigated the impact of mental tasks on binary classification performance in BCI users with central nervous system (CNS) tissue damage such as persons with stroke or spinal cord injury (SCI). Motor imagery (MI), that is the kinesthetic imagination of movement (e.g. squeezing a rubber ball with the right hand), is the "gold standard" and mainly used to modulate EEG patterns. Based on our recent results in able-bodied users, we hypothesized that pair-wise combination of "brain-teaser" (e.g. mental subtraction and mental word association) and "dynamic imagery" (e.g. hand and feet MI) tasks significantly increases classification performance of induced EEG patterns in the selected end-user group. Within-day (How stable is the classification within a day?) and between-day (How well does a model trained on day one perform on unseen data of day two?) analysis of variability of mental task pair classification in nine individuals confirmed the hypothesis. We found that the use of the classical MI task pair hand vs. feed leads to significantly lower classification accuracy - in average up to 15% less - in most users with stroke or SCI. User-specific selection of task pairs was again essential to enhance performance. We expect that the gained evidence will significantly contribute to make imagery-based BCI technology become accessible to a larger population of users including individuals with special needs due to CNS damage. PMID:25992718

Motif-Synchronization: A new method for analysis of dynamic brain networks with EEG

NASA Astrophysics Data System (ADS)

Rosário, R. S.; Cardoso, P. T.; Muñoz, M. A.; Montoya, P.; Miranda, J. G. V.

2015-12-01

The major aim of this work was to propose a new association method known as Motif-Synchronization. This method was developed to provide information about the synchronization degree and direction between two nodes of a network by counting the number of occurrences of some patterns between any two time series. The second objective of this work was to present a new methodology for the analysis of dynamic brain networks, by combining the Time-Varying Graph (TVG) method with a directional association method. We further applied the new algorithms to a set of human electroencephalogram (EEG) signals to perform a dynamic analysis of the brain functional networks (BFN).

Prognostic EEG patterns in patients resuscitated from cardiac arrest with particular focus on Generalized Periodic Epileptiform Discharges (GPEDs).

PubMed

Milani, P; Malissin, I; Tran-Dinh, Y R; Deye, N; Baud, F; Lévy, B I; Kubis, N

2014-04-01

We assessed clinical and early electrophysiological characteristics, in particular Generalized Periodic Epileptiform Discharges (GPEDs) patterns, of consecutive patients during a 1-year period, hospitalized in the Intensive Care Unit (ICU) after resuscitation following cardiac arrest (CA). Consecutive patients resuscitated from cardiac arrest (CA) with first EEG recordings within 48hours were included. Clinical data were collected from hospital records, in particular therapeutic hypothermia. Electroencephalograms (EEGs) were re-analyzed retrospectively. Sixty-two patients were included. Forty-two patients (68%) were treated with therapeutic hypothermia according to international guidelines. Global mortality was 74% but not significantly different between patients who benefited from therapeutic hypothermia compared to those who did not. All the patients who did not have an initial background activity (36/62; 58%) died. By contrast, initial background activity was present in 26/62 (42%) and among these patients, 16/26 (61%) survived. Electroencephalography demonstrated GPEDs patterns in 5 patients, all treated by therapeutic hypothermia and antiepileptic drugs. One of these survived and showed persistent background activity with responsiveness to benzodiazepine intravenous injection. Patients presenting suppressed background activity, even when treated by hypothermia, have a high probability of poor outcome. Thorough analysis of EEG patterns might help to identify patients with a better chance of survival. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Index finger motor imagery EEG pattern recognition in BCI applications using dictionary cleaned sparse representation-based classification for healthy people

NASA Astrophysics Data System (ADS)

Miao, Minmin; Zeng, Hong; Wang, Aimin; Zhao, Fengkui; Liu, Feixiang

2017-09-01

Electroencephalogram (EEG)-based motor imagery (MI) brain-computer interface (BCI) has shown its effectiveness for the control of rehabilitation devices designed for large body parts of the patients with neurologic impairments. In order to validate the feasibility of using EEG to decode the MI of a single index finger and constructing a BCI-enhanced finger rehabilitation system, we collected EEG data during right hand index finger MI and rest state for five healthy subjects and proposed a pattern recognition approach for classifying these two mental states. First, Fisher's linear discriminant criteria and power spectral density analysis were used to analyze the event-related desynchronization patterns. Second, both band power and approximate entropy were extracted as features. Third, aiming to eliminate the abnormal samples in the dictionary and improve the classification performance of the conventional sparse representation-based classification (SRC) method, we proposed a novel dictionary cleaned sparse representation-based classification (DCSRC) method for final classification. The experimental results show that the proposed DCSRC method gives better classification accuracies than SRC and an average classification accuracy of 81.32% is obtained for five subjects. Thus, it is demonstrated that single right hand index finger MI can be decoded from the sensorimotor rhythms, and the feature patterns of index finger MI and rest state can be well recognized for robotic exoskeleton initiation.

Electroencephalogram-based indices applied to dogs' depth of anaesthesia monitoring.

PubMed

Brás, S; Georgakis, A; Ribeiro, L; Ferreira, D A; Silva, A; Antunes, L; Nunes, C S

2014-12-01

Hypnotic drug administration causes alterations in the electroencephalogram (EEG) in a dose-dependent manner. These changes cannot be identified easily in the raw EEG, therefore EEG based indices were adopted for assessing depth of anaesthesia (DoA). This study examines several indices for estimating dogs' DoA. Data (EEG, clinical end-points) were collected from 8 dogs anaesthetized with propofol. EEG was initially collected without propofol. Then, 100 ml h⁻¹ (1000 mg h⁻¹) of propofol 1% infusion rate was administered until a deep anaesthetic stage was reached. The infusion rate was temporarily increased to 200 ml h⁻¹ (2000 mg h⁻¹) to achieve 80% of burst suppression. The index performance was accessed by correlation coefficient with the propofol concentrations, and prediction probability with the anaesthetic clinical end-points. The temporal entropy and the averaged instantaneous frequency were the best indices because they exhibit: (a) strong correlations with propofol concentrations, (b) high probabilities of predicting anaesthesia clinical end-points. Copyright © 2014 Elsevier Ltd. 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.

Neurodevelopmental Correlates of Theory of Mind in Preschool Children

ERIC Educational Resources Information Center

Sabbagh, Mark A.; Bowman, Lindsay C.; Evraire, Lyndsay E.; Ito, Jennie M. B.

2009-01-01

Baseline electroencephalogram (EEG) data were collected from twenty-nine 4-year-old children who also completed batteries of representational theory-of-mind (RTM) tasks and executive functioning (EF) tasks. Neural sources of children's EEG alpha (6-9 Hz) were estimated and analyzed to determine whether individual differences in regional EEG alpha…

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

Narrow band quantitative and multivariate electroencephalogram analysis of peri-adolescent period.

PubMed

Martinez, E I Rodríguez; Barriga-Paulino, C I; Zapata, M I; Chinchilla, C; López-Jiménez, A M; Gómez, C M

2012-08-24

The peri-adolescent period is a crucial developmental moment of transition from childhood to emergent adulthood. The present report analyses the differences in Power Spectrum (PS) of the Electroencephalogram (EEG) between late childhood (24 children between 8 and 13 years old) and young adulthood (24 young adults between 18 and 23 years old). The narrow band analysis of the Electroencephalogram was computed in the frequency range of 0-20 Hz. The analysis of mean and variance suggested that six frequency ranges presented a different rate of maturation at these ages, namely: low delta, delta-theta, low alpha, high alpha, low beta and high beta. For most of these bands the maturation seems to occur later in anterior sites than posterior sites. Correlational analysis showed a lower pattern of correlation between different frequencies in children than in young adults, suggesting a certain asynchrony in the maturation of different rhythms. The topographical analysis revealed similar topographies of the different rhythms in children and young adults. Principal Component Analysis (PCA) demonstrated the same internal structure for the Electroencephalogram of both age groups. Principal Component Analysis allowed to separate four subcomponents in the alpha range. All these subcomponents peaked at a lower frequency in children than in young adults. The present approaches complement and solve some of the incertitudes when the classical brain broad rhythm analysis is applied. Children have a higher absolute power than young adults for frequency ranges between 0-20 Hz, the correlation of Power Spectrum (PS) with age and the variance age comparison showed that there are six ranges of frequencies that can distinguish the level of EEG maturation in children and adults. The establishment of maturational order of different frequencies and its possible maturational interdependence would require a complete series including all the different ages.

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.

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

Quantitative EEG and Current Source Density Analysis of Combined Antiepileptic Drugs and Dopaminergic Agents in Genetic Epilepsy: Two Case Studies.

PubMed

Emory, Hamlin; Wells, Christopher; Mizrahi, Neptune

2015-07-01

Two adolescent females with absence epilepsy were classified, one as attention deficit and the other as bipolar disorder. Physical and cognitive exams identified hypotension, bradycardia, and cognitive dysfunction. Their initial electroencephalograms (EEGs) were considered slightly slow, but within normal limits. Quantitative EEG (QEEG) data included relative theta excess and low alpha mean frequencies. A combined treatment of antiepileptic drugs with a catecholamine agonist/reuptake inhibitor was sequentially used. Both patients' physical and cognitive functions improved and they have remained seizure free. The clinical outcomes were correlated with statistically significant changes in QEEG measures toward normal Z-scores in both anterior and posterior regions. In addition, low resolution electromagnetic tomography (LORETA) Z-scored source correlation analyses of the initial and treated QEEG data showed normalized patterns, supporting a neuroanatomic resolution. This study presents preliminary evidence for a neurophysiologic approach to patients with absence epilepsy and comorbid disorders and may provide a method for further research. © EEG and Clinical Neuroscience Society (ECNS) 2014.

Outcomes of patients with altered level of consciousness and abnormal electroencephalogram: A retrospective cohort study

PubMed Central

Ferrari-Marinho, Taissa; Naves, Pedro Vicente Ferreira; Ladeia-Frota, Carol; Caboclo, Luís Otávio

2017-01-01

Introduction Nonconvulsive seizures (NCS) are frequent in hospitalized patients and may further aggravate injury in the already damaged brain, potentially worsening outcomes in encephalopathic patients. Therefore, both early seizure recognition and treatment have been advocated to prevent further neurological damage. Objective Evaluate the main EEG patterns seen in patients with impaired consciousness and address the effect of treatment with antiepileptic drugs (AEDs), continuous intravenous anesthetic drugs (IVADs), or the combination of both, on outcomes. Methods This was a single center retrospective cohort study conducted in a private, tertiary care hospital. Consecutive adult patients with altered consciousness submitted to a routine EEG between January 2008 and February 2011 were included in this study. Based on EEG pattern, patients were assigned to one of three groups: Group Interictal Patterns (IP; EEG showing only interictal epileptiform discharges or triphasic waves), Group Rhythmic and Periodic Patterns (RPP; at least one EEG with rhythmic or periodic patterns), and Group Ictal (Ictal; at least one EEG showing ictal pattern). Groups were compared in terms of administered antiepileptic treatment and frequency of unfavorable outcomes (modified Rankin scale ≥3 and in-hospital mortality). Results Two hundred and six patients (475 EEGs) were included in this analysis. Interictal pattern was observed in 35.4% (73/206) of patients, RPP in 53.4% (110/206) and ictal in 11.2% (23/206) of patients. Treatment with AEDs, IVADs or a combination of both was administered in half of the patients. While all Ictal group patients received treatment (AEDs or IVADs), only 24/73 (32.9%) IP group patients and 55/108 (50.9%) RPP group patients were treated (p<0.001). Hospital length of stay (LOS) and frequency of unfavorable outcomes did not differ among the groups. In-hospital mortality was higher in IVADs treated RPP patients compared to AEDs treated RPP patients [11/19 (57.9%) vs. 11/36 (30.6%) patients, respectively, p = 0.049]. Hospital LOS, in-hospital mortality and frequency of unfavorable outcomes did not differ between Ictal patients treated exclusively with AEDs or IVADs. Conclusion In patients with acute altered consciousness and abnormal routine EEG, antiepileptic treatment did not improve outcomes regardless of the presence of periodic, rhythmic or ictal EEG patterns. PMID:28886073

Outcomes of patients with altered level of consciousness and abnormal electroencephalogram: A retrospective cohort study.

PubMed

Sanches, Paula Rodrigues; Corrêa, Thiago Domingos; Ferrari-Marinho, Taissa; Naves, Pedro Vicente Ferreira; Ladeia-Frota, Carol; Caboclo, Luís Otávio

2017-01-01

Nonconvulsive seizures (NCS) are frequent in hospitalized patients and may further aggravate injury in the already damaged brain, potentially worsening outcomes in encephalopathic patients. Therefore, both early seizure recognition and treatment have been advocated to prevent further neurological damage. Evaluate the main EEG patterns seen in patients with impaired consciousness and address the effect of treatment with antiepileptic drugs (AEDs), continuous intravenous anesthetic drugs (IVADs), or the combination of both, on outcomes. This was a single center retrospective cohort study conducted in a private, tertiary care hospital. Consecutive adult patients with altered consciousness submitted to a routine EEG between January 2008 and February 2011 were included in this study. Based on EEG pattern, patients were assigned to one of three groups: Group Interictal Patterns (IP; EEG showing only interictal epileptiform discharges or triphasic waves), Group Rhythmic and Periodic Patterns (RPP; at least one EEG with rhythmic or periodic patterns), and Group Ictal (Ictal; at least one EEG showing ictal pattern). Groups were compared in terms of administered antiepileptic treatment and frequency of unfavorable outcomes (modified Rankin scale ≥3 and in-hospital mortality). Two hundred and six patients (475 EEGs) were included in this analysis. Interictal pattern was observed in 35.4% (73/206) of patients, RPP in 53.4% (110/206) and ictal in 11.2% (23/206) of patients. Treatment with AEDs, IVADs or a combination of both was administered in half of the patients. While all Ictal group patients received treatment (AEDs or IVADs), only 24/73 (32.9%) IP group patients and 55/108 (50.9%) RPP group patients were treated (p<0.001). Hospital length of stay (LOS) and frequency of unfavorable outcomes did not differ among the groups. In-hospital mortality was higher in IVADs treated RPP patients compared to AEDs treated RPP patients [11/19 (57.9%) vs. 11/36 (30.6%) patients, respectively, p = 0.049]. Hospital LOS, in-hospital mortality and frequency of unfavorable outcomes did not differ between Ictal patients treated exclusively with AEDs or IVADs. In patients with acute altered consciousness and abnormal routine EEG, antiepileptic treatment did not improve outcomes regardless of the presence of periodic, rhythmic or ictal EEG patterns.

[Prognostic factors after cardiac arrest. Usefulness of early video-electroencephalogram].

PubMed

Arméstar, Fernando; Becerra Cuñat, Juan Luis; León Chan, Yariela; Mesalles Sanjuan, Eduard; Moreno, José Antonio; Jiménez González, Marta; Roca, Josep

2015-05-08

Predictors of unfavorable outcome in patients after cardiopulmonary arrest (CPA) are important to make decisions about the limitation of therapeutic efforts. The aim was to analyze the clinical variables in the prognosis of patients recovered after CPA. Retrospective study on comatose patients with recovered CPA. The variables were: age, sex, Glasgow Coma Score (GCS), pupillary light reflex, other variables related to CPA (cause, duration, witnessed or not witnessed), myoclonic status and electroencephalographic (EEG) patterns. Fifty patients were studied. The variables associated with mortality were the absence of pupillary light reflex (hazard ratio [HR] 0.277, 95% confidence interval [95% CI] 0.103-0.741, P=.01), a low GCS (HR 0.701, 95% CI 0.542-0.908, P=.007) and myoclonic state (HR 0.38, 95% CI 0.176-0.854, P=.01). We evaluated the EEG patterns in 22 patients. No statistical significance was observed. The absence of pupillary light reflex, a low GCS and myoclonic state are prognostic factors in patients recovered after a CPA. The EEG patterns showed a nonsignificant association with prognosis. Copyright © 2014 Elsevier España, S.L.U. All rights reserved.

Individual neurophysiological profile in external effects investigation

NASA Astrophysics Data System (ADS)

Schastlivtseva, Daria; Tatiana Kotrovskaya, D..

Cortex biopotentials are the significant elements in human psychophysiological individuality. Considered that cortical biopotentials are diverse and individually stable, therefore there is the existence of certain dependence between the basic properties of higher nervous activity and cerebral bioelectric activity. The main purpose of the study was to reveal the individual neurophysiological profile and CNS initial functional state manifestation in human electroencephalogram (EEG) under effect of inert gases (argon, xenon, helium), hypoxia, pressure changes (0.02 and 0.2 MPa). We obtained 5-minute eyes closed background EEG on 19 scalp positions using Ag/AgCl electrodes mounted in an electrode cap. All EEG signals were re-referenced to average earlobes; Fast Furies Transformation analysis was used to calculate the relative power spectrum of delta-, theta-, alpha- and beta frequency band in artifact-free EEG. The study involved 26 healthy men who provided written informed consent, aged 20 to 35 years. Data obtained depend as individual EEG type and initial central nervous functional state as intensity, duration and mix of factors. Pronounced alpha rhythm in the raw EEG correlated with their adaptive capacity under studied factor exposure. Representation change and zonal distribution perversion of EEG alpha rhythm were accompanied by emotional instability, increased anxiety and difficulty adapting subjects. High power factor or combination factor with psychological and emotional or physical exertion minimizes individual EEG pattern.

Use of Electroencephalography (EEG) to Assess CNS Changes Produced by Pesticides with different Modes of Action: Effects of Permethrin, Deltamethrin, Fipronil, Imidacloprid, Carbaryl, and Triadimefon

EPA Science Inventory

The electroencephalogram (EEG) is an apical measure, capable of detecting changes in brain neuronal activity produced by internal or external stimuli. We assessed whether pesticides with different modes of action produced different changes in the EEG of adult male Long-Evans rats...

EEG patterns associated with nitrogen narcosis (breathing air at 9 ATA).

PubMed

Pastena, Lucio; Faralli, Fabio; Mainardi, Giovanni; Gagliardi, Riccardo

2005-11-01

The narcotic effect of nitrogen impairs diver performance and limits dive profiles, especially for deep dives using compressed air. It would be helpful to establish measurable correlates of nitrogen narcosis. The authors observed the electroencephalogram (EEG) of 10 subjects, ages 22-27 yr, who breathed air during a 3-min compression to a simulated depth of 80 msw (9 ATA). The EEG from a 19-electrode cap was recorded for 20 min while the subject reclined on a cot with eyes closed, first at 1 ATA before the dive and again at 9 ATA. Signals were analyzed using Fast Fourier Transform and brain mapping for frequency domains 0-4 Hz, 4-7 Hz, 7-12 Hz, and 12-15 Hz. Student's paired t-test and correlation tests were used to compare results for the two conditions. Two EEG patterns were observed. The first was an increase in delta and theta activity in all cortical regions that appeared in the first 2 min at depth and was related to exposure time. The second was an increase in delta and theta activity and shifting of alpha activity to the frontal regions at minute 6 of breathing air at 9 ATA and was related to the narcotic effects of nitrogen. If confirmed by studies with larger case series, this EEG pattern could be used to identify nitrogen narcosis for various gas mixtures and prevent the dangerous impact of nitrogen on diver performance.

Artifacts on electroencephalograms may influence the amplitude-integrated EEG classification: a qualitative analysis in neonatal encephalopathy.

PubMed

Hagmann, Cornelia Franziska; Robertson, Nicola Jayne; Azzopardi, Denis

2006-12-01

This is a case report and a descriptive study demonstrating that artifacts are common during long-term recording of amplitude-integrated electroencephalograms and may lead to erroneous classification of the amplitude-integrated electroencephalogram trace. Artifacts occurred in 12% of 200 hours of recording time sampled from a representative sample of 20 infants with neonatal encephalopathy. Artifacts derived from electrical or movement interference occurred with similar frequency; both types of artifacts influenced the voltage and width of the amplitude-integrated electroencephalogram band. This is important knowledge especially if amplitude-integrated electroencephalogram is used as a selection tool for neuroprotection intervention studies.

Automatic Artifact Removal from Electroencephalogram Data Based on A Priori Artifact Information.

PubMed

Zhang, Chi; Tong, Li; Zeng, Ying; Jiang, Jingfang; Bu, Haibing; Yan, Bin; Li, Jianxin

2015-01-01

Electroencephalogram (EEG) is susceptible to various nonneural physiological artifacts. Automatic artifact removal from EEG data remains a key challenge for extracting relevant information from brain activities. To adapt to variable subjects and EEG acquisition environments, this paper presents an automatic online artifact removal method based on a priori artifact information. The combination of discrete wavelet transform and independent component analysis (ICA), wavelet-ICA, was utilized to separate artifact components. The artifact components were then automatically identified using a priori artifact information, which was acquired in advance. Subsequently, signal reconstruction without artifact components was performed to obtain artifact-free signals. The results showed that, using this automatic online artifact removal method, there were statistical significant improvements of the classification accuracies in both two experiments, namely, motor imagery and emotion recognition.

Automatic Artifact Removal from Electroencephalogram Data Based on A Priori Artifact Information

PubMed Central

Zhang, Chi; Tong, Li; Zeng, Ying; Jiang, Jingfang; Bu, Haibing; Li, Jianxin

2015-01-01

Electroencephalogram (EEG) is susceptible to various nonneural physiological artifacts. Automatic artifact removal from EEG data remains a key challenge for extracting relevant information from brain activities. To adapt to variable subjects and EEG acquisition environments, this paper presents an automatic online artifact removal method based on a priori artifact information. The combination of discrete wavelet transform and independent component analysis (ICA), wavelet-ICA, was utilized to separate artifact components. The artifact components were then automatically identified using a priori artifact information, which was acquired in advance. Subsequently, signal reconstruction without artifact components was performed to obtain artifact-free signals. The results showed that, using this automatic online artifact removal method, there were statistical significant improvements of the classification accuracies in both two experiments, namely, motor imagery and emotion recognition. PMID:26380294

Stroop color-word interference and electroencephalogram activation: evidence for age-related decline of the anterior attention system.

PubMed

West, R; Bell, M A

1997-07-01

Groups of healthy, community-dwelling younger and older adults performed a Stroop task in which color and word could be congruent or incongruent and spatially integrated or separated. During the task, continuous electroencephalogram (EEG) was recorded from frontal, parietal, and occipital regions. The magnitude of the Stroop interference effect and task-related EEG activation was greater for older than younger adults when stimuli were integrated. This effect was significant over medial and lateral frontal and parietal, but not occipital, regions. In comparison, interference and EEG activation did not differ for younger and older adults when stimuli were separated. These findings support the hypothesis that the anterior attention system is more sensitive to the effects of increasing age than the posterior attention system.

Geometric subspace methods and time-delay embedding for EEG artifact removal and classification.

PubMed

Anderson, Charles W; Knight, James N; O'Connor, Tim; Kirby, Michael J; Sokolov, Artem

2006-06-01

Generalized singular-value decomposition is used to separate multichannel electroencephalogram (EEG) into components found by optimizing a signal-to-noise quotient. These components are used to filter out artifacts. Short-time principal components analysis of time-delay embedded EEG is used to represent windowed EEG data to classify EEG according to which mental task is being performed. Examples are presented of the filtering of various artifacts and results are shown of classification of EEG from five mental tasks using committees of decision trees.

Differences between state entropy and bispectral index during analysis of identical electroencephalogram signals: a comparison with two randomised anaesthetic techniques.

PubMed

Pilge, Stefanie; Kreuzer, Matthias; Karatchiviev, Veliko; Kochs, Eberhard F; Malcharek, Michael; Schneider, Gerhard

2015-05-01

It is claimed that bispectral index (BIS) and state entropy reflect an identical clinical spectrum, the hypnotic component of anaesthesia. So far, it is not known to what extent different devices display similar index values while processing identical electroencephalogram (EEG) signals. To compare BIS and state entropy during analysis of identical EEG data. Inspection of raw EEG input to detect potential causes of erroneous index calculation. Offline re-analysis of EEG data from a randomised, single-centre controlled trial using the Entropy Module and an Aspect A-2000 monitor. Klinikum rechts der Isar, Technische Universität München, Munich. Forty adult patients undergoing elective surgery under general anaesthesia. Blocked randomisation of 20 patients per anaesthetic group (sevoflurane/remifentanil or propofol/remifentanil). Isolated forearm technique for differentiation between consciousness and unconsciousness. Prediction probability (PK) of state entropy to discriminate consciousness from unconsciousness. Correlation and agreement between state entropy and BIS from deep to light hypnosis. Analysis of raw EEG compared with index values that are in conflict with clinical examination, with frequency measures (frequency bands/Spectral Edge Frequency 95) and visual inspection for physiological EEG patterns (e.g. beta or delta arousal), pathophysiological features such as high-frequency signals (electromyogram/high-frequency EEG or eye fluttering/saccades), different types of electro-oculogram or epileptiform EEG and technical artefacts. PK of state entropy was 0.80 and of BIS 0.84; correlation coefficient of state entropy with BIS 0.78. Nine percent BIS and 14% state entropy values disagreed with clinical examination. Highest incidence of disagreement occurred after state transitions, in particular for state entropy after loss of consciousness during sevoflurane anaesthesia. EEG sequences which led to false 'conscious' index values often showed high-frequency signals and eye blinks. High-frequency EEG/electromyogram signals were pooled because a separation into EEG and fast electro-oculogram, for example eye fluttering or saccades, on the basis of a single EEG channel may not be very reliable. These signals led to higher Spectral Edge Frequency 95 and ratio of relative beta and gamma band power than EEG signals, indicating adequate unconscious classification. The frequency of other artefacts that were assignable, for example technical artefacts, movement artefacts, was negligible and they were excluded from analysis. High-frequency signals and eye blinks may account for index values that falsely indicate consciousness. Compared with BIS, state entropy showed more false classifications of the clinical state at transition between consciousness and unconsciousness.

Flexible electroencephalogram (EEG) headband

NASA Technical Reports Server (NTRS)

Raggio, L. J.

1973-01-01

Headband incorporates sensors which are embedded in sponges and are exposed only on surface that touches skin. Electrode sponge system is continually fed electrolyte through forced feed vacuum system. Headband may be used for EEG testing in hospitals, clinical laboratories, rest homes, and law enforcement agencies.

Assessing the depth of hypnosis of xenon anaesthesia with the EEG.

PubMed

Stuttmann, Ralph; Schultz, Arthur; Kneif, Thomas; Krauss, Terence; Schultz, Barbara

2010-04-01

Xenon was approved as an inhaled anaesthetic in Germany in 2005 and in other countries of the European Union in 2007. Owing to its low blood/gas partition coefficient, xenons effects on the central nervous system show a fast onset and offset and, even after long xenon anaesthetics, the wake-up times are very short. The aim of this study was to examine which electroencephalogram (EEG) stages are reached during xenon application and whether these stages can be identified by an automatic EEG classification. Therefore, EEG recordings were performed during xenon anaesthetics (EEG monitor: Narcotrend®). A total of 300 EEG epochs were assessed visually with regard to the EEG stages. These epochs were also classified automatically by the EEG monitor Narcotrend® using multivariate algorithms. There was a high correlation between visual and automatic classification (Spearman's rank correlation coefficient r=0.957, prediction probability Pk=0.949). Furthermore, it was observed that very deep stages of hypnosis were reached which are characterised by EEG activity in the low frequency range (delta waves). The burst suppression pattern was not seen. In deep hypnosis, in contrast to the xenon EEG, the propofol EEG was characterised by a marked superimposed higher frequency activity. To ensure an optimised dosage for the single patient, anaesthetic machines for xenon should be combined with EEG monitoring. To date, only a few anaesthetic machines for xenon are available. Because of the high price of xenon, new and further developments of machines focus on optimizing xenon consumption.

A Procedural Electroencephalogram Simulator for Evaluation of Anesthesia Monitors.

PubMed

Petersen, Christian Leth; Görges, Matthias; Massey, Roslyn; Dumont, Guy Albert; Ansermino, J Mark

2016-11-01

Recent research and advances in the automation of anesthesia are driving the need to better understand electroencephalogram (EEG)-based anesthesia end points and to test the performance of anesthesia monitors. This effort is currently limited by the need to collect raw EEG data directly from patients. A procedural method to synthesize EEG signals was implemented in a mobile software application. The application is capable of sending the simulated signal to an anesthesia depth of hypnosis monitor. Systematic sweeps of the simulator generate functional monitor response profiles reminiscent of how network analyzers are used to test electronic components. Three commercial anesthesia monitors (Entropy, NeuroSENSE, and BIS) were compared with this new technology, and significant response and feature variations between the monitor models were observed; this includes reproducible, nonmonotonic apparent multistate behavior and significant hysteresis at light levels of anesthesia. Anesthesia monitor response to a procedural simulator can reveal significant differences in internal signal processing algorithms. The ability to synthesize EEG signals at different anesthetic depths potentially provides a new method for systematically testing EEG-based monitors and automated anesthesia systems with all sensor hardware fully operational before human trials.

Approximate Entropy in the Electroencephalogram During Wake and Sleep

PubMed Central

Burioka, Naoto; Miyata, Masanori; Cornélissen, Germaine; Halberg, Franz; Takeshima, Takao; Kaplan, Daniel T.; Suyama, Hisashi; Endo, Masanori; Maegaki, Yoshihiro; Nomura, Takashi; Tomita, Yutaka; Nakashima, Kenji; Shimizu, Eiji

2006-01-01

Entropy measurement can discriminate among complex systems, including deterministic, stochastic and composite systems. We evaluated the changes of approximate entropy (ApEn) in signals of the electroencephalogram (EEG) during sleep. EEG signals were recorded from eight healthy volunteers during nightly sleep. We estimated the values of ApEn in EEG signals in each sleep stage. The ApEn values for EEG signals (mean ± SD) were 0.896 ± 0.264 during eyes-closed waking state, 0.738 ± 0.089 during Stage I, 0.615 ± 0.107 during Stage II, 0.487 ± 0.101 during Stage III, 0.397 ± 0.078 during Stage IV and 0.789 ± 0.182 during REM sleep. The ApEn values were found to differ with statistical significance among the six different stages of consciousness (ANOVA, p<0.001). ApEn of EEG was statistically significantly lower during Stage IV and higher during wake and REM sleep. We conclude that ApEn measurement can be useful to estimate sleep stages and the complexity in brain activity. PMID:15683194

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 =…

An experimental study to investigate the effects of a motion tracking electromagnetic sensor during EEG data acquisition.

PubMed

Bashashati, Ali; Noureddin, Borna; Ward, Rabab K; Lawrence, Peter D; Birch, Gary E

2006-03-01

A power spectral analysis study was conducted to investigate the effects of using an electromagnetic motion tracking sensor on an electroencephalogram (EEG) recording system. The results showed that the sensors do not generate any consistent frequency component(s) in the power spectrum of the EEG in the frequencies of interest (0.1-55 Hz).

Genetic Programming and Frequent Itemset Mining to Identify Feature Selection Patterns of iEEG and fMRI Epilepsy Data

PubMed Central

Smart, Otis; Burrell, Lauren

2014-01-01

Pattern classification for intracranial electroencephalogram (iEEG) and functional magnetic resonance imaging (fMRI) signals has furthered epilepsy research toward understanding the origin of epileptic seizures and localizing dysfunctional brain tissue for treatment. Prior research has demonstrated that implicitly selecting features with a genetic programming (GP) algorithm more effectively determined the proper features to discern biomarker and non-biomarker interictal iEEG and fMRI activity than conventional feature selection approaches. However for each the iEEG and fMRI modalities, it is still uncertain whether the stochastic properties of indirect feature selection with a GP yield (a) consistent results within a patient data set and (b) features that are specific or universal across multiple patient data sets. We examined the reproducibility of implicitly selecting features to classify interictal activity using a GP algorithm by performing several selection trials and subsequent frequent itemset mining (FIM) for separate iEEG and fMRI epilepsy patient data. We observed within-subject consistency and across-subject variability with some small similarity for selected features, indicating a clear need for patient-specific features and possible need for patient-specific feature selection or/and classification. For the fMRI, using nearest-neighbor classification and 30 GP generations, we obtained over 60% median sensitivity and over 60% median selectivity. For the iEEG, using nearest-neighbor classification and 30 GP generations, we obtained over 65% median sensitivity and over 65% median selectivity except one patient. PMID:25580059

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.

An exploratory data analysis of electroencephalograms using the functional boxplots approach

PubMed Central

Ngo, Duy; Sun, Ying; Genton, Marc G.; Wu, Jennifer; Srinivasan, Ramesh; Cramer, Steven C.; Ombao, Hernando

2015-01-01

Many model-based methods have been developed over the last several decades for analysis of electroencephalograms (EEGs) in order to understand electrical neural data. In this work, we propose to use the functional boxplot (FBP) to analyze log periodograms of EEG time series data in the spectral domain. The functional bloxplot approach produces a median curve—which is not equivalent to connecting medians obtained from frequency-specific boxplots. In addition, this approach identifies a functional median, summarizes variability, and detects potential outliers. By extending FBPs analysis from one-dimensional curves to surfaces, surface boxplots are also used to explore the variation of the spectral power for the alpha (8–12 Hz) and beta (16–32 Hz) frequency bands across the brain cortical surface. By using rank-based nonparametric tests, we also investigate the stationarity of EEG traces across an exam acquired during resting-state by comparing the spectrum during the early vs. late phases of a single resting-state EEG exam. PMID:26347598

Electroencephalogram associations to cognitive performance in clinically active nurses.

PubMed

Lees, Ty; Khushaba, Rami; Lal, Sara

2016-07-01

Cognitive impairment is traditionally identified via cognitive screening tools that have limited ability in detecting early or transitional stages of impairment. The dynamic nature of physiological variables such as the electroencephalogram (EEG) may provide alternate means for detecting these transitions. However, previous research examining EEG and cognitive performance is largely confined to samples with diagnosed cognitive impairments, and research examining non-impaired, and occupation specific samples, is limited. The present study aimed to investigate the associations between frontal pole and central EEG and cognitive performance in a sample of male and female nurses, and to determine the significance of these associations. Fifty seven nurses participated in the study, in which two lead bipolar EEG was recorded at positions Fp1 (frontal polar), Fp2, C3 (central) and C4 during a baseline and an active phase involving the common neuropsychological Stroop test. Participants' cognitive performance was assessed using the mini-mental state exam (MMSE) and Cognistat screening tools. Significant correlations between EEG beta activity and the outcome of MMSE and Cognistat were revealed, where an increased beta activity was associated to an increased global cognitive performance. Additionally, domain specific cognitive performance was also significantly associated to various EEG variables. The study identified potential EEG biomarkers for global and domain specific cognitive performance, and provides initial groundwork for the development of future EEG based biomarkers for detection of cognitive pathologies.

Qualitative and Quantitative Characteristics of the Electroencephalogram in Normal Horses during Administration of Inhaled Anesthesia.

PubMed

Williams, D C; Brosnan, R J; Fletcher, D J; Aleman, M; Holliday, T A; Tharp, B; Kass, P H; LeCouteur, R A; Steffey, E P

2016-01-01

The effects of anesthesia on the equine electroencephalogram (EEG) after administration of various drugs for sedation, induction, and maintenance are known, but not that the effect of inhaled anesthetics alone for EEG recording. To determine the effects of isoflurane and halothane, administered as single agents at multiple levels, on the EEG and quantitative EEG (qEEG) of normal horses. Six healthy horses. Prospective study. Digital EEG with video and quantitative EEG (qEEG) were recorded after the administration of one of the 2 anesthetics, isoflurane or halothane, at 3 alveolar doses (1.2, 1.4 and 1.6 MAC). Segments of EEG during controlled ventilation (CV), spontaneous ventilation (SV), and with peroneal nerve stimulation (ST) at each MAC multiple for each anesthetic were selected, analyzed, and compared. Multiple non-EEG measurements were also recorded. Specific raw EEG findings were indicative of changes in the depth of anesthesia. However, there was considerable variability in EEG between horses at identical MAC multiples/conditions and within individual horses over segments of a given epoch. Statistical significance for qEEG variables differed between anesthetics with bispectral index (BIS) CV MAC and 95% spectral edge frequency (SEF95) SV MAC differences in isoflurane only and median frequency (MED) differences in SV MAC with halothane only. Unprocessed EEG features (background and transients) appear to be beneficial for monitoring the depth of a particular anesthetic, but offer little advantage over the use of changes in mean arterial pressure for this purpose. Copyright © 2015 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine.

Statistical geometric affinity in human brain electric activity

NASA Astrophysics Data System (ADS)

Chornet-Lurbe, A.; Oteo, J. A.; Ros, J.

2007-05-01

The representation of the human electroencephalogram (EEG) records by neurophysiologists demands standardized time-amplitude scales for their correct conventional interpretation. In a suite of graphical experiments involving scaling affine transformations we have been able to convert electroencephalogram samples corresponding to any particular sleep phase and relaxed wakefulness into each other. We propound a statistical explanation for that finding in terms of data collapse. As a sequel, we determine characteristic time and amplitude scales and outline a possible physical interpretation. An analysis for characteristic times based on lacunarity is also carried out as well as a study of the synchrony between left and right EEG channels.

Atypical clinical course subacute sclerosing panencephalitis presenting as acute Encephalitis

PubMed Central

Komur, Mustafa; Arslankoylu, Ali E; Okuyaz, Cetin; Kuyucu, Necdet

2012-01-01

We report a 14-year-old boy who presented with loss of consciousness and gait instability. The electroencephalogram (EEG) showed generalized slowing with irregular activity and cerebral magnetic imaging revealed asymmetrical nonspecific signals on basal ganglia. His second electroencephalogram revealed periodical generalized high-voltage slow wave complexes which did not disappear with diazepam induction. Subacute sclerosing panencephalitis (SSPE) was considered and the diagnosis was confirmed with the identification of measles antibodies in cerebrospinal fluid. Our findings show that SSPE should be in mind in the differential diagnosis of meningoencephalitis and acute disseminated encephalomyelitis and highlight the significance of EEG in the diagnosis of unidentified cases. PMID:23248691

Human Supervision of Time Critical Control Systems. Addendum

DTIC Science & Technology

2010-02-26

signals such as electroencephalogram (EEG) and electrooculography ( EOG ). Current research has demonstrated these signals ’ ability to respond to changing...relationships often present in EEG/ EOG data; they routinely achieve classification accuracy greater than 80%. However, the discrete output of these...present data there were seven EEG and EOG signals recorded, thus, ICA assumes each were a mixture of seven independent components (Stone, 2002). Some

Developing an Adaptability Training Strategy and Policy for the DoD

DTIC Science & Technology

2008-10-01

might include monitoring of trainees using electroencephalogram ( EEG ) technology to gain neurofeedback during scenario performance. In order to...group & adequate sample; pre and post iii. Possibly including EEG monitoring (and even neurofeedback ) 4. Should seek to determine general...Dr. John Cowan has developed a system called the Peak Achievement Trainer (PAT) EEG , which traces electrical activity in the brain and provides

Neurophysiological prediction of neurological good and poor outcome in post-anoxic coma.

PubMed

Grippo, A; Carrai, R; Scarpino, M; Spalletti, M; Lanzo, G; Cossu, C; Peris, A; Valente, S; Amantini, A

2017-06-01

Investigation of the utility of association between electroencephalogram (EEG) and somatosensory-evoked potentials (SEPs) for the prediction of neurological outcome in comatose patients resuscitated after cardiac arrest (CA) treated with therapeutic hypothermia, according to different recording times after CA. Glasgow Coma Scale, EEG and SEPs performed at 12, 24 and 48-72 h after CA were assessed in 200 patients. Outcome was evaluated by Cerebral Performance Category 6 months after CA. Within 12 h after CA, grade 1 EEG predicted good outcome and bilaterally absent (BA) SEPs predicted poor outcome. Because grade 1 EEG and BA-SEPs were never found in the same patient, the recording of both EEG and SEPs allows us to correctly prognosticate a greater number of patients with respect to the use of a single test within 12 h after CA. At 48-72 h after CA, both grade 2 EEG and BA-SEPs predicted poor outcome with FPR=0.0%. When these neurophysiological patterns are both present in the same patient, they confirm and strengthen their prognostic value, but because they also occurred independently in eight patients, poor outcome is predictable in a greater number of patients. The combination of EEG/SEP findings allows prediction of good and poor outcome (within 12 h after CA) and of poor outcome (after 48-72 h). Recording of EEG and SEPs in the same patients allows always an increase in the number of cases correctly classified, and an increase of the reliability of prognostication in a single patient due to concordance of patterns. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Impact of Dronabinol on Quantitative Electroencephalogram (qEEG) Measures of Sleep in Obstructive Sleep Apnea Syndrome

PubMed Central

Farabi, Sarah S.; Prasad, Bharati; Quinn, Lauretta; Carley, David W.

2014-01-01

Study Objectives: To determine the effects of dronabinol on quantitative electroencephalogram (EEG) markers of the sleep process, including power distribution and ultradian cycling in 15 patients with obstructive sleep apnea (OSA). Methods: EEG (C4-A1) relative power (% total) in the delta, theta, alpha, and sigma bands was quantified by fast Fourier transformation (FFT) over 28-second intervals. An activation ratio (AR = [alpha + sigma] / [delta + theta]) also was computed for each interval. To assess ultradian rhythms, the best-fitting cosine wave was determined for AR and each frequency band in each polysomnogram (PSG). Results: Fifteen subjects were included in the analysis. Dronabinol was associated with significantly increased theta power (p = 0.002). During the first half of the night, dronabinol decreased sigma power (p = 0.03) and AR (p = 0.03), and increased theta power (p = 0.0006). At increasing dronabinol doses, ultradian rhythms accounted for a greater fraction of EEG power variance in the delta band (p = 0.04) and AR (p = 0.03). Females had higher amplitude ultradian rhythms than males (theta: p = 0.01; sigma: p = 0.01). Decreasing AHI was associated with increasing ultradian rhythm amplitudes (sigma: p < 0.001; AR: p = 0.02). At the end of treatment, lower relative power in the theta band (p = 0.02) and lower AHI (p = 0.05) correlated with a greater decrease in sleepiness from baseline. Conclusions: This exploratory study demonstrates that in individuals with OSA, dronabinol treatment may yield a shift in EEG power toward delta and theta frequencies and a strengthening of ultradian rhythms in the sleep EEG. Citation: Farabi SS; Prasad B; Quinn L; Carley DW. Impact of dronabinol on quantitative electroencephalogram (qEEG) measures of sleep in obstructive sleep apnea syndrome. J Clin Sleep Med 2014;10(1):49-56. PMID:24426820

Fast entrainment of human electroencephalogram to a theta-band photic flicker during successful memory encoding.

PubMed

Sato, Naoyuki

2013-01-01

Theta band power (4-8 Hz) in the scalp electroencephalogram (EEG) is thought to be stronger during memory encoding for subsequently remembered items than for forgotten items. According to simultaneous EEG-functional magnetic resonance imaging (fMRI) measurements, the memory-dependent EEG theta is associated with multiple regions of the brain. This suggests that the multiple regions cooperate with EEG theta synchronization during successful memory encoding. However, a question still remains: What kind of neural dynamic organizes such a memory-dependent global network? In this study, the modulation of the EEG theta entrainment property during successful encoding was hypothesized to lead to EEG theta synchronization among a distributed network. Then, a transient response of EEG theta to a theta-band photic flicker with a short duration was evaluated during memory encoding. In the results, flicker-induced EEG power increased and decreased with a time constant of several hundred milliseconds following the onset and the offset of the flicker, respectively. Importantly, the offset response of EEG power was found to be significantly decreased during successful encoding. Moreover, the offset response of the phase locking index was also found to associate with memory performance. According to computational simulations, the results are interpreted as a smaller time constant (i.e., faster response) of a driven harmonic oscillator rather than a change in the spontaneous oscillatory input. This suggests that the fast response of EEG theta forms a global EEG theta network among memory-related regions during successful encoding, and it contributes to a flexible formation of the network along the time course.

Nitrous oxide has different effects on the EEG and somatosensory evoked potentials during isoflurane anaesthesia in patients.

PubMed

Porkkala, T; Jäntti, V; Kaukinen, S; Häkkinen, V

1997-04-01

Electroencephalogram (EEG) and somatosensory evoked potentials (SEPs) are altered by inhalation anaesthesia. Nitrous oxide is commonly used in combination with volatile anaesthetics. We have studied the effects of nitrous oxide on both EEG and SEPs simultaneously during isoflurane burst-suppression anaesthesia. Twelve ASA I-II patients undergoing abdominal or orthopaedic surgery were anaesthetized with isoflurane by mask. After intubation and relaxation the isoflurane concentration was increased to a level at which an EEG burst-suppression pattern occurred (mean isoflurane end-tidal concentration 1.9 (SD 0.2) %. With a stable isoflurane concentration, the patients received isoflurane-air-oxygen and isoflurane-nitrous oxide-oxygen (FiO2 0.4) in a randomized cross-over manner. EEG and SEPs were simultaneously recorded before, and after wash-out or wash-in periods for nitrous oxide. The proportion of EEG suppressions as well as SEP amplitudes for cortical N20 were calculated. The proportion of EEG suppressions decreased from 53.5% to 34% (P < 0.05) when air was replaced by nitrous oxide. At the same time, the cortical N20 amplitude was reduced by 69% (P < 0.01). The results suggest that during isoflurane anaesthesia, nitrous oxide has a different effect on EEG and cortical SEP at the same time. The effects of nitrous oxide may be mediated by cortical and subcortical generators.

Epileptic seizure detection in EEG signal using machine learning techniques.

PubMed

Jaiswal, Abeg Kumar; Banka, Haider

2018-03-01

Epilepsy is a well-known nervous system disorder characterized by seizures. Electroencephalograms (EEGs), which capture brain neural activity, can detect epilepsy. Traditional methods for analyzing an EEG signal for epileptic seizure detection are time-consuming. Recently, several automated seizure detection frameworks using machine learning technique have been proposed to replace these traditional methods. The two basic steps involved in machine learning are feature extraction and classification. Feature extraction reduces the input pattern space by keeping informative features and the classifier assigns the appropriate class label. In this paper, we propose two effective approaches involving subpattern based PCA (SpPCA) and cross-subpattern correlation-based PCA (SubXPCA) with Support Vector Machine (SVM) for automated seizure detection in EEG signals. Feature extraction was performed using SpPCA and SubXPCA. Both techniques explore the subpattern correlation of EEG signals, which helps in decision-making process. SVM is used for classification of seizure and non-seizure EEG signals. The SVM was trained with radial basis kernel. All the experiments have been carried out on the benchmark epilepsy EEG dataset. The entire dataset consists of 500 EEG signals recorded under different scenarios. Seven different experimental cases for classification have been conducted. The classification accuracy was evaluated using tenfold cross validation. The classification results of the proposed approaches have been compared with the results of some of existing techniques proposed in the literature to establish the claim.

The effect of CPAP treatment on EEG of OSAS patients.

PubMed

Zhang, Cheng; Lv, Jun; Zhou, Junhong; Su, Li; Feng, Liping; Ma, Jing; Wang, Guangfa; Zhang, Jue

2015-12-01

Continuous positive airway pressure (CPAP) is currently the most effective treatment method for obstructive sleep apnea syndrome (OSAS). The purpose of this study was to compare the sleep electroencephalogram (EEG) changes before and after the application of CPAP to OSAS patients. A retrospective study was conducted and 45 sequential patients who received both polysomnography (PSG) and CPAP titration were included. The raw data of sleep EEG were extracted and analyzed by engineers using two main factors: fractal dimension (FD) and the zero-crossing rate of detrended FD (zDFD). FD was an effective indicator reflecting the EEG complexity and zDFD was useful to reflect the variability of the EEG complexity. The FD and zDFD indexes of sleep EEG of 45 OSAS patients before and after CPAP titration were analyzed. The age of 45 OSAS patients was 52.7 ± 5.6 years old and the patients include 12 females and 33 males. After CPAP treatment, FD of EEG in non-rapid eye movement (NREM) sleep decreased significantly (P < 0.05), while FD of EEG increased in rapid eye movement (REM) sleep (P < 0.05). Meanwhile, zDFD were decreased remarkably in both NREM and REM sleep after CPAP therapy (P < 0.05, respectively). CPAP therapy had a significant influence on sleep EEG in patients with OSAHS, which lead to a more stable EEG pattern. This may be one of the mechanisms that CPAP could improve sleep quality and brain function of OSAS patients.

An exploration of EEG features during recovery following stroke - implications for BCI-mediated neurorehabilitation therapy.

PubMed

Leamy, Darren J; Kocijan, Juš; Domijan, Katarina; Duffin, Joseph; Roche, Richard Ap; Commins, Sean; Collins, Ronan; Ward, Tomas E

2014-01-28

Brain-Computer Interfaces (BCI) can potentially be used to aid in the recovery of lost motor control in a limb following stroke. BCIs are typically used by subjects with no damage to the brain therefore relatively little is known about the technical requirements for the design of a rehabilitative BCI for stroke. 32-channel electroencephalogram (EEG) was recorded during a finger-tapping task from 10 healthy subjects for one session and 5 stroke patients for two sessions approximately 6 months apart. An off-line BCI design based on Filter Bank Common Spatial Patterns (FBCSP) was implemented to test and compare the efficacy and accuracy of training a rehabilitative BCI with both stroke-affected and healthy data. Stroke-affected EEG datasets have lower 10-fold cross validation results than healthy EEG datasets. When training a BCI with healthy EEG, average classification accuracy of stroke-affected EEG is lower than the average for healthy EEG. Classification accuracy of the late session stroke EEG is improved by training the BCI on the corresponding early stroke EEG dataset. This exploratory study illustrates that stroke and the accompanying neuroplastic changes associated with the recovery process can cause significant inter-subject changes in the EEG features suitable for mapping as part of a neurofeedback therapy, even when individuals have scored largely similar with conventional behavioural measures. It appears such measures can mask this individual variability in cortical reorganization. Consequently we believe motor retraining BCI should initially be tailored to individual patients.

On the use of EEG or MEG brain imaging tools in neuromarketing research.

PubMed

Vecchiato, Giovanni; Astolfi, Laura; De Vico Fallani, Fabrizio; Toppi, Jlenia; Aloise, Fabio; Bez, Francesco; Wei, Daming; Kong, Wanzeng; Dai, Jounging; Cincotti, Febo; Mattia, Donatella; Babiloni, Fabio

2011-01-01

Here we present an overview of some published papers of interest for the marketing research employing electroencephalogram (EEG) and magnetoencephalogram (MEG) methods. The interest for these methodologies relies in their high-temporal resolution as opposed to the investigation of such problem with the functional Magnetic Resonance Imaging (fMRI) methodology, also largely used in the marketing research. In addition, EEG and MEG technologies have greatly improved their spatial resolution in the last decades with the introduction of advanced signal processing methodologies. By presenting data gathered through MEG and high resolution EEG we will show which kind of information it is possible to gather with these methodologies while the persons are watching marketing relevant stimuli. Such information will be related to the memorization and pleasantness related to such stimuli. We noted that temporal and frequency patterns of brain signals are able to provide possible descriptors conveying information about the cognitive and emotional processes in subjects observing commercial advertisements. These information could be unobtainable through common tools used in standard marketing research. We also show an example of how an EEG methodology could be used to analyze cultural differences between fruition of video commercials of carbonated beverages in Western and Eastern countries.

On the Use of EEG or MEG Brain Imaging Tools in Neuromarketing Research

PubMed Central

Vecchiato, Giovanni; Astolfi, Laura; De Vico Fallani, Fabrizio; Toppi, Jlenia; Aloise, Fabio; Bez, Francesco; Wei, Daming; Kong, Wanzeng; Dai, Jounging; Cincotti, Febo; Mattia, Donatella; Babiloni, Fabio

2011-01-01

Here we present an overview of some published papers of interest for the marketing research employing electroencephalogram (EEG) and magnetoencephalogram (MEG) methods. The interest for these methodologies relies in their high-temporal resolution as opposed to the investigation of such problem with the functional Magnetic Resonance Imaging (fMRI) methodology, also largely used in the marketing research. In addition, EEG and MEG technologies have greatly improved their spatial resolution in the last decades with the introduction of advanced signal processing methodologies. By presenting data gathered through MEG and high resolution EEG we will show which kind of information it is possible to gather with these methodologies while the persons are watching marketing relevant stimuli. Such information will be related to the memorization and pleasantness related to such stimuli. We noted that temporal and frequency patterns of brain signals are able to provide possible descriptors conveying information about the cognitive and emotional processes in subjects observing commercial advertisements. These information could be unobtainable through common tools used in standard marketing research. We also show an example of how an EEG methodology could be used to analyze cultural differences between fruition of video commercials of carbonated beverages in Western and Eastern countries. PMID:21960996

Electroencephalogram and Heart Rate Regulation to Familiar and Unfamiliar People in Children with Autism Spectrum Disorders

ERIC Educational Resources Information Center

Van Hecke, Amy Vaughan; Lebow, Jocelyn; Bal, Elgiz; Lamb, Damon; Harden, Emily; Kramer, Alexis; Denver, John; Bazhenova, Olga; Porges, Stephen W.

2009-01-01

Few studies have examined whether familiarity of partner affects social responses in children with autism. This study investigated heart rate regulation (respiratory sinus arrhythmia [RSA]: The myelinated vagus nerve's regulation of heart rate) and temporal-parietal electroencephalogram (EEG) activity while nineteen 8- to 12-year-old children with…

Familial Clustering and DRD4 Effects on Electroencephalogram Measures in Multiplex Families with Attention Deficit/Hyperactivity Disorder

ERIC Educational Resources Information Center

Loo, Sandra K.; Hale, T. Sigi; Hanada, Grant; Macion, James; Shrestha, Anshu; McGough, James J.; McCracken, James T.; Nelson, Stanley; Smalley, Susan L.

2010-01-01

Objective: The current study tests electroencephalogram (EEG) measures as a potential endophenotype for attention deficit/hyperactivity disorder (ADHD) by examining sibling and parent-offspring similarity, familial clustering with the disorder, and association with the dopamine receptor D4 (DRD4) candidate gene. Method: The sample consists of 531…

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.

[The contribution of the clinical examination, electroencephalogram, and brain MRI in assessing the prognosis in term newborns with neonatal encephalopathy. A cohort of 30 newborns before the introduction of treatment with hypothermia].

PubMed

Jadas, V; Brasseur-Daudruy, M; Chollat, C; Pellerin, L; Devaux, A M; Marret, S

2014-02-01

Perinatal asphyxia complicated by hypoxic ischemic brain injury remains a source of neurological lesions. A major aim of neonatologists is to evaluate the severity of neonatal encephalopathy (NE) and to evaluate prognosis. The purpose of this study was to determine the contribution of brain MRI compared to electroencephalogram (EEG) and clinical data in assessing patients' prognosis. Thirty newborns from the pediatric resuscitation unit at Rouen university hospital were enrolled in a retrospective study between January 2006 and December 2008, prior to introduction of hypothermia treatment. All 30 newborns had at least two anamnestic criteria of perinatal asphyxia, one brain MRI in the first 5 days of life and another after 7 days of life as well as an early EEG in the first 2 days of life. Then, the infants were seen in consultation to assess neurodevelopment. This study showed a relation between NE stage and prognosis. During stage 1, prognosis was good, whereas stage 3 was associated with poor neurodevelopment outcome. Normal clinical examination before the 8th day of life was a good prognostic factor in this study. There was a relationship between severity of EEG after the 5th day of life and poor outcome. During stage 2, EEG patterns varied in severity, and brain MRI provided a better prognosis. Lesions of the basal ganglia and a decreased or absent signal of the posterior limb of the internal capsule were poor prognostic factors during brain MRI. These lesions were underestimated during standard MRI in the first days of life but were visible with diffusion sequences. Cognitive impairment affected 40% of surviving children, justifying extended pediatric follow-up. This study confirms the usefulness of brain MRI as a diagnostic tool in hypoxic ischemic encephalopathy in association with clinical data and EEG tracings. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

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…

Atypical EEG Power Correlates with Indiscriminately Friendly Behavior in Internationally Adopted Children

ERIC Educational Resources Information Center

Tarullo, Amanda R.; Garvin, Melissa C.; Gunnar, Megan R.

2011-01-01

While effects of institutional care on behavioral development have been studied extensively, effects on neural systems underlying these socioemotional and attention deficits are only beginning to be examined. The current study assessed electroencephalogram (EEG) power in 18-month-old internationally adopted, postinstitutionalized children (n = 37)…

Speech Presentation Cues Moderate Frontal EEG Asymmetry in Socially Withdrawn Young Adults

ERIC Educational Resources Information Center

Cole, Claire; Zapp, Daniel J.; Nelson, S. Katherine; Perez-Edgar, Koraly

2012-01-01

Socially withdrawn individuals display solitary behavior across wide contexts with both unfamiliar and familiar peers. This tendency to withdraw may be driven by either past or anticipated negative social encounters. In addition, socially withdrawn individuals often exhibit right frontal electroencephalogram (EEG) asymmetry at baseline and when…

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.

Epileptic seizure detection from EEG signals with phase-amplitude cross-frequency coupling and support vector machine

NASA Astrophysics Data System (ADS)

Liu, Yang; Wang, Jiang; Cai, Lihui; Chen, Yingyuan; Qin, Yingmei

2018-03-01

As a pattern of cross-frequency coupling (CFC), phase-amplitude coupling (PAC) depicts the interaction between the phase and amplitude of distinct frequency bands from the same signal, and has been proved to be closely related to the brain’s cognitive and memory activities. This work utilized PAC and support vector machine (SVM) classifier to identify the epileptic seizures from electroencephalogram (EEG) data. The entropy-based modulation index (MI) matrixes are used to express the strength of PAC, from which we extracted features as the input for classifier. Based on the Bonn database, which contains five datasets of EEG segments obtained from healthy volunteers and epileptic subjects, a 100% classification accuracy is achieved for identifying seizure ictal from healthy data, and an accuracy of 97.67% is reached in the classification of ictal EEG signals from inter-ictal EEGs. Based on the CHB-MIT database which is a group of continuously recorded epileptic EEGs by scalp electrodes, a 97.50% classification accuracy is obtained and a raising sign of MI value is found at 6s before seizure onset. The classification performance in this work is effective, and PAC can be considered as a useful tool for detecting and predicting the epileptic seizures and providing reference for clinical diagnosis.

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.

Atypical EEG power correlates with indiscriminately friendly behavior in internationally adopted children.

PubMed

Tarullo, Amanda R; Garvin, Melissa C; Gunnar, Megan R

2011-03-01

While effects of institutional care on behavioral development have been studied extensively, effects on neural systems underlying these socioemotional and attention deficits are only beginning to be examined. The current study assessed electroencephalogram (EEG) power in 18-month-old internationally adopted, postinstitutionalized children (n = 37) and comparison groups of nonadopted children (n = 47) and children internationally adopted from foster care (n = 39). For their age, postinstitutionalized children had an atypical EEG power distribution, with relative power concentrated in lower frequency bands compared with nonadopted children. Both internationally adopted groups had lower absolute alpha power than nonadopted children. EEG power was not related to growth at adoption or to global cognitive ability. Atypical EEG power distribution at 18 months predicted indiscriminate friendliness and poorer inhibitory control at 36 months. Both postinstitutionalized and foster care children were more likely than nonadopted children to exhibit indiscriminate friendliness. Results are consistent with a cortical hypoactivation model of the effects of early deprivation on neural development and provide initial evidence associating this atypical EEG pattern with indiscriminate friendliness. Outcomes observed in the foster care children raise questions about the specificity of institutional rearing as a risk factor and emphasize the need for broader consideration of the effects of early deprivation and disruptions in care. PsycINFO Database Record (c) 2011 APA, all rights reserved.

Decoding English Alphabet Letters Using EEG Phase Information

PubMed Central

Wang, YiYan; Wang, Pingxiao; Yu, Yuguo

2018-01-01

Increasing evidence indicates that the phase pattern and power of the low frequency oscillations of brain electroencephalograms (EEG) contain significant information during the human cognition of sensory signals such as auditory and visual stimuli. Here, we investigate whether and how the letters of the alphabet can be directly decoded from EEG phase and power data. In addition, we investigate how different band oscillations contribute to the classification and determine the critical time periods. An English letter recognition task was assigned, and statistical analyses were conducted to decode the EEG signal corresponding to each letter visualized on a computer screen. We applied support vector machine (SVM) with gradient descent method to learn the potential features for classification. It was observed that the EEG phase signals have a higher decoding accuracy than the oscillation power information. Low-frequency theta and alpha oscillations have phase information with higher accuracy than do other bands. The decoding performance was best when the analysis period began from 180 to 380 ms after stimulus presentation, especially in the lateral occipital and posterior temporal scalp regions (PO7 and PO8). These results may provide a new approach for brain-computer interface techniques (BCI) and may deepen our understanding of EEG oscillations in cognition. PMID:29467615

Automated diagnosis of autism: in search of a mathematical marker.

PubMed

Bhat, Shreya; Acharya, U Rajendra; Adeli, Hojjat; Bairy, G Muralidhar; Adeli, Amir

2014-01-01

Autism is a type of neurodevelopmental disorder affecting the memory, behavior, emotion, learning ability, and communication of an individual. An early detection of the abnormality, due to irregular processing in the brain, can be achieved using electroencephalograms (EEG). The variations in the EEG signals cannot be deciphered by mere visual inspection. Computer-aided diagnostic tools can be used to recognize the subtle and invisible information present in the irregular EEG pattern and diagnose autism. This paper presents a state-of-the-art review of automated EEG-based diagnosis of autism. Various time domain, frequency domain, time-frequency domain, and nonlinear dynamics for the analysis of autistic EEG signals are described briefly. A focus of the review is the use of nonlinear dynamics and chaos theory to discover the mathematical biomarkers for the diagnosis of the autism analogous to biological markers. A combination of the time-frequency and nonlinear dynamic analysis is the most effective approach to characterize the nonstationary and chaotic physiological signals for the automated EEG-based diagnosis of autism spectrum disorder (ASD). The features extracted using these nonlinear methods can be used as mathematical markers to detect the early stage of autism and aid the clinicians in their diagnosis. This will expedite the administration of appropriate therapies to treat the disorder.

Atypical EEG Power Correlates With Indiscriminately Friendly Behavior in Internationally Adopted Children

PubMed Central

Tarullo, Amanda R.; Garvin, Melissa C.; Gunnar, Megan R.

2012-01-01

While effects of institutional care on behavioral development have been studied extensively, effects on neural systems underlying these socioemotional and attention deficits are only beginning to be examined. The current study assessed electroencephalogram (EEG) power in 18-month-old internationally adopted, post-institutionalized children (n = 37) and comparison groups of non-adopted children (n = 47) and children internationally adopted from foster care (n = 39). For their age, post-institutionalized children had an atypical EEG power distribution, with relative power concentrated in lower frequency bands compared to non-adopted children. Both internationally adopted groups had lower absolute alpha power than non-adopted children. EEG power was not related to growth at adoption or to global cognitive ability. Atypical EEG power distribution at 18 months predicted indiscriminate friendliness and poorer inhibitory control at 36 months. Both post-institutionalized and foster care children were more likely than non-adopted children to exhibit indiscriminate friendliness. Results are consistent with a cortical hypoactivation model of the effects of early deprivation on neural development and provide initial evidence associating this atypical EEG pattern with indiscriminate friendliness. Outcomes observed in the foster care children raise questions about the specificity of institutional rearing as a risk factor and emphasize the need for broader consideration of the effects of early deprivation and disruptions in care. PMID:21171750

[An Electroencephalogram-driven Personalized Affective Music Player System: Algorithms and Preliminary Implementation].

PubMed

Ma, Yong; Li, Juan; Lu, Bin

2016-02-01

In order to monitor the emotional state changes of audience on real-time and to adjust the music playlist, we proposed an algorithm framework of an electroencephalogram (EEG) driven personalized affective music recommendation system based on the portable dry electrode shown in this paper. We also further finished a preliminary implementation on the Android platform. We used a two-dimensional emotional model of arousal and valence as the reference, and mapped the EEG data and the corresponding seed songs to the emotional coordinate quadrant in order to establish the matching relationship. Then, Mel frequency cepstrum coefficients were applied to evaluate the similarity between the seed songs and the songs in music library. In the end, during the music playing state, we used the EEG data to identify the audience's emotional state, and played and adjusted the corresponding song playlist based on the established matching relationship.

Noise Estimation in Electroencephalogram Signal by Using Volterra Series Coefficients

PubMed Central

Hassani, Malihe; Karami, Mohammad Reza

2015-01-01

The Volterra model is widely used for nonlinearity identification in practical applications. In this paper, we employed Volterra model to find the nonlinearity relation between electroencephalogram (EEG) signal and the noise that is a novel approach to estimate noise in EEG signal. We show that by employing this method. We can considerably improve the signal to noise ratio by the ratio of at least 1.54. An important issue in implementing Volterra model is its computation complexity, especially when the degree of nonlinearity is increased. Hence, in many applications it is urgent to reduce the complexity of computation. In this paper, we use the property of EEG signal and propose a new and good approximation of delayed input signal to its adjacent samples in order to reduce the computation of finding Volterra series coefficients. The computation complexity is reduced by the ratio of at least 1/3 when the filter memory is 3. PMID:26284176

Directionality volatility in electroencephalogram time series

NASA Astrophysics Data System (ADS)

Mansor, Mahayaudin M.; Green, David A.; Metcalfe, Andrew V.

2016-06-01

We compare time series of electroencephalograms (EEGs) from healthy volunteers with EEGs from subjects diagnosed with epilepsy. The EEG time series from the healthy group are recorded during awake state with their eyes open and eyes closed, and the records from subjects with epilepsy are taken from three different recording regions of pre-surgical diagnosis: hippocampal, epileptogenic and seizure zone. The comparisons for these 5 categories are in terms of deviations from linear time series models with constant variance Gaussian white noise error inputs. One feature investigated is directionality, and how this can be modelled by either non-linear threshold autoregressive models or non-Gaussian errors. A second feature is volatility, which is modelled by Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) processes. Other features include the proportion of variability accounted for by time series models, and the skewness and the kurtosis of the residuals. The results suggest these comparisons may have diagnostic potential for epilepsy and provide early warning of seizures.

Permanency analysis on human electroencephalogram signals for pervasive Brain-Computer Interface systems.

PubMed

Sadeghi, Koosha; Junghyo Lee; Banerjee, Ayan; Sohankar, Javad; Gupta, Sandeep K S

2017-07-01

Brain-Computer Interface (BCI) systems use some permanent features of brain signals to recognize their corresponding cognitive states with high accuracy. However, these features are not perfectly permanent, and BCI system should be continuously trained over time, which is tedious and time consuming. Thus, analyzing the permanency of signal features is essential in determining how often to repeat training. In this paper, we monitor electroencephalogram (EEG) signals, and analyze their behavior through continuous and relatively long period of time. In our experiment, we record EEG signals corresponding to rest state (eyes open and closed) from one subject everyday, for three and a half months. The results show that signal features such as auto-regression coefficients remain permanent through time, while others such as power spectral density specifically in 5-7 Hz frequency band are not permanent. In addition, eyes open EEG data shows more permanency than eyes closed data.

Emergency electroencephalogram: Usefulness in the diagnosis of nonconvulsive status epilepticus by the on-call neurologist.

PubMed

Máñez Miró, J U; Díaz de Terán, F J; Alonso Singer, P; Aguilar-Amat Prior, M J

2018-03-01

We aim to describe the use of emergency electroencephalogram (EmEEG) by the on-call neurologist when nonconvulsive status epilepticus (NCSE) is suspected, and in other indications, in a tertiary hospital. Observational retrospective cohort study of emergency EEG (EmEEG) recordings with 8-channel systems performed and analysed by the on-call neurologist in the emergency department and in-hospital wards between July 2013 and May 2015. Variables recorded were sex, age, symptoms, first diagnosis, previous seizure and cause, previous stroke, cancer, brain computed tomography, diagnosis after EEG, treatment, patient progress, routine control EEG (rEEG), and final diagnosis. We analysed frequency data, sensitivity, and specificity in the diagnosis of NCSE. The study included 135 EEG recordings performed in 129 patients; 51.4% were men and their median age was 69 years. In 112 cases (83%), doctors ruled out suspected NCSE because of altered level of consciousness in 42 (37.5%), behavioural abnormalities in 38 (33.9%), and aphasia in 32 (28.5%). The EmEEG diagnosis was NCSE in 37 patients (33%), and this was confirmed in 35 (94.6%) as the final diagnosis. In 3 other cases, NCSE was the diagnosis on discharge as confirmed by rEEG although the EmEEG missed this condition at first. EmEEG performed to rule out NCSE showed 92.1% sensitivity, 97.2% specificity, a positive predictive value of 94.6%, and a negative predictive value of 96%. Our experience finds that, in an appropriate clinical context, EmEEG performed by the on-call neurologist is a sensitive and specific tool for diagnosing NCSE. Copyright © 2016 Sociedad Española de Neurología. Publicado por Elsevier España, S.L.U. All rights reserved.

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.

Emotion and resilience: a multilevel investigation of hemispheric electroencephalogram asymmetry and emotion regulation in maltreated and nonmaltreated children.

PubMed

Curtis, W John; Cicchetti, Dante

2007-01-01

The current study was a multilevel investigation of resilience, emotion regulation, and hemispheric electroencephalogram (EEG) asymmetry in a sample of maltreated and nonmaltreated school age children. It was predicted that the positive emotionality and increased emotion regulatory ability associated with resilient functioning would be associated with relatively greater left frontal EEG activity. The study also investigated differences in pathways to resilience between maltreated and nonmaltreated children. The findings indicated that EEG asymmetry across central cortical regions distinguished between resilient and nonresilient children, with greater left hemisphere activity characterizing those who were resilient. In addition, nonmaltreated children showed greater left hemisphere EEG activity across parietal cortical regions. There was also a significant interaction between resilience, maltreatment status, and gender for asymmetry at anterior frontal electrodes, where nonmaltreated resilient females had greater relative left frontal activity compared to more right frontal activity exhibited by resilient maltreated females. An observational measure of emotion regulation significantly contributed to the prediction of resilience in the maltreated and nonmaltreated children, but EEG asymmetry in central cortical regions independently predicted resilience only in the maltreated group. The findings are discussed in terms of their meaning for the development of resilient functioning.

Narrow band quantitative and multivariate electroencephalogram analysis of peri-adolescent period

PubMed Central

2012-01-01

Background The peri-adolescent period is a crucial developmental moment of transition from childhood to emergent adulthood. The present report analyses the differences in Power Spectrum (PS) of the Electroencephalogram (EEG) between late childhood (24 children between 8 and 13 years old) and young adulthood (24 young adults between 18 and 23 years old). Results The narrow band analysis of the Electroencephalogram was computed in the frequency range of 0–20 Hz. The analysis of mean and variance suggested that six frequency ranges presented a different rate of maturation at these ages, namely: low delta, delta-theta, low alpha, high alpha, low beta and high beta. For most of these bands the maturation seems to occur later in anterior sites than posterior sites. Correlational analysis showed a lower pattern of correlation between different frequencies in children than in young adults, suggesting a certain asynchrony in the maturation of different rhythms. The topographical analysis revealed similar topographies of the different rhythms in children and young adults. Principal Component Analysis (PCA) demonstrated the same internal structure for the Electroencephalogram of both age groups. Principal Component Analysis allowed to separate four subcomponents in the alpha range. All these subcomponents peaked at a lower frequency in children than in young adults. Conclusions The present approaches complement and solve some of the incertitudes when the classical brain broad rhythm analysis is applied. Children have a higher absolute power than young adults for frequency ranges between 0-20 Hz, the correlation of Power Spectrum (PS) with age and the variance age comparison showed that there are six ranges of frequencies that can distinguish the level of EEG maturation in children and adults. The establishment of maturational order of different frequencies and its possible maturational interdependence would require a complete series including all the different ages. PMID:22920159

Signal Analysis Techniques for Interpreting Electroencephalograms

DTIC Science & Technology

1980-12-01

investigations by Lansing and Barlow (61). The relation between VER, adaptation attention fatigue, etc., has been studied quite extensively with invasive...in order to restore the highly abnormal EEG to near normal. Anatomical and Neurophysiological Considerations of VER Changes For studies of visual...Computer Analysis of Electroencephalograms, Digest of the 7th International Conf. on Medical and Biological Engineering, Stockholm, pp. 257-260, 1967. 4

Classification of EEG Signals Based on Pattern Recognition Approach.

PubMed

Amin, Hafeez Ullah; Mumtaz, Wajid; Subhani, Ahmad Rauf; Saad, Mohamad Naufal Mohamad; Malik, Aamir Saeed

2017-01-01

Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a "pattern recognition" approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher's discriminant ratio (FDR) and principal component analysis (PCA). A high density EEG dataset validated the proposed method (128-channels) by identifying two classifications: (1) EEG signals recorded during complex cognitive tasks using Raven's Advance Progressive Metric (RAPM) test; (2) EEG signals recorded during a baseline task (eyes open). Classifiers such as, K-nearest neighbors (KNN), Support Vector Machine (SVM), Multi-layer Perceptron (MLP), and Naïve Bayes (NB) were then employed. Outcomes yielded 99.11% accuracy via SVM classifier for coefficient approximations (A5) of low frequencies ranging from 0 to 3.90 Hz. Accuracy rates for detailed coefficients were 98.57 and 98.39% for SVM and KNN, respectively; and for detailed coefficients (D5) deriving from the sub-band range (3.90-7.81 Hz). Accuracy rates for MLP and NB classifiers were comparable at 97.11-89.63% and 91.60-81.07% for A5 and D5 coefficients, respectively. In addition, the proposed approach was also applied on public dataset for classification of two cognitive tasks and achieved comparable classification results, i.e., 93.33% accuracy with KNN. The proposed scheme yielded significantly higher classification performances using machine learning classifiers compared to extant quantitative feature extraction. These results suggest the proposed feature extraction method reliably classifies EEG signals recorded during cognitive tasks with a higher degree of accuracy.

Classification of EEG Signals Based on Pattern Recognition Approach

PubMed Central

Amin, Hafeez Ullah; Mumtaz, Wajid; Subhani, Ahmad Rauf; Saad, Mohamad Naufal Mohamad; Malik, Aamir Saeed

2017-01-01

Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a “pattern recognition” approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher's discriminant ratio (FDR) and principal component analysis (PCA). A high density EEG dataset validated the proposed method (128-channels) by identifying two classifications: (1) EEG signals recorded during complex cognitive tasks using Raven's Advance Progressive Metric (RAPM) test; (2) EEG signals recorded during a baseline task (eyes open). Classifiers such as, K-nearest neighbors (KNN), Support Vector Machine (SVM), Multi-layer Perceptron (MLP), and Naïve Bayes (NB) were then employed. Outcomes yielded 99.11% accuracy via SVM classifier for coefficient approximations (A5) of low frequencies ranging from 0 to 3.90 Hz. Accuracy rates for detailed coefficients were 98.57 and 98.39% for SVM and KNN, respectively; and for detailed coefficients (D5) deriving from the sub-band range (3.90–7.81 Hz). Accuracy rates for MLP and NB classifiers were comparable at 97.11–89.63% and 91.60–81.07% for A5 and D5 coefficients, respectively. In addition, the proposed approach was also applied on public dataset for classification of two cognitive tasks and achieved comparable classification results, i.e., 93.33% accuracy with KNN. The proposed scheme yielded significantly higher classification performances using machine learning classifiers compared to extant quantitative feature extraction. These results suggest the proposed feature extraction method reliably classifies EEG signals recorded during cognitive tasks with a higher degree of accuracy. PMID:29209190

Study of EEG during Sternberg Tasks with Different Direction of Arrangement for Letters

NASA Astrophysics Data System (ADS)

Kamihoriuchi, Kenji; Nuruki, Atsuo; Matae, Tadashi; Kurono, Asutsugu; Yunokuchi, Kazutomo

In previous study, we recorded electroencephalogram (EEG) of patients with dementia and healthy subjects during Sternberg task. But, only one presentation method of Sternberg task was considered in previous study. Therefore, we examined whether the EEG was different in two different presentation methods wrote letters horizontally and wrote letters vertically in this study. We recorded EEG of six healthy subjects during Sternberg task using two different presentation methods. The result was not different in EEG topography of all subjects. In all subjects, correct rate increased in case of vertically arranged letters.

Electroencephalographic Assessment in Vocational Counselling. Special Report.

ERIC Educational Resources Information Center

Visser, B. L.

A study examined the role of electroencephalograms (EEGs) in vocational counseling. A total of sixth-eight subjects, fifty of whom were under twenty and seventeen of whom were between the ages of twenty-one and twenty-nine, were referred for EEGs after being diagnosed as having concentration and learning difficulties. Various members of this group…

Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEG.

PubMed

Bai, Ou; Lin, Peter; Vorbach, Sherry; Li, Jiang; Furlani, Steve; Hallett, Mark

2007-12-01

To explore effective combinations of computational methods for the prediction of movement intention preceding the production of self-paced right and left hand movements from single trial scalp electroencephalogram (EEG). Twelve naïve subjects performed self-paced movements consisting of three key strokes with either hand. EEG was recorded from 128 channels. The exploration was performed offline on single trial EEG data. We proposed that a successful computational procedure for classification would consist of spatial filtering, temporal filtering, feature selection, and pattern classification. A systematic investigation was performed with combinations of spatial filtering using principal component analysis (PCA), independent component analysis (ICA), common spatial patterns analysis (CSP), and surface Laplacian derivation (SLD); temporal filtering using power spectral density estimation (PSD) and discrete wavelet transform (DWT); pattern classification using linear Mahalanobis distance classifier (LMD), quadratic Mahalanobis distance classifier (QMD), Bayesian classifier (BSC), multi-layer perceptron neural network (MLP), probabilistic neural network (PNN), and support vector machine (SVM). A robust multivariate feature selection strategy using a genetic algorithm was employed. The combinations of spatial filtering using ICA and SLD, temporal filtering using PSD and DWT, and classification methods using LMD, QMD, BSC and SVM provided higher performance than those of other combinations. Utilizing one of the better combinations of ICA, PSD and SVM, the discrimination accuracy was as high as 75%. Further feature analysis showed that beta band EEG activity of the channels over right sensorimotor cortex was most appropriate for discrimination of right and left hand movement intention. Effective combinations of computational methods provide possible classification of human movement intention from single trial EEG. Such a method could be the basis for a potential brain-computer interface based on human natural movement, which might reduce the requirement of long-term training. Effective combinations of computational methods can classify human movement intention from single trial EEG with reasonable accuracy.

Electroencephalogram Signal Classification for Automated Epileptic Seizure Detection Using Genetic Algorithm

PubMed Central

Nanthini, B. Suguna; Santhi, B.

2017-01-01

Background: Epilepsy causes when the repeated seizure occurs in the brain. Electroencephalogram (EEG) test provides valuable information about the brain functions and can be useful to detect brain disorder, especially for epilepsy. In this study, application for an automated seizure detection model has been introduced successfully. Materials and Methods: The EEG signals are decomposed into sub-bands by discrete wavelet transform using db2 (daubechies) wavelet. The eight statistical features, the four gray level co-occurrence matrix and Renyi entropy estimation with four different degrees of order, are extracted from the raw EEG and its sub-bands. Genetic algorithm (GA) is used to select eight relevant features from the 16 dimension features. The model has been trained and tested using support vector machine (SVM) classifier successfully for EEG signals. The performance of the SVM classifier is evaluated for two different databases. Results: The study has been experimented through two different analyses and achieved satisfactory performance for automated seizure detection using relevant features as the input to the SVM classifier. Conclusion: Relevant features using GA give better accuracy performance for seizure detection. PMID:28781480

Treating seizures in Creutzfeldt-Jakob disease.

PubMed

Ng, Marcus C; Westover, M Brandon; Cole, Andrew J

2014-01-01

Seizures are known to occur in Creutzfeldt-Jakob disease (CJD). In the setting of a rapidly progressive condition with no effective therapy, determining appropriate treatment for seizures can be difficult if clinical morbidity is not obvious yet the electroencephalogram (EEG) demonstrates a worrisome pattern such as status epilepticus. Herein, we present the case of a 39-year-old man with CJD and electrographic seizures, discuss how this case challenges conventional definitions of seizures, and discuss a rational approach toward treatment. Coincidentally, our case is the first report of CJD in a patient with Stickler syndrome.

Enabling computer decisions based on EEG input.

PubMed

Culpepper, Benjamin J; Keller, Robert M

2003-12-01

Multilayer neural networks were successfully trained to classify segments of 12-channel electroencephalogram (EEG) data into one of five classes corresponding to five cognitive tasks performed by a subject. Independent component analysis (ICA) was used to segregate obvious artifact EEG components from other sources, and a frequency-band representation was used to represent the sources computed by ICA. Examples of results include an 85% accuracy rate on differentiation between two tasks, using a segment of EEG only 0.05 s long and a 95% accuracy rate using a 0.5-s-long segment.

Enabling computer decisions based on EEG input

NASA Technical Reports Server (NTRS)

Culpepper, Benjamin J.; Keller, Robert M.

2003-01-01

Multilayer neural networks were successfully trained to classify segments of 12-channel electroencephalogram (EEG) data into one of five classes corresponding to five cognitive tasks performed by a subject. Independent component analysis (ICA) was used to segregate obvious artifact EEG components from other sources, and a frequency-band representation was used to represent the sources computed by ICA. Examples of results include an 85% accuracy rate on differentiation between two tasks, using a segment of EEG only 0.05 s long and a 95% accuracy rate using a 0.5-s-long segment.

EEG Artifact Removal Using a Wavelet Neural Network

NASA Technical Reports Server (NTRS)

Nguyen, Hoang-Anh T.; Musson, John; Li, Jiang; McKenzie, Frederick; Zhang, Guangfan; Xu, Roger; Richey, Carl; Schnell, Tom

2011-01-01

!n this paper we developed a wavelet neural network. (WNN) algorithm for Electroencephalogram (EEG) artifact removal without electrooculographic (EOG) recordings. The algorithm combines the universal approximation characteristics of neural network and the time/frequency property of wavelet. We. compared the WNN algorithm with .the ICA technique ,and a wavelet thresholding method, which was realized by using the Stein's unbiased risk estimate (SURE) with an adaptive gradient-based optimal threshold. Experimental results on a driving test data set show that WNN can remove EEG artifacts effectively without diminishing useful EEG information even for very noisy data.

Epileptic seizure onset detection based on EEG and ECG data fusion.

PubMed

Qaraqe, Marwa; Ismail, Muhammad; Serpedin, Erchin; Zulfi, Haneef

2016-05-01

This paper presents a novel method for seizure onset detection using fused information extracted from multichannel electroencephalogram (EEG) and single-channel electrocardiogram (ECG). In existing seizure detectors, the analysis of the nonlinear and nonstationary ECG signal is limited to the time-domain or frequency-domain. In this work, heart rate variability (HRV) extracted from ECG is analyzed using a Matching-Pursuit (MP) and Wigner-Ville Distribution (WVD) algorithm in order to effectively extract meaningful HRV features representative of seizure and nonseizure states. The EEG analysis relies on a common spatial pattern (CSP) based feature enhancement stage that enables better discrimination between seizure and nonseizure features. The EEG-based detector uses logical operators to pool SVM seizure onset detections made independently across different EEG spectral bands. Two fusion systems are adopted. In the first system, EEG-based and ECG-based decisions are directly fused to obtain a final decision. The second fusion system adopts an override option that allows for the EEG-based decision to override the fusion-based decision in the event that the detector observes a string of EEG-based seizure decisions. The proposed detectors exhibit an improved performance, with respect to sensitivity and detection latency, compared with the state-of-the-art detectors. Experimental results demonstrate that the second detector achieves a sensitivity of 100%, detection latency of 2.6s, and a specificity of 99.91% for the MAJ fusion case. Copyright © 2016 Elsevier Inc. All rights reserved.

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.

Correlation between perceived stigma and EEG paroxysmal abnormality in childhood epilepsy.

PubMed

Kanemura, Hideaki; Sano, Fumikazu; Ohyama, Tetsuo; Sugita, Kanji; Aihara, Masao

2015-11-01

We investigated the relationship between abnormal electroencephalogram (EEG) findings such as localized EEG paroxysmal abnormality (PA) and the perception of stigma to determine EEG factors associated with perceived stigma in childhood epilepsy. Participants comprised 40 patients (21 boys, 19 girls; mean age, 14.6 years) with epilepsy at enrollment. The criteria for inclusion were as follows: 1) age of 12-18 years, inclusive; 2) ≥6 months after epilepsy onset; 3) the ability to read and speak Japanese; and 4) the presence of EEG PA. Fifteen healthy seizure-free children were included as a control group. Participants were asked to rate how often they felt or acted in the ways described in the items of the Child Stigma Scale using a 5-point scale. Electroencephalogram paroxysms were classified based on the presence of spikes, sharp waves, or spike-wave complexes, whether focal or generalized. Participants showed significantly higher stigma scores than healthy subjects (p<0.01). A higher score reflects a greater perception of stigma. The average total scores of patients presenting with EEG PA at generalized, frontal, RD, midtemporal, and occipital regions were 2.3, 4.0, 2.4, 3.2, and 2.2, respectively. The scores of all questions were higher in the frontal group than those in other regions (p<0.01). Children presenting with frontal EEG PA perceived a greater stigma than children presenting with nonfrontal EEG PA (p<0.01). A relationship was identified between frontal EEG PA and a greater perception of stigma. Further studies are needed to confirm whether frontal EEG PA may function as a mediator of emotional responses such as perceived stigma in childhood epilepsy. Copyright © 2015 Elsevier Inc. All rights reserved.

Epileptic seizure detection in EEG signal with GModPCA and support vector machine.

PubMed

Jaiswal, Abeg Kumar; Banka, Haider

2017-01-01

Epilepsy is one of the most common neurological disorders caused by recurrent seizures. Electroencephalograms (EEGs) record neural activity and can detect epilepsy. Visual inspection of an EEG signal for epileptic seizure detection is a time-consuming process and may lead to human error; therefore, recently, a number of automated seizure detection frameworks were proposed to replace these traditional methods. Feature extraction and classification are two important steps in these procedures. Feature extraction focuses on finding the informative features that could be used for classification and correct decision-making. Therefore, proposing effective feature extraction techniques for seizure detection is of great significance. Principal Component Analysis (PCA) is a dimensionality reduction technique used in different fields of pattern recognition including EEG signal classification. Global modular PCA (GModPCA) is a variation of PCA. In this paper, an effective framework with GModPCA and Support Vector Machine (SVM) is presented for epileptic seizure detection in EEG signals. The feature extraction is performed with GModPCA, whereas SVM trained with radial basis function kernel performed the classification between seizure and nonseizure EEG signals. Seven different experimental cases were conducted on the benchmark epilepsy EEG dataset. The system performance was evaluated using 10-fold cross-validation. In addition, we prove analytically that GModPCA has less time and space complexities as compared to PCA. The experimental results show that EEG signals have strong inter-sub-pattern correlations. GModPCA and SVM have been able to achieve 100% accuracy for the classification between normal and epileptic signals. Along with this, seven different experimental cases were tested. The classification results of the proposed approach were better than were compared the results of some of the existing methods proposed in literature. It is also found that the time and space complexities of GModPCA are less as compared to PCA. This study suggests that GModPCA and SVM could be used for automated epileptic seizure detection in EEG signal.

Visual Masking in Schizophrenia: Overview and Theoretical Implications

PubMed Central

Green, Michael F.; Lee, Junghee; Wynn, Jonathan K.; Mathis, Kristopher I.

2011-01-01

Visual masking provides several key advantages for exploring the earliest stages of visual processing in schizophrenia: it allows for control over timing at the millisecond level, there are several well-supported theories of the underlying neurobiology of visual masking, and it is amenable to examination by electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI). In this paper, we provide an overview of the visual masking impairment schizophrenia, including the relevant theoretical mechanisms for masking impairment. We will discuss its relationship to clinical symptoms, antipsychotic medications, diagnostic specificity, and presence in at-risk populations. As part of this overview, we will cover the neural correlates of visual masking based on recent findings from EEG and fMRI. Finally, we will suggest a possible mechanism that could explain the patterns of masking findings and other visual processing findings in schizophrenia. PMID:21606322

Support vector machine and fuzzy C-mean clustering-based comparative evaluation of changes in motor cortex electroencephalogram under chronic alcoholism.

PubMed

Kumar, Surendra; Ghosh, Subhojit; Tetarway, Suhash; Sinha, Rakesh Kumar

2015-07-01

In this study, the magnitude and spatial distribution of frequency spectrum in the resting electroencephalogram (EEG) were examined to address the problem of detecting alcoholism in the cerebral motor cortex. The EEG signals were recorded from chronic alcoholic conditions (n = 20) and the control group (n = 20). Data were taken from motor cortex region and divided into five sub-bands (delta, theta, alpha, beta-1 and beta-2). Three methodologies were adopted for feature extraction: (1) absolute power, (2) relative power and (3) peak power frequency. The dimension of the extracted features is reduced by linear discrimination analysis and classified by support vector machine (SVM) and fuzzy C-mean clustering. The maximum classification accuracy (88 %) with SVM clustering was achieved with the EEG spectral features with absolute power frequency on F4 channel. Among the bands, relatively higher classification accuracy was found over theta band and beta-2 band in most of the channels when computed with the EEG features of relative power. Electrodes wise CZ, C3 and P4 were having more alteration. Considering the good classification accuracy obtained by SVM with relative band power features in most of the EEG channels of motor cortex, it can be suggested that the noninvasive automated online diagnostic system for the chronic alcoholic condition can be developed with the help of EEG signals.

Association between brain structural anomalies, electroencephalogram and history of seizures in Mucopolysaccharidosis type II (Hunter syndrome).

PubMed

Jiménez-Arredondo, Ramón Ernesto; Brambila-Tapia, Aniel Jessica Leticia; Mercado-Silva, Francisco Miguel; Ortiz-Aranda, Martha; Benites-Godinez, Verónica; Olmos-García-de-Alba, Graciela; Figuera, Luis Eduardo

2017-03-01

Mucopolysaccharidosis type II or Hunter syndrome (MPS II) is a genetic disease that can course with intellectual impairment and central nervous system (CNS) alterations. To date, no report has documented electroencephalogram (EEG) measures associated with CNS alterations, detected by imaging studies, and the history of seizures in patients with MPS II. Therefore, we decided to search this association. We included 9 patients with MPS II and performed imaging studies of the brain to detect the presence of cortico-subcortical atrophy, enlarged subarachnoid space and supratentorial ventricular size. Additionally, we performed EEG studies in sleep and awake conditions and a complete clinical description. Five out of the nine patients presented history of seizures and all except one patient (88.9%) presented some CNS structural alteration in the imaging studies, being the most frequent the cortico-subcortical atrophy (77.8%). The EEG results showed low amplitude in all patients and low voltage in sleep condition in eight patients with interhemispheric asymmetry in six patients during awake and sleep conditions. Although the five patients with history of seizures did not present a distinctive EEG anomaly, four of them presented some structural alteration in the imaging studies. In conclusion, most patients presented structural alterations in the CNS; likewise, all of them presented EEG anomalies mainly during sleep conditions. However, a clear association between EEG, CNS and the history of seizures was not established.

Advanced Physiological Estimation of Cognitive Status (APECS)

DTIC Science & Technology

2009-09-15

REPORT Advanced Physiological Estimation of Cognitive Status (APECS) Final Report 14. ABSTRACT 16. SECURITY CLASSIFICATION OF: EEG...fitness and transmit data to command and control systems. Some of the signals that the physiological sensors measure are readily interpreted, such as...electroencephalogram (EEG) and other signals requires a complex series of mathematical transformations or algorithms. Overall, research on algorithms

[FREQUENCY-TEMPORAL STRUCTURE OF HUMAN ELECTROENCEPHALOGRAM IN THE CONDITION OF ARTIFICIAL HYPOGRAVITY: DRY IMMERSION MODEL].

PubMed

Kuznetsova, G D; Gabova, A V; Lazarev, I E; Obukhov, Iu V; Obukhov, K Iu; Morozov, A A; Kulikov, M A; Shchatskova, A B; Vasil'eva, O N; Tomilovskaia, E S

2015-01-01

Frequency-temporal electroencephalogram (EEG) reactions to hypogravity were studied in 7 male subjects at the age of 20 to 27 years. The experiment was conducted using dry immersion (DI) as the best known method of simulating the space microgravity effects on the Earth. This hypogravity model reproduces hypokinesia, i.e. the weight-bearing and mechanic load removal, which is typical of microgravity. EEG was recorded by Neuroscan-2 (Compumedics) before the experiment (baseline data) and at the end of day 2 in DI. Comparative analysis of the EEG frequency-temporal structure was performed with the use of 2 techniques: Fourier transform and modified wavelet analysis. The Fourier transform elicited that after 2 days in DI the main shifts occurring to the EEG spectral composition are a decline in the alpha power and a slight though reliable growth of theta power. Similar frequency shifts were detected in the same records analyzed using the wavelet transform. According to wavelet analysis, during DI shifts in EEG frequency spectrum are accompanied by frequency desorganization of the EEG dominant rhythm and gross impairment of total stability of the electrical activity with time. Wavelet transform provides an opportunity to quantify changes in the frequency-temporal structure of the electrical activity of the brain. Quantitative evidence of frequency desorganization and temporal instability of EEG wavelet spectrograms may be the key to the understanding of mechanisms that drive functional disorders in the brain cortex in the conditions of hypogravity.

Sex differences on a mental rotation task: variations in electroencephalogram hemispheric activation between children and college students.

PubMed

Roberts, J E; Bell, M A

2000-01-01

The area of cognitive research that has produced the most consistent sex differences is spatial ability. In particular, men usually perform better on mental rotation tasks than women. Performance on mental rotation tasks has been associated with right parietal activation levels, both during task performance and prior to performance during baseline recordings. This study examined the relations among sex, age, electroencephalogram (EEG) hemispheric activation (at the 10.5 Hz to 13.5 Hz frequency band), and 2-D mental rotation task ability. Nineteen 8-year-olds (10 boys) and 20 college students (10 men) had EEG recorded at baseline and while performing a mental rotation task. Men had a faster reaction time on the mental rotation task than women, whereas there were no differences between boys and girls. After covarying for baseline EEG power values, men exhibited more activation (lower EEG power values) than women in the parietal and posterior temporal regions, whereas boys' and girls' power values did not differ in the parietal or posterior temporal regions. Furthermore, during the baseline condition, men generally exhibited more activation (lower EEG power values) throughout all regions of the scalp. Results support the hypothesis that a change that affects both brain activation and performance on mental rotation tasks occurs sometime between childhood and adulthood.

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

Permutation auto-mutual information of electroencephalogram in anesthesia

NASA Astrophysics Data System (ADS)

Liang, Zhenhu; Wang, Yinghua; Ouyang, Gaoxiang; Voss, Logan J.; Sleigh, Jamie W.; Li, Xiaoli

2013-04-01

Objective. The dynamic change of brain activity in anesthesia is an interesting topic for clinical doctors and drug designers. To explore the dynamical features of brain activity in anesthesia, a permutation auto-mutual information (PAMI) method is proposed to measure the information coupling of electroencephalogram (EEG) time series obtained in anesthesia. Approach. The PAMI is developed and applied on EEG data collected from 19 patients under sevoflurane anesthesia. The results are compared with the traditional auto-mutual information (AMI), SynchFastSlow (SFS, derived from the BIS index), permutation entropy (PE), composite PE (CPE), response entropy (RE) and state entropy (SE). Performance of all indices is assessed by pharmacokinetic/pharmacodynamic (PK/PD) modeling and prediction probability. Main results. The PK/PD modeling and prediction probability analysis show that the PAMI index correlates closely with the anesthetic effect. The coefficient of determination R2 between PAMI values and the sevoflurane effect site concentrations, and the prediction probability Pk are higher in comparison with other indices. The information coupling in EEG series can be applied to indicate the effect of the anesthetic drug sevoflurane on the brain activity as well as other indices. The PAMI of the EEG signals is suggested as a new index to track drug concentration change. Significance. The PAMI is a useful index for analyzing the EEG dynamics during general anesthesia.

A case of schizencephaly has a normal surface EEG but abnormal intracranial EEG: epilepsia partialis continua or dystonia?

PubMed

Lv, Yudan; Ma, Dihui; Meng, Hongmei; Zan, Wang; Li, Cui

2013-10-01

Schizencephaly is a congenital malformation of the cerebral hemispheres, with communication between the lateral ventricle and the subarachnoid space. Marinelli reported that schizencephaly may be associated with continuous involuntary hand movements, such as dystonia or epilepsia partialis continua (EPC). We describe a young Chinese patient with continuous involuntary movements of the contralateral hand affected by schizencephaly. He has a normal scalp electroencephalogram (EEG) but abnormal intracranial EEG, with synchronized periodic lateralized epileptiform discharges. The results obtained from these EEG investigations and the clinical features of the involuntary movements are in favor of a diagnosis of secondary EPC.

Paroxysmal occipital discharges suppressed by eye opening: spectrum of clinical and imaging features at a tertiary care center in India.

PubMed

Kaul, Bhavna; Shukla, Garima; Goyal, Vinay; Srivastava, Achal; Behari, Madhuri

2012-01-01

Paroxysmal occipital discharges (PODs) demonstrating the phenomena of fixation-off sensitivity have classically been described in childhood epilepsies with occipital paroxysms. We attempted to delineate the demographic, clinical and imaging characteristics of patients whose interictal electroencephalograms (EEGs) showed occipital discharges with fixation-off sensitivity at our center. During the period between 2003 and 2005, patients whose interictal EEGs showed PODs were included in the study. A detailed history, clinical examination and EEG findings along with imaging characteristics were analyzed. Of the 9,104 interictal EEGs screened during the study period, 11 patients (6 females and 5 males) aged between 5 and 17 years were identified to have PODs with fixation-off sensitivity. Five had history of generalized tonic-clonic seizures. Three patients could be classified under Panayiotopoulos syndrome; the remaining 8 (72.2%) patients had symptomatic epilepsy. This study suggests that the phenomenon of fixation-off sensitivity is found not only in patients of idiopathic focal epilepsies, but also in a substantial number of patients of symptomatic epilepsy. The high proportion of symptomatic epilepsy with phenomenon of fixation-off sensitivity may be related to the referral pattern.

Computer-aided diagnosis of alcoholism-related EEG signals.

PubMed

Acharya, U Rajendra; S, Vidya; Bhat, Shreya; Adeli, Hojjat; Adeli, Amir

2014-12-01

Alcoholism is a severe disorder that affects the functionality of neurons in the central nervous system (CNS) and alters the behavior of the affected person. Electroencephalogram (EEG) signals can be used as a diagnostic tool in the evaluation of subjects with alcoholism. The neurophysiological interpretation of EEG signals in persons with alcoholism (PWA) is based on observation and interpretation of the frequency and power in their EEGs compared to EEG signals from persons without alcoholism. This paper presents a review of the known features of EEGs obtained from PWA and proposes that the impact of alcoholism on the brain can be determined by computer-aided analysis of EEGs through extracting the minute variations in the EEG signals that can differentiate the EEGs of PWA from those of nonaffected persons. The authors advance the idea of automated computer-aided diagnosis (CAD) of alcoholism by employing the EEG signals. This is achieved through judicious combination of signal processing techniques such as wavelet, nonlinear dynamics, and chaos theory and pattern recognition and classification techniques. A CAD system is cost-effective and efficient and can be used as a decision support system by physicians in the diagnosis and treatment of alcoholism especially those who do not specialize in alcoholism or neurophysiology. It can also be of great value to rehabilitation centers to assess PWA over time and to monitor the impact of treatment aimed at minimizing or reversing the effects of the disease on the brain. A CAD system can be used to determine the extent of alcoholism-related changes in EEG signals (low, medium, high) and the effectiveness of therapeutic plans. Copyright © 2014 Elsevier Inc. All rights reserved.

Electroencephalogram measurement using polymer-based dry microneedle electrode

NASA Astrophysics Data System (ADS)

Arai, Miyako; Nishinaka, Yuya; Miki, Norihisa

2015-06-01

In this paper, we report a successful electroencephalogram (EEG) measurement using polymer-based dry microneedle electrodes. The electrodes consist of needle-shaped substrates of SU-8, a silver film, and a nanoporous parylene protective film. Differently from conventional wet electrodes, microneedle electrodes do not require skin preparation and a conductive gel. SU-8 is superior as a structural material to poly(dimethylsiloxane) (PDMS; Dow Corning Toray Sylgard 184) in terms of hardness, which was used in our previous work, and facilitates the penetration of needles through the stratum corneum. SU-8 microneedles can be successfully inserted into the skin without breaking and could maintain a sufficiently low skin-electrode contact impedance for EEG measurement. The electrodes successfully measured EEG from the frontal pole, and the quality of acquired signals was verified to be as high as those obtained using commercially available wet electrodes without any skin preparation or a conductive gel. The electrodes are readily applicable to record brain activities for a long period with little stress involved in skin preparation to the users.

Artificial bee colony algorithm for single-trial electroencephalogram analysis.

PubMed

Hsu, Wei-Yen; Hu, Ya-Ping

2015-04-01

In this study, we propose an analysis system combined with feature selection to further improve the classification accuracy of single-trial electroencephalogram (EEG) data. Acquiring event-related brain potential data from the sensorimotor cortices, the system comprises artifact and background noise removal, feature extraction, feature selection, and feature classification. First, the artifacts and background noise are removed automatically by means of independent component analysis and surface Laplacian filter, respectively. Several potential features, such as band power, autoregressive model, and coherence and phase-locking value, are then extracted for subsequent classification. Next, artificial bee colony (ABC) algorithm is used to select features from the aforementioned feature combination. Finally, selected subfeatures are classified by support vector machine. Comparing with and without artifact removal and feature selection, using a genetic algorithm on single-trial EEG data for 6 subjects, the results indicate that the proposed system is promising and suitable for brain-computer interface applications. © EEG and Clinical Neuroscience Society (ECNS) 2014.

Clinical observations on attention-deficit hyperactivity disorder (ADHD) in children with frontal lobe epilepsy.

PubMed

Zhang, Dong-Qing; Li, Fu-Hai; Zhu, Xiao-Bo; Sun, Ruo-Peng

2014-01-01

The objective was to investigate the prevalence of attention-deficit hyperactivity disorder (ADHD) in children with frontal lobe epilepsy and related factors. The medical records of 190 children diagnosed with frontal lobe epilepsy at Qilu Hospital of Shandong University between 2006 and 2011 were retrospectively collected, and a follow-up analysis of the prevalence of ADHD in these children was conducted. Of the 161 children with an effective follow-up, 59.0% (95/161) with frontal lobe epilepsy suffered from ADHD as well. Analysis of epilepsy and ADHD-related factors indicated that the incidence of ADHD was 89.4% (76/85) in children with abnormal electroencephalogram (EEG) discharges on the most recent EEG, which was significantly higher than the ADHD incidence of 25% (19/76) in children with normal readings on the most recent EEG (P < .01). Children with frontal lobe epilepsy have a high incidence of ADHD. Sustained abnormal discharge on the electroencephalogram is associated with increased comorbidity of ADHD with frontal lobe epilepsy.

Odds Ratio Product of Sleep EEG as a Continuous Measure of Sleep State

PubMed Central

Younes, Magdy; Ostrowski, Michele; Soiferman, Marc; Younes, Henry; Younes, Mark; Raneri, Jill; Hanly, Patrick

2015-01-01

Study Objectives: To develop and validate an algorithm that provides a continuous estimate of sleep depth from the electroencephalogram (EEG). Design: Retrospective analysis of polysomnograms. Setting: Research laboratory. Participants: 114 patients who underwent clinical polysomnography in sleep centers at the University of Manitoba (n = 58) and the University of Calgary (n = 56). Interventions: None. Measurements and Results: Power spectrum of EEG was determined in 3-second epochs and divided into delta, theta, alpha-sigma, and beta frequency bands. The range of powers in each band was divided into 10 aliquots. EEG patterns were assigned a 4-digit number that reflects the relative power in the 4 frequency ranges (10,000 possible patterns). Probability of each pattern occurring in 30-s epochs staged awake was determined, resulting in a continuous probability value from 0% to 100%. This was divided by 40 (% of epochs staged awake) producing the odds ratio product (ORP), with a range of 0–2.5. In validation testing, average ORP decreased progressively as EEG progressed from wakefulness (2.19 ± 0.29) to stage N3 (0.13 ± 0.05). ORP < 1.0 predicted sleep and ORP > 2.0 predicted wakefulness in > 95% of 30-s epochs. Epochs with intermediate ORP occurred in unstable sleep with a high arousal index (> 70/h) and were subject to much interrater scoring variability. There was an excellent correlation (r2 = 0.98) between ORP in current 30-s epochs and the likelihood of arousal or awakening occurring in the next 30-s epoch. Conclusions: Our results support the use of the odds ratio product (ORP) as a continuous measure of sleep depth. Citation: Younes M, Ostrowski M, Soiferman M, Younes H, Younes M, Raneri J, Hanly P. Odds ratio product of sleep EEG as a continuous measure of sleep state. SLEEP 2015;38(4):641–654. PMID:25348125

Sleep EEG of Microcephaly in Zika Outbreak.

PubMed

Kanda, Paulo Afonso Medeiros; Aguiar, Aline de Almeida Xavier; Miranda, Jose Lucivan; Falcao, Alexandre Loverde; Andrade, Claudia Suenia; Reis, Luigi Neves Dos Santos; Almeida, Ellen White R Bacelar; Bello, Yanes Brum; Monfredinho, Arthur; Kanda, Rafael Guimaraes

2018-01-01

Microcephaly (MC), previously considered rare, is now a health emergency of international concern because of the devastating Zika virus pandemic outbreak of 2015. The authors describe the electroencephalogram (EEG) findings in sleep EEG of epileptic children who were born with microcephaly in areas of Brazil with active Zika virus transmission between 2014 and 2017. The authors reviewed EEGs from 23 children. Nine were females (39.2%), and the age distribution varied from 4 to 48 months. MC was associated with mother positive serology to toxoplasmosis (toxo), rubella (rub), herpes, and dengue (1 case); toxo (1 case); chikungunya virus (CHIKV) (1 case); syphilis (1 case); and Zika virus (ZIKV) (10 cases). In addition, 1 case was associated with perinatal hypoxia and causes of 9 cases remain unknown. The main background EEG abnormality was diffuse slowing (10 cases), followed by classic (3 cases) and modified (5 cases) hypsarrhythmia. A distinct EEG pattern was seen in ZIKV (5 cases), toxo (2 cases), and undetermined cause (1 case). It was characterized by runs of frontocentrotemporal 4.5-13 Hz activity (7 cases) or diffuse and bilateral runs of 18-24 Hz (1 case). In ZIKV, this rhythmic activity was associated with hypsarrhythmia or slow background. Further studies are necessary to determine if this association is suggestive of ZIKV infection. The authors believe that EEG should be included in the investigation of all newly diagnosed congenital MC, especially those occurring in areas of autochthonous transmission of ZIKV.

Lateralization patterns of covert but not overt movements change with age: An EEG neurofeedback study.

PubMed

Zich, Catharina; Debener, Stefan; De Vos, Maarten; Frerichs, Stella; Maurer, Stefanie; Kranczioch, Cornelia

2015-08-01

The mental practice of movements has been suggested as a promising add-on therapy to facilitate motor recovery after stroke. In the case of mentally practised movements, electroencephalogram (EEG) can be utilized to provide feedback about an otherwise covert act. The main target group for such an intervention are elderly patients, though research so far is largely focused on young populations (<30 years). The present study therefore aimed to examine the influence of age on the neural correlates of covert movements (CMs) in a real-time EEG neurofeedback framework. CM-induced event-related desynchronization (ERD) was studied in young (mean age: 23.6 years) and elderly (mean age: 62.7 years) healthy adults. Participants performed covert and overt hand movements. CMs were based on kinesthetic motor imagery (MI) or quasi-movements (QM). Based on previous studies investigating QM in the mu frequency range (8-13Hz) QM were expected to result in more lateralized ERD% patterns and accordingly higher classification accuracies. Independent of CM strategy the elderly were characterized by a significantly reduced lateralization of ERD%, due to stronger ipsilateral ERD%, and in consequence, reduced classification accuracies. QM were generally perceived as more vivid, but no differences were evident between MI and QM in ERD% or classification accuracies. EEG feedback enhanced task-related activity independently of strategy and age. ERD% measures of overt and covert movements were strongly related in young adults, whereas in the elderly ERD% lateralization is dissociated. In summary, we did not find evidence in support of more pronounced ERD% lateralization patterns in QM. Our finding of a less lateralized activation pattern in the elderly is in accordance to previous research and with the idea that compensatory processes help to overcome neurodegenerative changes related to normal ageing. Importantly, it indicates that EEG neurofeedback studies should place more emphasis on the age of the potential end-users. Copyright © 2015 Elsevier Inc. All rights reserved.

Electroencephalographic Abnormalities during Sleep in Children with Developmental Speech-Language Disorders: A Case-Control Study

ERIC Educational Resources Information Center

Parry-Fielder, Bronwyn; Collins, Kevin; Fisher, John; Keir, Eddie; Anderson, Vicki; Jacobs, Rani; Scheffer, Ingrid E.; Nolan, Terry

2009-01-01

Earlier research has suggested a link between epileptiform activity in the electroencephalogram (EEG) and developmental speech-language disorder (DSLD). This study investigated the strength of this association by comparing the frequency of EEG abnormalities in 45 language-normal children (29 males, 16 females; mean age 6y 11mo, SD 1y 10mo, range…

How Sex and College Major Relate to Mental Rotation Accuracy and Preferred Strategy: An Electroencephalographic (EEG) Investigation

ERIC Educational Resources Information Center

Li, Yingli; O'Boyle, Michael

2013-01-01

The electroencephalogram (EEG) was used to investigate variation in mental rotation (MR) strategies between males and females and different college majors. Beta activation was acquired from 40 participants (10 males and 10 females in physical science; 10 males and 10 females in social science) when performing the Vandenberg and Kuse (1978) mental…

Electroencephalogram power changes as a correlate of chemotherapy-associated fatigue and cognitive dysfunction.

PubMed

Moore, Halle C F; Parsons, Michael W; Yue, Guang H; Rybicki, Lisa A; Siemionow, Wlodzimierz

2014-08-01

Persistent fatigue and cognitive dysfunction are poorly understood potential long-term effects of adjuvant chemotherapy. In this pilot study, we assessed the value of electroencephalogram (EEG) power measurements as a means to evaluate physical and mental fatigue associated with chemotherapy. Women planning to undergo adjuvant chemotherapy for breast cancer and healthy controls underwent neurophysiologic assessments at baseline, during the time of chemotherapy treatment, and at 1 year. Repeated measures analysis of variance was used to analyze the data. Compared with controls, patients reported more subjective fatigue at baseline that increased during chemotherapy and did not entirely resolve by 1 year. Performance on endurance testing was similar in patients versus controls at all time points; however, values of EEG power increased after a physical task in patients during chemotherapy but not controls. Compared with controls, subjective mental fatigue was similar for patients at baseline and 1 year but worsened during chemotherapy. Patients performed similarly to controls on formal cognitive testing at all time points, but EEG activity after the cognitive task was increased in patients only during chemotherapy. EEG power measurement has the potential to provide a sensitive neurophysiologic correlate of cancer treatment-related fatigue and cognitive dysfunction.

Optimal electrode selection for multi-channel electroencephalogram based detection of auditory steady-state responses.

PubMed

Van Dun, Bram; Wouters, Jan; Moonen, Marc

2009-07-01

Auditory steady-state responses (ASSRs) are used for hearing threshold estimation at audiometric frequencies. Hearing impaired newborns, in particular, benefit from this technique as it allows for a more precise diagnosis than traditional techniques, and a hearing aid can be better fitted at an early age. However, measurement duration of current single-channel techniques is still too long for clinical widespread use. This paper evaluates the practical performance of a multi-channel electroencephalogram (EEG) processing strategy based on a detection theory approach. A minimum electrode set is determined for ASSRs with frequencies between 80 and 110 Hz using eight-channel EEG measurements of ten normal-hearing adults. This set provides a near-optimal hearing threshold estimate for all subjects and improves response detection significantly for EEG data with numerous artifacts. Multi-channel processing does not significantly improve response detection for EEG data with few artifacts. In this case, best response detection is obtained when noise-weighted averaging is applied on single-channel data. The same test setup (eight channels, ten normal-hearing subjects) is also used to determine a minimum electrode setup for 10-Hz ASSRs. This configuration allows to record near-optimal signal-to-noise ratios for 80% of subjects.

Postnatal development of EEG patterns, catecholamine contents and myelination, and effect of hyperthyroidism in Suncus brain.

PubMed

Takeuchi, T; Sitizyo, K; Harada, E

1998-03-01

The postnatal development of the central nervous system (CNS) in house musk shrew in the early stage of maturation was studied. The electroencephalogram (EEG) and visual evoked potential (VEP) in association with catecholamine contents and myelin basic protein (MBP) immunoreactivity were carried out from the 1st to the 20th day of postnatal age. Different EEG patterns which were specific to behavioral states (awake and drowsy) were first recorded on the 5th day, and the total power which was obtained by power spectrum analysis increased after this stage. The latencies of all peaks in VEP markedly shortened between the 5th and the 7th day. Noradrenalin (NA) content of the brain showed a slight increase after the 3rd day, and reached maximum levels on the 7th day, which was delayed a few days compared to dopamine (DA). In hyperthyroidism, the peak latency of VEP was shortened and biosynthesis of NA in cerebral cortex and DA in hippocampus was accelerated. The most obvious change in MBP-immunoreactivity of the telencephalon occurred from the 7th to the 10th day. These morphological changes in the brain advanced at the identical time-course to those in the electrophysiological development and increment of DA and NA contents.

Design of an Adaptive Human-Machine System Based on Dynamical Pattern Recognition of Cognitive Task-Load.

PubMed

Zhang, Jianhua; Yin, Zhong; Wang, Rubin

2017-01-01

This paper developed a cognitive task-load (CTL) classification algorithm and allocation strategy to sustain the optimal operator CTL levels over time in safety-critical human-machine integrated systems. An adaptive human-machine system is designed based on a non-linear dynamic CTL classifier, which maps a set of electroencephalogram (EEG) and electrocardiogram (ECG) related features to a few CTL classes. The least-squares support vector machine (LSSVM) is used as dynamic pattern classifier. A series of electrophysiological and performance data acquisition experiments were performed on seven volunteer participants under a simulated process control task environment. The participant-specific dynamic LSSVM model is constructed to classify the instantaneous CTL into five classes at each time instant. The initial feature set, comprising 56 EEG and ECG related features, is reduced to a set of 12 salient features (including 11 EEG-related features) by using the locality preserving projection (LPP) technique. An overall correct classification rate of about 80% is achieved for the 5-class CTL classification problem. Then the predicted CTL is used to adaptively allocate the number of process control tasks between operator and computer-based controller. Simulation results showed that the overall performance of the human-machine system can be improved by using the adaptive automation strategy proposed.

Using the nonlinear control of anaesthesia-induced hypersensitivity of EEG at burst suppression level to test the effects of radiofrequency radiation on brain function

PubMed Central

Lipping, Tarmo; Rorarius, Michael; Jäntti, Ville; Annala, Kari; Mennander, Ari; Ferenets, Rain; Toivonen, Tommi; Toivo, Tim; Värri, Alpo; Korpinen, Leena

2009-01-01

Background In this study, investigating the effects of mobile phone radiation on test animals, eleven pigs were anaesthetised to the level where burst-suppression pattern appears in the electroencephalogram (EEG). At this level of anaesthesia both human subjects and animals show high sensitivity to external stimuli which produce EEG bursts during suppression. The burst-suppression phenomenon represents a nonlinear control system, where low-amplitude EEG abruptly switches to very high amplitude bursts. This switching can be triggered by very minor stimuli and the phenomenon has been described as hypersensitivity. To test if also radio frequency (RF) stimulation can trigger this nonlinear control, the animals were exposed to pulse modulated signal of a GSM mobile phone at 890 MHz. In the first phase of the experiment electromagnetic field (EMF) stimulation was randomly switched on and off and the relation between EEG bursts and EMF stimulation onsets and endpoints were studied. In the second phase a continuous RF stimulation at 31 W/kg was applied for 10 minutes. The ECG, the EEG, and the subcutaneous temperature were recorded. Results No correlation between the exposure and the EEG burst occurrences was observed in phase I measurements. No significant changes were observed in the EEG activity of the pigs during phase II measurements although several EEG signal analysis methods were applied. The temperature measured subcutaneously from the pigs' head increased by 1.6°C and the heart rate by 14.2 bpm on the average during the 10 min exposure periods. Conclusion The hypothesis that RF radiation would produce sensory stimulation of somatosensory, auditory or visual system or directly affect the brain so as to produce EEG bursts during suppression was not confirmed. PMID:19615084

An EEG-Based Person Authentication System with Open-Set Capability Combining Eye Blinking Signals

PubMed Central

Wu, Qunjian; Zeng, Ying; Zhang, Chi; Tong, Li; Yan, Bin

2018-01-01

The electroencephalogram (EEG) signal represents a subject’s specific brain activity patterns and is considered as an ideal biometric given its superior forgery prevention. However, the accuracy and stability of the current EEG-based person authentication systems are still unsatisfactory in practical application. In this paper, a multi-task EEG-based person authentication system combining eye blinking is proposed, which can achieve high precision and robustness. Firstly, we design a novel EEG-based biometric evoked paradigm using self- or non-self-face rapid serial visual presentation (RSVP). The designed paradigm could obtain a distinct and stable biometric trait from EEG with a lower time cost. Secondly, the event-related potential (ERP) features and morphological features are extracted from EEG signals and eye blinking signals, respectively. Thirdly, convolutional neural network and back propagation neural network are severally designed to gain the score estimation of EEG features and eye blinking features. Finally, a score fusion technology based on least square method is proposed to get the final estimation score. The performance of multi-task authentication system is improved significantly compared to the system using EEG only, with an increasing average accuracy from 92.4% to 97.6%. Moreover, open-set authentication tests for additional imposters and permanence tests for users are conducted to simulate the practical scenarios, which have never been employed in previous EEG-based person authentication systems. A mean false accepted rate (FAR) of 3.90% and a mean false rejected rate (FRR) of 3.87% are accomplished in open-set authentication tests and permanence tests, respectively, which illustrate the open-set authentication and permanence capability of our systems. PMID:29364848

An EEG-Based Person Authentication System with Open-Set Capability Combining Eye Blinking Signals.

PubMed

Wu, Qunjian; Zeng, Ying; Zhang, Chi; Tong, Li; Yan, Bin

2018-01-24

The electroencephalogram (EEG) signal represents a subject's specific brain activity patterns and is considered as an ideal biometric given its superior forgery prevention. However, the accuracy and stability of the current EEG-based person authentication systems are still unsatisfactory in practical application. In this paper, a multi-task EEG-based person authentication system combining eye blinking is proposed, which can achieve high precision and robustness. Firstly, we design a novel EEG-based biometric evoked paradigm using self- or non-self-face rapid serial visual presentation (RSVP). The designed paradigm could obtain a distinct and stable biometric trait from EEG with a lower time cost. Secondly, the event-related potential (ERP) features and morphological features are extracted from EEG signals and eye blinking signals, respectively. Thirdly, convolutional neural network and back propagation neural network are severally designed to gain the score estimation of EEG features and eye blinking features. Finally, a score fusion technology based on least square method is proposed to get the final estimation score. The performance of multi-task authentication system is improved significantly compared to the system using EEG only, with an increasing average accuracy from 92.4% to 97.6%. Moreover, open-set authentication tests for additional imposters and permanence tests for users are conducted to simulate the practical scenarios, which have never been employed in previous EEG-based person authentication systems. A mean false accepted rate (FAR) of 3.90% and a mean false rejected rate (FRR) of 3.87% are accomplished in open-set authentication tests and permanence tests, respectively, which illustrate the open-set authentication and permanence capability of our systems.

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

A case of epilepsy induced by eating or by visual stimuli of food made of minced meat.

PubMed

Mimura, Naoya; Inoue, Takeshi; Shimotake, Akihiro; Matsumoto, Riki; Ikeda, Akio; Takahashi, Ryosuke

2017-08-31

We report a 34-year-old woman with eating epilepsy induced not only by eating but also seeing foods made of minced meat. In her early 20s of age, she started having simple partial seizures (SPS) as flashback and epigastric discomfort induced by particular foods. When she was 33 years old, she developed SPS, followed by secondarily generalized tonic-clonic seizure (sGTCS) provoked by eating a hot dog, and 6 months later, only seeing the video of dumpling. We performed video electroencephalogram (EEG) monitoring while she was seeing the video of soup dumpling, which most likely caused sGTCS. Ictal EEG showed rhythmic theta activity in the left frontal to mid-temporal area, followed by generalized seizure pattern. In this patient, seizures were provoked not only by eating particular foods but also by seeing these. This suggests a form of epilepsy involving visual stimuli.

Study of emotion-based neurocardiology through wearable systems

NASA Astrophysics Data System (ADS)

Ramasamy, Mouli; Varadan, Vijay

2016-04-01

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 phenomena. 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 modalities 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.

New KF-PP-SVM classification method for EEG in brain-computer interfaces.

PubMed

Yang, Banghua; Han, Zhijun; Zan, Peng; Wang, Qian

2014-01-01

Classification methods are a crucial direction in the current study of brain-computer interfaces (BCIs). To improve the classification accuracy for electroencephalogram (EEG) signals, a novel KF-PP-SVM (kernel fisher, posterior probability, and support vector machine) classification method is developed. Its detailed process entails the use of common spatial patterns to obtain features, based on which the within-class scatter is calculated. Then the scatter is added into the kernel function of a radial basis function to construct a new kernel function. This new kernel is integrated into the SVM to obtain a new classification model. Finally, the output of SVM is calculated based on posterior probability and the final recognition result is obtained. To evaluate the effectiveness of the proposed KF-PP-SVM method, EEG data collected from laboratory are processed with four different classification schemes (KF-PP-SVM, KF-SVM, PP-SVM, and SVM). The results showed that the overall average improvements arising from the use of the KF-PP-SVM scheme as opposed to KF-SVM, PP-SVM and SVM schemes are 2.49%, 5.83 % and 6.49 % respectively.

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.

[The role of ambulatory electroencephalogram monitoring: experience and results in 264 records].

PubMed

González de la Aleja, J; Saiz Díaz, R A; Martín García, H; Juntas, R; Pérez-Martínez, D; de la Peña, P

2008-11-01

Ambulatory electroencephalogram (EEG) monitoring allows for long-term, mobile electroencephalographic recordings of patients. This study aims to describe and analyze the results obtained with ambulatory EEG in our clinical practice. We have analyzed the results of 264 ambulatory EEG records, grouped according to the reason for the request: a) group 1: diagnostic evaluation of episodes of epileptic nature; b) group 2: diagnostic evaluation of paroxysmal episodes, and c) group 3: evaluation of the risk of relapse during anti-seizure treatment withdrawal in certain epileptic patients. a) Group 1 (n=137): normal results were found in 54 records (39.4%). There was generalized epileptic activity in 20 (14.6%) of them (5 with ictal activity) and focal epileptic activity was detected in 57 cases (42%) (8 with ictal activity). No EEG diagnosis could be reached in 6 (4%) recordings due to the presence of artefacts; b) group 2 (n=99): in 47 records (47.5 %), there were no episodes and the Holter-EEG was normal. There was a clinically documented episode without anomalies during Holter-EEG registration in 14 cases (14.2%). In 29 records (29.3%), focal epileptic activity was recorded (ictal 4) and generalized epileptic activity (ictal in 1) was recorded in 4 patients (4%). No EEG diagnosis could be reached in 5 cases (5%), and c) group 3 (n=28): the study was normal in 15 cases (53.6%) and showed focal interictal epileptic activity in 8 (28.6 %) and generalized interictal epileptic activity in 5 of them (17.8%). We believe that the ambulatory EEG recordings in correctly selected cases can provide important additional information regarding global assessment of patients with epilepsy.

Changes of the Prefrontal EEG (Electroencephalogram) Activities According to the Repetition of Audio-Visual Learning.

ERIC Educational Resources Information Center

Kim, Yong-Jin; Chang, Nam-Kee

2001-01-01

Investigates the changes of neuronal response according to a four time repetition of audio-visual learning. Obtains EEG data from the prefrontal (Fp1, Fp2) lobe from 20 subjects at the 8th grade level. Concludes that the habituation of neuronal response shows up in repetitive audio-visual learning and brain hemisphericity can be changed by…

Letting Thoughts Take Wing

NASA Technical Reports Server (NTRS)

Jorgensen, Chuck; Wheeler, Kevin

2002-01-01

Scientists are conducting research into electroencephalograms (EEGs) of brainwave activity, and electromyography (EMG) of muscle activity, in order to develop systems which can control an aircraft with only a pilot's thoughts. This article describes some EEG and EMG signals, and how they might be analyzed and interpreted to operate an aircraft. The development of a system to detect and interpret fine muscle movements is also profiled in the article.

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.

Evaluating the effectiveness of using electroencephalogram power indices to measure visual fatigue.

PubMed

Hsu, Bin-Wei; Wang, Mao-Jiun J

2013-02-01

Electroencephalography (EEG) is widely used in cognitive and behavioral research. This study evaluates the effectiveness of using the EEG power index to measure visual fatigue. Three common visual fatigue measures, critical-flicker fusion (CFF), near-point accommodation (NPA), and subjective eye-fatigue rating, were used for comparison. The study participants were 20 men with a mean age of 20.4 yr. (SD = 1.5). The experimental task was a car-racing video game. Results indicated that the EEG power indices were valid as a visual fatigue measure and the sensitivity of the objective measures (CFF and EEG power index) was higher than the subjective measure. The EEG beta and EEG alpha were effective for measuring visual fatigue in short- and long-duration tasks, respectively. EEG beta/alpha were the most effective power indexes for the visual fatigue measure.

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.

Estimation of alertness levels with changes in decibel scale wavelength of EEG during dual-task simulation of auditory sonar target detection.

PubMed

Arjunan, Sridhar P; Kumar, Dinesh K; Jung, Tzyy-Ping

2010-01-01

Changes in alertness levels can have dire consequences for people operating and controlling motorized equipment. Past research studies have shown the relationship of Electroencephalogram (EEG) with alertness of the person. This research reports the fractal analysis of EEG and estimation of the alertness levels of the individual based on the changes in the maximum fractal length (MFL) of EEG. The results indicate that MFL of only 2 channels of EEG can be used to identify the loss of alertness of the individual with mean (inverse) correlation coefficient = 0.82. This study has also reported that using the changes in MFL of EEG, the changes in alertness level of a person was estimated with a mean correlation coefficient = 0.69.

Age-Related Neural Oscillation Patterns During the Processing of Temporally Manipulated Speech.

PubMed

Rufener, Katharina S; Oechslin, Mathias S; Wöstmann, Malte; Dellwo, Volker; Meyer, Martin

2016-05-01

This EEG-study aims to investigate age-related differences in the neural oscillation patterns during the processing of temporally modulated speech. Viewing from a lifespan perspective, we recorded the electroencephalogram (EEG) data of three age samples: young adults, middle-aged adults and older adults. Stimuli consisted of temporally degraded sentences in Swedish-a language unfamiliar to all participants. We found age-related differences in phonetic pattern matching when participants were presented with envelope-degraded sentences, whereas no such age-effect was observed in the processing of fine-structure-degraded sentences. Irrespective of age, during speech processing the EEG data revealed a relationship between envelope information and the theta band (4-8 Hz) activity. Additionally, an association between fine-structure information and the gamma band (30-48 Hz) activity was found. No interaction, however, was found between acoustic manipulation of stimuli and age. Importantly, our main finding was paralleled by an overall enhanced power in older adults in high frequencies (gamma: 30-48 Hz). This occurred irrespective of condition. For the most part, this result is in line with the Asymmetric Sampling in Time framework (Poeppel in Speech Commun 41:245-255, 2003), which assumes an isomorphic correspondence between frequency modulations in neurophysiological patterns and acoustic oscillations in spoken language. We conclude that speech-specific neural networks show strong stability over adulthood, despite initial processes of cortical degeneration indicated by enhanced gamma power. The results of our study therefore confirm the concept that sensory and cognitive processes undergo multidirectional trajectories within the context of healthy aging.

No short-term effects of digital mobile radio telephone on the awake human electroencephalogram

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

Roeschke, J.; Mann, K.

1997-05-01

A recent study reported the results of an exploratory study of alterations of the quantitative sleep profile due to the effects of a digital mobile radio telephone. Rapid eye movement (REM) was suppressed, and the spectral power density in the 8--13 Hz frequency range during REM sleep was altered. The aim of the present study was to illuminate the influence of digital mobile radio telephone on the awake electroencephalogram (EEG) of healthy subjects. For this purpose, the authors investigated 34 male subjects in a single-blind cross-over design experiment by measuring spontaneous EEGs under closed-eyes condition from scalp positions C{sub 3}more » and C{sub 4} and comparing the effects of an active and an inactive digital mobile radio telephone (GSM) system. During exposure of nearly 3.5 min to the 900 MHz electromagnetic field pulsed at a frequency of 217 Hz and with a pulse width of 580 {micro}s, the authors could not detect any difference in the awake EEGs in terms of spectral power density measures.« less

Classification of epilepsy types through global network analysis of scalp electroencephalograms

NASA Astrophysics Data System (ADS)

Lee, Uncheol; Kim, Seunghwan; Jung, Ki-Young

2006-04-01

Epilepsy is a dynamic disease in which self-organization and emergent structures occur dynamically at multiple levels of neuronal integration. Therefore, the transient relationship within multichannel electroencephalograms (EEGs) is crucial for understanding epileptic processes. In this paper, we show that the global relationship within multichannel EEGs provides us with more useful information in classifying two different epilepsy types than pairwise relationships such as cross correlation. To demonstrate this, we determine the global network structure within channels of the scalp EEG based on the minimum spanning tree method. The topological dissimilarity of the network structures from different types of temporal lobe epilepsy is described in the form of the divergence rate and is computed for 11 patients with left (LTLE) and right temporal lobe epilepsy (RTLE). We find that patients with LTLE and RTLE exhibit different large scale network structures, which emerge at the epoch immediately before the seizure onset, not in the preceding epochs. Our results suggest that patients with the two different epilepsy types display distinct large scale dynamical networks with characteristic epileptic network structures.

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

Toward Automated Cochlear Implant Fitting Procedures Based on Event-Related Potentials.

PubMed

Finke, Mareike; Billinger, Martin; Büchner, Andreas

Cochlear implants (CIs) restore hearing to the profoundly deaf by direct electrical stimulation of the auditory nerve. To provide an optimal electrical stimulation pattern the CI must be individually fitted to each CI user. To date, CI fitting is primarily based on subjective feedback from the user. However, not all CI users are able to provide such feedback, for example, small children. This study explores the possibility of using the electroencephalogram (EEG) to objectively determine if CI users are able to hear differences in tones presented to them, which has potential applications in CI fitting or closed loop systems. Deviant and standard stimuli were presented to 12 CI users in an active auditory oddball paradigm. The EEG was recorded in two sessions and classification of the EEG data was performed with shrinkage linear discriminant analysis. Also, the impact of CI artifact removal on classification performance and the possibility to reuse a trained classifier in future sessions were evaluated. Overall, classification performance was above chance level for all participants although performance varied considerably between participants. Also, artifacts were successfully removed from the EEG without impairing classification performance. Finally, reuse of the classifier causes only a small loss in classification performance. Our data provide first evidence that EEG can be automatically classified on single-trial basis in CI users. Despite the slightly poorer classification performance over sessions, classifier and CI artifact correction appear stable over successive sessions. Thus, classifier and artifact correction weights can be reused without repeating the set-up procedure in every session, which makes the technique easier applicable. With our present data, we can show successful classification of event-related cortical potential patterns in CI users. In the future, this has the potential to objectify and automate parts of CI fitting procedures.

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.

A stable pattern of EEG spectral coherence distinguishes children with autism from neuro-typical controls - a large case control study.

PubMed

Duffy, Frank H; Als, Heidelise

2012-06-26

The autism rate has recently increased to 1 in 100 children. Genetic studies demonstrate poorly understood complexity. Environmental factors apparently also play a role. Magnetic resonance imaging (MRI) studies demonstrate increased brain sizes and altered connectivity. Electroencephalogram (EEG) coherence studies confirm connectivity changes. However, genetic-, MRI- and/or EEG-based diagnostic tests are not yet available. The varied study results likely reflect methodological and population differences, small samples and, for EEG, lack of attention to group-specific artifact. Of the 1,304 subjects who participated in this study, with ages ranging from 1 to 18 years old and assessed with comparable EEG studies, 463 children were diagnosed with autism spectrum disorder (ASD); 571 children were neuro-typical controls (C). After artifact management, principal components analysis (PCA) identified EEG spectral coherence factors with corresponding loading patterns. The 2- to 12-year-old subsample consisted of 430 ASD- and 554 C-group subjects (n = 984). Discriminant function analysis (DFA) determined the spectral coherence factors' discrimination success for the two groups. Loading patterns on the DFA-selected coherence factors described ASD-specific coherence differences when compared to controls. Total sample PCA of coherence data identified 40 factors which explained 50.8% of the total population variance. For the 2- to 12-year-olds, the 40 factors showed highly significant group differences (P < 0.0001). Ten randomly generated split half replications demonstrated high-average classification success (C, 88.5%; ASD, 86.0%). Still higher success was obtained in the more restricted age sub-samples using the jackknifing technique: 2- to 4-year-olds (C, 90.6%; ASD, 98.1%); 4- to 6-year-olds (C, 90.9%; ASD 99.1%); and 6- to 12-year-olds (C, 98.7%; ASD, 93.9%). Coherence loadings demonstrated reduced short-distance and reduced, as well as increased, long-distance coherences for the ASD-groups, when compared to the controls. Average spectral loading per factor was wide (10.1 Hz). Classification success suggests a stable coherence loading pattern that differentiates ASD- from C-group subjects. This might constitute an EEG coherence-based phenotype of childhood autism. The predominantly reduced short-distance coherences may indicate poor local network function. The increased long-distance coherences may represent compensatory processes or reduced neural pruning. The wide average spectral range of factor loadings may suggest over-damped neural networks.

EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme

PubMed Central

Ji, Hongfei; Li, Jie; Lu, Rongrong; Gu, Rong; Cao, Lei; Gong, Xiaoliang

2016-01-01

Electroencephalogram- (EEG-) based brain-computer interface (BCI) systems usually utilize one type of changes in the dynamics of brain oscillations for control, such as event-related desynchronization/synchronization (ERD/ERS), steady state visual evoked potential (SSVEP), and P300 evoked potentials. There is a recent trend to detect more than one of these signals in one system to create a hybrid BCI. However, in this case, EEG data were always divided into groups and analyzed by the separate processing procedures. As a result, the interactive effects were ignored when different types of BCI tasks were executed simultaneously. In this work, we propose an improved tensor based multiclass multimodal scheme especially for hybrid BCI, in which EEG signals are denoted as multiway tensors, a nonredundant rank-one tensor decomposition model is proposed to obtain nonredundant tensor components, a weighted fisher criterion is designed to select multimodal discriminative patterns without ignoring the interactive effects, and support vector machine (SVM) is extended to multiclass classification. Experiment results suggest that the proposed scheme can not only identify the different changes in the dynamics of brain oscillations induced by different types of tasks but also capture the interactive effects of simultaneous tasks properly. Therefore, it has great potential use for hybrid BCI. PMID:26880873

EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme.

PubMed

Ji, Hongfei; Li, Jie; Lu, Rongrong; Gu, Rong; Cao, Lei; Gong, Xiaoliang

2016-01-01

Electroencephalogram- (EEG-) based brain-computer interface (BCI) systems usually utilize one type of changes in the dynamics of brain oscillations for control, such as event-related desynchronization/synchronization (ERD/ERS), steady state visual evoked potential (SSVEP), and P300 evoked potentials. There is a recent trend to detect more than one of these signals in one system to create a hybrid BCI. However, in this case, EEG data were always divided into groups and analyzed by the separate processing procedures. As a result, the interactive effects were ignored when different types of BCI tasks were executed simultaneously. In this work, we propose an improved tensor based multiclass multimodal scheme especially for hybrid BCI, in which EEG signals are denoted as multiway tensors, a nonredundant rank-one tensor decomposition model is proposed to obtain nonredundant tensor components, a weighted fisher criterion is designed to select multimodal discriminative patterns without ignoring the interactive effects, and support vector machine (SVM) is extended to multiclass classification. Experiment results suggest that the proposed scheme can not only identify the different changes in the dynamics of brain oscillations induced by different types of tasks but also capture the interactive effects of simultaneous tasks properly. Therefore, it has great potential use for hybrid BCI.

EEG feature selection method based on decision tree.

PubMed

Duan, Lijuan; Ge, Hui; Ma, Wei; Miao, Jun

2015-01-01

This paper aims to solve automated feature selection problem in brain computer interface (BCI). In order to automate feature selection process, we proposed a novel EEG feature selection method based on decision tree (DT). During the electroencephalogram (EEG) signal processing, a feature extraction method based on principle component analysis (PCA) was used, and the selection process based on decision tree was performed by searching the feature space and automatically selecting optimal features. Considering that EEG signals are a series of non-linear signals, a generalized linear classifier named support vector machine (SVM) was chosen. In order to test the validity of the proposed method, we applied the EEG feature selection method based on decision tree to BCI Competition II datasets Ia, and the experiment showed encouraging results.

Evaluation of driver fatigue on two channels of EEG data.

PubMed

Li, Wei; He, Qi-chang; Fan, Xiu-min; Fei, Zhi-min

2012-01-11

Electroencephalogram (EEG) data is an effective indicator to evaluate driver fatigue. The 16 channels of EEG data are collected and transformed into three bands (θ, α, and β) in the current paper. First, 12 types of energy parameters are computed based on the EEG data. Then, Grey Relational Analysis (GRA) is introduced to identify the optimal indicator of driver fatigue, after which, the number of significant electrodes is reduced using Kernel Principle Component Analysis (KPCA). Finally, the evaluation model for driver fatigue is established with the regression equation based on the EEG data from two significant electrodes (Fp1 and O1). The experimental results verify that the model is effective in evaluating driver fatigue. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Changes in decibel scale wavelength properties of EEG with alertness levels while performing sustained attention tasks.

PubMed

Arjunan, Sridhar P; Kumar, Dinesh K; Jung, Tzyy-Ping

2009-01-01

Loss of alertness can have dire consequences for people controlling motorized equipment or for people in professions such as defense. Electroencephalogram (EEG) is known to be related to alertness of the person, but due to high level of noise and low signal strength, the use of EEG for such applications has been considered to be unreliable. This study reports the fractal analysis of EEG and identifies the use of maximum fractal length (MFL) as a feature that is inversely correlated with the alertness of the subject. The results show that MFL (of only single channel of EEG) indicates the loss of alertness of the individual with mean (inverse) correlation coefficient = 0.82.

[Control of intelligent car based on electroencephalogram and neurofeedback].

PubMed

Li, Song; Xiong, Xin; Fu, Yunfa

2018-02-01

To improve the performance of brain-controlled intelligent car based on motor imagery (MI), a method based on neurofeedback (NF) with electroencephalogram (EEG) for controlling intelligent car is proposed. A mental strategy of MI in which the energy column diagram of EEG features related to the mental activity is presented to subjects with visual feedback in real time to train them to quickly master the skills of MI and regulate their EEG activity, and combination of multi-features fusion of MI and multi-classifiers decision were used to control the intelligent car online. The average, maximum and minimum accuracy of identifying instructions achieved by the trained group (trained by the designed feedback system before the experiment) were 85.71%, 90.47% and 76.19%, respectively and the corresponding accuracy achieved by the control group (untrained) were 73.32%, 80.95% and 66.67%, respectively. For the trained group, the average, longest and shortest time consuming were 92 s, 101 s, and 85 s, respectively, while for the control group the corresponding time were 115.7 s, 120 s, and 110 s, respectively. According to the results described above, it is expected that this study may provide a new idea for the follow-up development of brain-controlled intelligent robot by the neurofeedback with EEG related to MI.

Impact of dronabinol on quantitative electroencephalogram (qEEG) measures of sleep in obstructive sleep apnea syndrome.

PubMed

Farabi, Sarah S; Prasad, Bharati; Quinn, Lauretta; Carley, David W

2014-01-15

To determine the effects of dronabinol on quantitative electroencephalogram (EEG) markers of the sleep process, including power distribution and ultradian cycling in 15 patients with obstructive sleep apnea (OSA). EEG (C4-A1) relative power (% total) in the delta, theta, alpha, and sigma bands was quantified by fast Fourier transformation (FFT) over 28-second intervals. An activation ratio (AR = [alpha + sigma] / [delta + theta]) also was computed for each interval. To assess ultradian rhythms, the best-fitting cosine wave was determined for AR and each frequency band in each polysomnogram (PSG). Fifteen subjects were included in the analysis. Dronabinol was associated with significantly increased theta power (p = 0.002). During the first half of the night, dronabinol decreased sigma power (p = 0.03) and AR (p = 0.03), and increased theta power (p = 0.0006). At increasing dronabinol doses, ultradian rhythms accounted for a greater fraction of EEG power variance in the delta band (p = 0.04) and AR (p = 0.03). Females had higher amplitude ultradian rhythms than males (theta: p = 0.01; sigma: p = 0.01). Decreasing AHI was associated with increasing ultradian rhythm amplitudes (sigma: p < 0.001; AR: p = 0.02). At the end of treatment, lower relative power in the theta band (p = 0.02) and lower AHI (p = 0.05) correlated with a greater decrease in sleepiness from baseline. This exploratory study demonstrates that in individuals with OSA, dronabinol treatment may yield a shift in EEG power toward delta and theta frequencies and a strengthening of ultradian rhythms in the sleep EEG.

Comparison of Bispectral Index and Entropy values with electroencephalogram during surgical anaesthesia with sevoflurane.

PubMed

Aho, A J; Kamata, K; Jäntti, V; Kulkas, A; Hagihira, S; Huhtala, H; Yli-Hankala, A

2015-08-01

Concomitantly recorded Bispectral Index® (BIS) and Entropy™ values sometimes show discordant trends during general anaesthesia. Previously, no attempt had been made to discover which EEG characteristics cause discrepancies between BIS and Entropy. We compared BIS and Entropy values, and analysed the changes in the raw EEG signal during surgical anaesthesia with sevoflurane. In this prospective, open-label study, 65 patients receiving general anaesthesia with sevoflurane were enrolled. BIS, Entropy and multichannel digital EEG were recorded. Concurrent BIS and State Entropy (SE) values were selected. Whenever BIS and SE values showed ≥10-unit disagreement for ≥60 s, the raw EEG signal was analysed both in time and frequency domain. A ≥10-unit disagreement ≥60 s was detected 428 times in 51 patients. These 428 episodes accounted for 5158 (11%) out of 45 918 analysed index pairs. During EEG burst suppression, SE was higher than BIS in 35 out of 49 episodes. During delta-theta dominance, BIS was higher than SE in 141 out of 157 episodes. During alpha or beta activity, SE was higher than BIS in all 49 episodes. During electrocautery, both BIS and SE changed, sometimes in the opposite direction, but returned to baseline values after electrocautery. Electromyography caused index disagreement four times (BIS > SE). Certain specific EEG patterns, and artifacts, are associated with discrepancies between BIS and SE. Time and frequency domain analyses of the original EEG improve the interpretation of studies involving BIS, Entropy and other EEG-based indices. NCT01077674. © 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.

EMG (Electromyography) (For Parents)

MedlinePlus

... topic for: Parents Kids Teens ECG (Electrocardiogram) Muscular Dystrophy Bones, Muscles, and Joints EEG (Electroencephalogram) Getting an EKG (Video) Medical Tests and Procedures (Video Landing Page) EKG (Video) Medical Tests: What to Expect ... About Us Contact Us Partners Editorial ...

Effects of some antipsychotics and a benzodiazepine hypnotic on the sleep-wake pattern in an animal model of schizophrenia.

PubMed

Ishida, Takayuki; Obara, Yoshihito; Kamei, Chiaki

2009-09-01

We studied the effects of antipsychotics and a hypnotic on sleep disturbance in schizophrenia using an animal model of the disease. Electrodes for the electroencephalogram (EEG) and electromyogram (EMG) were chronically implanted into the cortex and the dorsal neck muscle of rats. EEG and EMG were recorded with an electroencephalograph for 6 h (10:00 - 16:00). SleepSign ver. 2.0 was used for EEG and EMG analysis. Haloperidol and olanzapine had an antagonizing effect on the increases in sleep latency and total awake time and the decrease in total non-rapid eye movement (NREM) sleep time induced by MK-801. Olanzapine also antagonized the decrease in total rapid eye movement (REM) sleep time induced by MK-801. Aripiprazole antagonized only the increase in sleep latency induced by MK-801, whereas, risperidone, quetiapine, and flunitrazepam had no effect in the changes of sleep-wake pattern induced by MK-801. Olanzapine increased delta activity and decreased beta activity during NREM sleep. In contrast, flunitrazepam had an opposite effect. It was clarified that haloperidol and olanzapine were effective for decrease of sleep time in this animal model of schizophrenia. In addition, aripiprazole showed a sleep-inducing effect in schizophrenia model rat. On the other hand, flunitrazepam showed no beneficial effect on sleep disturbance in schizophrenia model rat.

Role of EEG as Biomarker in the Early Detection and Classification of Dementia

PubMed Central

Al-Qazzaz, Noor Kamal; Ali, Sawal Hamid Bin MD.; Ahmad, Siti Anom; Chellappan, Kalaivani; Islam, Md. Shabiul; Escudero, Javier

2014-01-01

The early detection and classification of dementia are important clinical support tasks for medical practitioners in customizing patient treatment programs to better manage the development and progression of these diseases. Efforts are being made to diagnose these neurodegenerative disorders in the early stages. Indeed, early diagnosis helps patients to obtain the maximum treatment benefit before significant mental decline occurs. The use of electroencephalogram as a tool for the detection of changes in brain activities and clinical diagnosis is becoming increasingly popular for its capabilities in quantifying changes in brain degeneration in dementia. This paper reviews the role of electroencephalogram as a biomarker based on signal processing to detect dementia in early stages and classify its severity. The review starts with a discussion of dementia types and cognitive spectrum followed by the presentation of the effective preprocessing denoising to eliminate possible artifacts. It continues with a description of feature extraction by using linear and nonlinear techniques, and it ends with a brief explanation of vast variety of separation techniques to classify EEG signals. This paper also provides an idea from the most popular studies that may help in diagnosing dementia in early stages and classifying through electroencephalogram signal processing and analysis. PMID:25093211

Role of EEG as biomarker in the early detection and classification of dementia.

PubMed

Al-Qazzaz, Noor Kamal; Ali, Sawal Hamid Bin Md; Ahmad, Siti Anom; Chellappan, Kalaivani; Islam, Md Shabiul; Escudero, Javier

2014-01-01

The early detection and classification of dementia are important clinical support tasks for medical practitioners in customizing patient treatment programs to better manage the development and progression of these diseases. Efforts are being made to diagnose these neurodegenerative disorders in the early stages. Indeed, early diagnosis helps patients to obtain the maximum treatment benefit before significant mental decline occurs. The use of electroencephalogram as a tool for the detection of changes in brain activities and clinical diagnosis is becoming increasingly popular for its capabilities in quantifying changes in brain degeneration in dementia. This paper reviews the role of electroencephalogram as a biomarker based on signal processing to detect dementia in early stages and classify its severity. The review starts with a discussion of dementia types and cognitive spectrum followed by the presentation of the effective preprocessing denoising to eliminate possible artifacts. It continues with a description of feature extraction by using linear and nonlinear techniques, and it ends with a brief explanation of vast variety of separation techniques to classify EEG signals. This paper also provides an idea from the most popular studies that may help in diagnosing dementia in early stages and classifying through electroencephalogram signal processing and analysis.

Electroencephalograms in epilepsy: analysis and seizure prediction within the framework of Lyapunov theory

NASA Astrophysics Data System (ADS)

Moser, H. R.; Weber, B.; Wieser, H. G.; Meier, P. F.

1999-06-01

Epileptic seizures are defined as the clinical manifestation of excessive and hypersynchronous activity of neurons in the cerebral cortex and represent one of the most frequent malfunctions of the human central nervous system. Therefore, the search for precursors and predictors of a seizure is of utmost clinical relevance and may even guide us to a deeper understanding of the seizure generating mechanisms. We extract chaos-indicators such as Lyapunov exponents and Kolmogorov entropies from different types of electroencephalograms (EEGs): this covers mainly intracranial EEGs (semi-invasive and invasive recording techniques), but also scalp-EEGs from the surface of the skin. Among the analytical methods we tested up to now, we find that the spectral density of the local expansion exponents is best suited to predict the onset of a forthcoming seizure. We also evaluate the time-evolution of the dissipation in these signals: it exhibits strongly significant variations that clearly relate to the time relative to a seizure onset. This article is mainly devoted to an assessment of these methods with respect to their sensitivity to EEG changes, e.g., prior to a seizure. Further, we investigate interictal EEGs (i.e., far away from a seizure) in order to characterize their more general properties, such as the convergence of the reconstructed quantities with respect to the number of phase space dimensions. Generally we use multichannel reconstruction, but we also present a comparison with the delay-embedding technique.

Hypoglycemia-associated changes in the electroencephalogram in patients with type 1 diabetes and normal hypoglycemia awareness or unawareness.

PubMed

Sejling, Anne-Sophie; Kjær, Troels W; Pedersen-Bjergaard, Ulrik; Diemar, Sarah S; Frandsen, Christian S S; Hilsted, Linda; Faber, Jens; Holst, Jens J; Tarnow, Lise; Nielsen, Martin N; Remvig, Line S; Thorsteinsson, Birger; Juhl, Claus B

2015-05-01

Hypoglycemia is associated with increased activity in the low-frequency bands in the electroencephalogram (EEG). We investigated whether hypoglycemia awareness and unawareness are associated with different hypoglycemia-associated EEG changes in patients with type 1 diabetes. Twenty-four patients participated in the study: 10 with normal hypoglycemia awareness and 14 with hypoglycemia unawareness. The patients were studied at normoglycemia (5-6 mmol/L) and hypoglycemia (2.0-2.5 mmol/L), and during recovery (5-6 mmol/L) by hyperinsulinemic glucose clamp. During each 1-h period, EEG, cognitive function, and hypoglycemia symptom scores were recorded, and the counterregulatory hormonal response was measured. Quantitative EEG analysis showed that the absolute amplitude of the θ band and α-θ band up to doubled during hypoglycemia with no difference between the two groups. In the recovery period, the θ amplitude remained increased. Cognitive function declined equally during hypoglycemia in both groups and during recovery reaction time was still prolonged in a subset of tests. The aware group reported higher hypoglycemia symptom scores and had higher epinephrine and cortisol responses compared with the unaware group. In patients with type 1 diabetes, EEG changes and cognitive performance during hypoglycemia are not affected by awareness status during a single insulin-induced episode with hypoglycemia. © 2015 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.

Time-related interdependence between low-frequency cortical electrical activity and respiratory activity in lizard, Gallotia galloti.

PubMed

de Vera, Luis; Pereda, Ernesto; Santana, Alejandro; González, Julián J

2005-03-01

Electroencephalograms of medial cortex and electromyograms of intercostal muscles (EMG-icm) were simultaneously recorded in the lizard, Gallotia galloti, during two daily time periods (at daytime, DTP: 1200-1600 h; by night, NTP: 0000-0400 h), to investigate whether a relationship exists between the respiratory and cortical electrical activity of reptiles, and, if so, how this relationship changes during the night rest period. Testing was carried out by studying interdependence between cortical electrical and respiratory activities, by means of linear and nonlinear signal analysis techniques. Both physiological activities were evaluated through simultaneous power signals, derived from the power of the low-frequency band of the electroencephalogram (pEEG-LF), and from the power of the EMG-icm (pEMG-icm), respectively. During both DTP and NTP, there was a significant coherence between both signals in the main frequency band of pEMG-icm. During both DTP and NTP, the nonlinear index N measured significant linear asymmetric interdependence between pEEG-LF and pEMG-icm. The N value obtained between pEEG-LF vs. pEMG-icm was greater than the one between pEMG-icm vs. pEEG-LF. This means that the system that generates the pEEG-LF is more complex than the one that generates the pEMG-icm, and suggests that the temporal variability of power in the low-frequency cortical electrical activity is driven by the power of the respiratory activity.

Emotional stress recognition using a new fusion link between electroencephalogram and peripheral signals

PubMed Central

Hosseini, Seyyed Abed; Khalilzadeh, Mohammad Ali; Naghibi-Sistani, Mohammad Bagher; Homam, Seyyed Mehran

2015-01-01

Background: This paper proposes a new emotional stress assessment system using multi-modal bio-signals. Electroencephalogram (EEG) is the reflection of brain activity and is widely used in clinical diagnosis and biomedical research. Methods: We design an efficient acquisition protocol to acquire the EEG signals in five channels (FP1, FP2, T3, T4 and Pz) and peripheral signals such as blood volume pulse, skin conductance (SC) and respiration, under images induction (calm-neutral and negatively excited) for the participants. The visual stimuli images are selected from the subset International Affective Picture System database. The qualitative and quantitative evaluation of peripheral signals are used to select suitable segments of EEG signals for improving the accuracy of signal labeling according to emotional stress states. After pre-processing, wavelet coefficients, fractal dimension, and Lempel-Ziv complexity are used to extract the features of the EEG signals. The vast number of features leads to the problem of dimensionality, which is solved using the genetic algorithm as a feature selection method. Results: The results show that the average classification accuracy is 89.6% for two categories of emotional stress states using the support vector machine (SVM). Conclusion: This is a great improvement in results compared to other similar researches. We achieve a noticeable improvement of 11.3% in accuracy using SVM classifier, in compared to previous studies. Therefore, a new fusion between EEG and peripheral signals are more robust in comparison to the separate signals. PMID:26622979

Probability distributions of the electroencephalogram envelope of preterm infants.

PubMed

Saji, Ryoya; Hirasawa, Kyoko; Ito, Masako; Kusuda, Satoshi; Konishi, Yukuo; Taga, Gentaro

2015-06-01

To determine the stationary characteristics of electroencephalogram (EEG) envelopes for prematurely born (preterm) infants and investigate the intrinsic characteristics of early brain development in preterm infants. Twenty neurologically normal sets of EEGs recorded in infants with a post-conceptional age (PCA) range of 26-44 weeks (mean 37.5 ± 5.0 weeks) were analyzed. Hilbert transform was applied to extract the envelope. We determined the suitable probability distribution of the envelope and performed a statistical analysis. It was found that (i) the probability distributions for preterm EEG envelopes were best fitted by lognormal distributions at 38 weeks PCA or less, and by gamma distributions at 44 weeks PCA; (ii) the scale parameter of the lognormal distribution had positive correlations with PCA as well as a strong negative correlation with the percentage of low-voltage activity; (iii) the shape parameter of the lognormal distribution had significant positive correlations with PCA; (iv) the statistics of mode showed significant linear relationships with PCA, and, therefore, it was considered a useful index in PCA prediction. These statistics, including the scale parameter of the lognormal distribution and the skewness and mode derived from a suitable probability distribution, may be good indexes for estimating stationary nature in developing brain activity in preterm infants. The stationary characteristics, such as discontinuity, asymmetry, and unimodality, of preterm EEGs are well indicated by the statistics estimated from the probability distribution of the preterm EEG envelopes. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Emotional stress recognition using a new fusion link between electroencephalogram and peripheral signals.

PubMed

Hosseini, Seyyed Abed; Khalilzadeh, Mohammad Ali; Naghibi-Sistani, Mohammad Bagher; Homam, Seyyed Mehran

2015-07-06

This paper proposes a new emotional stress assessment system using multi-modal bio-signals. Electroencephalogram (EEG) is the reflection of brain activity and is widely used in clinical diagnosis and biomedical research. We design an efficient acquisition protocol to acquire the EEG signals in five channels (FP1, FP2, T3, T4 and Pz) and peripheral signals such as blood volume pulse, skin conductance (SC) and respiration, under images induction (calm-neutral and negatively excited) for the participants. The visual stimuli images are selected from the subset International Affective Picture System database. The qualitative and quantitative evaluation of peripheral signals are used to select suitable segments of EEG signals for improving the accuracy of signal labeling according to emotional stress states. After pre-processing, wavelet coefficients, fractal dimension, and Lempel-Ziv complexity are used to extract the features of the EEG signals. The vast number of features leads to the problem of dimensionality, which is solved using the genetic algorithm as a feature selection method. The results show that the average classification accuracy is 89.6% for two categories of emotional stress states using the support vector machine (SVM). This is a great improvement in results compared to other similar researches. We achieve a noticeable improvement of 11.3% in accuracy using SVM classifier, in compared to previous studies. Therefore, a new fusion between EEG and peripheral signals are more robust in comparison to the separate signals.

Genome-wide association identifies candidate genes that influence the human electroencephalogram

PubMed Central

Hodgkinson, Colin A.; Enoch, Mary-Anne; Srivastava, Vibhuti; Cummins-Oman, Justine S.; Ferrier, Cherisse; Iarikova, Polina; Sankararaman, Sriram; Yamini, Goli; Yuan, Qiaoping; Zhou, Zhifeng; Albaugh, Bernard; White, Kenneth V.; Shen, Pei-Hong; Goldman, David

2010-01-01

Complex psychiatric disorders are resistant to whole-genome analysis due to genetic and etiological heterogeneity. Variation in resting electroencephalogram (EEG) is associated with common, complex psychiatric diseases including alcoholism, schizophrenia, and anxiety disorders, although not diagnostic for any of them. EEG traits for an individual are stable, variable between individuals, and moderately to highly heritable. Such intermediate phenotypes appear to be closer to underlying molecular processes than are clinical symptoms, and represent an alternative approach for the identification of genetic variation that underlies complex psychiatric disorders. We performed a whole-genome association study on alpha (α), beta (β), and theta (θ) EEG power in a Native American cohort of 322 individuals to take advantage of the genetic and environmental homogeneity of this population isolate. We identified three genes (SGIP1, ST6GALNAC3, and UGDH) with nominal association to variability of θ or α power. SGIP1 was estimated to account for 8.8% of variance in θ power, and this association was replicated in US Caucasians, where it accounted for 3.5% of the variance. Bayesian analysis of prior probability of association based upon earlier linkage to chromosome 1 and enrichment for vesicle-related transport proteins indicates that the association of SGIP1 with θ power is genuine. We also found association of SGIP1 with alcoholism, an effect that may be mediated via the same brain mechanisms accessed by θ EEG, and which also provides validation of the use of EEG as an endophenotype for alcoholism. PMID:20421487

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

Alterations in Resting-State Activity Relate to Performance in a Verbal Recognition Task

PubMed Central

López Zunini, Rocío A.; Thivierge, Jean-Philippe; Kousaie, Shanna; Sheppard, Christine; Taler, Vanessa

2013-01-01

In the brain, resting-state activity refers to non-random patterns of intrinsic activity occurring when participants are not actively engaged in a task. We monitored resting-state activity using electroencephalogram (EEG) both before and after a verbal recognition task. We show a strong positive correlation between accuracy in verbal recognition and pre-task resting-state alpha power at posterior sites. We further characterized this effect by examining resting-state post-task activity. We found marked alterations in resting-state alpha power when comparing pre- and post-task periods, with more pronounced alterations in participants that attained higher task accuracy. These findings support a dynamical view of cognitive processes where patterns of ongoing brain activity can facilitate –or interfere– with optimal task performance. PMID:23785436

Subspace techniques to remove artifacts from EEG: a quantitative analysis.

PubMed

Teixeira, A R; Tome, A M; Lang, E W; Martins da Silva, A

2008-01-01

In this work we discuss and apply projective subspace techniques to both multichannel as well as single channel recordings. The single-channel approach is based on singular spectrum analysis(SSA) and the multichannel approach uses the extended infomax algorithm which is implemented in the opensource toolbox EEGLAB. Both approaches will be evaluated using artificial mixtures of a set of selected EEG signals. The latter were selected visually to contain as the dominant activity one of the characteristic bands of an electroencephalogram (EEG). The evaluation is performed both in the time and frequency domain by using correlation coefficients and coherence function, respectively.

EEG analysis using wavelet-based information tools.

PubMed

Rosso, O A; Martin, M T; Figliola, A; Keller, K; Plastino, A

2006-06-15

Wavelet-based informational tools for quantitative electroencephalogram (EEG) record analysis are reviewed. Relative wavelet energies, wavelet entropies and wavelet statistical complexities are used in the characterization of scalp EEG records corresponding to secondary generalized tonic-clonic epileptic seizures. In particular, we show that the epileptic recruitment rhythm observed during seizure development is well described in terms of the relative wavelet energies. In addition, during the concomitant time-period the entropy diminishes while complexity grows. This is construed as evidence supporting the conjecture that an epileptic focus, for this kind of seizures, triggers a self-organized brain state characterized by both order and maximal complexity.

Low-Complexity Discriminative Feature Selection From EEG Before and After Short-Term Memory Task.

PubMed

Behzadfar, Neda; Firoozabadi, S Mohammad P; Badie, Kambiz

2016-10-01

A reliable and unobtrusive quantification of changes in cortical activity during short-term memory task can be used to evaluate the efficacy of interfaces and to provide real-time user-state information. In this article, we investigate changes in electroencephalogram signals in short-term memory with respect to the baseline activity. The electroencephalogram signals have been analyzed using 9 linear and nonlinear/dynamic measures. We applied statistical Wilcoxon examination and Davis-Bouldian criterion to select optimal discriminative features. The results show that among the features, the permutation entropy significantly increased in frontal lobe and the occipital second lower alpha band activity decreased during memory task. These 2 features reflect the same mental task; however, their correlation with memory task varies in different intervals. In conclusion, it is suggested that the combination of the 2 features would improve the performance of memory based neurofeedback systems. © EEG and Clinical Neuroscience Society (ECNS) 2016.

Frontal EEG Asymmetry and Temperament Across Infancy and Early Childhood: An Exploration of Stability and Bidirectional Relations

PubMed Central

Howarth, Grace Z.; Fettig, Nicole B.; Curby, Timothy W.; Bell, Martha Ann

2015-01-01

The stability of frontal electroencephalogram (EEG) asymmetry, temperamental activity level and fear, as well as bidirectional relations between asymmetry and temperament across the first four years of life were examined in a sample of 183 children. Children participated in annual lab visits through 48 months, providing EEG and maternal report of temperament. EEG asymmetry showed moderate stability between 10 and 24 months. Analyses revealed that more left asymmetry predicted later activity level across the first three years. Conversely, asymmetry did not predict fear. Rather, fear at 36 months predicted more right asymmetry at 48 months. Results highlight the need for additional longitudinal research of infants and children to increase understanding of bidirectional relations between EEG and temperament in typically developing populations. PMID:26659466

Cortical connectivity and memory performance in cognitive decline: A study via graph theory from EEG data.

PubMed

Vecchio, F; Miraglia, F; Quaranta, D; Granata, G; Romanello, R; Marra, C; Bramanti, P; Rossini, P M

2016-03-01

Functional brain abnormalities including memory loss are found to be associated with pathological changes in connectivity and network neural structures. Alzheimer's disease (AD) interferes with memory formation from the molecular level, to synaptic functions and neural networks organization. Here, we determined whether brain connectivity of resting-state networks correlate with memory in patients affected by AD and in subjects with mild cognitive impairment (MCI). One hundred and forty-four subjects were recruited: 70 AD (MMSE Mini Mental State Evaluation 21.4), 50 MCI (MMSE 25.2) and 24 healthy subjects (MMSE 29.8). Undirected and weighted cortical brain network was built to evaluate graph core measures to obtain Small World parameters. eLORETA lagged linear connectivity as extracted by electroencephalogram (EEG) signals was used to weight the network. A high statistical correlation between Small World and memory performance was found. Namely, higher Small World characteristic in EEG gamma frequency band during the resting state, better performance in short-term memory as evaluated by the digit span tests. Such Small World pattern might represent a biomarker of working memory impairment in older people both in physiological and pathological conditions. Copyright © 2015 IBRO. Published by Elsevier Ltd. All rights reserved.

Multivariate and multiorgan analysis of cardiorespiratory variability signals: the CAP sleep case.

PubMed

Bianchi, Anna M; Ferini-Strambi, Luigi; Castronovo, Vincenza; Cerutti, Sergio

2006-10-01

Signals from different systems are analyzed during sleep on a beat-to-beat basis to provide a quantitative measure of synchronization with the heart rate variability (HRV) signal, oscillations of which reflect the action of the autonomic nervous system. Beat-to-beat variability signals synchronized to QRS occurrence on ECG signals were extracted from respiration, electroencephalogram (EEG) and electromyogram (EMG) traces. The analysis was restricted to sleep stage 2. Cyclic alternating pattern (CAP) periods were detected from EEG signals and the following conditions were identified: stage 2 non-CAP (2 NCAP), stage 2 CAP (2 CAP) and stage 2 CAP with myoclonus (2 CAP MC). The coupling relationships between pairs of variability signals were studied in both the time and frequency domains. Passing from 2 NCAP to 2 CAP, sympathetic activation is indicated by tachycardia and reduced respiratory arrhythmia in the heart rate signal. At the same time, we observed a marked link between EEG and HRV at the CAP frequency. During 2 CAP MC, the increased synchronization involved myoclonus and respiration. The underlying mechanism seems to be related to a global control system at the central level that involves the different systems.

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.

Hybrid EEG--Eye Tracker: Automatic Identification and Removal of Eye Movement and Blink Artifacts from Electroencephalographic Signal.

PubMed

Mannan, Malik M Naeem; Kim, Shinjung; Jeong, Myung Yung; Kamran, M Ahmad

2016-02-19

Contamination of eye movement and blink artifacts in Electroencephalogram (EEG) recording makes the analysis of EEG data more difficult and could result in mislead findings. Efficient removal of these artifacts from EEG data is an essential step in improving classification accuracy to develop the brain-computer interface (BCI). In this paper, we proposed an automatic framework based on independent component analysis (ICA) and system identification to identify and remove ocular artifacts from EEG data by using hybrid EEG and eye tracker system. The performance of the proposed algorithm is illustrated using experimental and standard EEG datasets. The proposed algorithm not only removes the ocular artifacts from artifactual zone but also preserves the neuronal activity related EEG signals in non-artifactual zone. The comparison with the two state-of-the-art techniques namely ADJUST based ICA and REGICA reveals the significant improved performance of the proposed algorithm for removing eye movement and blink artifacts from EEG data. Additionally, results demonstrate that the proposed algorithm can achieve lower relative error and higher mutual information values between corrected EEG and artifact-free EEG data.

Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier.

PubMed

Raghu, S; Sriraam, N; Kumar, G Pradeep

2017-02-01

Electroencephalogram shortly termed as EEG is considered as the fundamental segment for the assessment of the neural activities in the brain. In cognitive neuroscience domain, EEG-based assessment method is found to be superior due to its non-invasive ability to detect deep brain structure while exhibiting superior spatial resolutions. Especially for studying the neurodynamic behavior of epileptic seizures, EEG recordings reflect the neuronal activity of the brain and thus provide required clinical diagnostic information for the neurologist. This specific proposed study makes use of wavelet packet based log and norm entropies with a recurrent Elman neural network (REN) for the automated detection of epileptic seizures. Three conditions, normal, pre-ictal and epileptic EEG recordings were considered for the proposed study. An adaptive Weiner filter was initially applied to remove the power line noise of 50 Hz from raw EEG recordings. Raw EEGs were segmented into 1 s patterns to ensure stationarity of the signal. Then wavelet packet using Haar wavelet with a five level decomposition was introduced and two entropies, log and norm were estimated and were applied to REN classifier to perform binary classification. The non-linear Wilcoxon statistical test was applied to observe the variation in the features under these conditions. The effect of log energy entropy (without wavelets) was also studied. It was found from the simulation results that the wavelet packet log entropy with REN classifier yielded a classification accuracy of 99.70 % for normal-pre-ictal, 99.70 % for normal-epileptic and 99.85 % for pre-ictal-epileptic.

Predictive value of early amplitude-integrated electroencephalography for later diagnosed cerebral white matter damage in preterm infants.

PubMed

Song, Juan; Zhu, Changlian; Xu, Falin; Guo, Jiajia; Zhang, Yanhua

2014-10-01

 The aim of the article is to assess the predictive value of amplitude-integrated electroencephalogram (aEEG) for cerebral white matter damage (WMD) in preterm infants. Patients and  Preterms ≤ 32 weeks' gestational age (GA) born between March 2012 and December 2012 were enrolled. The aEEG patterns within 72 hours were classified and recorded to predict their neurodevelopmental prognosis and the predictive results were used to compare with the results by cerebral ultrasound examination. Neurobehavioral disorder (neonatal behavioral neurological assessment score < 35, dyskinesia or dysgnosia) or death was thought as poor neurodevelopmental prognosis. Psychomotor development index (PDI) or mental development index (MDI) ≤ 79 was regarded as dyskinesia or dysgnosia, respectively.  Of the 63 preterms, 3.2% were born < 27 weeks' gestation and 96.8% at 27 to 32 weeks' gestation. The median GA was 29.3 weeks and the median birth weight was 1,030 g. On the basis of the aEEG results, normal, mildly abnormal, and severely abnormal cases were 10, 24, and 29; whereas determined by cerebral ultrasound, normal, mild, and severe cases were 17, 20, and 26, respectively. The aEEG degree showed significantly positive correlations with both WMD and poor neurodevelopmental prognosis (p < 0.01).  Abnormal aEEG of preterm infants within 72 hours after birth may imply WMD occurrence and poor neurodevelopmental prognosis. Georg Thieme Verlag KG Stuttgart · New York.

Anti-deception: reliable EEG-based biometrics with real-time capability from the neural response of face rapid serial visual presentation.

PubMed

Wu, Qunjian; Yan, Bin; Zeng, Ying; Zhang, Chi; Tong, Li

2018-05-03

The electroencephalogram (EEG) signal represents a subject's specific brain activity patterns and is considered as an ideal biometric given its superior invisibility, non-clonality, and non-coercion. In order to enhance its applicability in identity authentication, a novel EEG-based identity authentication method is proposed based on self- or non-self-face rapid serial visual presentation. In contrast to previous studies that extracted EEG features from rest state or motor imagery, the designed paradigm could obtain a distinct and stable biometric trait with a lower time cost. Channel selection was applied to select specific channels for each user to enhance system portability and improve discriminability between users and imposters. Two different imposter scenarios were designed to test system security, which demonstrate the capability of anti-deception. Fifteen users and thirty imposters participated in the experiment. The mean authentication accuracy values for the two scenarios were 91.31 and 91.61%, with 6 s time cost, which illustrated the precision and real-time capability of the system. Furthermore, in order to estimate the repeatability and stability of our paradigm, another data acquisition session is conducted for each user. Using the classification models generated from the previous sessions, a mean false rejected rate of 7.27% has been achieved, which demonstrates the robustness of our paradigm. Experimental results reveal that the proposed paradigm and methods are effective for EEG-based identity authentication.

Prolonged ictal aphasia: a diagnosis to consider.

PubMed

Herskovitz, Moshe; Schiller, Yitzhak

2012-11-01

Aphasia is a common symptom encountered by clinical neurologists. It is usually caused by strokes or lesions involving language regions of the brain, yet prolonged aphasia is rarely the sole manifestation of a simple partial status epilepticus. We report six patients, who suffered from prolonged ictal aphasia. All but one patient had a structural lesion in the left hemisphere, only three suffered from clinical seizures during or shortly prior to the aphasic episode. All patients had ictal patterns on the electroencephalogram (EEG), four of whom had periodic lateralized epileptiform discharges, and five showed frequent recurrent electrographic seizures during the aphasic state. The aphasia lasted several days in all patients, and it resolved after administration of antiepileptic drug treatment. In conclusion, prolonged ictal aphasia is a rare but important treatable cause of aphasia. Surface EEG recordings should be obtained in all patients with unexplained prolonged aphasia to diagnose this rare but treatable entity. Crown Copyright © 2012. Published by Elsevier Ltd. All rights reserved.

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.

Recognizing emotions from EEG subbands using wavelet analysis.

PubMed

Candra, Henry; Yuwono, Mitchell; Handojoseno, Ardi; Chai, Rifai; Su, Steven; Nguyen, Hung T

2015-01-01

Objectively recognizing emotions is a particularly important task to ensure that patients with emotional symptoms are given the appropriate treatments. The aim of this study was to develop an emotion recognition system using Electroencephalogram (EEG) signals to identify four emotions including happy, sad, angry, and relaxed. We approached this objective by firstly investigating the relevant EEG frequency band followed by deciding the appropriate feature extraction method. Two features were considered namely: 1. Wavelet Energy, and 2. Wavelet Entropy. EEG Channels reduction was then implemented to reduce the complexity of the features. The ground truth emotional states of each subject were inferred using Russel's circumplex model of emotion, that is, by mapping the subjectively reported degrees of valence (pleasure) and arousal to the appropriate emotions - for example, an emotion with high valence and high arousal is equivalent to a `happy' emotional state, while low valence and low arousal is equivalent to a `sad' emotional state. The Support Vector Machine (SVM) classifier was then used for mapping each feature vector into corresponding discrete emotions. The results presented in this study indicated thatWavelet features extracted from alpha, beta and gamma bands seem to provide the necessary information for describing the aforementioned emotions. Using the DEAP (Dataset for Emotion Analysis using electroencephalogram, Physiological and Video Signals), our proposed method achieved an average sensitivity and specificity of 77.4% ± 14.1% and 69.1% ± 12.8%, respectively.

Household wireless electroencephalogram hat

NASA Astrophysics Data System (ADS)

Szu, Harold; Hsu, Charles; Moon, Gyu; Yamakawa, Takeshi; Tran, Binh

2012-06-01

We applied Compressive Sensing to design an affordable, convenient Brain Machine Interface (BMI) measuring the high spatial density, and real-time process of Electroencephalogram (EEG) brainwaves by a Smartphone. It is useful for therapeutic and mental health monitoring, learning disability biofeedback, handicap interfaces, and war gaming. Its spec is adequate for a biomedical laboratory, without the cables hanging over the head and tethered to a fixed computer terminal. Our improved the intrinsic signal to noise ratio (SNR) by using the non-uniform placement of the measuring electrodes to create the proximity of measurement to the source effect. We computing a spatiotemporal average the larger magnitude of EEG data centers in 0.3 second taking on tethered laboratory data, using fuzzy logic, and computing the inside brainwave sources, by Independent Component Analysis (ICA). Consequently, we can overlay them together by non-uniform electrode distribution enhancing the signal noise ratio and therefore the degree of sparseness by threshold. We overcame the conflicting requirements between a high spatial electrode density and precise temporal resolution (beyond Event Related Potential (ERP) P300 brainwave at 0.3 sec), and Smartphone wireless bottleneck of spatiotemporal throughput rate. Our main contribution in this paper is the quality and the speed of iterative compressed image recovery algorithm based on a Block Sparse Code (Baranuick et al, IEEE/IT 2008). As a result, we achieved real-time wireless dynamic measurement of EEG brainwaves, matching well with traditionally tethered high density EEG.

Noninvasive Electroencephalogram Based Control of a Robotic Arm for Writing Task Using Hybrid BCI System.

PubMed

Gao, Qiang; Dou, Lixiang; Belkacem, Abdelkader Nasreddine; Chen, Chao

2017-01-01

A novel hybrid brain-computer interface (BCI) based on the electroencephalogram (EEG) signal which consists of a motor imagery- (MI-) based online interactive brain-controlled switch, "teeth clenching" state detector, and a steady-state visual evoked potential- (SSVEP-) based BCI was proposed to provide multidimensional BCI control. MI-based BCI was used as single-pole double throw brain switch (SPDTBS). By combining the SPDTBS with 4-class SSEVP-based BCI, movement of robotic arm was controlled in three-dimensional (3D) space. In addition, muscle artifact (EMG) of "teeth clenching" condition recorded from EEG signal was detected and employed as interrupter, which can initialize the statement of SPDTBS. Real-time writing task was implemented to verify the reliability of the proposed noninvasive hybrid EEG-EMG-BCI. Eight subjects participated in this study and succeeded to manipulate a robotic arm in 3D space to write some English letters. The mean decoding accuracy of writing task was 0.93 ± 0.03. Four subjects achieved the optimal criteria of writing the word "HI" which is the minimum movement of robotic arm directions (15 steps). Other subjects had needed to take from 2 to 4 additional steps to finish the whole process. These results suggested that our proposed hybrid noninvasive EEG-EMG-BCI was robust and efficient for real-time multidimensional robotic arm control.

Noninvasive Electroencephalogram Based Control of a Robotic Arm for Writing Task Using Hybrid BCI System

PubMed Central

Gao, Qiang

2017-01-01

A novel hybrid brain-computer interface (BCI) based on the electroencephalogram (EEG) signal which consists of a motor imagery- (MI-) based online interactive brain-controlled switch, “teeth clenching” state detector, and a steady-state visual evoked potential- (SSVEP-) based BCI was proposed to provide multidimensional BCI control. MI-based BCI was used as single-pole double throw brain switch (SPDTBS). By combining the SPDTBS with 4-class SSEVP-based BCI, movement of robotic arm was controlled in three-dimensional (3D) space. In addition, muscle artifact (EMG) of “teeth clenching” condition recorded from EEG signal was detected and employed as interrupter, which can initialize the statement of SPDTBS. Real-time writing task was implemented to verify the reliability of the proposed noninvasive hybrid EEG-EMG-BCI. Eight subjects participated in this study and succeeded to manipulate a robotic arm in 3D space to write some English letters. The mean decoding accuracy of writing task was 0.93 ± 0.03. Four subjects achieved the optimal criteria of writing the word “HI” which is the minimum movement of robotic arm directions (15 steps). Other subjects had needed to take from 2 to 4 additional steps to finish the whole process. These results suggested that our proposed hybrid noninvasive EEG-EMG-BCI was robust and efficient for real-time multidimensional robotic arm control. PMID:28660211

Infant frontal EEG asymmetry in relation with postnatal maternal depression and parenting behavior.

PubMed

Wen, D J; Soe, N N; Sim, L W; Sanmugam, S; Kwek, K; Chong, Y-S; Gluckman, P D; Meaney, M J; Rifkin-Graboi, A; Qiu, A

2017-03-14

Right frontal electroencephalogram (EEG) asymmetry associates with negative affect and depressed mood, which, among children, are predicted by maternal depression and poor parenting. This study examined associations of maternal depression and maternal sensitivity with infant frontal EEG asymmetry based on 111 mother-6-month-infant dyads. There were no significant effects of postnatal maternal depression or maternal sensitivity, or their interaction, on infant EEG frontal asymmetry. However, in a subsample for which the infant spent at least 50% of his/her day time hours with his/her mother, both lower maternal sensitivity and higher maternal depression predicted greater relative right frontal EEG asymmetry. Our study further showed that greater relative right frontal EEG asymmetry of 6-month-old infants predicted their greater negative emotionality at 12 months of age. Our study suggested that among infants with sufficient postnatal maternal exposure, both maternal sensitivity and mental health are important influences on early brain development.

Electroencephalogram, circulation, and lung function after high-velocity behind armor blunt trauma.

PubMed

Drobin, Dan; Gryth, Dan; Persson, Jonas K E; Rocksén, David; Arborelius, Ulf P; Olsson, Lars-Gunnar; Bursell, Jenny; Kjellström, B Thomas

2007-08-01

Behind armor blunt trauma (BABT) is defined as the nonpenetrating injury resulting from a ballistic impact on personal body armor. The protective vest may impede the projectile, but some of the kinetic energy is transferred to the body, causing internal injuries and occasionally death. The aim in this study was to investigate changes in electroencephalogram (EEG) and physiologic parameters after high-velocity BABT. Eight anesthetized pigs, wearing body armor (including a ceramic plate) on the right side of their thorax, were shot with a 7.62-mm assault rifle (velocity approximately 800 m/s). The shots did not penetrate the armor and these animals were compared with control animals (n = 4), shot with blank ammunition. EEG and several physiologic parameters were thereafter monitored during a 2-hour period after the shot. All animals survived during the experimental period. Five of the exposed animals showed a temporary effect on EEG. Furthermore, exposed animals displayed decreased cardiac capacity and an impaired oxygenation of the blood. Postmortem examination revealed subcutaneous hematomas and crush injuries to the right lung. The results in our animal model indicate that high-velocity BABT induce circulatory and respiratory dysfunction, and in some cases even transient cerebral functional disturbances.

Predicting seizure by modeling synaptic plasticity based on EEG signals - a case study of inherited epilepsy

NASA Astrophysics Data System (ADS)

Zhang, Honghui; Su, Jianzhong; Wang, Qingyun; Liu, Yueming; Good, Levi; Pascual, Juan M.

2018-03-01

This paper explores the internal dynamical mechanisms of epileptic seizures through quantitative modeling based on full brain electroencephalogram (EEG) signals. Our goal is to provide seizure prediction and facilitate treatment for epileptic patients. Motivated by an earlier mathematical model with incorporated synaptic plasticity, we studied the nonlinear dynamics of inherited seizures through a differential equation model. First, driven by a set of clinical inherited electroencephalogram data recorded from a patient with diagnosed Glucose Transporter Deficiency, we developed a dynamic seizure model on a system of ordinary differential equations. The model was reduced in complexity after considering and removing redundancy of each EEG channel. Then we verified that the proposed model produces qualitatively relevant behavior which matches the basic experimental observations of inherited seizure, including synchronization index and frequency. Meanwhile, the rationality of the connectivity structure hypothesis in the modeling process was verified. Further, through varying the threshold condition and excitation strength of synaptic plasticity, we elucidated the effect of synaptic plasticity to our seizure model. Results suggest that synaptic plasticity has great effect on the duration of seizure activities, which support the plausibility of therapeutic interventions for seizure control.

Compact continuum brain model for human electroencephalogram

NASA Astrophysics Data System (ADS)

Kim, J. W.; Shin, H.-B.; Robinson, P. A.

2007-12-01

A low-dimensional, compact brain model has recently been developed based on physiologically based mean-field continuum formulation of electric activity of the brain. The essential feature of the new compact model is a second order time-delayed differential equation that has physiologically plausible terms, such as rapid corticocortical feedback and delayed feedback via extracortical pathways. Due to its compact form, the model facilitates insight into complex brain dynamics via standard linear and nonlinear techniques. The model successfully reproduces many features of previous models and experiments. For example, experimentally observed typical rhythms of electroencephalogram (EEG) signals are reproduced in a physiologically plausible parameter region. In the nonlinear regime, onsets of seizures, which often develop into limit cycles, are illustrated by modulating model parameters. It is also shown that a hysteresis can occur when the system has multiple attractors. As a further illustration of this approach, power spectra of the model are fitted to those of sleep EEGs of two subjects (one with apnea, the other with narcolepsy). The model parameters obtained from the fittings show good matches with previous literature. Our results suggest that the compact model can provide a theoretical basis for analyzing complex EEG signals.

Automatic detection of ischemic stroke based on scaling exponent electroencephalogram using extreme learning machine

NASA Astrophysics Data System (ADS)

Adhi, H. A.; Wijaya, S. K.; Prawito; Badri, C.; Rezal, M.

2017-03-01

Stroke is one of cerebrovascular diseases caused by the obstruction of blood flow to the brain. Stroke becomes the leading cause of death in Indonesia and the second in the world. Stroke also causes of the disability. Ischemic stroke accounts for most of all stroke cases. Obstruction of blood flow can cause tissue damage which results the electrical changes in the brain that can be observed through the electroencephalogram (EEG). In this study, we presented the results of automatic detection of ischemic stroke and normal subjects based on the scaling exponent EEG obtained through detrended fluctuation analysis (DFA) using extreme learning machine (ELM) as the classifier. The signal processing was performed with 18 channels of EEG in the range of 0-30 Hz. Scaling exponents of the subjects were used as the input for ELM to classify the ischemic stroke. The performance of detection was observed by the value of accuracy, sensitivity and specificity. The result showed, performance of the proposed method to classify the ischemic stroke was 84 % for accuracy, 82 % for sensitivity and 87 % for specificity with 120 hidden neurons and sine as the activation function of ELM.

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.

Kmeans-ICA based automatic method for ocular artifacts removal in a motorimagery classification.

PubMed

Bou Assi, Elie; Rihana, Sandy; Sawan, Mohamad

2014-01-01

Electroencephalogram (EEG) recordings aroused as inputs of a motor imagery based BCI system. Eye blinks contaminate the spectral frequency of the EEG signals. Independent Component Analysis (ICA) has been already proved for removing these artifacts whose frequency band overlap with the EEG of interest. However, already ICA developed methods, use a reference lead such as the ElectroOculoGram (EOG) to identify the ocular artifact components. In this study, artifactual components were identified using an adaptive thresholding by means of Kmeans clustering. The denoised EEG signals have been fed into a feature extraction algorithm extracting the band power, the coherence and the phase locking value and inserted into a linear discriminant analysis classifier for a motor imagery classification.

Unlocking the Secrets of the Brain, Part II: A Continuing Look at Techniques for Exploring the Brain.

ERIC Educational Resources Information Center

Powledge, Tabitha M.

1997-01-01

Describes techniques for delving into the brain including positron emission tomography (PET), single photon emission computed tomography (SPECT), electroencephalogram (EEG), magnetoencephalography (MEG), transcranial magnetic stimulation (TMS), and low-tech indirect studies. (JRH)

Analysis of cross-correlations in electroencephalogram signals as an approach to proactive diagnosis of schizophrenia

NASA Astrophysics Data System (ADS)

Timashev, Serge F.; Panischev, Oleg Yu.; Polyakov, Yuriy S.; Demin, Sergey A.; Kaplan, Alexander Ya.

2012-02-01

We apply flicker-noise spectroscopy (FNS), a time series analysis method operating on structure functions and power spectrum estimates, to study the clinical electroencephalogram (EEG) signals recorded in children/adolescents (11 to 14 years of age) with diagnosed schizophrenia-spectrum symptoms at the National Center for Psychiatric Health (NCPH) of the Russian Academy of Medical Sciences. The EEG signals for these subjects were compared with the signals for a control sample of chronically depressed children/adolescents. The purpose of the study is to look for diagnostic signs of subjects' susceptibility to schizophrenia in the FNS parameters for specific electrodes and cross-correlations between the signals simultaneously measured at different points on the scalp. Our analysis of EEG signals from scalp-mounted electrodes at locations F3 and F4, which are symmetrically positioned in the left and right frontal areas of cerebral cortex, respectively, demonstrates an essential role of frequency-phase synchronization, a phenomenon representing specific correlations between the characteristic frequencies and phases of excitations in the brain. We introduce quantitative measures of frequency-phase synchronization and systematize the values of FNS parameters for the EEG data. The comparison of our results with the medical diagnoses for 84 subjects performed at NCPH makes it possible to group the EEG signals into 4 categories corresponding to different risk levels of subjects' susceptibility to schizophrenia. We suggest that the introduced quantitative characteristics and classification of cross-correlations may be used for the diagnosis of schizophrenia at the early stages of its development.

Effects of green and black tea consumption on brain wave activities in healthy volunteers as measured by a simplified Electroencephalogram (EEG): A feasibility study.

PubMed

Okello, Edward J; Abadi, Awatf M; Abadi, Saad A

2016-06-01

Tea has been associated with many mental benefits, such as attention enhancement, clarity of mind, and relaxation. These psychosomatic states can be measured in terms of brain activity using an electroencephalogram (EEG). Brain activity can be assessed either during a state of passive activity or when performing attention tasks and it can provide useful information about the brain's state. This study investigated the effects of green and black consumption on brain activity as measured by a simplified EEG, during passive activity. Eight healthy volunteers participated in the study. The EEG measurements were performed using a two channel EEG brain mapping instrument - HeadCoach™. Fast Fourier transform algorithm and EEGLAB toolbox using the Matlab software were used for data processing and analysis. Alpha, theta, and beta wave activities were all found to increase after 1 hour of green and black tea consumption, albeit, with very considerable inter-individual variations. Our findings provide further evidence for the putative beneficial effects of tea. The highly significant increase in theta waves (P < 0.004) between 30 minutes and 1 hour post-consumption of green tea may be an indication of its putative role in cognitive function, specifically alertness and attention. There were considerable inter-individual variations in response to the two teas which may be due genetic polymorphisms in metabolism and/or influence of variety/blend, dose and content of the selected products whose chemistry and therefore efficacy will have been influenced by 'from field to shelf practices'.

The theta burst transcranial magnetic stimulation over the right PFC affects electroencephalogram oscillation during emotional processing.

PubMed

Cao, Dan; Li, Yingjie; Niznikiewicz, Margaret A; Tang, Yingying; Wang, Jijun

2018-03-02

Prefrontal cortex (PFC) plays an important role in emotional processing and therefore is one of the most frequently targeted regions for non-invasive brain stimulation such as repetitive transcranial magnetic stimulation (rTMS) in clinical trials, especially in the treatment of emotional disorders. As an approach to enhance the effectiveness of rTMS, continuous theta burst stimulation (cTBS) has been demonstrated to be efficient and safe. However, it is unclear how cTBS affects brain processes related to emotion. In particular, psychophysiological studies on the underlying neural mechanisms are sparse. In the current study, we investigated how the cTBS influences emotional processing when applied over the right PFC. Participants performed an emotion recognition Go/NoGo task, which asked them to select a GO response to either happy or fearful faces after the cTBS or after sham stimulation, while 64-channel electroencephalogram (EEG) was recorded. EEG oscillation was examined using event-related spectral perturbation (ERSP) in a time-interval between 170 and 310ms after face stimuli onset. In the sham group, we found a significant difference in the alpha band between response to happy and fearful stimuli but that effect did not exist in the cTBS group. The alpha band activity at the scalp was reduced suggesting the excitatory effect at the brain level. The beta and gamma band activity was not sensitive to cTBS intervention. The results of the current study demonstrate that cTBS does affect emotion processing and the effect is reflected in changes in EEG oscillations in the alpha band specifically. The results confirm the role of prefrontal cortex in emotion processing. We also suggest that this pattern of cTBS results elucidates mechanisms by which mood improvement in depressive disorders is achieved using cTBS intervention. Copyright © 2017 Elsevier Inc. All rights reserved.

Real time system design of motor imagery brain-computer interface based on multi band CSP and SVM

NASA Astrophysics Data System (ADS)

Zhao, Li; Li, Xiaoqin; Bian, Yan

2018-04-01

Motion imagery (MT) is an effective method to promote the recovery of limbs in patients after stroke. Though an online MT brain computer interface (BCT) system, which apply MT, can enhance the patient's participation and accelerate their recovery process. The traditional method deals with the electroencephalogram (EEG) induced by MT by common spatial pattern (CSP), which is used to extract information from a frequency band. Tn order to further improve the classification accuracy of the system, information of two characteristic frequency bands is extracted. The effectiveness of the proposed feature extraction method is verified by off-line analysis of competition data and the analysis of online system.

[Risk factors for interictal epileptiform discharges on electroencephalogram in children with spastic hemiplegic cerebral palsy].

PubMed

Li, Su-Yun; Qian, Xu-Guang; Zhao, Yi-Li; Fu, Wen-Jie; Tan, Xiao-Ru; Liu, Zhen-Huan

2015-12-01

To investigate the clinical symptoms and features of interictal epileptiform discharges (IED) on electroencephalogram (EEG) in children with spastic hemiplegic cerebral palsy (CP) and to analyze the risk factors for IED. Eighty-three children with spastic hemiplegic CP were recruited, and their clinical data, results of video-electroencephalogram, imaging findings, and cognitive levels were collected. The influencing factors for IED were determined by multiple logistic regression analysis. The incidence of epilepsy was 13% in children with spastic hemiplegic CP; 34% of these cases had IED. The incidence of epilepsy in children with IED (32%) was significantly higher than that in those without IED (4%) (P<0.01). The incidence of IED in children with complications and brain cortex impairment increased significantly (P<0.01). The incidence of IED varied significantly between patients with different cognitive levels (P<0.01). Brain cortex impairment (OR=11.521) and low cognitive level (OR=2.238)were risk factors for IED in children with spastic hemiplegic CP (P<0.05). Spastic hemiplegic CP is often found with IED on EEG, and the incidence of epilepsy is higher in children with IED than in those without IED. Brain cortex impairment and low cognitive level have predictive values for IED in children with spastic hemiplegic CP.

A stable pattern of EEG spectral coherence distinguishes children with autism from neuro-typical controls - a large case control study

PubMed Central

2012-01-01

Background The autism rate has recently increased to 1 in 100 children. Genetic studies demonstrate poorly understood complexity. Environmental factors apparently also play a role. Magnetic resonance imaging (MRI) studies demonstrate increased brain sizes and altered connectivity. Electroencephalogram (EEG) coherence studies confirm connectivity changes. However, genetic-, MRI- and/or EEG-based diagnostic tests are not yet available. The varied study results likely reflect methodological and population differences, small samples and, for EEG, lack of attention to group-specific artifact. Methods Of the 1,304 subjects who participated in this study, with ages ranging from 1 to 18 years old and assessed with comparable EEG studies, 463 children were diagnosed with autism spectrum disorder (ASD); 571 children were neuro-typical controls (C). After artifact management, principal components analysis (PCA) identified EEG spectral coherence factors with corresponding loading patterns. The 2- to 12-year-old subsample consisted of 430 ASD- and 554 C-group subjects (n = 984). Discriminant function analysis (DFA) determined the spectral coherence factors' discrimination success for the two groups. Loading patterns on the DFA-selected coherence factors described ASD-specific coherence differences when compared to controls. Results Total sample PCA of coherence data identified 40 factors which explained 50.8% of the total population variance. For the 2- to 12-year-olds, the 40 factors showed highly significant group differences (P < 0.0001). Ten randomly generated split half replications demonstrated high-average classification success (C, 88.5%; ASD, 86.0%). Still higher success was obtained in the more restricted age sub-samples using the jackknifing technique: 2- to 4-year-olds (C, 90.6%; ASD, 98.1%); 4- to 6-year-olds (C, 90.9%; ASD 99.1%); and 6- to 12-year-olds (C, 98.7%; ASD, 93.9%). Coherence loadings demonstrated reduced short-distance and reduced, as well as increased, long-distance coherences for the ASD-groups, when compared to the controls. Average spectral loading per factor was wide (10.1 Hz). Conclusions Classification success suggests a stable coherence loading pattern that differentiates ASD- from C-group subjects. This might constitute an EEG coherence-based phenotype of childhood autism. The predominantly reduced short-distance coherences may indicate poor local network function. The increased long-distance coherences may represent compensatory processes or reduced neural pruning. The wide average spectral range of factor loadings may suggest over-damped neural networks. PMID:22730909

Classification of EEG abnormalities in partial epilepsy with simultaneous EEG-fMRI recordings.

PubMed

Pedreira, C; Vaudano, A E; Thornton, R C; Chaudhary, U J; Vulliemoz, S; Laufs, H; Rodionov, R; Carmichael, D W; Lhatoo, S D; Guye, M; Quian Quiroga, R; Lemieux, L

2014-10-01

Scalp EEG recordings and the classification of interictal epileptiform discharges (IED) in patients with epilepsy provide valuable information about the epileptogenic network, particularly by defining the boundaries of the "irritative zone" (IZ), and hence are helpful during pre-surgical evaluation of patients with severe refractory epilepsies. The current detection and classification of epileptiform signals essentially rely on expert observers. This is a very time-consuming procedure, which also leads to inter-observer variability. Here, we propose a novel approach to automatically classify epileptic activity and show how this method provides critical and reliable information related to the IZ localization beyond the one provided by previous approaches. We applied Wave_clus, an automatic spike sorting algorithm, for the classification of IED visually identified from pre-surgical simultaneous Electroencephalogram-functional Magnetic Resonance Imagining (EEG-fMRI) recordings in 8 patients affected by refractory partial epilepsy candidate for surgery. For each patient, two fMRI analyses were performed: one based on the visual classification and one based on the algorithmic sorting. This novel approach successfully identified a total of 29 IED classes (compared to 26 for visual identification). The general concordance between methods was good, providing a full match of EEG patterns in 2 cases, additional EEG information in 2 other cases and, in general, covering EEG patterns of the same areas as expert classification in 7 of the 8 cases. Most notably, evaluation of the method with EEG-fMRI data analysis showed hemodynamic maps related to the majority of IED classes representing improved performance than the visual IED classification-based analysis (72% versus 50%). Furthermore, the IED-related BOLD changes revealed by using the algorithm were localized within the presumed IZ for a larger number of IED classes (9) in a greater number of patients than the expert classification (7 and 5, respectively). In contrast, in only one case presented the new algorithm resulted in fewer classes and activation areas. We propose that the use of automated spike sorting algorithms to classify IED provides an efficient tool for mapping IED-related fMRI changes and increases the EEG-fMRI clinical value for the pre-surgical assessment of patients with severe epilepsy. Copyright © 2014 Elsevier Inc. All rights reserved.

EEG Sleep Stages Classification Based on Time Domain Features and Structural Graph Similarity.

PubMed

Diykh, Mohammed; Li, Yan; Wen, Peng

2016-11-01

The electroencephalogram (EEG) signals are commonly used in diagnosing and treating sleep disorders. Many existing methods for sleep stages classification mainly depend on the analysis of EEG signals in time or frequency domain to obtain a high classification accuracy. In this paper, the statistical features in time domain, the structural graph similarity and the K-means (SGSKM) are combined to identify six sleep stages using single channel EEG signals. Firstly, each EEG segment is partitioned into sub-segments. The size of a sub-segment is determined empirically. Secondly, statistical features are extracted, sorted into different sets of features and forwarded to the SGSKM to classify EEG sleep stages. We have also investigated the relationships between sleep stages and the time domain features of the EEG data used in this paper. The experimental results show that the proposed method yields better classification results than other four existing methods and the support vector machine (SVM) classifier. A 95.93% average classification accuracy is achieved by using the proposed method.

Near-lossless multichannel EEG compression based on matrix and tensor decompositions.

PubMed

Dauwels, Justin; Srinivasan, K; Reddy, M Ramasubba; Cichocki, Andrzej

2013-05-01

A novel near-lossless compression algorithm for multichannel electroencephalogram (MC-EEG) is proposed based on matrix/tensor decomposition models. MC-EEG is represented in suitable multiway (multidimensional) forms to efficiently exploit temporal and spatial correlations simultaneously. Several matrix/tensor decomposition models are analyzed in view of efficient decorrelation of the multiway forms of MC-EEG. A compression algorithm is built based on the principle of “lossy plus residual coding,” consisting of a matrix/tensor decomposition-based coder in the lossy layer followed by arithmetic coding in the residual layer. This approach guarantees a specifiable maximum absolute error between original and reconstructed signals. The compression algorithm is applied to three different scalp EEG datasets and an intracranial EEG dataset, each with different sampling rate and resolution. The proposed algorithm achieves attractive compression ratios compared to compressing individual channels separately. For similar compression ratios, the proposed algorithm achieves nearly fivefold lower average error compared to a similar wavelet-based volumetric MC-EEG compression algorithm.

Artifact removal from EEG signals using adaptive filters in cascade

NASA Astrophysics Data System (ADS)

Garcés Correa, A.; Laciar, E.; Patiño, H. D.; Valentinuzzi, M. E.

2007-11-01

Artifacts in EEG (electroencephalogram) records are caused by various factors, like line interference, EOG (electro-oculogram) and ECG (electrocardiogram). These noise sources increase the difficulty in analyzing the EEG and to obtaining clinical information. For this reason, it is necessary to design specific filters to decrease such artifacts in EEG records. In this paper, a cascade of three adaptive filters based on a least mean squares (LMS) algorithm is proposed. The first one eliminates line interference, the second adaptive filter removes the ECG artifacts and the last one cancels EOG spikes. Each stage uses a finite impulse response (FIR) filter, which adjusts its coefficients to produce an output similar to the artifacts present in the EEG. The proposed cascade adaptive filter was tested in five real EEG records acquired in polysomnographic studies. In all cases, line-frequency, ECG and EOG artifacts were attenuated. It is concluded that the proposed filter reduces the common artifacts present in EEG signals without removing significant information embedded in these records.

A capacitive, biocompatible and adhesive electrode for long-term and cap-free monitoring of EEG signals.

PubMed

Lee, Seung Min; Kim, Jeong Hun; Byeon, Hang Jin; Choi, Yoon Young; Park, Kwang Suk; Lee, Sang-Hoon

2013-06-01

Long-term electroencephalogram (EEG) monitoring broadens EEG applications to various areas, but it requires cap-free recording of EEG signals. Our objective here is to develop a capacitive, small-sized, adhesive and biocompatible electrode for the cap-free and long-term EEG monitoring. We have developed an electrode made of polydimethylsiloxane (PDMS) and adhesive PDMS for EEG monitoring. This electrode can be attached to a hairy scalp and be completely hidden by the hair. We tested its electrical and mechanical (adhesive) properties by measuring voltage gain to frequency and adhesive force using 30 repeat cycles of the attachment and detachment test. Electrode performance on EEG was evaluated by alpha rhythm detection and measuring steady state visually evoked potential and N100 auditory evoked potential. We observed the successful recording of alpha rhythm and evoked signals to diverse stimuli with high signal quality. The biocompatibility of the electrode was verified and a survey found that the electrode was comfortable and convenient to wear. These results indicate that the proposed EEG electrode is suitable and convenient for long term EEG monitoring.

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

Classifying visuomotor workload in a driving simulator using subject specific spatial brain patterns

PubMed Central

Dijksterhuis, Chris; de Waard, Dick; Brookhuis, Karel A.; Mulder, Ben L. J. M.; de Jong, Ritske

2013-01-01

A passive Brain Computer Interface (BCI) is a system that responds to the spontaneously produced brain activity of its user and could be used to develop interactive task support. A human-machine system that could benefit from brain-based task support is the driver-car interaction system. To investigate the feasibility of such a system to detect changes in visuomotor workload, 34 drivers were exposed to several levels of driving demand in a driving simulator. Driving demand was manipulated by varying driving speed and by asking the drivers to comply to individually set lane keeping performance targets. Differences in the individual driver's workload levels were classified by applying the Common Spatial Pattern (CSP) and Fisher's linear discriminant analysis to frequency filtered electroencephalogram (EEG) data during an off line classification study. Several frequency ranges, EEG cap configurations, and condition pairs were explored. It was found that classifications were most accurate when based on high frequencies, larger electrode sets, and the frontal electrodes. Depending on these factors, classification accuracies across participants reached about 95% on average. The association between high accuracies and high frequencies suggests that part of the underlying information did not originate directly from neuronal activity. Nonetheless, average classification accuracies up to 75–80% were obtained from the lower EEG ranges that are likely to reflect neuronal activity. For a system designer, this implies that a passive BCI system may use several frequency ranges for workload classifications. PMID:23970851

Variation in neurophysiological function and evidence of quantitative electroencephalogram discordance: predicting cocaine-dependent treatment attrition.

PubMed

Venneman, Sandy; Leuchter, Andrew; Bartzokis, George; Beckson, Mace; Simon, Sara L; Schaefer, Melodie; Rawson, Richard; Newton, Tom; Cook, Ian A; Uijtdehaage, Sebastian; Ling, Walter

2006-01-01

Cocaine treatment trials suffer from a high rate of attrition. We examined pretreatment neurophysiological factors to identify participants at greatest risk. Twenty-five participants were divided into concordant and discordant groups following electroencephalogram (EEG) measures recorded prior to a double-blind, placebo-controlled treatment trial. Three possible outcomes were examined: successful completion, dropout, and removal. Concordant (high perfusion correlate) participants had an 85% rate of successful completion, while discordant participants had a 15% rate of successful completion. Twenty-five percent of dropouts and 50% of participants removed were discordant (low perfusion correlate), while only 25% of those who completed were discordant. Failure to complete the trial was not explained by depression, craving, benzoylecgonine levels or quantitative electroencephalogram (QEEG) power; thus cordance may help identify attrition risk.

Passive Transport Disrupts Grid Signals in the Parahippocampal Cortex.

PubMed

Winter, Shawn S; Mehlman, Max L; Clark, Benjamin J; Taube, Jeffrey S

2015-10-05

Navigation is usually thought of relative to landmarks, but neural signals representing space also use information generated by an animal's movements. These signals include grid cells, which fire at multiple locations, forming a repeating grid pattern. Grid cell generation depends upon theta rhythm, a 6-10 Hz electroencephalogram (EEG) oscillation that is modulated by the animals' movement velocity. We passively moved rats in a clear cart to eliminate motor related self-movement cues that drive moment-to-moment changes in theta rhythmicity. We found that passive movement maintained theta power and frequency at levels equivalent to low active movement velocity, spared overall head-direction (HD) cell characteristics, but abolished both velocity modulation of theta rhythmicity and grid cell firing patterns. These results indicate that self-movement motor cues are necessary for generating grid-specific firing patterns, possibly by driving velocity modulation of theta rhythmicity, which may be used as a speed signal to generate the repeating pattern of grid cells. Copyright © 2015 Elsevier Ltd. All rights reserved.

Neurophysiological differences between patients clinically at high risk for schizophrenia and neurotypical controls--first steps in development of a biomarker.

PubMed

Duffy, Frank H; D'Angelo, Eugene; Rotenberg, Alexander; Gonzalez-Heydrich, Joseph

2015-11-02

Schizophrenia is a severe, disabling and prevalent mental disorder without cure and with a variable, incomplete pharmacotherapeutic response. Prior to onset in adolescence or young adulthood a prodromal period of abnormal symptoms lasting weeks to years has been identified and operationalized as clinically high risk (CHR) for schizophrenia. However, only a minority of subjects prospectively identified with CHR convert to schizophrenia, thereby limiting enthusiasm for early intervention(s). This study utilized objective resting electroencephalogram (EEG) quantification to determine whether CHR constitutes a cohesive entity and an evoked potential to assess CHR cortical auditory processing. This study constitutes an EEG-based quantitative neurophysiological comparison between two unmedicated subject groups: 35 neurotypical controls (CON) and 22 CHR patients. After artifact management, principal component analysis (PCA) identified EEG spectral and spectral coherence factors described by associated loading patterns. Discriminant function analysis (DFA) determined factors' discrimination success between subjects in the CON and CHR groups. Loading patterns on DFA-selected factors described CHR-specific spectral and coherence differences when compared to controls. The frequency modulated auditory evoked response (FMAER) explored functional CON-CHR differences within the superior temporal gyri. Variable reduction by PCA identified 40 coherence-based factors explaining 77.8% of the total variance and 40 spectral factors explaining 95.9% of the variance. DFA demonstrated significant CON-CHR group difference (P <0.00001) and successful jackknifed subject classification (CON, 85.7%; CHR, 86.4% correct). The population distribution plotted along the canonical discriminant variable was clearly bimodal. Coherence factors delineated loading patterns of altered connectivity primarily involving the bilateral posterior temporal electrodes. However, FMAER analysis showed no CON-CHR group differences. CHR subjects form a cohesive group, significantly separable from CON subjects by EEG-derived indices. Symptoms of CHR may relate to altered connectivity with the posterior temporal regions but not to primary auditory processing abnormalities within these regions.

Professional musicians listen differently to music.

PubMed

Mikutta, C A; Maissen, G; Altorfer, A; Strik, W; Koenig, T

2014-05-30

Experience-based adaptation of emotional responses is an important faculty for cognitive and emotional functioning. Professional musicians represent an ideal model in which to elicit experience-driven changes in the emotional processing domain. The changes of the central representation of emotional arousal due to musical expertise are still largely unknown. The aim of the present study was to investigate the electroencephalogram (EEG) correlates of experience-driven changes in the domain of emotional arousal. Therefore, the differences in perceived (subjective arousal via ratings) and physiologically measured (EEG) arousal between amateur and professional musicians were examined. A total of 15 professional and 19 amateur musicians listened to the first movement of Ludwig van Beethoven's 5th symphony (duration=∼7.4min), during which a continuous 76-channel EEG was recorded. In a second session, the participants evaluated their emotional arousal during listening. In a tonic analysis, we examined the average EEG data over the time course of the music piece. For a phasic analysis, a fast Fourier transform was performed and covariance maps of spectral power were computed in association with the subjective arousal ratings. The subjective arousal ratings of the professional musicians were more consistent than those of the amateur musicians. In the tonic EEG analysis, a mid-frontal theta activity was observed in the professionals. In the phasic EEG, the professionals exhibited an increase of posterior alpha, central delta, and beta rhythm during high arousal. Professionals exhibited different and/or more intense patterns of emotional activation when they listened to the music. The results of the present study underscore the impact of music experience on emotional reactions. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

De novo status epilepticus with isolated aphasia.

PubMed

Flügel, Dominique; Kim, Olaf Chan-Hi; Felbecker, Ansgar; Tettenborn, Barbara

2015-08-01

Sudden onset of aphasia is usually due to stroke. Rapid diagnostic workup is necessary if reperfusion therapy is considered. Ictal aphasia is a rare condition but has to be excluded. Perfusion imaging may differentiate acute ischemia from other causes. In dubious cases, EEG is required but is time-consuming and laborious. We report a case where we considered de novo status epilepticus as a cause of aphasia without any lesion even at follow-up. A 62-year-old right-handed woman presented to the emergency department after nurses found her aphasic. She had undergone operative treatment of varicosis 3 days earlier. Apart from hypertension and obesity, no cardiovascular risk factors and no intake of medication other than paracetamol were reported. Neurological examination revealed global aphasia and right pronation in the upper extremity position test. Computed tomography with angiography and perfusion showed no abnormalities. Electroencephalogram performed after the CT scan showed left-sided slowing with high-voltage rhythmic 2/s delta waves but no clear ictal pattern. Intravenous lorazepam did improve EEG slightly, while aphasia did not change. Lumbar puncture was performed which likely excluded encephalitis. Magnetic resonance imaging showed cortical pathological diffusion imaging (restriction) and cortical hyperperfusion in the left parietal region. Intravenous anticonvulsant therapy under continuous EEG resolved neurological symptoms. The patient was kept on anticonvulsant therapy. Magnetic resonance imaging after 6 months showed no abnormalities along with no clinical abnormalities. Magnetic resonance imaging findings were only subtle, and EEG was without clear ictal pattern, so the diagnosis of aphasic status remains with some uncertainty. However, status epilepticus can mimic stroke symptoms and has to be considered in patients with aphasia even when no previous stroke or structural lesions are detectable and EEG shows no epileptic discharges. Epileptic origin is favored when CT or MR imaging reveal no hypoperfusion. In this case, MRI was superior to CT in detecting hyperperfusion. This article is part of a Special Issue entitled "Status Epilepticus". Copyright © 2015 Elsevier Inc. All rights reserved.

[EEG technician-nurse collaboration during stereo-electroencephalography].

PubMed

Jomard, Caroline; Benghezal, Mouna; Cheramy, Isabelle; De Beaumont, Ségolène

2017-01-01

Drug-resistant epilepsy has significant repercussions on the daily life of children. Surgery may represent a hope. The nurse and the electroencephalogram technician carry out important teamwork during pre-surgical assessment tests and notably the stereo-electroencephalography. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

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.

The Utility of EEG in Attention Deficit Hyperactivity Disorder: A Replication Study.

PubMed

Swatzyna, Ronald J; Tarnow, Jay D; Roark, Alexandra; Mardick, Jacob

2017-07-01

The routine use of stimulants in pediatrics has increased dramatically over the past 3 decades and the long-term consequences have yet to be fully studied. Since 1978 there have been 7 articles identifying electroencephalogram (EEG) abnormalities, particularly epileptiform discharges in children with attention deficit hyperactivity disorder (ADHD). Many have studied the prevalence of these discharges in this population with varying results. An article published in 2011 suggests that EEG technology should be considered prior to prescribing stimulants to children diagnosed with ADHD due to a high prevalence of epileptiform discharges. The 2011 study found a higher prevalence (26%) of epileptiform discharges when using 23-hour and sleep-deprived EEGs in comparison with other methods of activation (hyperventilation or photostimulation) and conventional EEG. We sought to replicate the 2011 results using conventional EEG with the added qEEG technologies of automatic spike detection and low-resolution electromagnetic tomography analysis (LORETA) brain mapping. Our results showed 32% prevalence of epileptiform discharges, which suggests that an EEG should be considered prior to prescribing stimulant medications.

Electroencephalogram signatures of loss and recovery of consciousness from propofol

PubMed Central

Purdon, Patrick L.; Pierce, Eric T.; Mukamel, Eran A.; Prerau, Michael J.; Walsh, John L.; Wong, Kin Foon K.; Salazar-Gomez, Andres F.; Harrell, Priscilla G.; Sampson, Aaron L.; Cimenser, Aylin; Ching, ShiNung; Kopell, Nancy J.; Tavares-Stoeckel, Casie; Habeeb, Kathleen; Merhar, Rebecca; Brown, Emery N.

2013-01-01

Unconsciousness is a fundamental component of general anesthesia (GA), but anesthesiologists have no reliable ways to be certain that a patient is unconscious. To develop EEG signatures that track loss and recovery of consciousness under GA, we recorded high-density EEGs in humans during gradual induction of and emergence from unconsciousness with propofol. The subjects executed an auditory task at 4-s intervals consisting of interleaved verbal and click stimuli to identify loss and recovery of consciousness. During induction, subjects lost responsiveness to the less salient clicks before losing responsiveness to the more salient verbal stimuli; during emergence they recovered responsiveness to the verbal stimuli before recovering responsiveness to the clicks. The median frequency and bandwidth of the frontal EEG power tracked the probability of response to the verbal stimuli during the transitions in consciousness. Loss of consciousness was marked simultaneously by an increase in low-frequency EEG power (<1 Hz), the loss of spatially coherent occipital alpha oscillations (8–12 Hz), and the appearance of spatially coherent frontal alpha oscillations. These dynamics reversed with recovery of consciousness. The low-frequency phase modulated alpha amplitude in two distinct patterns. During profound unconsciousness, alpha amplitudes were maximal at low-frequency peaks, whereas during the transition into and out of unconsciousness, alpha amplitudes were maximal at low-frequency nadirs. This latter phase–amplitude relationship predicted recovery of consciousness. Our results provide insights into the mechanisms of propofol-induced unconsciousness, establish EEG signatures of this brain state that track transitions in consciousness precisely, and suggest strategies for monitoring the brain activity of patients receiving GA. PMID:23487781

Comparison of Sensorimotor Rhythm (SMR) and Beta Training on Selective Attention and Symptoms in Children with Attention Deficit/Hyperactivity Disorder (ADHD): A Trend Report.

PubMed

Mohammadi, Mohammad Reza; Malmir, Nastaran; Khaleghi, Ali; Aminiorani, Majd

2015-06-01

The aim of this study was to assess and compare the effect of two neurofeedback protocols (SMR/theta and beta/theta) on ADHD symptoms, selective attention and EEG (electroencephalogram) parameters in children with ADHD. The sample consisted of 16 children (9-15 year old: 13 boys; 3 girls) with ADHD-combined type (ADHD-C). All of children used methylphenidate (MPH) during the study. The neurofeedback training consisted of two phases of 15 sessions, each lasting 45 minutes. In the first phase, participants were trained to enhance sensorimotor rhythm (12-15 Hz) and reduce theta activity (4-8 Hz) at C4 and in the second phase; they had to increase beta (15-18 Hz) and reduce theta activity at C3. Assessments consisted of d2 attention endurance test, ADHD rating scale (parent form) at three time periods: before, middle and the end of the training. EEG signals were recorded just before and after the training. Based on parents' reports, inattention after beta/theta training, and hyperactivity/impulsivity were improved after the end of the training. All subscales of d2 test were improved except for the difference between maximum and minimum responses. However, EEG analysis showed no significant differences. Neurofeedback in conjunction with Methylphenidate may cause further improvement in ADHD symptoms reported by parents and selective attention without long-term impact on EEG patterns. However, determining the exact relationship between EEG parameters, neurofeedback protocols and ADHD symptoms remain unclear.

Beyond Epilepsy: How Can Quantitative Electroencephalography Improve Conventional Electroencephalography Findings? A Systematic Review of Comparative EEG Studies.

PubMed

Martins, Cassio Henrique Taques; Assunção, Catarina De Marchi

2018-01-01

It is a fundamental element in both research and clinical applications of electroencephalography to know the frequency composition of brain electrical activity. The quantitative analysis of brain electrical activity uses computer resources to evaluate the electroencephalography and allows quantification of the data. The contribution of the quantitative perspective is unique, since conventional electroencephalography based on the visual examination of the tracing is not as objective. A systematic review was performed on the MEDLINE database in October 2017. The authors independently analyzed the studies, by title and abstract, and selected articles that met the inclusion criteria: comparative studies, not older than 30 years, that compared the use of conventional electroencephalogram (EEG) with the use of quantitative electroencephalogram (QEEG) in the English language. One hundred twelve articles were automatically selected by the MEDLINE search engine, but only six met the above criteria. The review found that given a 95% confidence interval, QEEG had no statistically higher sensitivity than EEG in four of the six studies reviewed. However, these results must be viewed with appropriate caution, particularly as groups in between studies were not matched on important variables such as gender, age, type of illness, recovery stage, and treatment. The authors' findings in this systematic review are suggestive of the importance of QEEG as an auxiliary tool to traditional EEG, and as such, justifying further refinement, standardization, and eventually the future execution of a head-to-head prospective study on comparing the two methods.

Hybrid EEG—Eye Tracker: Automatic Identification and Removal of Eye Movement and Blink Artifacts from Electroencephalographic Signal

PubMed Central

Mannan, Malik M. Naeem; Kim, Shinjung; Jeong, Myung Yung; Kamran, M. Ahmad

2016-01-01

Contamination of eye movement and blink artifacts in Electroencephalogram (EEG) recording makes the analysis of EEG data more difficult and could result in mislead findings. Efficient removal of these artifacts from EEG data is an essential step in improving classification accuracy to develop the brain-computer interface (BCI). In this paper, we proposed an automatic framework based on independent component analysis (ICA) and system identification to identify and remove ocular artifacts from EEG data by using hybrid EEG and eye tracker system. The performance of the proposed algorithm is illustrated using experimental and standard EEG datasets. The proposed algorithm not only removes the ocular artifacts from artifactual zone but also preserves the neuronal activity related EEG signals in non-artifactual zone. The comparison with the two state-of-the-art techniques namely ADJUST based ICA and REGICA reveals the significant improved performance of the proposed algorithm for removing eye movement and blink artifacts from EEG data. Additionally, results demonstrate that the proposed algorithm can achieve lower relative error and higher mutual information values between corrected EEG and artifact-free EEG data. PMID:26907276

Failure warning of hydrous sandstone based on electroencephalogram technique

NASA Astrophysics Data System (ADS)

Tao, Kai; Zheng, Wei

2018-06-01

Sandstone is a type of rock mass that widely exists in nature. Moisture is an important factor that leads to sandstone structural failure. The major failure assessment methods of hydrous sandstone at present cannot satisfy real-time and portability requirements, especially lacks of warning function. In this study, acoustic emission (AE) and computed tomography (CT) techniques are combined for real-time failure assessment of hydrous sandstone. Eight visual colors for warning are screened according to different failure states, and an electroencephalogram (EEG) experiment is conducted to demonstrate their diverse excitations of the human brain's concentration.

[DESCRIPTION AND PRESENTATION OF THE RESULTS OF ELECTROENCEPHALOGRAM PROCESSING USING AN INFORMATION MODEL].

PubMed

Myznikov, I L; Nabokov, N L; Rogovanov, D Yu; Khankevich, Yu R

2016-01-01

The paper proposes to apply the informational modeling of correlation matrix developed by I.L. Myznikov in early 1990s in neurophysiological investigations, such as electroencephalogram recording and analysis, coherence description of signals from electrodes on the head surface. The authors demonstrate information models built using the data from studies of inert gas inhalation by healthy human subjects. In the opinion of the authors, information models provide an opportunity to describe physiological processes with a high level of generalization. The procedure of presenting the EEG results holds great promise for the broad application.

Combining Different Tools for EEG Analysis to Study the Distributed Character of Language Processing

PubMed Central

da Rocha, Armando Freitas; Foz, Flávia Benevides; Pereira, Alfredo

2015-01-01

Recent studies on language processing indicate that language cognition is better understood if assumed to be supported by a distributed intelligent processing system enrolling neurons located all over the cortex, in contrast to reductionism that proposes to localize cognitive functions to specific cortical structures. Here, brain activity was recorded using electroencephalogram while volunteers were listening or reading small texts and had to select pictures that translate meaning of these texts. Several techniques for EEG analysis were used to show this distributed character of neuronal enrollment associated with the comprehension of oral and written descriptive texts. Low Resolution Tomography identified the many different sets (s i) of neurons activated in several distinct cortical areas by text understanding. Linear correlation was used to calculate the information H(e i) provided by each electrode of the 10/20 system about the identified s i. H(e i) Principal Component Analysis (PCA) was used to study the temporal and spatial activation of these sources s i. This analysis evidenced 4 different patterns of H(e i) covariation that are generated by neurons located at different cortical locations. These results clearly show that the distributed character of language processing is clearly evidenced by combining available EEG technologies. PMID:26713089

A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface.

PubMed

Zhou, Bangyan; Wu, Xiaopei; Lv, Zhao; Zhang, Lei; Guo, Xiaojin

2016-01-01

Independent component analysis (ICA) as a promising spatial filtering method can separate motor-related independent components (MRICs) from the multichannel electroencephalogram (EEG) signals. However, the unpredictable burst interferences may significantly degrade the performance of ICA-based brain-computer interface (BCI) system. In this study, we proposed a new algorithm frame to address this issue by combining the single-trial-based ICA filter with zero-training classifier. We developed a two-round data selection method to identify automatically the badly corrupted EEG trials in the training set. The "high quality" training trials were utilized to optimize the ICA filter. In addition, we proposed an accuracy-matrix method to locate the artifact data segments within a single trial and investigated which types of artifacts can influence the performance of the ICA-based MIBCIs. Twenty-six EEG datasets of three-class motor imagery were used to validate the proposed methods, and the classification accuracies were compared with that obtained by frequently used common spatial pattern (CSP) spatial filtering algorithm. The experimental results demonstrated that the proposed optimizing strategy could effectively improve the stability, practicality and classification performance of ICA-based MIBCI. The study revealed that rational use of ICA method may be crucial in building a practical ICA-based MIBCI system.

Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates

NASA Astrophysics Data System (ADS)

Jamal, Wasifa; Das, Saptarshi; Oprescu, Ioana-Anastasia; Maharatna, Koushik; Apicella, Fabio; Sicca, Federico

2014-08-01

Objective. The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. Approach. Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful, happy and neutral faces. The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects. Among different supervised learning techniques, we here explored the discriminant analysis and support vector machine both with polynomial kernels for the classification task. Main results. The leave one out cross-validation of the classification algorithm gives 94.7% accuracy as the best performance with corresponding sensitivity and specificity values as 85.7% and 100% respectively. Significance. The proposed method gives high classification accuracies and outperforms other contemporary research results. The effectiveness of the proposed method for classification of autistic and typical children suggests the possibility of using it on a larger population to validate it for clinical practice.

Age dependent electroencephalographic changes in attention-deficit/hyperactivity disorder (ADHD).

PubMed

Poil, S-S; Bollmann, S; Ghisleni, C; O'Gorman, R L; Klaver, P; Ball, J; Eich-Höchli, D; Brandeis, D; Michels, L

2014-08-01

Objective biomarkers for attention-deficit/hyperactivity disorder (ADHD) could improve diagnostics or treatment monitoring of this psychiatric disorder. The resting electroencephalogram (EEG) provides non-invasive spectral markers of brain function and development. Their accuracy as ADHD markers is increasingly questioned but may improve with pattern classification. This study provides an integrated analysis of ADHD and developmental effects in children and adults using regression analysis and support vector machine classification of spectral resting (eyes-closed) EEG biomarkers in order to clarify their diagnostic value. ADHD effects on EEG strongly depend on age and frequency. We observed typical non-linear developmental decreases in delta and theta power for both ADHD and control groups. However, for ADHD adults we found a slowing in alpha frequency combined with a higher power in alpha-1 (8-10Hz) and beta (13-30Hz). Support vector machine classification of ADHD adults versus controls yielded a notable cross validated sensitivity of 67% and specificity of 83% using power and central frequency from all frequency bands. ADHD children were not classified convincingly with these markers. Resting state electrophysiology is altered in ADHD, and these electrophysiological impairments persist into adulthood. Spectral biomarkers may have both diagnostic and prognostic value. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Analysis of wavelet-filtered tonic-clonic electroencephalogram recordings.

PubMed

Rosso, O A; Figliola, A; Creso, J; Serrano, E

2004-07-01

EEG signals obtained during tonic-clonic epileptic seizures can be severely contaminated by muscle and physiological noise. Heavily contaminated EEG signals are hard to analyse quantitatively and also are usually rejected for visual inspection by physicians, resulting in a considerable loss of collected information. The aim of this work was to develop a computer-based method of time series analysis for such EEGs. A method is presented for filtering those frequencies associated with muscle activity using a wavelet transform. One of the advantages of this method over traditional filtering is that wavelet filtering of some frequency bands does not modify the pattern of the remaining ones. In consequence, the dynamics associated with them do not change. After generation of a 'noise free' signal by removal of the muscle artifacts using wavelets, a dynamic analysis was performed using non-linear dynamics metric tools. The characteristic parameters evaluated (correlation dimension D2 and largest Lyapunov exponent lambda1) were compatible with those obtained in previous works. The average values obtained were: D2=4.25 and lambda1=3.27 for the pre-ictal stage; D2=4.03 and lambda1=2.68 for the tonic seizure stage; D2=4.11 and lambda1=2.46 for the clonic seizure stage.

Combining Different Tools for EEG Analysis to Study the Distributed Character of Language Processing.

PubMed

Rocha, Armando Freitas da; Foz, Flávia Benevides; Pereira, Alfredo

2015-01-01

Recent studies on language processing indicate that language cognition is better understood if assumed to be supported by a distributed intelligent processing system enrolling neurons located all over the cortex, in contrast to reductionism that proposes to localize cognitive functions to specific cortical structures. Here, brain activity was recorded using electroencephalogram while volunteers were listening or reading small texts and had to select pictures that translate meaning of these texts. Several techniques for EEG analysis were used to show this distributed character of neuronal enrollment associated with the comprehension of oral and written descriptive texts. Low Resolution Tomography identified the many different sets (s i ) of neurons activated in several distinct cortical areas by text understanding. Linear correlation was used to calculate the information H(e i ) provided by each electrode of the 10/20 system about the identified s i . H(e i ) Principal Component Analysis (PCA) was used to study the temporal and spatial activation of these sources s i . This analysis evidenced 4 different patterns of H(e i ) covariation that are generated by neurons located at different cortical locations. These results clearly show that the distributed character of language processing is clearly evidenced by combining available EEG technologies.

``Seeing'' electroencephalogram through the skull: imaging prefrontal cortex with fast optical signal

NASA Astrophysics Data System (ADS)

Medvedev, Andrei V.; Kainerstorfer, Jana M.; Borisov, Sergey V.; Gandjbakhche, Amir H.; Vanmeter, John

2010-11-01

Near-infrared spectroscopy is a novel imaging technique potentially sensitive to both brain hemodynamics (slow signal) and neuronal activity (fast optical signal, FOS). The big challenge of measuring FOS noninvasively lies in the presumably low signal-to-noise ratio. Thus, detectability of the FOS has been controversially discussed. We present reliable detection of FOS from 11 individuals concurrently with electroencephalogram (EEG) during a Go-NoGo task. Probes were placed bilaterally over prefrontal cortex. Independent component analysis (ICA) was used for artifact removal. Correlation coefficient in the best correlated FOS-EEG ICA pairs was highly significant (p < 10-8), and event-related optical signal (EROS) was found in all subjects. Several EROS components were similar to the event-related potential (ERP) components. The most robust ``optical N200'' at t = 225 ms coincided with the N200 ERP; both signals showed significant difference between targets and nontargets, and their timing correlated with subject's reaction time. Correlation between FOS and EEG even in single trials provides further evidence that at least some FOS components ``reflect'' electrical brain processes directly. The data provide evidence for the early involvement of prefrontal cortex in rapid object recognition. EROS is highly localized and can provide cost-effective imaging tools for cortical mapping of cognitive processes.

Deep Learning from EEG Reports for Inferring Underspecified Information

PubMed Central

Goodwin, Travis R.; Harabagiu, Sanda M.

2017-01-01

Secondary use1of electronic health records (EHRs) often relies on the ability to automatically identify and extract information from EHRs. Unfortunately, EHRs are known to suffer from a variety of idiosyncrasies – most prevalently, they have been shown to often omit or underspecify information. Adapting traditional machine learning methods for inferring underspecified information relies on manually specifying features characterizing the specific information to recover (e.g. particular findings, test results, or physician’s impressions). By contrast, in this paper, we present a method for jointly (1) automatically extracting word- and report-level features and (2) inferring underspecified information from EHRs. Our approach accomplishes these two tasks jointly by combining recent advances in deep neural learning with access to textual data in electroencephalogram (EEG) reports. We evaluate the performance of our model on the problem of inferring the neurologist’s over-all impression (normal or abnormal) from electroencephalogram (EEG) reports and report an accuracy of 91.4% precision of 94.4% recall of 91.2% and F1 measure of 92.8% (a 40% improvement over the performance obtained using Doc2Vec). These promising results demonstrate the power of our approach, while error analysis reveals remaining obstacles as well as areas for future improvement. PMID:28815118

“Seeing” electroencephalogram through the skull: imaging prefrontal cortex with fast optical signal

PubMed Central

Medvedev, Andrei V.; Kainerstorfer, Jana M.; Borisov, Sergey V.; Gandjbakhche, Amir H.; VanMeter, John

2010-01-01

Near-infrared spectroscopy is a novel imaging technique potentially sensitive to both brain hemodynamics (slow signal) and neuronal activity (fast optical signal, FOS). The big challenge of measuring FOS noninvasively lies in the presumably low signal-to-noise ratio. Thus, detectability of the FOS has been controversially discussed. We present reliable detection of FOS from 11 individuals concurrently with electroencephalogram (EEG) during a Go-NoGo task. Probes were placed bilaterally over prefrontal cortex. Independent component analysis (ICA) was used for artifact removal. Correlation coefficient in the best correlated FOS–EEG ICA pairs was highly significant (p < 10−8), and event-related optical signal (EROS) was found in all subjects. Several EROS components were similar to the event-related potential (ERP) components. The most robust “optical N200” at t = 225 ms coincided with the N200 ERP; both signals showed significant difference between targets and nontargets, and their timing correlated with subject’s reaction time. Correlation between FOS and EEG even in single trials provides further evidence that at least some FOS components “reflect” electrical brain processes directly. The data provide evidence for the early involvement of prefrontal cortex in rapid object recognition. EROS is highly localized and can provide cost-effective imaging tools for cortical mapping of cognitive processes. PMID:21198150

Quantitative EEG and neurofeedback in children and adolescents: anxiety disorders, depressive disorders, comorbid addiction and attention-deficit/hyperactivity disorder, and brain injury.

PubMed

Simkin, Deborah R; Thatcher, Robert W; Lubar, Joel

2014-07-01

This article explores the science surrounding neurofeedback. Both surface neurofeedback (using 2-4 electrodes) and newer interventions, such as real-time z-score neurofeedback (electroencephalogram [EEG] biofeedback) and low-resolution electromagnetic tomography neurofeedback, are reviewed. The limited literature on neurofeedback research in children and adolescents is discussed regarding treatment of anxiety, mood, addiction (with comorbid attention-deficit/hyperactivity disorder), and traumatic brain injury. Future potential applications, the use of quantitative EEG for determining which patients will be responsive to medications, the role of randomized controlled studies in neurofeedback research, and sensible clinical guidelines are considered. Copyright © 2014 Elsevier Inc. All rights reserved.

Next-day residual effects of hypnotics in DSM-IV primary insomnia: a driving simulator study with simultaneous electroencephalogram monitoring.

PubMed

Staner, Luc; Ertlé, Stéphane; Boeijinga, Peter; Rinaudo, Gilbert; Arnal, Marie Agnès; Muzet, Alain; Luthringer, Rémy

2005-10-01

Most studies that investigated the next-day residual effects of hypnotic drugs on daytime driving performances were performed on healthy subjects and after a single drug administration. In the present study, we further examine whether the results of these studies could be generalised to insomniac patients and after repeated drug administration. Single and repeated (7 day) doses of zolpidem (10 mg), zopiclone (7.5 mg), lormetazepam (1 mg) or placebo were administered at bedtime in a crossover design to 23 patients (9 men and 14 women aged 38.8+/-2.0 years) with Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) primary insomnia. Driving tests were performed 9-11 h post-dose. Results showed that treatment effects were evidenced for subjective sleep, for driving abilities, and for electroencephalogram (EEG) recorded before (resting EEG) and during the driving simulation test (driving EEG). Compared to placebo, zopiclone increased the number of collisions and lormetazepam increased deviation from speed limit and deviation from absolute speed, whereas zolpidem did not differentiate from placebo on these analyses. EEG recordings showed that in contrast to zolpidem, lormetazepam and zopiclone induced typical benzodiazepine-like alterations, suggesting that next-day poor driving performance could relate to a prolonged central nervous system effect of these two hypnotics. The present results corroborate studies on healthy volunteers showing that residual effects of hypnotics increase with their half-lives. The results further suggest that drugs preserving physiological EEG rhythms before and during the driving simulation test 9-11 h post-dose, such as zolpidem, do not influence next-day driving abilities.

Muscle artifacts in single trial EEG data distinguish patients with Parkinson's disease from healthy individuals.

PubMed

Weyhenmeyer, Jonathan; Hernandez, Manuel E; Lainscsek, Claudia; Sejnowski, Terrence J; Poizner, Howard

2014-01-01

Parkinson's disease (PD) is known to lead to marked alterations in cortical-basal ganglia activity that may be amenable to serve as a biomarker for PD diagnosis. Using non-linear delay differential equations (DDE) for classification of PD patients on and off dopaminergic therapy (PD-on, PD-off, respectively) from healthy age-matched controls (CO), we show that 1 second of quasi-resting state clean and raw electroencephalogram (EEG) data can be used to classify CO from PD-on/off based on the area under the receiver operating characteristic curve (AROC). Raw EEG is shown to classify more robustly (AROC=0.59-0.86) than clean EEG data (AROC=0.57-0.72). Decomposition of the raw data into stereotypical and non-stereotypical artifacts provides evidence that increased classification of raw EEG time series originates from muscle artifacts. Thus, non-linear feature extraction and classification of raw EEG data in a low dimensional feature space is a potential biomarker for Parkinson's disease.

Automatic removal of eye-movement and blink artifacts from EEG signals.

PubMed

Gao, Jun Feng; Yang, Yong; Lin, Pan; Wang, Pei; Zheng, Chong Xun

2010-03-01

Frequent occurrence of electrooculography (EOG) artifacts leads to serious problems in interpreting and analyzing the electroencephalogram (EEG). In this paper, a robust method is presented to automatically eliminate eye-movement and eye-blink artifacts from EEG signals. Independent Component Analysis (ICA) is used to decompose EEG signals into independent components. Moreover, the features of topographies and power spectral densities of those components are extracted to identify eye-movement artifact components, and a support vector machine (SVM) classifier is adopted because it has higher performance than several other classifiers. The classification results show that feature-extraction methods are unsuitable for identifying eye-blink artifact components, and then a novel peak detection algorithm of independent component (PDAIC) is proposed to identify eye-blink artifact components. Finally, the artifact removal method proposed here is evaluated by the comparisons of EEG data before and after artifact removal. The results indicate that the method proposed could remove EOG artifacts effectively from EEG signals with little distortion of the underlying brain signals.

Classification of epileptic EEG signals based on simple random sampling and sequential feature selection.

PubMed

Ghayab, Hadi Ratham Al; Li, Yan; Abdulla, Shahab; Diykh, Mohammed; Wan, Xiangkui

2016-06-01

Electroencephalogram (EEG) signals are used broadly in the medical fields. The main applications of EEG signals are the diagnosis and treatment of diseases such as epilepsy, Alzheimer, sleep problems and so on. This paper presents a new method which extracts and selects features from multi-channel EEG signals. This research focuses on three main points. Firstly, simple random sampling (SRS) technique is used to extract features from the time domain of EEG signals. Secondly, the sequential feature selection (SFS) algorithm is applied to select the key features and to reduce the dimensionality of the data. Finally, the selected features are forwarded to a least square support vector machine (LS_SVM) classifier to classify the EEG signals. The LS_SVM classifier classified the features which are extracted and selected from the SRS and the SFS. The experimental results show that the method achieves 99.90, 99.80 and 100 % for classification accuracy, sensitivity and specificity, respectively.

Automatic classification of sleep stages based on the time-frequency image of EEG signals.

PubMed

Bajaj, Varun; Pachori, Ram Bilas

2013-12-01

In this paper, a new method for automatic sleep stage classification based on time-frequency image (TFI) of electroencephalogram (EEG) signals is proposed. Automatic classification of sleep stages is an important part for diagnosis and treatment of sleep disorders. The smoothed pseudo Wigner-Ville distribution (SPWVD) based time-frequency representation (TFR) of EEG signal has been used to obtain the time-frequency image (TFI). The segmentation of TFI has been performed based on the frequency-bands of the rhythms of EEG signals. The features derived from the histogram of segmented TFI have been used as an input feature set to multiclass least squares support vector machines (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for automatic classification of sleep stages from EEG signals. The experimental results are presented to show the effectiveness of the proposed method for classification of sleep stages from EEG signals. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

EEG and Heart Rate Measures of Working Memory at 5 and 10 Months of Age

PubMed Central

Cuevas, Kimberly; Bell, Martha Ann; Marcovitch, Stuart; Calkins, Susan D.

2013-01-01

We recorded electroencephalogram (EEG; 6–9 Hz) and heart rate (HR) from infants at 5 and 10 months of age during baseline and performance on the looking A-not-B task of infant working memory (WM). Longitudinal baseline-to-task comparisons revealed WM-related increases in EEG power (all electrodes) and EEG coherence (medial frontal-occipital electrode pairs) at both ages. WM-related decreases in HR were only present at 5 months, and WM-related increases in EEG coherence became more localized by 10 months. Regression analyses revealed that baseline-to-task changes in psychophysiology accounted for variability in WM performance at 10, but not 5, months. HR and EEG power (medial frontal and lateral frontal electrodes) were unique predictors of variability in 10-month WM performance. These findings are discussed in relation to frontal lobe development, and represent the first comprehensive longitudinal analysis of age-related changes in the behavioral and psychophysiological correlates of WM. PMID:22148943

Spectral electroencephalogram in liver cirrhosis with minimal hepatic encephalopathy before and after lactulose therapy.

PubMed

Singh, Jatinderpal; Sharma, Barjesh Chander; Maharshi, Sudhir; Puri, Vinod; Srivastava, Siddharth

2016-06-01

Minimal hepatic encephalopathy (MHE) represents the mildest form of hepatic encephalopathy. Spectral electroencephalogram (sEEG) analysis improves the recognition of MHE by decreasing inter-operator variability and providing quantitative parameters of brain dysfunction. We compared sEEG in patients with cirrhosis with and without MHE and the effects of lactulose on sEEG in patients with MHE. One hundred patients with cirrhosis (50 with and 50 without MHE) were enrolled. Diagnosis of MHE was based on psychometric hepatic encephalopathy score (PHES) of ≤ -5. Critical flicker frequency, model of end-stage liver disease score, and sEEG were performed at baseline in all patients. The spectral variables considered were the mean dominant frequency (MDF) and relative power in beta, alpha, theta, and delta bands. Patients with MHE were given 3 months of lactulose, and all parameters were repeated. Spectral electroencephalogram analysis showed lower MDF (7.8 ± 1.7 vs 8.7 ± 1.3 Hz, P < 0.05) and higher theta relative power (34.29 ± 4.8 vs 24 ± 6.7%, P = 001) while lower alpha relative power (28.6 ± 4.0 vs 33.5 ± 5.3%, P = .001) in patients with MHE than in patients without MHE. With theta relative power, sensitivity 96%, specificity 84%, and accuracy of 90% were obtained for diagnosis of MHE. After lactulose treatment, MHE improved in 21 patients, and significant changes were seen in MDF (7.8 ± 0.5 vs 8.5 ± 0.6), theta (34.2 ± 4.8 vs 23.3 ± 4.1%), alpha (28.6 ± 4.0 vs 35.5 ± 4.5%), and delta relative power (13.7 ± 3.5 vs 17.0 ± 3.3%) after treatment (P ≤ 0.05). Spectral EEG is a useful objective and quantitative tool for diagnosis and to assess the response to treatment in patients with cirrhosis with MHE. © 2015 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

Age-Related Differences in Sleep Architecture and Electroencephalogram in Adolescents in the National Consortium on Alcohol and Neurodevelopment in Adolescence Sample

PubMed Central

Baker, Fiona C.; Willoughby, Adrian R.; de Zambotti, Massimiliano; Franzen, Peter L.; Prouty, Devin; Javitz, Harold; Hasler, Brant; Clark, Duncan B.; Colrain, Ian M.

2016-01-01

Study Objectives: To investigate age-related differences in polysomnographic and sleep electroencephalographic (EEG) measures, considering sex, pubertal stage, ethnicity, and scalp topography in a large group of adolescents in the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA). Methods: Following an adaptation/clinical screening night, 141 healthy adolescents (12–21 y, 64 girls) had polysomnographic recordings, from which sleep staging and EEG measures were derived. The setting was the SRI International Human Sleep Laboratory and University of Pittsburgh Pediatric Sleep Laboratory. Results: Older age was associated with a lower percentage of N3 sleep, accompanied by higher percentages of N2, N1, and rapid eye movement (REM) sleep. Older boys compared with younger boys had more frequent awakenings and wakefulness after sleep onset, effects that were absent in girls. Delta (0.3–4 Hz) EEG power in nonrapid eye movement NREM sleep was lower in older than younger adolescents at all electrode sites, with steeper slopes of decline over the occipital scalp. EEG power in higher frequency bands was also lower in older adolescents than younger adolescents, with equal effects across electrodes. Percent delta power in the first NREM period was similar across age. African Americans had lower EEG power across frequency bands (delta to sigma) compared with Caucasians. Finally, replacing age with pubertal status in the models showed similar relationships. Conclusions: Substantial differences in sleep architecture and EEG were evident across adolescence in this large group, with sex modifying some relationships. Establishment and follow-up of this cohort allows the investigation of sleep EEG-brain structural relationships and the effect of behaviors, such as alcohol and substance use, on sleep EEG maturation. Citation: Baker FC, Willoughby AR, de Zambotti M, Franzen PL, Prouty D, Javitz H, Hasler B, Clark DB, Colrain IM. Age-related differences in sleep architecture and electroencephalogram in adolescents in the national consortium on alcohol and neurodevelopment in adolescence sample. SLEEP 2016;39(7):1429–1439. PMID:27253763

Biotelemetry system for ambulatory patients

NASA Technical Reports Server (NTRS)

Fryer, T. B.

1978-01-01

Compact transmitter for multichannel telemetry of medical data is carried in patient's belt. Pulse-code modulation (PCM), is used for high-quality signal, and low-power CMOS integrated circuits make miniaturization possible. Transmitter is useful for electro-encephalograms (EEG) and electro-cardiograms (EKG) and other biomedical patient-monitoring situations.

Modelling Peri-Perceptual Brain Processes in a Deep Learning Spiking Neural Network Architecture.

PubMed

Gholami Doborjeh, Zohreh; Kasabov, Nikola; Gholami Doborjeh, Maryam; Sumich, Alexander

2018-06-11

Familiarity of marketing stimuli may affect consumer behaviour at a peri-perceptual processing level. The current study introduces a method for deep learning of electroencephalogram (EEG) data using a spiking neural network (SNN) approach that reveals the complexity of peri-perceptual processes of familiarity. The method is applied to data from 20 participants viewing familiar and unfamiliar logos. The results support the potential of SNN models as novel tools in the exploration of peri-perceptual mechanisms that respond differentially to familiar and unfamiliar stimuli. Specifically, the activation pattern of the time-locked response identified by the proposed SNN model at approximately 200 milliseconds post-stimulus suggests greater connectivity and more widespread dynamic spatio-temporal patterns for familiar than unfamiliar logos. The proposed SNN approach can be applied to study other peri-perceptual or perceptual brain processes in cognitive and computational neuroscience.

Analysis of EEG Related Saccadic Eye Movement

NASA Astrophysics Data System (ADS)

Funase, Arao; Kuno, Yoshiaki; Okuma, Shigeru; Yagi, Tohru

Our final goal is to establish the model for saccadic eye movement that connects the saccade and the electroencephalogram(EEG). As the first step toward this goal, we recorded and analyzed the saccade-related EEG. In the study recorded in this paper, we tried detecting a certain EEG that is peculiar to the eye movement. In these experiments, each subject was instructed to point their eyes toward visual targets (LEDs) or the direction of the sound sources (buzzers). In the control cases, the EEG was recorded in the case of no eye movemens. As results, in the visual experiments, we found that the potential of EEG changed sharply on the occipital lobe just before eye movement. Furthermore, in the case of the auditory experiments, similar results were observed. In the case of the visual experiments and auditory experiments without eye movement, we could not observed the EEG changed sharply. Moreover, when the subject moved his/her eyes toward a right-side target, a change in EEG potential was found on the right occipital lobe. On the contrary, when the subject moved his/her eyes toward a left-side target, a sharp change in EEG potential was found on the left occipital lobe.

Miniaturized, on-head, invasive electrode connector integrated EEG data acquisition system.

PubMed

Ives, John R; Mirsattari, Seyed M; Jones, D

2007-07-01

Intracranial electroencephalogram (EEG) monitoring involves recording multi-contact electrodes. The current systems require separate wires from each recording contact to the data acquisition unit resulting in many connectors and cables. To overcome limitations of such systems such as noise, restrictions in patient mobility and compliance, we developed a miniaturized EEG monitoring system with the amplifiers and multiplexers integrated into the electrode connectors and mounted on the head. Small, surface-mounted instrumentation amplifiers, coupled with 8:1 analog multiplexers, were assembled into 8-channel modular units to connect to 16:1 analog multiplexer manifold to create a small (55 cm(3)) head-mounted 128-channel system. A 6-conductor, 30 m long cable was used to transmit the EEG signals from the patient to the remote data acquisition system. Miniaturized EEG amplifiers and analog multiplexers were integrated directly into the electrode connectors. Up to 128-channels of EEG were amplified and analog multiplexed directly on the patient's head. The amplified EEG data were obtained over one long wire. A miniaturized system of invasive EEG recording has the potential to reduce artefact, simplify trouble-shooting, lower nursing care and increase patient compliance. Miniaturization technology improves intracranial EEG monitoring and leads to >128-channel capacity.

Translating the Science of Alertness and Performance from Laboratory to Field: Using State-of-the-Art Monitoring Imaging and Performance Enhancement Technologies to Improve the Alertness and Safety of the Military and Civilian Workforce

DTIC Science & Technology

2008-06-01

imaging (fMRI) environments, b) custom 32 channel electrode caps for use in fMRI environmentsnew EEG/ EOG signal analysts software, c) ambulatory...personnel 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: REPORT b. ABSTRACT u c. THIS PAGE U 17. LIMITATION OF ABSTRACT SAR 18. NUMBER...digital electroencephalogram (EEG) and electrooculogram ( EOG ) recording systems for ambulatory use as well as for use in functional magnet-resonance

Frontotemporal Functional Connectivity and Executive Functions Contribute to Episodic Memory Performance

PubMed Central

Blankenship, Tashauna L.; O'Neill, Meagan; Deater-Deckard, Kirby; Diana, Rachel A.; Bell, Martha Ann

2016-01-01

The contributions of hemispheric-specific electrophysiology (electroencephalogram or EEG) and independent executive functions (inhibitory control, working memory, cognitive flexibility) to episodic memory performance were examined using abstract paintings. Right hemisphere frontotemporal functional connectivity during encoding and retrieval, measured via EEG alpha coherence, statistically predicted performance on recency but not recognition judgments for the abstract paintings. Theta coherence, however, did not predict performance. Likewise, cognitive flexibility statistically predicted performance on recency judgments, but not recognition. These findings suggest that recognition and recency operate via separate electrophysiological and executive mechanisms. PMID:27388478

[Syncope, transient ischemic attacks, transient global amnesia and migraine].

PubMed

Hartl, E

2017-10-01

Epileptic seizures can manifest with a variety of clinical symptoms. Depending on the dominant symptom, several differential diagnoses have to be considered. Their differentiation can be challenging, especially after the first episode. The review article summarizes the most common differential diagnoses as well as their characteristics compared to epileptic seizures, aiming at providing guidelines for their clinical differentiation. Whenever a clear diagnosis is not possible based on the patient history and clinical signs, diagnostic evaluation with, e. g. an electroencephalogram (EEG) and finally EEG video monitoring can be helpful.

Efficacy of brain-computer interface-driven neuromuscular electrical stimulation for chronic paresis after stroke.

PubMed

Mukaino, Masahiko; Ono, Takashi; Shindo, Keiichiro; Fujiwara, Toshiyuki; Ota, Tetsuo; Kimura, Akio; Liu, Meigen; Ushiba, Junichi

2014-04-01

Brain computer interface technology is of great interest to researchers as a potential therapeutic measure for people with severe neurological disorders. The aim of this study was to examine the efficacy of brain computer interface, by comparing conventional neuromuscular electrical stimulation and brain computer interface-driven neuromuscular electrical stimulation, using an A-B-A-B withdrawal single-subject design. A 38-year-old male with severe hemiplegia due to a putaminal haemorrhage participated in this study. The design involved 2 epochs. In epoch A, the patient attempted to open his fingers during the application of neuromuscular electrical stimulation, irrespective of his actual brain activity. In epoch B, neuromuscular electrical stimulation was applied only when a significant motor-related cortical potential was observed in the electroencephalogram. The subject initially showed diffuse functional magnetic resonance imaging activation and small electro-encephalogram responses while attempting finger movement. Epoch A was associated with few neurological or clinical signs of improvement. Epoch B, with a brain computer interface, was associated with marked lateralization of electroencephalogram (EEG) and blood oxygenation level dependent responses. Voluntary electromyogram (EMG) activity, with significant EEG-EMG coherence, was also prompted. Clinical improvement in upper-extremity function and muscle tone was observed. These results indicate that self-directed training with a brain computer interface may induce activity- dependent cortical plasticity and promote functional recovery. This preliminary clinical investigation encourages further research using a controlled design.

The Neuronal Transition Probability (NTP) Model for the Dynamic Progression of Non-REM Sleep EEG: The Role of the Suprachiasmatic Nucleus

PubMed Central

Merica, Helli; Fortune, Ronald D.

2011-01-01

Little attention has gone into linking to its neuronal substrates the dynamic structure of non-rapid-eye-movement (NREM) sleep, defined as the pattern of time-course power in all frequency bands across an entire episode. Using the spectral power time-courses in the sleep electroencephalogram (EEG), we showed in the typical first episode, several moves towards-and-away from deep sleep, each having an identical pattern linking the major frequency bands beta, sigma and delta. The neuronal transition probability model (NTP) – in fitting the data well – successfully explained the pattern as resulting from stochastic transitions of the firing-rates of the thalamically-projecting brainstem-activating neurons, alternating between two steady dynamic-states (towards-and-away from deep sleep) each initiated by a so-far unidentified flip-flop. The aims here are to identify this flip-flop and to demonstrate that the model fits well all NREM episodes, not just the first. Using published data on suprachiasmatic nucleus (SCN) activity we show that the SCN has the information required to provide a threshold-triggered flip-flop for timing the towards-and-away alternations, information provided by sleep-relevant feedback to the SCN. NTP then determines the pattern of spectral power within each dynamic-state. NTP was fitted to individual NREM episodes 1–4, using data from 30 healthy subjects aged 20–30 years, and the quality of fit for each NREM measured. We show that the model fits well all NREM episodes and the best-fit probability-set is found to be effectively the same in fitting all subject data. The significant model-data agreement, the constant probability parameter and the proposed role of the SCN add considerable strength to the model. With it we link for the first time findings at cellular level and detailed time-course data at EEG level, to give a coherent picture of NREM dynamics over the entire night and over hierarchic brain levels all the way from the SCN to the EEG. PMID:21886801

Emotion recognition from multichannel EEG signals using K-nearest neighbor classification.

PubMed

Li, Mi; Xu, Hongpei; Liu, Xingwang; Lu, Shengfu

2018-04-27

Many studies have been done on the emotion recognition based on multi-channel electroencephalogram (EEG) signals. This paper explores the influence of the emotion recognition accuracy of EEG signals in different frequency bands and different number of channels. We classified the emotional states in the valence and arousal dimensions using different combinations of EEG channels. Firstly, DEAP default preprocessed data were normalized. Next, EEG signals were divided into four frequency bands using discrete wavelet transform, and entropy and energy were calculated as features of K-nearest neighbor Classifier. The classification accuracies of the 10, 14, 18 and 32 EEG channels based on the Gamma frequency band were 89.54%, 92.28%, 93.72% and 95.70% in the valence dimension and 89.81%, 92.24%, 93.69% and 95.69% in the arousal dimension. As the number of channels increases, the classification accuracy of emotional states also increases, the classification accuracy of the gamma frequency band is greater than that of the beta frequency band followed by the alpha and theta frequency bands. This paper provided better frequency bands and channels reference for emotion recognition based on EEG.

An EEG (electroencephalogram) recording system with carbon wire electrodes for simultaneous EEG-fMRI (functional magnetic resonance imaging) recording

PubMed Central

Negishi, Michiro; Abildgaard, Mark; Laufer, Ilan; Nixon, Terry; Constable, Robert Todd

2008-01-01

Simultaneous EEG-fMRI (Electroencephalography-functional Magnetic Resonance Imaging) recording provides a means for acquiring high temporal resolution electrophysiological data and high spatial resolution metabolic data of the brain in the same experimental runs. Carbon wire electrodes (not metallic EEG electrodes with carbon wire leads) are suitable for simultaneous EEG-fMRI recording, because they cause less RF (radio-frequency) heating and susceptibility artifacts than metallic electrodes. These characteristics are especially desirable for recording the EEG in high field MRI scanners. Carbon wire electrodes are also comfortable to wear during long recording sessions. However, carbon electrodes have high electrode-electrolyte potentials compared to widely used Ag/AgCl (silver/silver-chloride) electrodes, which may cause slow voltage drifts. This paper introduces a prototype EEG recording system with carbon wire electrodes and a circuit that suppresses the slow voltage drift. The system was tested for the voltage drift, RF heating, susceptibility artifact, and impedance, and was also evaluated in a simultaneous ERP (event-related potential)-fMRI experiment. PMID:18588913

Automatic seizure detection based on the combination of newborn multi-channel EEG and HRV information

NASA Astrophysics Data System (ADS)

Mesbah, Mostefa; Balakrishnan, Malarvili; Colditz, Paul B.; Boashash, Boualem

2012-12-01

This article proposes a new method for newborn seizure detection that uses information extracted from both multi-channel electroencephalogram (EEG) and a single channel electrocardiogram (ECG). The aim of the study is to assess whether additional information extracted from ECG can improve the performance of seizure detectors based solely on EEG. Two different approaches were used to combine this extracted information. The first approach, known as feature fusion, involves combining features extracted from EEG and heart rate variability (HRV) into a single feature vector prior to feeding it to a classifier. The second approach, called classifier or decision fusion, is achieved by combining the independent decisions of the EEG and the HRV-based classifiers. Tested on recordings obtained from eight newborns with identified EEG seizures, the proposed neonatal seizure detection algorithms achieved 95.20% sensitivity and 88.60% specificity for the feature fusion case and 95.20% sensitivity and 94.30% specificity for the classifier fusion case. These results are considerably better than those involving classifiers using EEG only (80.90%, 86.50%) or HRV only (85.70%, 84.60%).

Speech Presentation Cues Moderate Frontal EEG Asymmetry in Socially Withdrawn Young Adults

PubMed Central

Cole, Claire; Zapp, Daniel J.; Nelson, S. Katherine; Pérez-Edgar, Koraly

2011-01-01

Socially withdrawn individuals display solitary behavior across wide contexts with both unfamiliar and familiar peers. This tendency to withdraw may be driven by either past or anticipated negative social encounters. In addition, socially withdrawn individuals often exhibit right frontal electroencephalogram (EEG) asymmetry at baseline and when under stress. In the current study we examined shifts in frontal EEG activity in young adults (N=41) at baseline, as they viewed either an anxiety-provoking or a benign speech video, and as they subsequently prepared for their own speech. Results indicated that right frontal EEG activity increased, relative to the left, only for socially withdrawn participants exposed to the anxious video. These results suggest that contextual affective cues may prime an individual’s response to stress, particularly if they illustrate or substantiate an anticipated negative event. PMID:22169714

n-Iterative Exponential Forgetting Factor for EEG Signals Parameter Estimation

PubMed Central

Palma Orozco, Rosaura

2018-01-01

Electroencephalograms (EEG) signals are of interest because of their relationship with physiological activities, allowing a description of motion, speaking, or thinking. Important research has been developed to take advantage of EEG using classification or predictor algorithms based on parameters that help to describe the signal behavior. Thus, great importance should be taken to feature extraction which is complicated for the Parameter Estimation (PE)–System Identification (SI) process. When based on an average approximation, nonstationary characteristics are presented. For PE the comparison of three forms of iterative-recursive uses of the Exponential Forgetting Factor (EFF) combined with a linear function to identify a synthetic stochastic signal is presented. The one with best results seen through the functional error is applied to approximate an EEG signal for a simple classification example, showing the effectiveness of our proposal. PMID:29568310

The Use of Electrophysiology in the Study of Early Development

ERIC Educational Resources Information Center

Szucs, Denes

2005-01-01

Electrophysiology is a timely and important tool in the study of early cognitive development. This commentary polishes the definition of event-related potential (ERP) components; often interpreted as expressions of mental processes. Further, attention is drawn to time-frequency analysis of the electroencephalogram (EEG) which conveys much more…

Alpha Asymmetry in Infants at Risk for Autism Spectrum Disorders

ERIC Educational Resources Information Center

Gabard-Durnam, Laurel; Tierney, Adrienne L.; Vogel-Farley, Vanessa; Tager-Flusberg, Helen; Nelson, Charles A.

2015-01-01

An emerging focus of research on autism spectrum disorder (ASD) targets the identification of early-developing ASD endophenotypes using infant siblings of affected children. One potential neural endophenotype is resting frontal electroencephalogram (EEG) alpha asymmetry, a metric of hemispheric organization. Here, we examined the development of…

Musical Cognition at Birth: A Qualitative Study

ERIC Educational Resources Information Center

Hefer, Michal; Weintraub, Zalman; Cohen, Veronika

2009-01-01

This paper describes research on newborns' responses to music. Video observation and electroencephalogram (EEG) were collected to see whether newborns' responses to random sounds differed from their responses to music. The data collected were subjected to both qualitative and quantitative analysis. This paper will focus on the qualitative study,…

Amplitude-integrated EEG colored according to spectral edge frequency.

PubMed

Kobayashi, Katsuhiro; Mimaki, Nobuyoshi; Endoh, Fumika; Inoue, Takushi; Yoshinaga, Harumi; Ohtsuka, Yoko

2011-10-01

To improve the interpretability of figures containing an amplitude-integrated electroencephalogram (aEEG), we devised a color scale that allows us to incorporate spectral edge frequency (SEF) information into aEEG figures. Preliminary clinical assessment of this novel technique, which we call aEEG/SEF, was performed using neonatal and early infantile seizure data. We created aEEG, color density spectral array (DSA), and aEEG/SEF figures for focal seizures recorded in seven infants. Each seizure was paired with an interictal period from the same patient. After receiving instructions on how to interpret the figures, eight test reviewers examined each of the 72 figures displaying compressed data in aEEG, DSA, or aEEG/SEF form (12 seizures and 12 corresponding interictal periods) and attempted to identify each as a seizure or otherwise. They were not provided with any information regarding the original record. The median number of correctly identified seizures, out of a total of 12, was 7 (58.3%) for aEEG figures, 8 (66.7%) for DSA figures and 10 (83.3%) for aEEG/SEF figures; the differences among these are statistically significant (p=0.011). All reviewers concluded that aEEG/SEF figures were the easiest to interpret. The aEEG/SEF data presentation technique is a valid option in aEEG recordings of seizures. Copyright © 2011 Elsevier B.V. All rights reserved.

Effects of noxious stimuli on the electroencephalogram of anaesthetised chickens (Gallus gallus domesticus).

PubMed

McIlhone, Amanda E; Beausoleil, Ngaio J; Kells, Nikki J; Mellor, David J; Johnson, Craig B

2018-01-01

The reliable assessment and management of avian pain is important in the context of animal welfare. Overtly expressed signs of pain vary substantially between and within species, strains and individuals, limiting the use of behaviour in pain studies. Similarly, physiological indices of pain can also vary and may be confounded by influence from non-painful stimuli. In mammals, changes in the frequency spectrum of the electroencephalogram (EEG) recorded under light anaesthesia (the minimal anaesthesia model; MAM) have been shown to reliably indicate cerebral responses to noxious stimuli in a range of species. The aim of the current study was to determine whether the MAM can be applied to the study of nociception in birds. Ten chickens were lightly anaesthetised with halothane and their EEG recorded using surface electrodes during the application of supramaximal mechanical, thermal and electrical noxious stimuli. Spectral analysis revealed no EEG responses to any of these stimuli. Given that birds possess the neural apparatus to detect and process pain, and that the applied noxious stimuli elicit behavioural signs of pain in conscious chickens, this lack of response probably relates to methodological limitations. Anatomical differences between the avian and mammalian brains, along with a paucity of knowledge regarding specific sites of pain processing in the avian brain, could mean that EEG recorded from the head surface is insensitive to changes in neural activity in the pain processing regions of the avian brain. Future investigations should examine alternative electrode placement sites, based on avian homologues of the mammalian brain regions involved in pain processing.

Effects of noxious stimuli on the electroencephalogram of anaesthetised chickens (Gallus gallus domesticus)

PubMed Central

McIlhone, Amanda E.; Beausoleil, Ngaio J.; Mellor, David J.; Johnson, Craig B.

2018-01-01

The reliable assessment and management of avian pain is important in the context of animal welfare. Overtly expressed signs of pain vary substantially between and within species, strains and individuals, limiting the use of behaviour in pain studies. Similarly, physiological indices of pain can also vary and may be confounded by influence from non-painful stimuli. In mammals, changes in the frequency spectrum of the electroencephalogram (EEG) recorded under light anaesthesia (the minimal anaesthesia model; MAM) have been shown to reliably indicate cerebral responses to noxious stimuli in a range of species. The aim of the current study was to determine whether the MAM can be applied to the study of nociception in birds. Ten chickens were lightly anaesthetised with halothane and their EEG recorded using surface electrodes during the application of supramaximal mechanical, thermal and electrical noxious stimuli. Spectral analysis revealed no EEG responses to any of these stimuli. Given that birds possess the neural apparatus to detect and process pain, and that the applied noxious stimuli elicit behavioural signs of pain in conscious chickens, this lack of response probably relates to methodological limitations. Anatomical differences between the avian and mammalian brains, along with a paucity of knowledge regarding specific sites of pain processing in the avian brain, could mean that EEG recorded from the head surface is insensitive to changes in neural activity in the pain processing regions of the avian brain. Future investigations should examine alternative electrode placement sites, based on avian homologues of the mammalian brain regions involved in pain processing. PMID:29698446

Electroencephalogram spindle activity during dexmedetomidine sedation and physiological sleep.

PubMed

Huupponen, E; Maksimow, A; Lapinlampi, P; Särkelä, M; Saastamoinen, A; Snapir, A; Scheinin, H; Scheinin, M; Meriläinen, P; Himanen, S-L; Jääskeläinen, S

2008-02-01

Dexmedetomidine, a selective alpha(2)-adrenoceptor agonist, induces a unique, sleep-like state of sedation. The objective of the present work was to study human electroencephalogram (EEG) sleep spindles during dexmedetomidine sedation and compare them with spindles during normal physiological sleep, to test the hypothesis that dexmedetomidine exerts its effects via normal sleep-promoting pathways. EEG was continuously recorded from a bipolar frontopolar-laterofrontal derivation with Entropy Module (GE Healthcare) during light and deep dexmedetomidine sedation (target-controlled infusions set at 0.5 and 3.2 ng/ml) in 11 healthy subjects, and during physiological sleep in 10 healthy control subjects. Sleep spindles were visually scored and quantitatively analyzed for density, duration, amplitude (band-pass filtering) and frequency content (matching pursuit approach), and compared between the two groups. In visual analysis, EEG activity during dexmedetomidine sedation was similar to physiological stage 2 (S2) sleep with slight to moderate amount of slow-wave activity and abundant sleep spindle activity. In quantitative EEG analyses, sleep spindles were similar during dexmedetomidine sedation and normal sleep. No statistically significant differences were found in spindle density, amplitude or frequency content, but the spindles during dexmedetomidine sedation had longer duration (mean 1.11 s, SD 0.14 s) than spindles in normal sleep (mean 0.88 s, SD 0.14 s; P=0.0014). Analysis of sleep spindles shows that dexmedetomidine produces a state closely resembling physiological S2 sleep in humans, which gives further support to earlier experimental evidence for activation of normal non-rapid eye movement sleep-promoting pathways by this sedative agent.

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.

A preliminary study of muscular artifact cancellation in single-channel EEG.

PubMed

Chen, Xun; Liu, Aiping; Peng, Hu; Ward, Rabab K

2014-10-01

Electroencephalogram (EEG) recordings are often contaminated with muscular artifacts that strongly obscure the EEG signals and complicates their analysis. For the conventional case, where the EEG recordings are obtained simultaneously over many EEG channels, there exists a considerable range of methods for removing muscular artifacts. In recent years, there has been an increasing trend to use EEG information in ambulatory healthcare and related physiological signal monitoring systems. For practical reasons, a single EEG channel system must be used in these situations. Unfortunately, there exist few studies for muscular artifact cancellation in single-channel EEG recordings. To address this issue, in this preliminary study, we propose a simple, yet effective, method to achieve the muscular artifact cancellation for the single-channel EEG case. This method is a combination of the ensemble empirical mode decomposition (EEMD) and the joint blind source separation (JBSS) techniques. We also conduct a study that compares and investigates all possible single-channel solutions and demonstrate the performance of these methods using numerical simulations and real-life applications. The proposed method is shown to significantly outperform all other methods. It can successfully remove muscular artifacts without altering the underlying EEG activity. It is thus a promising tool for use in ambulatory healthcare systems.

Effect of bilateral eye movements on frontal interhemispheric gamma EEG coherence: implications for EMDR therapy.

PubMed

Propper, Ruth E; Pierce, Jenna; Geisler, Mark W; Christman, Stephen D; Bellorado, Nathan

2007-09-01

The use of bilateral eye movements (EMs) is an important component of Eye Movement Desensitization and Reprocessing (EMDR) therapy for posttraumatic stress disorder. The neural mechanisms underlying EMDR remain unclear. However, prior behavioral work looking at the effects of bilateral EMs on the retrieval of episodic memories suggests that the EMs enhance interhemispheric interaction. The present study examined the effects of the EMs used in EMDR on interhemispheric electroencephalogram coherence. Relative to noneye-movement controls, engaging in bilateral EMs led to decreased interhemispheric gamma electroencephalogram coherence. Implications for future work on EMDR and episodic memory are discussed.

Automated EEG-based screening of depression using deep convolutional neural network.

PubMed

Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adeli, Hojjat; Subha, D P

2018-07-01

In recent years, advanced neurocomputing and machine learning techniques have been used for Electroencephalogram (EEG)-based diagnosis of various neurological disorders. In this paper, a novel computer model is presented for EEG-based screening of depression using a deep neural network machine learning approach, known as Convolutional Neural Network (CNN). The proposed technique does not require a semi-manually-selected set of features to be fed into a classifier for classification. It learns automatically and adaptively from the input EEG signals to differentiate EEGs obtained from depressive and normal subjects. The model was tested using EEGs obtained from 15 normal and 15 depressed patients. The algorithm attained accuracies of 93.5% and 96.0% using EEG signals from the left and right hemisphere, respectively. It was discovered in this research that the EEG signals from the right hemisphere are more distinctive in depression than those from the left hemisphere. This discovery is consistent with recent research and revelation that the depression is associated with a hyperactive right hemisphere. An exciting extension of this research would be diagnosis of different stages and severity of depression and development of a Depression Severity Index (DSI). Copyright © 2018 Elsevier B.V. All rights reserved.

An Inflatable and Wearable Wireless System for Making 32-Channel Electroencephalogram Measurements.

PubMed

Yu, Yi-Hsin; Lu, Shao-Wei; Chuang, Chun-Hsiang; King, Jung-Tai; Chang, Che-Lun; Chen, Shi-An; Chen, Sheng-Fu; Lin, Chin-Teng

2016-07-01

Potable electroencephalography (EEG) devices have become critical for important research. They have various applications, such as in brain-computer interfaces (BCI). Numerous recent investigations have focused on the development of dry sensors, but few concern the simultaneous attachment of high-density dry sensors to different regions of the scalp to receive qualified EEG signals from hairy sites. An inflatable and wearable wireless 32-channel EEG device was designed, prototyped, and experimentally validated for making EEG signal measurements; it incorporates spring-loaded dry sensors and a novel gasbag design to solve the problem of interference by hair. The cap is ventilated and incorporates a circuit board and battery with a high-tolerance wireless (Bluetooth) protocol and low power consumption characteristics. The proposed system provides a 500/250 Hz sampling rate, and 24 bit EEG data to meet the BCI system data requirement. Experimental results prove that the proposed EEG system is effective in measuring audio event-related potential, measuring visual event-related potential, and rapid serial visual presentation. Results of this work demonstrate that the proposed EEG cap system performs well in making EEG measurements and is feasible for practical applications.

Aspects of Complexity in Sleep Analysis

NASA Astrophysics Data System (ADS)

Leitão, José M. N.; Da Rosa, Agostinho C.

The paper presents a selection of sleep analysis problems where some aspects and concepts of complexity come about. Emphasis is given to the electroencephalogram (EEG) as the most important sleep related variable. The conception of the EEG as a message to be deciphered stresses the importance of the communication and information theories in this field. An optimal detector of K complexes and vertex sharp waves based on a stochastic model of sleep EEG is considered. Besides detecting, the algorithm is also able to follow the evolution of the basic ongoing activity. It is shown that both the ostructure and microstructure of sleep can be described in terms of symbols and interpreted as sentences of a language. Syntactic models and Markov chain representations play in this context an important role.

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.

Effects of isoflurane, sevoflurane and methoxyflurane on the electroencephalogram of the chicken.

PubMed

McIlhone, Amanda E; Beausoleil, Ngaio J; Johnson, Craig B; Mellor, David J

2014-11-01

Anaesthetics have differing effects on mammalian electroencephalogram (EEG) but little is known about the effects on avian EEG. This study explored how inhalant anaesthetics affect chicken EEG. Experimental study. Twelve female Hyline Brown chickens aged 6-11 weeks. Each chicken was anaesthetized with isoflurane, sevoflurane, and methoxyflurane. For each, anaesthesia was adjusted to 1, 1.5 and 2 times Minimum Anaesthetic Concentration (MAC). Total Power (Ptot), Median Frequency (F50), Spectral Edge Frequency (F95) and Burst Suppression Ratio (BSR) were calculated at each volume concentration. BSR data were analyzed using doubly repeated measures anova. Neither isoflurane nor sevoflurane could be included in analysis of F50, F95 and Ptot because of extensive burst suppression; Methoxyflurane data were analyzed using RM anova. There was a significant interaction between anaesthetic and concentration on BSR [F(4,22) = 10.65, p < 0.0001]. For both isoflurane and sevoflurane, BSR increased with concentration. Isoflurane caused less suppression than sevoflurane at 1.5 MAC and at final 1 MAC while methoxyflurane caused virtually no burst suppression. Methoxyflurane concentration had a significant effect on F50 [F(2,20) = 3.83, p = 0.04], F95 [F(2,20) = 4.03, p = 0.03] and Ptot [F(2,20) = 5.22, p = 0.02]. Decreasing methoxyflurane from 2 to 1 MAC increased F50 and F95. Ptot increased when concentration decreased from 1.5 to 1 MAC and tended to be higher at 1 MAC than at 2 MAC. Isoflurane and sevoflurane suppressed chicken EEG in a dose-dependent manner. Higher concentrations of methoxyflurane caused an increasing degree of synchronization of EEG. Isoflurane and sevoflurane suppressed EEG activity to a greater extent than did methoxyflurane at equivalent MAC multiples. Isoflurane caused less suppression than sevoflurane at intermediate concentrations. These results indicate the similarity between avian and mammalian EEG responses to inhalant anaesthetics and reinforce the difference between MAC and anaesthetic effects on brain activity in birds. © 2014 Association of Veterinary Anaesthetists and the American College of Veterinary Anesthesia and Analgesia.

A brief history of typical absence seizures - Petit mal revisited.

PubMed

Brigo, Francesco; Trinka, Eugen; Lattanzi, Simona; Bragazzi, Nicola Luigi; Nardone, Raffaele; Martini, Mariano

2018-03-01

In this article, we have traced back the history of typical absence seizures, from their initial clinical description to the more recent nosological position. The first description of absence seizures was made by Poupart in 1705 and Tissot in 1770. In 1824, Calmeil introduced the term "absences", and in 1838, Esquirol for the first time used the term petit mal. Reynolds instead used the term "epilepsia mitior" (milder epilepsy) and provided a comprehensive description of absence seizures (1861). In 1854, Delasiauve ranked absences as the seizure type with lower severity and introduced the concept of idiopathic epilepsy. Otto Binswanger (1899) discussed the role of cortex in the pathophysiology of "abortive seizures", whereas William Gowers (1901) emphasized the importance of a detailed clinical history to identify nonmotor seizures or very mild motor phenomena which otherwise may go unnoticed or considered not epileptic. At the beginning of the 20th Century, the term pyknolepsy was introduced, but initially was not universally considered as a type of epilepsy; it was definitely recognized as an epileptic entity only in 1945, based on electroencephalogram (EEG) recordings. Hans Berger, the inventor of the EEG, made also the first EEG recording of an atypical absence (his results were published only in 1933), whereas the characteristic EEG pattern was reported by neurophysiologists of the Harvard Medical School in 1935. The discovery of EEG made it also possible to differentiate absence seizures from so called "psychomotor" seizures occurring in temporal lobe epilepsy. Penfield and Jasper (1938) considered absences as expression of "centrencephalic epilepsy". Typical absences seizures are now classified by the International League Against Epilepsy among generalized nonmotor (absence) seizures. Copyright © 2018 Elsevier Inc. 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

Monitoring and diagnosis of Alzheimer's disease using noninvasive compressive sensing EEG

NASA Astrophysics Data System (ADS)

Morabito, F. C.; Labate, D.; Morabito, G.; Palamara, I.; Szu, H.

2013-05-01

The majority of elderly with Alzheimer's Disease (AD) receive care at home from caregivers. In contrast to standard tethered clinical settings, a wireless, real-time, body-area smartphone-based remote monitoring of electroencephalogram (EEG) can be extremely advantageous for home care of those patients. Such wearable tools pave the way to personalized medicine, for example giving the opportunity to control the progression of the disease and the effect of drugs. By applying Compressive Sensing (CS) techniques it is in principle possible to overcome the difficulty raised by smartphones spatial-temporal throughput rate bottleneck. Unfortunately, EEG and other physiological signals are often non-sparse. In this paper, it is instead shown that the EEG of AD patients becomes actually more compressible with the progression of the disease. EEG of Mild Cognitive Impaired (MCI) subjects is also showing clear tendency to enhanced compressibility. This feature favor the use of CS techniques and ultimately the use of telemonitoring with wearable sensors.

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.

A Discriminative Approach to EEG Seizure Detection

PubMed Central

Johnson, Ashley N.; Sow, Daby; Biem, Alain

2011-01-01

Seizures are abnormal sudden discharges in the brain with signatures represented in electroencephalograms (EEG). The efficacy of the application of speech processing techniques to discriminate between seizure and non-seizure states in EEGs is reported. The approach accounts for the challenges of unbalanced datasets (seizure and non-seizure), while also showing a system capable of real-time seizure detection. The Minimum Classification Error (MCE) algorithm, which is a discriminative learning algorithm with wide-use in speech processing, is applied and compared with conventional classification techniques that have already been applied to the discrimination between seizure and non-seizure states in the literature. The system is evaluated on 22 pediatric patients multi-channel EEG recordings. Experimental results show that the application of speech processing techniques and MCE compare favorably with conventional classification techniques in terms of classification performance, while requiring less computational overhead. The results strongly suggests the possibility of deploying the designed system at the bedside. PMID:22195192

Technical Tips: Performing EEGs and Polysomnograms on Children with Neurodevelopmental Disabilities

PubMed Central

Paasch, Valerie; Hoosier, Teresa M.; Accardo, Jennifer; Ewen, Joshua B.; Slifer, Keith J.

2013-01-01

Electroencephalograms (EEGs) and polysomnograms (PSGs) are critical and frequently ordered tests in the care of children with neurodevelopmental disabilities (NDD). Performing studies with this population can be very intimidating, given that the referral reasons and seizure types can be unique, and children with NDD may have any combination of behavioral or sensory challenges that can make it difficult to successfully complete a study. This article presents a variety of strategies that can be used to overcome these challenges through good preparation, patience, caregiver involvement, effective behavioral management techniques, and education about the medical aspects of EEG/PSG in NDD. This Technical Tips article features ideas and experiences from an EEG/PSG technologist, two board-certified child neurologists (one who is further certified in Clinical Neurophysiology, while the other is further certified in Sleep Medicine), and two behaviorally trained pediatric psychologists. PMID:23301283

Classification of EEG signals using a genetic-based machine learning classifier.

PubMed

Skinner, B T; Nguyen, H T; Liu, D K

2007-01-01

This paper investigates the efficacy of the genetic-based learning classifier system XCS, for the classification of noisy, artefact-inclusive human electroencephalogram (EEG) signals represented using large condition strings (108bits). EEG signals from three participants were recorded while they performed four mental tasks designed to elicit hemispheric responses. Autoregressive (AR) models and Fast Fourier Transform (FFT) methods were used to form feature vectors with which mental tasks can be discriminated. XCS achieved a maximum classification accuracy of 99.3% and a best average of 88.9%. The relative classification performance of XCS was then compared against four non-evolutionary classifier systems originating from different learning techniques. The experimental results will be used as part of our larger research effort investigating the feasibility of using EEG signals as an interface to allow paralysed persons to control a powered wheelchair or other devices.

Wearable ear EEG for brain interfacing

NASA Astrophysics Data System (ADS)

Schroeder, Eric D.; Walker, Nicholas; Danko, Amanda S.

2017-02-01

Brain-computer interfaces (BCIs) measuring electrical activity via electroencephalogram (EEG) have evolved beyond clinical applications to become wireless consumer products. Typically marketed for meditation and neu- rotherapy, these devices are limited in scope and currently too obtrusive to be a ubiquitous wearable. Stemming from recent advancements made in hearing aid technology, wearables have been shrinking to the point that the necessary sensors, circuitry, and batteries can be fit into a small in-ear wearable device. In this work, an ear-EEG device is created with a novel system for artifact removal and signal interpretation. The small, compact, cost-effective, and discreet device is demonstrated against existing consumer electronics in this space for its signal quality, comfort, and usability. A custom mobile application is developed to process raw EEG from each device and display interpreted data to the user. Artifact removal and signal classification is accomplished via a combination of support matrix machines (SMMs) and soft thresholding of relevant statistical properties.

EEG-guided meditation: A personalized approach.

PubMed

Fingelkurts, Andrew A; Fingelkurts, Alexander A; Kallio-Tamminen, Tarja

2015-12-01

The therapeutic potential of meditation for physical and mental well-being is well documented, however the possibility of adverse effects warrants further discussion of the suitability of any particular meditation practice for every given participant. This concern highlights the need for a personalized approach in the meditation practice adjusted for a concrete individual. This can be done by using an objective screening procedure that detects the weak and strong cognitive skills in brain function, thus helping design a tailored meditation training protocol. Quantitative electroencephalogram (qEEG) is a suitable tool that allows identification of individual neurophysiological types. Using qEEG screening can aid developing a meditation training program that maximizes results and minimizes risk of potential negative effects. This brief theoretical-conceptual review provides a discussion of the problem and presents some illustrative results on the usage of qEEG screening for the guidance of mediation personalization. Copyright © 2015 Elsevier Ltd. All rights reserved.

Spectral F-test power evaluation in the EEG during intermittent photic stimulation.

PubMed

de Sá, Antonio Mauricio F L Miranda; Cagy, Mauricio; Lazarev, Vladimir V; Infantosi, Antonio Fernando C

2006-06-01

Intermittent photic stimulation (IPS) is an important functional test, which can induce the photic driving in the electroencephalogram (EEG). It is capable of enhancing latent oscillations manifestations not present in the resting EEG. However, for adequate quantitative evaluation of the photic driving, these changes should be assessed on a statistical basis. With this aim, the sampling distribution of spectral F test was investigated. On this basis, confidence limits of the SFT-estimate could be obtained for different practical situations, in which the signal-to-noise ratio and the number of epochs used in the estimation may vary. The technique was applied to the EEG of 10 normal subjects during IPS, and allowed detecting responses not only at the fundamental IPS frequency but also at higher harmonics. It also permitted to assess the strength of the photic driving responses and to compare them in different derivations and in different subjects.

A Proposed Multisite Double-Blind Randomized Clinical Trial of Neurofeedback for ADHD: Need, Rationale, and Strategy

ERIC Educational Resources Information Center

Kerson, Cynthia

2013-01-01

Objective: Additional treatments with persisting benefit are needed for ADHD. Because ADHD often shows excessive theta electroencephalogram (EEG) power, low beta, and excessive theta-beta ratio (TBR), a promising treatment is neurofeedback (NF) downtraining TBR. Although several nonblind randomized clinical trials (RCTs) show a medium-large…

Neurophysiologic Analysis of the Effects of Interactive Tailored Health Videos on Attention to Health Messages

ERIC Educational Resources Information Center

Lee, Jung A.

2011-01-01

Web-based tailored approaches hold much promise as effective means for delivering health education and improving public health. This study examines the effects of interactive tailored health videos on attention to health messages using neurophysiological changes measured by Electroencephalogram (EEG) and Electrocardiogram (EKG). Sixty-eight…

Multiscale Entropy of Electroencephalogram as a Potential Predictor for the Prognosis of Neonatal Seizures.

PubMed

Lu, Wen-Yu; Chen, Jyun-Yu; Chang, Chi-Feng; Weng, Wen-Chin; Lee, Wang-Tso; Shieh, Jiann-Shing

2015-01-01

Increasing animal studies supported the harmful effects of prolonged or frequent neonatal seizures in developing brain, including increased risk of later epilepsy. Various nonlinear analytic measures had been applied to investigate the change of brain complexity with age. This study focuses on clarifying the relationship between later epilepsy and the changes of electroencephalogram (EEG) complexity in neonatal seizures. EEG signals from 19 channels of the whole brain from 32 neonates below 2 months old were acquired. The neonates were classified into 3 groups: 9 were normal controls, 9 were neonatal seizures without later epilepsy, and 14 were neonatal seizures with later epilepsy. Sample entropy (SamEn), multiscale entropy (MSE) and complexity index (CI) were analyzed. Although there was no significant change in SamEn, the CI values showed significantly decreased over Channels C3, C4, and Cz in patients with neonatal seizures and later epilepsy compared with control group. More multifocal epileptiform discharges in EEG, more abnormal neuroimaging findings, and higher incidence of future developmental delay were noted in the group with later epilepsy. Decreased MSE and CI values in patients with neonatal seizures and later epilepsy may reflect the mixed effects of acute insults, underlying brain immaturity, and prolonged seizures-related injuries. The analysis of MSE and CI can therefore provide a quantifiable and accurate way to decrypt the mystery of neonatal seizures, and could be a promising predictor.

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.

Electroencephalogram complexity analysis in children with attention-deficit/hyperactivity disorder during a visual cognitive task.

PubMed

Zarafshan, Hadi; Khaleghi, Ali; Mohammadi, Mohammad Reza; Moeini, Mahdi; Malmir, Nastaran

2016-01-01

The aim of this study was to investigate electroencephalogram (EEG) dynamics using complexity analysis in children with attention-deficit/hyperactivity disorder (ADHD) compared with healthy control children when performing a cognitive task. Thirty 7-12-year-old children meeting Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition (DSM-5) criteria for ADHD and 30 healthy control children underwent an EEG evaluation during a cognitive task, and Lempel-Ziv complexity (LZC) values were computed. There were no significant differences between ADHD and control groups on age and gender. The mean LZC of the ADHD children was significantly larger than healthy children over the right anterior and right posterior regions during the cognitive performance. In the ADHD group, complexity of the right hemisphere was higher than that of the left hemisphere, but the complexity of the left hemisphere was higher than that of the right hemisphere in the normal group. Although fronto-striatal dysfunction is considered conclusive evidence for the pathophysiology of ADHD, our arithmetic mental task has provided evidence of structural and functional changes in the posterior regions and probably cerebellum in ADHD.

Changes in the somatosensory evoked potentials and spontaneous electroencephalogram of hens during stunning in argon-induced anoxia.

PubMed

Raj, A B; Gregory, N G; Wotton, S B

1991-01-01

This study examined the time to loss of consciousness in hens during stunning in argon-induced anoxia. Somatosensory evoked potentials (SEPs) and the spontaneous electroencephalogram (EEG) were recorded in 12 culled hens prior to and during stunning in less than 2% oxygen (air displaced by argon). An additional 20 hens were stunned with a similar concentration of oxygen and the time to loss of posture, eye closure, and the onset and duration of clonic and tonic convulsions were recorded. A further 10 hens were immersed in less than 2% oxygen for 15-17 s and their response to comb pinching was tested as soon as they had been transferred to atmospheric air. It is concluded that the birds had not lost the primary response in their SEPs by the time they started convulsing, but the reduction in the amplitude of the SEPs, changes in their spontaneous EEG and a negative response to comb pinch before the start of the convulsions indicated that the birds were unconscious when they convulsed.

EEG and ocular correlates of circadian melatonin phase and human performance decrements during sleep loss

NASA Technical Reports Server (NTRS)

Cajochen, C.; Khalsa, S. B.; Wyatt, J. K.; Czeisler, C. A.; Dijk, D. J.

1999-01-01

The aim of this study was to quantify the associations between slow eye movements (SEMs), eye blink rate, waking electroencephalogram (EEG) power density, neurobehavioral performance, and the circadian rhythm of plasma melatonin in a cohort of 10 healthy men during up to 32 h of sustained wakefulness. The time course of neurobehavioral performance was characterized by fairly stable levels throughout the first 16 h of wakefulness followed by deterioration during the phase of melatonin secretion. This deterioration was closely associated with an increase in SEMs. Frontal low-frequency EEG activity (1-7 Hz) exhibited a prominent increase with time awake and little circadian modulation. EEG alpha activity exhibited circadian modulation. The dynamics of SEMs and EEG activity were phase locked to changes in neurobehavioral performance and lagged the plasma melatonin rhythm. The data indicate that frontal areas of the brain are more susceptible to sleep loss than occipital areas. Frontal EEG activity and ocular parameters may be used to monitor and predict changes in neurobehavioral performance associated with sleep loss and circadian misalignment.

Hybrid EEG-EOG brain-computer interface system for practical machine control.

PubMed

Punsawad, Yunyong; Wongsawat, Yodchanan; Parnichkun, Manukid

2010-01-01

Practical issues such as accuracy with various subjects, number of sensors, and time for training are important problems of existing brain-computer interface (BCI) systems. In this paper, we propose a hybrid framework for the BCI system that can make machine control more practical. The electrooculogram (EOG) is employed to control the machine in the left and right directions while the electroencephalogram (EEG) is employed to control the forword, no action, and complete stop motions of the machine. By using only 2-channel biosignals, the average classification accuracy of more than 95% can be achieved.

The Effect of Electroencephalogram (EEG) Reference Choice on Information-Theoretic Measures of the Complexity and Integration of EEG Signals

PubMed Central

Trujillo, Logan T.; Stanfield, Candice T.; Vela, Ruben D.

2017-01-01

Converging evidence suggests that human cognition and behavior emerge from functional brain networks interacting on local and global scales. We investigated two information-theoretic measures of functional brain segregation and integration—interaction complexity CI(X), and integration I(X)—as applied to electroencephalographic (EEG) signals and how these measures are affected by choice of EEG reference. CI(X) is a statistical measure of the system entropy accounted for by interactions among its elements, whereas I(X) indexes the overall deviation from statistical independence of the individual elements of a system. We recorded 72 channels of scalp EEG from human participants who sat in a wakeful resting state (interleaved counterbalanced eyes-open and eyes-closed blocks). CI(X) and I(X) of the EEG signals were computed using four different EEG references: linked-mastoids (LM) reference, average (AVG) reference, a Laplacian (LAP) “reference-free” transformation, and an infinity (INF) reference estimated via the Reference Electrode Standardization Technique (REST). Fourier-based power spectral density (PSD), a standard measure of resting state activity, was computed for comparison and as a check of data integrity and quality. We also performed dipole source modeling in order to assess the accuracy of neural source CI(X) and I(X) estimates obtained from scalp-level EEG signals. CI(X) was largest for the LAP transformation, smallest for the LM reference, and at intermediate values for the AVG and INF references. I(X) was smallest for the LAP transformation, largest for the LM reference, and at intermediate values for the AVG and INF references. Furthermore, across all references, CI(X) and I(X) reliably distinguished between resting-state conditions (larger values for eyes-open vs. eyes-closed). These findings occurred in the context of the overall expected pattern of resting state PSD. Dipole modeling showed that simulated scalp EEG-level CI(X) and I(X) reflected changes in underlying neural source dependencies, but only for higher levels of integration and with highest accuracy for the LAP transformation. Our observations suggest that the Laplacian-transformation should be preferred for the computation of scalp-level CI(X) and I(X) due to its positive impact on EEG signal quality and statistics, reduction of volume-conduction, and the higher accuracy this provides when estimating scalp-level EEG complexity and integration. PMID:28790884

Electrical source localization by LORETA in patients with epilepsy: Confirmation by postoperative MRI

PubMed Central

Akdeniz, Gülsüm

2016-01-01

Background: Few studies have been conducted that have compared electrical source localization (ESL) results obtained by analyzing ictal patterns in scalp electroencephalogram (EEG) with the brain areas that are found to be responsible for seizures using other brain imaging techniques. Additionally, adequate studies have not been performed to confirm the accuracy of ESL methods. Materials and Methods: In this study, ESL was conducted using LORETA (Low Resolution Brain Electromagnetic Tomography) in 9 patients with lesions apparent on magnetic resonance imaging (MRI) and in 6 patients who did not exhibit lesions on their MRIs. EEGs of patients who underwent surgery for epilepsy and had follow-ups for at least 1 year after operations were analyzed for ictal spike, rhythmic, paroxysmal fast, and obscured EEG activities. Epileptogenic zones identified in postoperative MRIs were then compared with localizations obtained by LORETA model we employed. Results: We found that brain areas determined via ESL were in concordance with resected brain areas for 13 of the 15 patients evaluated, and those 13 patients were post-operatively determined as being seizure-free. Conclusion: ESL, which is a noninvasive technique, may contribute to the correct delineation of epileptogenic zones in patients who will eventually undergo surgery to treat epilepsy, (regardless of neuroimaging status). Moreover, ESL may aid in deciding on the number and localization of intracranial electrodes to be used in patients who are candidates for invasive recording. PMID:27011626

Electrical source localization by LORETA in patients with epilepsy: Confirmation by postoperative MRI.

PubMed

Akdeniz, Gülsüm

2016-01-01

Few studies have been conducted that have compared electrical source localization (ESL) results obtained by analyzing ictal patterns in scalp electroencephalogram (EEG) with the brain areas that are found to be responsible for seizures using other brain imaging techniques. Additionally, adequate studies have not been performed to confirm the accuracy of ESL methods. In this study, ESL was conducted using LORETA (Low Resolution Brain Electromagnetic Tomography) in 9 patients with lesions apparent on magnetic resonance imaging (MRI) and in 6 patients who did not exhibit lesions on their MRIs. EEGs of patients who underwent surgery for epilepsy and had follow-ups for at least 1 year after operations were analyzed for ictal spike, rhythmic, paroxysmal fast, and obscured EEG activities. Epileptogenic zones identified in postoperative MRIs were then compared with localizations obtained by LORETA model we employed. We found that brain areas determined via ESL were in concordance with resected brain areas for 13 of the 15 patients evaluated, and those 13 patients were post-operatively determined as being seizure-free. ESL, which is a noninvasive technique, may contribute to the correct delineation of epileptogenic zones in patients who will eventually undergo surgery to treat epilepsy, (regardless of neuroimaging status). Moreover, ESL may aid in deciding on the number and localization of intracranial electrodes to be used in patients who are candidates for invasive recording.

EEG artifact elimination by extraction of ICA-component features using image processing algorithms.

PubMed

Radüntz, T; Scouten, J; Hochmuth, O; Meffert, B

2015-03-30

Artifact rejection is a central issue when dealing with electroencephalogram recordings. Although independent component analysis (ICA) separates data in linearly independent components (IC), the classification of these components as artifact or EEG signal still requires visual inspection by experts. In this paper, we achieve automated artifact elimination using linear discriminant analysis (LDA) for classification of feature vectors extracted from ICA components via image processing algorithms. We compare the performance of this automated classifier to visual classification by experts and identify range filtering as a feature extraction method with great potential for automated IC artifact recognition (accuracy rate 88%). We obtain almost the same level of recognition performance for geometric features and local binary pattern (LBP) features. Compared to the existing automated solutions the proposed method has two main advantages: First, it does not depend on direct recording of artifact signals, which then, e.g. have to be subtracted from the contaminated EEG. Second, it is not limited to a specific number or type of artifact. In summary, the present method is an automatic, reliable, real-time capable and practical tool that reduces the time intensive manual selection of ICs for artifact removal. The results are very promising despite the relatively small channel resolution of 25 electrodes. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Dual Adaptive Filtering by Optimal Projection Applied to Filter Muscle Artifacts on EEG and Comparative Study

PubMed Central

Peyrodie, Laurent; Szurhaj, William; Bolo, Nicolas; Pinti, Antonio; Gallois, Philippe

2014-01-01

Muscle artifacts constitute one of the major problems in electroencephalogram (EEG) examinations, particularly for the diagnosis of epilepsy, where pathological rhythms occur within the same frequency bands as those of artifacts. This paper proposes to use the method dual adaptive filtering by optimal projection (DAFOP) to automatically remove artifacts while preserving true cerebral signals. DAFOP is a two-step method. The first step consists in applying the common spatial pattern (CSP) method to two frequency windows to identify the slowest components which will be considered as cerebral sources. The two frequency windows are defined by optimizing convolutional filters. The second step consists in using a regression method to reconstruct the signal independently within various frequency windows. This method was evaluated by two neurologists on a selection of 114 pages with muscle artifacts, from 20 clinical recordings of awake and sleeping adults, subject to pathological signals and epileptic seizures. A blind comparison was then conducted with the canonical correlation analysis (CCA) method and conventional low-pass filtering at 30 Hz. The filtering rate was 84.3% for muscle artifacts with a 6.4% reduction of cerebral signals even for the fastest waves. DAFOP was found to be significantly more efficient than CCA and 30 Hz filters. The DAFOP method is fast and automatic and can be easily used in clinical EEG recordings. PMID:25298967

Non-invasive monitoring of spreading depression.

PubMed

Bastany, Zoya J R; Askari, Shahbaz; Dumont, Guy A; Speckmann, Erwin-Josef; Gorji, Ali

2016-10-01

Spreading depression (SD), a slow propagating depolarization wave, plays an important role in pathophysiology of different neurological disorders. Yet, research into SD-related disorders has been hampered by the lack of non-invasive recording techniques of SD. Here we compared the manifestations of SD in continuous non-invasive electroencephalogram (EEG) recordings to invasive electrocorticographic (ECoG) recordings in order to obtain further insights into generator structures and electrogenic mechanisms of surface recording of SD. SD was induced by KCl application and simultaneous SD recordings were performed by scalp EEG as well as ECoG electrodes of somatosensory neocortex of rats using a novel homemade EEG amplifier, AgCl recording electrodes, and high chloride conductive gel. Different methods were used to analyze the data; including the spectrogram, bi-spectrogram, pattern distribution, relative spectrum power, and multivariable Gaussian fit analysis. The negative direct current (DC) shifts recorded by scalp electrodes exhibited a high homogeneity to those recorded by ECoG electrodes. Furthermore, this novel method of recording and analysis was able to separate SD recorded by scalp electrodes from non-neuronal DC shifts induced by other potential generators, such as the skin, muscles, arteries, dura, etc. These data suggest a novel application for continuous non-invasive monitoring of DC potential changes, such as SD. Non-invasive monitoring of SD would allow early intervention and improve outcome in SD-related neurological disorders. Copyright © 2016 IBRO. All rights reserved.

Mating Signals Indicating Sexual Receptiveness Induce Unique Spatio-Temporal EEG Theta Patterns in an Anuran Species

PubMed Central

Fang, Guangzhan; Yang, Ping; Cui, Jianguo; Yao, Dezhong; Brauth, Steven E.; Tang, Yezhong

2012-01-01

Female mate choice is of importance for individual fitness as well as a determining factor in genetic diversity and speciation. Nevertheless relatively little is known about how females process information acquired from males during mate selection. In the Emei music frog, Babina daunchina, males normally call from hidden burrows and females in the reproductive stage prefer male calls produced from inside burrows compared with ones from outside burrows. The present study evaluated changes in electroencephalogram (EEG) power output in four frequency bands induced by male courtship vocalizations on both sides of the telencephalon and mesencephalon in females. The results show that (1) both the values of left hemispheric theta relative power and global lateralization in the theta band are modulated by the sexual attractiveness of the acoustic stimulus in the reproductive stage, suggesting the theta oscillation is closely correlated with processing information associated with mate choice; (2) mean relative power in the beta band is significantly greater in the mesencephalon than the left telencephalon, regardless of reproductive status or the biological significance of signals, indicating it is associated with processing acoustic features and (3) relative power in the delta and alpha bands are not affected by reproductive status or acoustic stimuli. The results imply that EEG power in the theta and beta bands reflect different information processing mechanisms related to vocal recognition and auditory perception in anurans. PMID:23285010

Scale-free dynamics of the synchronization between sleep EEG power bands and the high frequency component of heart rate variability in normal men and patients with sleep apnea-hypopnea syndrome.

PubMed

Dumont, Martine; Jurysta, Fabrice; Lanquart, Jean-Pol; Noseda, André; van de Borne, Philippe; Linkowski, Paul

2007-12-01

To investigate the dynamics of the synchronization between heart rate variability and sleep electroencephalogram power spectra and the effect of sleep apnea-hypopnea syndrome. Heart rate and sleep electroencephalogram signals were recorded in controls and patients with sleep apnea-hypopnea syndrome that were matched for age, gender, sleep parameters, and blood pressure. Spectral analysis was applied to electrocardiogram and electroencephalogram sleep recordings to obtain power values every 20s. Synchronization likelihood was computed between time series of the normalized high frequency spectral component of RR-intervals and all electroencephalographic frequency bands. Detrended fluctuation analysis was applied to the synchronizations in order to qualify their dynamic behaviors. For all sleep bands, the fluctuations of the synchronization between sleep EEG and heart activity appear scale free and the scaling exponent is close to one as for 1/f noise. We could not detect any effect due to sleep apnea-hypopnea syndrome. The synchronizations between the high frequency component of heart rate variability and all sleep power bands exhibited robust fluctuations characterized by self-similar temporal behavior of 1/f noise type. No effects of sleep apnea-hypopnea syndrome were observed in these synchronizations. Sleep apnea-hypopnea syndrome does not affect the interdependence between the high frequency component of heart rate variability and all sleep power bands as measured by synchronization likelihood.

Stimulus type does not affect infant arousal response patterns.

PubMed

Richardson, Heidi L; Walker, Adrian M; Horne, Rosemary S C

2010-03-01

Previous studies have examined infant arousal responses to various arousal stimuli; however it is unclear whether the patterns of responses to different stimuli are comparable within subjects across early development. The aim of the study was to compare the effects of both respiratory and somatosensory stimulation on arousal processes in the same infants throughout the first 6 months of life. Ten healthy term infants were studied with daytime polysomnography at 2-4 weeks, 2-3 and 5-6 months. Infants were challenged with both hypoxia (15% O(2), balanced N(2)) and a pulsatile air-jet to the nostrils. Stimulus-induced sub-cortical activations (SCA) and cortical arousals (CA) were expressed as percentages of total arousals. Heart rate (HR) changes and electroencephalogram (EEG) desynchronization were also contrasted for the two stimuli. During active sleep (AS), there was no significant effect of stimulus type on proportions of CA at any of the ages studied. During quiet sleep (QS), hypoxia elicited higher CA proportions than the air-jet at 2-3 and 5-6 months (P < 0.01). Overall, HR responses associated with SCA and CA and the duration of EEG desynchronization during CA were similar for both stimuli. Mild hypoxia and nasal air-jet stimulation produce qualitatively similar patterns of arousal responses during the first 6 months of life, supporting the concept of a final common neural pathway of cortical activation. Quantitatively, full CA from QS is more likely with hypoxia, in keeping with it being a life-threatening stimulus. This study supports the nasal air-jet as an appropriate stimulus for assessing developmental patterns of infant arousal process.

Autoregressive model in the Lp norm space for EEG analysis.

PubMed

Li, Peiyang; Wang, Xurui; Li, Fali; Zhang, Rui; Ma, Teng; Peng, Yueheng; Lei, Xu; Tian, Yin; Guo, Daqing; Liu, Tiejun; Yao, Dezhong; Xu, Peng

2015-01-30

The autoregressive (AR) model is widely used in electroencephalogram (EEG) analyses such as waveform fitting, spectrum estimation, and system identification. In real applications, EEGs are inevitably contaminated with unexpected outlier artifacts, and this must be overcome. However, most of the current AR models are based on the L2 norm structure, which exaggerates the outlier effect due to the square property of the L2 norm. In this paper, a novel AR object function is constructed in the Lp (p≤1) norm space with the aim to compress the outlier effects on EEG analysis, and a fast iteration procedure is developed to solve this new AR model. The quantitative evaluation using simulated EEGs with outliers proves that the proposed Lp (p≤1) AR can estimate the AR parameters more robustly than the Yule-Walker, Burg and LS methods, under various simulated outlier conditions. The actual application to the resting EEG recording with ocular artifacts also demonstrates that Lp (p≤1) AR can effectively address the outliers and recover a resting EEG power spectrum that is more consistent with its physiological basis. Copyright © 2014 Elsevier B.V. All rights reserved.

Algorithm based on the short-term Rényi entropy and IF estimation for noisy EEG signals analysis.

PubMed

Lerga, Jonatan; Saulig, Nicoletta; Mozetič, Vladimir

2017-01-01

Stochastic electroencephalogram (EEG) signals are known to be nonstationary and often multicomponential. Detecting and extracting their components may help clinicians to localize brain neurological dysfunctionalities for patients with motor control disorders due to the fact that movement-related cortical activities are reflected in spectral EEG changes. A new algorithm for EEG signal components detection from its time-frequency distribution (TFD) has been proposed in this paper. The algorithm utilizes the modification of the Rényi entropy-based technique for number of components estimation, called short-term Rényi entropy (STRE), and upgraded by an iterative algorithm which was shown to enhance existing approaches. Combined with instantaneous frequency (IF) estimation, the proposed method was applied to EEG signal analysis both in noise-free and noisy environments for limb movements EEG signals, and was shown to be an efficient technique providing spectral description of brain activities at each electrode location up to moderate additive noise levels. Furthermore, the obtained information concerning the number of EEG signal components and their IFs show potentials to enhance diagnostics and treatment of neurological disorders for patients with motor control illnesses. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

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.

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

Artifact Removal from Biosignal using Fixed Point ICA Algorithm for Pre-processing in Biometric Recognition

NASA Astrophysics Data System (ADS)

Mishra, Puneet; Singla, Sunil Kumar

2013-01-01

In the modern world of automation, biological signals, especially Electroencephalogram (EEG) and Electrocardiogram (ECG), are gaining wide attention as a source of biometric information. Earlier studies have shown that EEG and ECG show versatility with individuals and every individual has distinct EEG and ECG spectrum. EEG (which can be recorded from the scalp due to the effect of millions of neurons) may contain noise signals such as eye blink, eye movement, muscular movement, line noise, etc. Similarly, ECG may contain artifact like line noise, tremor artifacts, baseline wandering, etc. These noise signals are required to be separated from the EEG and ECG signals to obtain the accurate results. This paper proposes a technique for the removal of eye blink artifact from EEG and ECG signal using fixed point or FastICA algorithm of Independent Component Analysis (ICA). For validation, FastICA algorithm has been applied to synthetic signal prepared by adding random noise to the Electrocardiogram (ECG) signal. FastICA algorithm separates the signal into two independent components, i.e. ECG pure and artifact signal. Similarly, the same algorithm has been applied to remove the artifacts (Electrooculogram or eye blink) from the EEG signal.

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.

Automated Identification of Abnormal Adult EEGs

PubMed Central

López, S.; Suarez, G.; Jungreis, D.; Obeid, I.; Picone, J.

2016-01-01

The interpretation of electroencephalograms (EEGs) is a process that is still dependent on the subjective analysis of the examiners. Though interrater agreement on critical events such as seizures is high, it is much lower on subtler events (e.g., when there are benign variants). The process used by an expert to interpret an EEG is quite subjective and hard to replicate by machine. The performance of machine learning technology is far from human performance. We have been developing an interpretation system, AutoEEG, with a goal of exceeding human performance on this task. In this work, we are focusing on one of the early decisions made in this process – whether an EEG is normal or abnormal. We explore two baseline classification algorithms: k-Nearest Neighbor (kNN) and Random Forest Ensemble Learning (RF). A subset of the TUH EEG Corpus was used to evaluate performance. Principal Components Analysis (PCA) was used to reduce the dimensionality of the data. kNN achieved a 41.8% detection error rate while RF achieved an error rate of 31.7%. These error rates are significantly lower than those obtained by random guessing based on priors (49.5%). The majority of the errors were related to misclassification of normal EEGs. PMID:27195311

Sleep Dysfunction and EEG Alterations in Mice Overexpressing Alpha-Synuclein

PubMed Central

McDowell, Kimberly A.; Shin, David; Roos, Kenneth P.; Chesselet, Marie-Françoise

2018-01-01

Background: Sleep disruptions occur early and frequently in Parkinson’s disease (PD). PD patients also show a slowing of resting state activity. Alpha-synuclein is causally linked to PD and accumulates in sleep-related brain regions. While sleep problems occur in over 75% of PD patients and severely impact the quality of life of patients and caregivers, their study is limited by a paucity of adequate animal models. Objective: The objective of this study was to determine whether overexpression of wildtype alpha-synuclein could lead to alterations in sleep patterns reminiscent of those observed in PD by measuring sleep/wake activity with rigorous quantitative methods in a well-characterized genetic mouse model. Methods: At 10 months of age, mice expressing human wildtype alpha-synuclein under the Thy-1 promoter (Thy1-aSyn) and wildtype littermates underwent the subcutaneous implantation of a telemetry device (Data Sciences International) for the recording of electromyograms (EMG) and electroencephalograms (EEG) in freely moving animals. Surgeries and data collection were performed without knowledge of mouse genotype. Results: Thy1-aSyn mice showed increased non-rapid eye movement sleep during their quiescent phase, increased active wake during their active phase, and decreased rapid eye movement sleep over a 24-h period, as well as a shift in the density of their EEG power spectra toward lower frequencies with a significant decrease in gamma power during wakefulness. Conclusions: Alpha-synuclein overexpression in mice produces sleep disruptions and altered oscillatory EEG activity reminiscent of PD, and this model provides a novel platform to assess mechanisms and therapeutic strategies for sleep dysfunction in PD. PMID:24867919

Fluctuations of the fractal dimension of the electroencephalogram during periodic breathing in heart failure patients.

PubMed

Maestri, Roberto; La Rovere, Maria Teresa; Robbi, Elena; Pinna, Gian Domenico

2010-06-01

The physiological mechanisms responsible for periodic breathing (PB) in heart failure (HF) patients are still debated. A role for rhythmic shifts in the level of wakefulness has been suggested, but their existence has never been proven. In this study we investigated the existence of an oscillation in EEG activity during PB in these patients and assessed its relationship with the ventilatory oscillation. EEG activity was measured by the fractal dimension (FD) and by a spectral technique (weighted mean frequency, WMF) in 17 stable HF patients (mean age +/- SD: 57+/-10 yrs, NYHA class: 2.6 +/- 0.4, LVEF: 24 +/- 6%), with sustained PB during supine rest. The relationship between minute ventilation (MV) signal and FD and WMF was assessed by coherence analysis. Most patients (10/17) showed a well defined oscillation in FD and WMF at the frequency of PB closely linked (coherence > 0.7) with the oscillation of MV. In the remaining patients, neither FD nor WMF showed a clear oscillatory pattern synchronous with MV. Overall, the two EEG-derived parameters showed the same coherence with the ventilatory oscillation (mean coherence +/- SD: 0.65 +/- 0.25 vs 0.66 +/- 0.23, for FD and WMF respectively, p = 0.44). Our results provide evidence that during PB in HF patients, EEG activity often, but not always, fluctuates synchronously with the ventilatory oscillation. These fluctuations can be effectively detected by the fractal dimension, but classical spectral methods provide substantially the same information. Other mechanisms, particularly chemical instability in the respiratory control system, are likely to play a role in the genesis of PB.

Filter Bank Regularized Common Spatial Pattern Ensemble for Small Sample Motor Imagery Classification.

PubMed

Park, Sang-Hoon; Lee, David; Lee, Sang-Goog

2018-02-01

For the last few years, many feature extraction methods have been proposed based on biological signals. Among these, the brain signals have the advantage that they can be obtained, even by people with peripheral nervous system damage. Motor imagery electroencephalograms (EEG) are inexpensive to measure, offer a high temporal resolution, and are intuitive. Therefore, these have received a significant amount of attention in various fields, including signal processing, cognitive science, and medicine. The common spatial pattern (CSP) algorithm is a useful method for feature extraction from motor imagery EEG. However, performance degradation occurs in a small-sample setting (SSS), because the CSP depends on sample-based covariance. Since the active frequency range is different for each subject, it is also inconvenient to set the frequency range to be different every time. In this paper, we propose the feature extraction method based on a filter bank to solve these problems. The proposed method consists of five steps. First, motor imagery EEG is divided by a using filter bank. Second, the regularized CSP (R-CSP) is applied to the divided EEG. Third, we select the features according to mutual information based on the individual feature algorithm. Fourth, parameter sets are selected for the ensemble. Finally, we classify using ensemble based on features. The brain-computer interface competition III data set IVa is used to evaluate the performance of the proposed method. The proposed method improves the mean classification accuracy by 12.34%, 11.57%, 9%, 4.95%, and 4.47% compared with CSP, SR-CSP, R-CSP, filter bank CSP (FBCSP), and SR-FBCSP. Compared with the filter bank R-CSP ( , ), which is a parameter selection version of the proposed method, the classification accuracy is improved by 3.49%. In particular, the proposed method shows a large improvement in performance in the SSS.

Electro-encephalographic disturbances due to chronic toxin abuse in young people, with special reference to glue-sniffing.

PubMed

Griesel, R D; Jansen, P; Richter, L M

1990-11-03

A study was carried out in order to document any abnormalities in the electro-encephalogram (EEG) that might appear in young adolescents who have deliberately inhaled the range of volatile substances loosely referred to as 'glue'. The EEGs of a group of 'street children' being assisted in a Johannesburg shelter were examined. The records were analysed for any clinical abnormalities and also subjected to spectral analysis in order to examine the overall characteristics of frequency, power and spatial distribution. The EEGs clearly revealed that, although at the time of the examination the subjects were ostensibly abstinent, both clinical and normative evidence of continuing brain disturbance was present. It was concluded that glue sniffing is likely to have long term electrocerebral sequelae.

Reduction of the Dimensionality of the EEG Channels during Scoliosis Correction Surgeries Using a Wavelet Decomposition Technique

PubMed Central

Al-Kadi, Mahmoud I.; Reaz, Mamun Bin Ibne; Ali, Mohd Alauddin Mohd; Liu, Chian Yong

2014-01-01

This paper presents a comparison between the electroencephalogram (EEG) channels during scoliosis correction surgeries. Surgeons use many hand tools and electronic devices that directly affect the EEG channels. These noises do not affect the EEG channels uniformly. This research provides a complete system to find the least affected channel by the noise. The presented system consists of five stages: filtering, wavelet decomposing (Level 4), processing the signal bands using four different criteria (mean, energy, entropy and standard deviation), finding the useful channel according to the criteria's value and, finally, generating a combinational signal from Channels 1 and 2. Experimentally, two channels of EEG data were recorded from six patients who underwent scoliosis correction surgeries in the Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM) (the Medical center of National University of Malaysia). The combinational signal was tested by power spectral density, cross-correlation function and wavelet coherence. The experimental results show that the system-outputted EEG signals are neatly switched without any substantial changes in the consistency of EEG components. This paper provides an efficient procedure for analyzing EEG signals in order to avoid averaging the channels that lead to redistribution of the noise on both channels, reducing the dimensionality of the EEG features and preparing the best EEG stream for the classification and monitoring stage. PMID:25051031

Incorporating an ERP Project into Undergraduate Instruction

PubMed Central

Nyhus, Erika; Curtis, Nancy

2016-01-01

Electroencephalogram (EEG) is a relatively non-invasive, simple technique, and recent advances in open source analysis tools make it feasible to implement EEG as a component in undergraduate neuroscience curriculum. We have successfully led students to design novel experiments, record EEG data, and analyze event-related potentials (ERPs) during a one-semester laboratory course for undergraduates in cognitive neuroscience. First, students learned how to set up an EEG recording and completed an analysis tutorial. Students then learned how to set up a novel EEG experiment; briefly, they formed groups of four and designed an EEG experiment on a topic of their choice. Over the course of two weeks students collected behavioral and EEG data. Each group then analyzed their behavioral and ERP data and presented their results both as a presentation and as a final paper. Upon completion of the group project students reported a deeper understanding of cognitive neuroscience methods and a greater appreciation for the strengths and weaknesses of the EEG technique. Although recent advances in open source software made this project possible, it also required access to EEG recording equipment and proprietary software. Future efforts should be directed at making publicly available datasets to learn ERP analysis techniques and making publicly available EEG recording and analysis software to increase the accessibility of hands-on research experience in undergraduate cognitive neuroscience laboratory courses. PMID:27385925

Early Standard Electroencephalogram Abnormalities Predict Mortality in Septic Intensive Care Unit Patients.

PubMed

Azabou, Eric; Magalhaes, Eric; Braconnier, Antoine; Yahiaoui, Lyria; Moneger, Guy; Heming, Nicholas; Annane, Djillali; Mantz, Jean; Chrétien, Fabrice; Durand, Marie-Christine; Lofaso, Frédéric; Porcher, Raphael; Sharshar, Tarek

2015-01-01

Sepsis is associated with increased mortality, delirium and long-term cognitive impairment in intensive care unit (ICU) patients. Electroencephalogram (EEG) abnormalities occurring at the acute stage of sepsis may correlate with severity of brain dysfunction. Predictive value of early standard EEG abnormalities for mortality in ICU septic patients remains to be assessed. In this prospective, single center, observational study, standard EEG was performed, analyzed and classified according to both Synek and Young EEG scales, in consecutive patients acutely admitted in ICU for sepsis. Delirium, coma and the level of sedation were assessed at the time of EEG recording; and duration of sedation, occurrence of in-ICU delirium or death were assessed during follow-up. Adjusted analyses were carried out using multiple logistic regression. One hundred ten patients were included, mean age 63.8 (±18.1) years, median SAPS-II score 38 (29-55). At the time of EEG recording, 46 patients (42%) were sedated and 22 (20%) suffered from delirium. Overall, 54 patients (49%) developed delirium, of which 32 (29%) in the days after EEG recording. 23 (21%) patients died in the ICU. Absence of EEG reactivity was observed in 27 patients (25%), periodic discharges (PDs) in 21 (19%) and electrographic seizures (ESZ) in 17 (15%). ICU mortality was independently associated with a delta-predominant background (OR: 3.36; 95% CI [1.08 to 10.4]), absence of EEG reactivity (OR: 4.44; 95% CI [1.37-14.3], PDs (OR: 3.24; 95% CI [1.03 to 10.2]), Synek grade ≥ 3 (OR: 5.35; 95% CI [1.66-17.2]) and Young grade > 1 (OR: 3.44; 95% CI [1.09-10.8]) after adjustment to Simplified Acute Physiology Score (SAPS-II) at admission and level of sedation. Delirium at the time of EEG was associated with ESZ in non-sedated patients (32% vs 10%, p = 0.037); with Synek grade ≥ 3 (36% vs 7%, p< 0.05) and Young grade > 1 (36% vs 17%, p< 0.001). Occurrence of delirium in the days after EEG was associated with a delta-predominant background (48% vs 15%, p = 0.001); absence of reactivity (39% vs 10%, p = 0.003), Synek grade ≥ 3 (42% vs 17%, p = 0.001) and Young grade >1 (58% vs 17%, p = 0.0001). In this prospective cohort of 110 septic ICU patients, early standard EEG was significantly disturbed. Absence of EEG reactivity, a delta-predominant background, PDs, Synek grade ≥ 3 and Young grade > 1 at day 1 to 3 following admission were independent predictors of ICU mortality and were associated with occurence of delirium. ESZ and PDs, found in about 20% of our patients. Their prevalence could have been higher, with a still higher predictive value, if they had been diagnosed more thoroughly using continuous EEG.

Early Standard Electroencephalogram Abnormalities Predict Mortality in Septic Intensive Care Unit Patients

PubMed Central

Azabou, Eric; Magalhaes, Eric; Braconnier, Antoine; Yahiaoui, Lyria; Moneger, Guy; Heming, Nicholas; Annane, Djillali; Mantz, Jean; Chrétien, Fabrice; Durand, Marie-Christine; Lofaso, Frédéric; Porcher, Raphael; Sharshar, Tarek

2015-01-01

Introduction Sepsis is associated with increased mortality, delirium and long-term cognitive impairment in intensive care unit (ICU) patients. Electroencephalogram (EEG) abnormalities occurring at the acute stage of sepsis may correlate with severity of brain dysfunction. Predictive value of early standard EEG abnormalities for mortality in ICU septic patients remains to be assessed. Methods In this prospective, single center, observational study, standard EEG was performed, analyzed and classified according to both Synek and Young EEG scales, in consecutive patients acutely admitted in ICU for sepsis. Delirium, coma and the level of sedation were assessed at the time of EEG recording; and duration of sedation, occurrence of in-ICU delirium or death were assessed during follow-up. Adjusted analyses were carried out using multiple logistic regression. Results One hundred ten patients were included, mean age 63.8 (±18.1) years, median SAPS-II score 38 (29–55). At the time of EEG recording, 46 patients (42%) were sedated and 22 (20%) suffered from delirium. Overall, 54 patients (49%) developed delirium, of which 32 (29%) in the days after EEG recording. 23 (21%) patients died in the ICU. Absence of EEG reactivity was observed in 27 patients (25%), periodic discharges (PDs) in 21 (19%) and electrographic seizures (ESZ) in 17 (15%). ICU mortality was independently associated with a delta-predominant background (OR: 3.36; 95% CI [1.08 to 10.4]), absence of EEG reactivity (OR: 4.44; 95% CI [1.37–14.3], PDs (OR: 3.24; 95% CI [1.03 to 10.2]), Synek grade ≥ 3 (OR: 5.35; 95% CI [1.66–17.2]) and Young grade > 1 (OR: 3.44; 95% CI [1.09–10.8]) after adjustment to Simplified Acute Physiology Score (SAPS-II) at admission and level of sedation. Delirium at the time of EEG was associated with ESZ in non-sedated patients (32% vs 10%, p = 0.037); with Synek grade ≥ 3 (36% vs 7%, p< 0.05) and Young grade > 1 (36% vs 17%, p< 0.001). Occurrence of delirium in the days after EEG was associated with a delta-predominant background (48% vs 15%, p = 0.001); absence of reactivity (39% vs 10%, p = 0.003), Synek grade ≥ 3 (42% vs 17%, p = 0.001) and Young grade >1 (58% vs 17%, p = 0.0001). Conclusions In this prospective cohort of 110 septic ICU patients, early standard EEG was significantly disturbed. Absence of EEG reactivity, a delta-predominant background, PDs, Synek grade ≥ 3 and Young grade > 1 at day 1 to 3 following admission were independent predictors of ICU mortality and were associated with occurence of delirium. ESZ and PDs, found in about 20% of our patients. Their prevalence could have been higher, with a still higher predictive value, if they had been diagnosed more thoroughly using continuous EEG. PMID:26447697

Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram.

PubMed

Truong, Nhan Duy; Nguyen, Anh Duy; Kuhlmann, Levin; Bonyadi, Mohammad Reza; Yang, Jiawei; Ippolito, Samuel; Kavehei, Omid

2018-05-07

Seizure prediction has attracted growing attention as one of the most challenging predictive data analysis efforts to improve the life of patients with drug-resistant epilepsy and tonic seizures. Many outstanding studies have reported great results in providing sensible indirect (warning systems) or direct (interactive neural stimulation) control over refractory seizures, some of which achieved high performance. However, to achieve high sensitivity and a low false prediction rate, many of these studies relied on handcraft feature extraction and/or tailored feature extraction, which is performed for each patient independently. This approach, however, is not generalizable, and requires significant modifications for each new patient within a new dataset. In this article, we apply convolutional neural networks to different intracranial and scalp electroencephalogram (EEG) datasets and propose a generalized retrospective and patient-specific seizure prediction method. We use the short-time Fourier transform on 30-s EEG windows to extract information in both the frequency domain and the time domain. The algorithm automatically generates optimized features for each patient to best classify preictal and interictal segments. The method can be applied to any other patient from any dataset without the need for manual feature extraction. The proposed approach achieves sensitivity of 81.4%, 81.2%, and 75% and a false prediction rate of 0.06/h, 0.16/h, and 0.21/h on the Freiburg Hospital intracranial EEG dataset, the Boston Children's Hospital-MIT scalp EEG dataset, and the American Epilepsy Society Seizure Prediction Challenge dataset, respectively. Our prediction method is also statistically better than an unspecific random predictor for most of the patients in all three datasets. Copyright © 2018 Elsevier Ltd. All rights reserved.

Differences in Seizure Expression Between Magnetic Seizure Therapy and Electroconvulsive Shock.

PubMed

Cycowicz, Yael M; Rowny, Stefan B; Luber, Bruce; Lisanby, Sarah H

2018-06-01

Evidence suggests that magnetic seizure therapy (MST) results in fewer side effects than electroconvulsive treatment, both in humans treated with electroconvulsive therapy (ECT) as well as in the animal preclinical model that uses electroconvulsive shock (ECS). Evidence suggests that MST results in fewer cognitive side effects than ECT. Although MST offers enhanced control over seizure induction and spread, little is known about how MST and ECT seizures differ. Seizure characteristics are associated with treatment effect. This study presents quantitative analyses of electroencephalogram (EEG) power after electrical and magnetic seizure induction and anesthesia-alone sham in an animal model. The aim was to test whether differential neurophysiological characteristics of the seizures could be identified that support earlier observations that the powers of theta, alpha, and beta but not delta frequency bands were lower after MST when compared with those after ECS. In a randomized, sham-controlled trial, 24 macaca mulatte received 6 weeks of daily sessions while scalp EEG was recorded. Electroencephalogram power was quantified within delta, theta, alpha, and beta frequency bands. Magnetic seizure therapy induced lower ictal expression in the theta, alpha and beta frequencies than ECS, but MST and ECS were indistinguishable in the delta band. Magnetic seizure therapy showed less postictal suppression than ECS. Increasing electrical dosage increased ictal power, whereas increasing MST dosage had no effect on EEG expression. Magnetic seizure therapy seizures have less robust electrophysiological expression than ECS, and these differences are largest in the alpha and beta bands. The relevance of these differences in higher frequency bands to clinical outcomes deserves further exploration. Contrasting EEG in ECS and MST may lead to insights on the physiological underpinnings of seizure-induced amnesia and to finding ways to reduce cognitive side effects.

Differential oscillatory electroencephalogram between attention-deficit/hyperactivity disorder subtypes and typically developing adolescents.

PubMed

Mazaheri, Ali; Fassbender, Catherine; Coffey-Corina, Sharon; Hartanto, Tadeus A; Schweitzer, Julie B; Mangun, George R

2014-09-01

A neurobiological-based classification of attention-deficit/hyperactivity disorder (ADHD) subtypes has thus far remained elusive. The aim of this study was to use oscillatory changes in the electroencephalogram (EEG) related to informative cue processing, motor preparation, and top-down control to investigate neurophysiological differences between typically developing (TD) adolescents, and those diagnosed with predominantly inattentive (IA) or combined (CB) (associated with symptoms of inattention as well as impulsivity/hyperactivity) subtypes of ADHD. The EEG was recorded from 57 rigorously screened adolescents (12 to 17 years of age; 23 TD, 17 IA, and 17 CB), while they performed a cued flanker task. We examined the oscillatory changes in theta (3-5 Hz), alpha (8-12 Hz), and beta (22-25 Hz) EEG bands after cues that informed participants with which hand they would subsequently be required to respond. Relative to TD adolescents, the IA group showed significantly less postcue alpha suppression, suggesting diminished processing of the cue in the visual cortex, whereas the CB group showed significantly less beta suppression at the electrode contralateral to the cued response hand, suggesting poor motor planning. Finally, both ADHD subtypes showed weak functional connectivity between frontal theta and posterior alpha, suggesting common top-down control impairment. We found both distinct and common task-related neurophysiological impairments in ADHD subtypes. Our results suggest that task-induced changes in EEG oscillations provide an objective measure, which in conjunction with other sources of information might help distinguish between ADHD subtypes and therefore aid in diagnoses and evaluation of treatment. Copyright © 2014 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Tracking the coupling of two electroencephalogram series in the isoflurane and remifentanil anesthesia.

PubMed

Liang, Zhenhu; Liang, Shujuan; Wang, Yinghua; Ouyang, Gaoxiang; Li, Xiaoli

2015-02-01

Coupling in multiple electroencephalogram (EEG) signals provides a perspective tool to understand the mechanism of brain communication. In this study, we propose a method based on permutation cross-mutual information (PCMI) to investigate whether or not the coupling between EEG series can be used to quantify the effect of specific anesthetic drugs (isoflurane and remifentanil) on brain activities. A Rössler-Lorenz system and surrogate analysis was first employed to compare histogram-based mutual information (HMI) and PCMI for estimating the coupling of two nonlinear systems. Then, the HMI and the PCMI indices of EEG recordings from two sides of the forehead of 12 patients undergoing combined remifentanil and isoflurane anesthesia were demonstrated for tracking the effect of drug on the coupling of brain activities. Performance of all indices was assessed by the correlation coefficients (Rij) and relative coefficient of variation (CV). The PCMI can track the coupling strength of two nonlinear systems, and it is sensitive to the phase change of the coupling systems. Compared to the HMI, the PCMI has a better correlation with the coupling strength in nonlinear systems. The PCMI could track the effect of anesthesia and distinguish the consciousness state from the unconsciousness state. Moreover, at the embedding dimension m=4 and lag τ=1, the PCMI had a better performance than HMI in tracking the effect of anesthesia drugs on brain activities. As a measure of coupling, the PCMI was able to reflect the state of consciousness from two EEG recordings. The PCMI is a promising new coupling measure for estimating the effect of isoflurane and remifentanil anesthetic drugs on the brain activity. Copyright © 2014 International Federation of Clinical Neurophysiology. All rights reserved.

The Emotion Recognition System Based on Autoregressive Model and Sequential Forward Feature Selection of Electroencephalogram Signals

PubMed Central

Hatamikia, Sepideh; Maghooli, Keivan; Nasrabadi, Ali Motie

2014-01-01

Electroencephalogram (EEG) is one of the useful biological signals to distinguish different brain diseases and mental states. In recent years, detecting different emotional states from biological signals has been merged more attention by researchers and several feature extraction methods and classifiers are suggested to recognize emotions from EEG signals. In this research, we introduce an emotion recognition system using autoregressive (AR) model, sequential forward feature selection (SFS) and K-nearest neighbor (KNN) classifier using EEG signals during emotional audio-visual inductions. The main purpose of this paper is to investigate the performance of AR features in the classification of emotional states. To achieve this goal, a distinguished AR method (Burg's method) based on Levinson-Durbin's recursive algorithm is used and AR coefficients are extracted as feature vectors. In the next step, two different feature selection methods based on SFS algorithm and Davies–Bouldin index are used in order to decrease the complexity of computing and redundancy of features; then, three different classifiers include KNN, quadratic discriminant analysis and linear discriminant analysis are used to discriminate two and three different classes of valence and arousal levels. The proposed method is evaluated with EEG signals of available database for emotion analysis using physiological signals, which are recorded from 32 participants during 40 1 min audio visual inductions. According to the results, AR features are efficient to recognize emotional states from EEG signals, and KNN performs better than two other classifiers in discriminating of both two and three valence/arousal classes. The results also show that SFS method improves accuracies by almost 10-15% as compared to Davies–Bouldin based feature selection. The best accuracies are %72.33 and %74.20 for two classes of valence and arousal and %61.10 and %65.16 for three classes, respectively. PMID:25298928

Characterization of the Theta to Beta Ratio in ADHD: Identifying Potential Sources of Heterogeneity

ERIC Educational Resources Information Center

Loo, Sandra K.; Cho, Alexander; Hale, T. Sigi; McGough, James; McCracken, James; Smalley, Susan L.

2013-01-01

Objective: The goal of this study is to characterize the theta to beta ratio (THBR) obtained from electroencephalogram (EEG) measures, in a large sample of community and clinical participants with regard to (a) ADHD diagnosis and subtypes, (b) common psychiatric comorbidities, and (c) cognitive correlates. Method: The sample includes 871…

Anger and Approach Motivation in Infancy: Relations to Early Childhood Inhibitory Control and Behavior Problems

ERIC Educational Resources Information Center

He, Jie; Degnan, Kathryn Amey; McDermott, Jennifer Martin; Henderson, Heather A.; Hane, Amie Ashley; Xu, Qinmei; Fox, Nathan A.

2010-01-01

The relations among infant anger reactivity, approach behavior, and frontal electroencephalogram (EEG) asymmetry, and their relations to inhibitory control and behavior problems in early childhood were examined within the context of a longitudinal study of temperament. Two hundred nine infants' anger expressions to arm restraint were observed at 4…

Neurologic Diseases - Multiple Languages

MedlinePlus

... Cantonese dialect) (繁體中文) French (français) Hindi (हिन्दी) Japanese (日本語) Korean (한국어) Russian (Русский) Somali (Af-Soomaali ) ... हिन्दी (Hindi) Bilingual PDF Health Information Translations Japanese (日本語) Expand Section EEG (Electroencephalogram) - 日本語 (Japanese) Bilingual ...

Learning in Balance: Using Oscillatory EEG Biomarkers of Attention, Motivation and Vigilance to Interpret Game-Based Learning

ERIC Educational Resources Information Center

Cowley, Benjamin; Ravaja, Niklas

2014-01-01

Motivated by the link between play and learning, proposed in literature to have a neurobiological basis, we study the electroencephalogram and associated psychophysiology of "learning game" players. Forty-five players were tested for topic comprehension by a questionnaire administered before and after solo playing of the game Peacemaker…

Exploring resting-state EEG complexity before migraine attacks.

PubMed

Cao, Zehong; Lai, Kuan-Lin; Lin, Chin-Teng; Chuang, Chun-Hsiang; Chou, Chien-Chen; Wang, Shuu-Jiun

2018-06-01

Objective Entropy-based approaches to understanding the temporal dynamics of complexity have revealed novel insights into various brain activities. Herein, electroencephalogram complexity before migraine attacks was examined using an inherent fuzzy entropy approach, allowing the development of an electroencephalogram-based classification model to recognize the difference between interictal and preictal phases. Methods Forty patients with migraine without aura and 40 age-matched normal control subjects were recruited, and the resting-state electroencephalogram signals of their prefrontal and occipital areas were prospectively collected. The migraine phases were defined based on the headache diary, and the preictal phase was defined as within 72 hours before a migraine attack. Results The electroencephalogram complexity of patients in the preictal phase, which resembled that of normal control subjects, was significantly higher than that of patients in the interictal phase in the prefrontal area (FDR-adjusted p < 0.05) but not in the occipital area. The measurement of test-retest reliability (n = 8) using the intra-class correlation coefficient was good with r1 = 0.73 ( p = 0.01). Furthermore, the classification model, support vector machine, showed the highest accuracy (76 ± 4%) for classifying interictal and preictal phases using the prefrontal electroencephalogram complexity. Conclusion Entropy-based analytical methods identified enhancement or "normalization" of frontal electroencephalogram complexity during the preictal phase compared with the interictal phase. This classification model, using this complexity feature, may have the potential to provide a preictal alert to migraine without aura patients.

Understanding perception of active noise control system through multichannel EEG analysis.

PubMed

Bagha, Sangeeta; Tripathy, R K; Nanda, Pranati; Preetam, C; Das, Debi Prasad

2018-06-01

In this Letter, a method is proposed to investigate the effect of noise with and without active noise control (ANC) on multichannel electroencephalogram (EEG) signal. The multichannel EEG signal is recorded during different listening conditions such as silent, music, noise, ANC with background noise and ANC with both background noise and music. The multiscale analysis of EEG signal of each channel is performed using the discrete wavelet transform. The multivariate multiscale matrices are formulated based on the sub-band signals of each EEG channel. The singular value decomposition is applied to the multivariate matrices of multichannel EEG at significant scales. The singular value features at significant scales and the extreme learning machine classifier with three different activation functions are used for classification of multichannel EEG signal. The experimental results demonstrate that, for ANC with noise and ANC with noise and music classes, the proposed method has sensitivity values of 75.831% ( p < 0.001 ) and 99.31% ( p < 0.001 ), respectively. The method has an accuracy value of 83.22% for the classification of EEG signal with music and ANC with music as stimuli. The important finding of this study is that by the introduction of ANC, music can be better perceived by the human brain.

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.

On the applicability of brain reading for predictive human-machine interfaces in robotics.

PubMed

Kirchner, Elsa Andrea; Kim, Su Kyoung; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Krell, Mario Michael; Tabie, Marc; Fahle, Manfred

2013-01-01

The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors.

On the Applicability of Brain Reading for Predictive Human-Machine Interfaces in Robotics

PubMed Central

Kirchner, Elsa Andrea; Kim, Su Kyoung; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Krell, Mario Michael; Tabie, Marc; Fahle, Manfred

2013-01-01

The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors. PMID:24358125

Distinct iEEG activity patterns in temporal-limbic and prefrontal sites induced by emotional intentionality

PubMed Central

Singer, Neomi; Podlipsky, Ilana; Esposito, Fabrizio; Okon-Singer, Hadas; Andelman, Fani; Kipervasser, Svetlana; Neufeld, Miri Y.; Goebel, Rainer; Fried, Itzhak; Hendler, Talma

2015-01-01

Our emotions tend to be directed towards someone or something. Such emotional intentionality calls for the integration between two streams of information; abstract hedonic value and its associated concrete content. In a previous functional magnetic resonance imaging (fMRI) study we found that the combination of these two streams, as modeled by short emotional music excerpts and neutral film clips, was associated with synergistic activation in both temporal-limbic (TL) and ventral-lateral PFC (vLPFC) regions. This additive effect implies the integration of domain-specific ‘affective’ and ‘cognitive’ processes. Yet, the low temporal resolution of the fMRI limits the characterization of such cross-domain integration. To this end, we complemented the fMRI data with intracranial electroencephalogram (iEEG) recordings from twelve patients with intractable epilepsy. As expected, the additive fMRI activation in the amygdala and vLPFC was associated with distinct spatio-temporal iEEG patterns among electrodes situated within the vicinity of the fMRI activation foci. On the one hand, TL channels exhibited a transient (0–500 msec) increase in gamma power (61–69 Hz), possibly reflecting initial relevance detection or hedonic value tagging. On the other hand, vLPFC channels showed sustained (1–12 sec) suppression of low frequency power (2.3–24 Hz), possibly mediating changes in gating, enabling an on-going readiness for content-based processing of emotionally tagged signals. Moreover, an additive effect in delta-gamma phase-amplitude coupling (PAC) was found among the TL channels, possibly reflecting the integration between distinct domain specific processes. Together, this study provides a multi-faceted neurophysiological signature for computations that possibly underlie emotional intentionality in humans. PMID:25288171

Distinct iEEG activity patterns in temporal-limbic and prefrontal sites induced by emotional intentionality.

PubMed

Singer, Neomi; Podlipsky, Ilana; Esposito, Fabrizio; Okon-Singer, Hadas; Andelman, Fani; Kipervasser, Svetlana; Neufeld, Miri Y; Goebel, Rainer; Fried, Itzhak; Hendler, Talma

2014-11-01

Our emotions tend to be directed towards someone or something. Such emotional intentionality calls for the integration between two streams of information; abstract hedonic value and its associated concrete content. In a previous functional magnetic resonance imaging (fMRI) study we found that the combination of these two streams, as modeled by short emotional music excerpts and neutral film clips, was associated with synergistic activation in both temporal-limbic (TL) and ventral-lateral PFC (vLPFC) regions. This additive effect implies the integration of domain-specific 'affective' and 'cognitive' processes. Yet, the low temporal resolution of the fMRI limits the characterization of such cross-domain integration. To this end, we complemented the fMRI data with intracranial electroencephalogram (iEEG) recordings from twelve patients with intractable epilepsy. As expected, the additive fMRI activation in the amygdala and vLPFC was associated with distinct spatio-temporal iEEG patterns among electrodes situated within the vicinity of the fMRI activation foci. On the one hand, TL channels exhibited a transient (0-500 msec) increase in gamma power (61-69 Hz), possibly reflecting initial relevance detection or hedonic value tagging. On the other hand, vLPFC channels showed sustained (1-12 sec) suppression of low frequency power (2.3-24 Hz), possibly mediating changes in gating, enabling an on-going readiness for content-based processing of emotionally tagged signals. Moreover, an additive effect in delta-gamma phase-amplitude coupling (PAC) was found among the TL channels, possibly reflecting the integration between distinct domain specific processes. Together, this study provides a multi-faceted neurophysiological signature for computations that possibly underlie emotional intentionality in humans. Copyright © 2014 Elsevier Ltd. All rights reserved.

Brain connectivity analysis from EEG signals using stable phase-synchronized states during face perception tasks

NASA Astrophysics Data System (ADS)

Jamal, Wasifa; Das, Saptarshi; Maharatna, Koushik; Pan, Indranil; Kuyucu, Doga

2015-09-01

Degree of phase synchronization between different Electroencephalogram (EEG) channels is known to be the manifestation of the underlying mechanism of information coupling between different brain regions. In this paper, we apply a continuous wavelet transform (CWT) based analysis technique on EEG data, captured during face perception tasks, to explore the temporal evolution of phase synchronization, from the onset of a stimulus. Our explorations show that there exists a small set (typically 3-5) of unique synchronized patterns or synchrostates, each of which are stable of the order of milliseconds. Particularly, in the beta (β) band, which has been reported to be associated with visual processing task, the number of such stable states has been found to be three consistently. During processing of the stimulus, the switching between these states occurs abruptly but the switching characteristic follows a well-behaved and repeatable sequence. This is observed in a single subject analysis as well as a multiple-subject group-analysis in adults during face perception. We also show that although these patterns remain topographically similar for the general category of face perception task, the sequence of their occurrence and their temporal stability varies markedly between different face perception scenarios (stimuli) indicating toward different dynamical characteristics for information processing, which is stimulus-specific in nature. Subsequently, we translated these stable states into brain complex networks and derived informative network measures for characterizing the degree of segregated processing and information integration in those synchrostates, leading to a new methodology for characterizing information processing in human brain. The proposed methodology of modeling the functional brain connectivity through the synchrostates may be viewed as a new way of quantitative characterization of the cognitive ability of the subject, stimuli and information integration/segregation capability.

Comparison between Scalp EEG and Behind-the-Ear EEG for Development of a Wearable Seizure Detection System for Patients with Focal Epilepsy

PubMed Central

Gu, Ying; Cleeren, Evy; Dan, Jonathan; Claes, Kasper; Hunyadi, Borbála

2017-01-01

A wearable electroencephalogram (EEG) device for continuous monitoring of patients suffering from epilepsy would provide valuable information for the management of the disease. Currently no EEG setup is small and unobtrusive enough to be used in daily life. Recording behind the ear could prove to be a solution to a wearable EEG setup. This article examines the feasibility of recording epileptic EEG from behind the ear. It is achieved by comparison with scalp EEG recordings. Traditional scalp EEG and behind-the-ear EEG were simultaneously acquired from 12 patients with temporal, parietal, or occipital lobe epilepsy. Behind-the-ear EEG consisted of cross-head channels and unilateral channels. The analysis on Electrooculography (EOG) artifacts resulting from eye blinking showed that EOG artifacts were absent on cross-head channels and had significantly small amplitudes on unilateral channels. Temporal waveform and frequency content during seizures from behind-the-ear EEG visually resembled that from scalp EEG. Further, coherence analysis confirmed that behind-the-ear EEG acquired meaningful epileptic discharges similarly to scalp EEG. Moreover, automatic seizure detection based on support vector machine (SVM) showed that comparable seizure detection performance can be achieved using these two recordings. With scalp EEG, detection had a median sensitivity of 100% and a false detection rate of 1.14 per hour, while, with behind-the-ear EEG, it had a median sensitivity of 94.5% and a false detection rate of 0.52 per hour. These findings demonstrate the feasibility of detecting seizures from EEG recordings behind the ear for patients with focal epilepsy. PMID:29295522

Quantitative EEG and LORETA: valuable tools in discerning FTD from AD?

PubMed

Caso, Francesca; Cursi, Marco; Magnani, Giuseppe; Fanelli, Giovanna; Falautano, Monica; Comi, Giancarlo; Leocani, Letizia; Minicucci, Fabio

2012-10-01

Drawing a clinical distinction between frontotemporal dementia (FTD) and Alzheimer's disease (AD) is tricky, particularly at the early stages of disease. This study evaluates the possibility in differentiating 39 FTD, 39 AD, and 39 controls (CTR) by means of power spectral analysis and standardized low resolution brain electromagnetic tomography (sLORETA) within delta, theta, alpha 1 and 2, beta 1, 2, and 3 frequency bands. Both analyses revealed in AD patients, relative to CTR, higher expression of diffuse delta/theta and lower central/posterior fast frequency (from alpha1 to beta2) bands. FTD patients showed diffuse increased theta power compared with CTR and lower delta relative to AD patients. Compared with FTD, AD patients showed diffuse higher theta power at spectral analysis and, at sLORETA, decreased alpha2 and beta1 values in central/temporal regions. Spectral analysis and sLORETA provided complementary information that might help characterizing different patterns of electroencephalogram (EEG) oscillatory activity in AD and FTD. Nevertheless, this differentiation was possible only at the group level because single patients could not be discerned with sufficient accuracy. Copyright © 2012 Elsevier Inc. All rights reserved.

Reduced mu suppression and altered motor resonance in euthymic bipolar disorder: Evidence for a dysfunctional mirror system?

PubMed

Andrews, Sophie C; Enticott, Peter G; Hoy, Kate E; Thomson, Richard H; Fitzgerald, Paul B

2016-01-01

Social cognitive difficulties are common in the acute phase of bipolar disorder and, to a lesser extent, during the euthymic stage, and imaging studies of social cognition in euthymic bipolar disorder have implicated mirror system brain regions. This study aimed to use a novel multimodal approach (i.e., including both transcranial magnetic stimulation (TMS) and electroencephalogram (EEG)) to investigate mirror systems in bipolar disorder. Fifteen individuals with euthymic bipolar disorder and 16 healthy controls participated in this study. Single-pulse TMS was applied to the optimal site in the primary motor cortex (M1), which stimulates the muscle of interest during the observation of hand movements (goal-directed or interacting) designed to elicit mirror system activity. Single EEG electrodes (C3, CZ, C4) recorded mu rhythm modulation concurrently. Results revealed that the patient group showed significantly less mu suppression compared to healthy controls. Surprisingly, motor resonance was not significantly different overall between groups; however, bipolar disorder participants showed a pattern of reduced reactivity on some conditions. Although preliminary, this study indicates a potential mirror system deficit in euthymic bipolar disorder, which may contribute to the pathophysiology of the disorder.

Effect of electrocardiogram interference on cortico-cortical connectivity analysis and a possible solution.

PubMed

Govindan, R B; Kota, Srinivas; Al-Shargabi, Tareq; Massaro, An N; Chang, Taeun; du Plessis, Adre

2016-09-01

Electroencephalogram (EEG) signals are often contaminated by the electrocardiogram (ECG) interference, which affects quantitative characterization of EEG. We propose null-coherence, a frequency-based approach, to attenuate the ECG interference in EEG using simultaneously recorded ECG as a reference signal. After validating the proposed approach using numerically simulated data, we apply this approach to EEG recorded from six newborns receiving therapeutic hypothermia for neonatal encephalopathy. We compare our approach with an independent component analysis (ICA), a previously proposed approach to attenuate ECG artifacts in the EEG signal. The power spectrum and the cortico-cortical connectivity of the ECG attenuated EEG was compared against the power spectrum and the cortico-cortical connectivity of the raw EEG. The null-coherence approach attenuated the ECG contamination without leaving any residual of the ECG in the EEG. We show that the null-coherence approach performs better than ICA in attenuating the ECG contamination without enhancing cortico-cortical connectivity. Our analysis suggests that using ICA to remove ECG contamination from the EEG suffers from redistribution problems, whereas the null-coherence approach does not. We show that both the null-coherence and ICA approaches attenuate the ECG contamination. However, the EEG obtained after ICA cleaning displayed higher cortico-cortical connectivity compared with that obtained using the null-coherence approach. This suggests that null-coherence is superior to ICA in attenuating the ECG interference in EEG for cortico-cortical connectivity analysis. Copyright © 2016 Elsevier B.V. All rights reserved.

Electroencephalography reactivity for prognostication of post-anoxic coma after cardiopulmonary resuscitation: A comparison of quantitative analysis and visual analysis.

PubMed

Liu, Gang; Su, Yingying; Jiang, Mengdi; Chen, Weibi; Zhang, Yan; Zhang, Yunzhou; Gao, Daiquan

2016-07-28

Electroencephalogram reactivity (EEG-R) is a positive predictive factor for assessing outcomes in comatose patients. Most studies assess the prognostic value of EEG-R utilizing visual analysis; however, this method is prone to subjectivity. We sought to categorize EEG-R with a quantitative approach. We retrospectively studied consecutive comatose patients who had an EEG-R recording performed 1-3 days after cardiopulmonary resuscitation (CPR) or during normothermia after therapeutic hypothermia. EEG-R was assessed via visual analysis and quantitative analysis separately. Clinical outcomes were followed-up at 3-month and dichotomized as recovery of awareness or no recovery of awareness. A total of 96 patients met the inclusion criteria, and 38 (40%) patients recovered awareness at 3-month followed-up. Of 27 patients with EEG-R measured with visual analysis, 22 patients recovered awareness; and of the 69 patients who did not demonstrated EEG-R, 16 patients recovered awareness. The sensitivity and specificity of visually measured EEG-R were 58% and 91%, respectively. The area under the receiver operating characteristic curve for the quantitative analysis was 0.92 (95% confidence interval, 0.87-0.97), with the best cut-off value of 0.10. EEG-R through quantitative analysis might be a good method in predicting the recovery of awareness in patients with post-anoxic coma after CPR. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A simple system for detection of EEG artifacts in polysomnographic recordings.

PubMed

Durka, P J; Klekowicz, H; Blinowska, K J; Szelenberger, W; Niemcewicz, Sz

2003-04-01

We present an efficient parametric system for automatic detection of electroencephalogram (EEG) artifacts in polysomnographic recordings. For each of the selected types of artifacts, a relevant parameter was calculated for a given epoch. If any of these parameters exceeded a threshold, the epoch was marked as an artifact. Performance of the system, evaluated on 18 overnight polysomnographic recordings, revealed concordance with decisions of human experts close to the interexpert agreement and the repeatability of expert's decisions, assessed via a double-blind test. Complete software (Matlab source code) for the presented system is freely available from the Internet at http://brain.fuw.edu.pl/artifacts.

Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients

NASA Astrophysics Data System (ADS)

Mormann, Florian; Lehnertz, Klaus; David, Peter; E. Elger, Christian

2000-10-01

We apply the concept of phase synchronization of chaotic and/or noisy systems and the statistical distribution of the relative instantaneous phases to electroencephalograms (EEGs) recorded from patients with temporal lobe epilepsy. Using the mean phase coherence as a statistical measure for phase synchronization, we observe characteristic spatial and temporal shifts in synchronization that appear to be strongly related to pathological activity. In particular, we observe distinct differences in the degree of synchronization between recordings from seizure-free intervals and those before an impending seizure, indicating an altered state of brain dynamics prior to seizure activity.

Time reversibility of intracranial human EEG recordings in mesial temporal lobe epilepsy

NASA Astrophysics Data System (ADS)

van der Heyden, M. J.; Diks, C.; Pijn, J. P. M.; Velis, D. N.

1996-02-01

Intracranial electroencephalograms from patients suffering from mesial temporal lobe epilepsy were tested for time reversibility. If the recorded time series is irreversible, the input of the recording system cannot be a realisation of a linear Gaussian random process. We confirmed experimentally that the measurement equipment did not introduce irreversibility in the recorded output when the input was a realisation of a linear Gaussian random process. In general, the non-seizure recordings are reversible, whereas the seizure recordings are irreversible. These results suggest that time reversibility is a useful property for the characterisation of human intracranial EEG recordings in mesial temporal lobe epilepsy.

The Assessment of Electroencephalographic Changes and Memory Disturbances in Acute Intoxications with Industrial Poisons

PubMed Central

Chalupa, B.; Synková, J.; Ševčík, M.

1960-01-01

A report is given of the results of the electroencephalogram (EEG) and of an experimental memory examination in a group of 22 cases of acute carbon monoxide and solvents poisoning of varying severity. An abnormal EEG recording, most often in the form of theta activity 5-6 sec., was found in 12 patients; memory disturbances were found in 13 cases. There was correlation between the results of the two examinations as well as with the clinical classification of the degree of intoxication. The methods are suitable for the solving of various theoretical and practical questions in industrial toxicology. PMID:13692202

Specificity of spontaneous EEG associated with different levels of cognitive and communicative dysfunctions in children.

PubMed

Kozhushko, Nadezhda Ju; Nagornova, Zhanna V; Evdokimov, Sergey A; Shemyakina, Natalia V; Ponomarev, Valery A; Tereshchenko, Ekaterina P; Kropotov, Jury D

2018-06-01

This study aimed to reveal electrophysiological markers of communicative and cognitive dysfunctions of different severity in children with autism spectrum disorder (ASD). Eyes-opened electroencephalograms (EEGs) of 42 children with ASD, divided into two groups according to the severity of their communicative and cognitive dysfunctions (24 with severe and 18 children with less severe ASD), and 70 age-matched controls aged 4-9 years were examined by means of spectral and group independent component (gIC) analyses. A predominance of theta and beta EEG activity in both groups of children with ASD compared to the activity in the control group was found in the global gIC together with a predominance of beta EEG activity in the right occipital region. The quantity of local gICs with enhanced slow and high-frequency EEG activity (within the frontal, temporal, and parietal cortex areas) in children 4-9 years of age might be considered a marker of cognitive and communicative dysfunction severity. Copyright © 2018 Elsevier B.V. All rights reserved.

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.

Multivariate evoked response detection based on the spectral F-test.

PubMed

Rocha, Paulo Fábio F; Felix, Leonardo B; Miranda de Sá, Antonio Mauricio F L; Mendes, Eduardo M A M

2016-05-01

Objective response detection techniques, such as magnitude square coherence, component synchrony measure, and the spectral F-test, have been used to automate the detection of evoked responses. The performance of these detectors depends on both the signal-to-noise ratio (SNR) and the length of the electroencephalogram (EEG) signal. Recently, multivariate detectors were developed to increase the detection rate even in the case of a low signal-to-noise ratio or of short data records originated from EEG signals. In this context, an extension to the multivariate case of the spectral F-test detector is proposed. The performance of this technique is assessed using Monte Carlo. As an example, EEG data from 12 subjects during photic stimulation is used to demonstrate the usefulness of the proposed detector. The multivariate method showed detection rates consistently higher than those ones when only one signal was used. It is shown that the response detection in EEG signals with the multivariate technique was statistically significant if two or more EEG derivations were used. Copyright © 2016 Elsevier B.V. All rights reserved.

Detection of seizures from small samples using nonlinear dynamic system theory.

PubMed

Yaylali, I; Koçak, H; Jayakar, P

1996-07-01

The electroencephalogram (EEG), like many other biological phenomena, is quite likely governed by nonlinear dynamics. Certain characteristics of the underlying dynamics have recently been quantified by computing the correlation dimensions (D2) of EEG time series data. In this paper, D2 of the unbiased autocovariance function of the scalp EEG data was used to detect electrographic seizure activity. Digital EEG data were acquired at a sampling rate of 200 Hz per channel and organized in continuous frames (duration 2.56 s, 512 data points). To increase the reliability of D2 computations with short duration data, raw EEG data were initially simplified using unbiased autocovariance analysis to highlight the periodic activity that is present during seizures. The D2 computation was then performed from the unbiased autocovariance function of each channel using the Grassberger-Procaccia method with Theiler's box-assisted correlation algorithm. Even with short duration data, this preprocessing proved to be computationally robust and displayed no significant sensitivity to implementation details such as the choices of embedding dimension and box size. The system successfully identified various types of seizures in clinical studies.

Role of the gluten-free diet on neurological-EEG findings and sleep disordered breathing in children with celiac disease.

PubMed

Parisi, P; Pietropaoli, N; Ferretti, A; Nenna, R; Mastrogiorgio, G; Del Pozzo, M; Principessa, L; Bonamico, M; Villa, M P

2015-02-01

To determine whether celiac children are at risk for EEG-neurological features and sleep disordered breathing (SDB), and whether an appropriate gluten-free diet (GFD) influences these disorders. We consecutively enrolled 19 children with a new biopsy-proven celiac disease (CD) diagnosis. At CD diagnosis and after 6 months of GFD, each patient underwent a general and neurological examination, an electroencephalogram, a questionnaire about neurological features, and a validated questionnaire about SDB: OSA (obstructive sleep apnea) scores<0 predict normality; values>0 predict OSA. At CD diagnosis, 37% of patients complained headache that affected daily activities and 32% showed positive OSA score. The EEG examinations revealed abnormal finding in 48% of children. After 6 months of GFD headache disappeared in 72% of children and EEG abnormalities in 78%; all children showed negative OSA score. According to our preliminary data, in the presence of unexplained EEG abnormalities and/or other neurological disorders/SDB an atypical or silent CD should also be taken into account. Copyright © 2014 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Automatic and Direct Identification of Blink Components from Scalp EEG

PubMed Central

Kong, Wanzeng; Zhou, Zhanpeng; Hu, Sanqing; Zhang, Jianhai; Babiloni, Fabio; Dai, Guojun

2013-01-01

Eye blink is an important and inevitable artifact during scalp electroencephalogram (EEG) recording. The main problem in EEG signal processing is how to identify eye blink components automatically with independent component analysis (ICA). Taking into account the fact that the eye blink as an external source has a higher sum of correlation with frontal EEG channels than all other sources due to both its location and significant amplitude, in this paper, we proposed a method based on correlation index and the feature of power distribution to automatically detect eye blink components. Furthermore, we prove mathematically that the correlation between independent components and scalp EEG channels can be translating directly from the mixing matrix of ICA. This helps to simplify calculations and understand the implications of the correlation. The proposed method doesn't need to select a template or thresholds in advance, and it works without simultaneously recording an electrooculography (EOG) reference. The experimental results demonstrate that the proposed method can automatically recognize eye blink components with a high accuracy on entire datasets from 15 subjects. PMID:23959240

Brain Monitoring with Electroencephalography and the Electroencephalogram-Derived Bispectral Index During Cardiac Surgery

PubMed Central

Kertai, Miklos D.; Whitlock, Elizabeth L.; Avidan, Michael S.

2011-01-01

Cardiac surgery presents particular challenges for the anesthesiologist. In addition to standard and advanced monitors typically used during cardiac surgery, anesthesiologists may consider monitoring the brain with raw or processed electroencephalography (EEG). There is strong evidence that a protocol incorporating the processed EEG Bispectral Index (BIS) decreases the incidence intraoperative awareness compared with standard practice. However there is conflicting evidence that incorporating the BIS into cardiac anesthesia practice improves “fast-tracking,” decreases anesthetic drug use, or detects cerebral ischemia. Recent research, including many cardiac surgical patients, shows that a protocol based on BIS monitoring is not superior to a protocol based on end tidal anesthetic concentration monitoring in preventing awareness. There has been a resurgence of interest in the anesthesia literature in limited montage EEG monitoring, including nonproprietary processed indices. This has been accompanied by research showing that with structured training, anesthesiologists can glean useful information from the raw EEG trace. In this review, we discuss both the hypothesized benefits and limitations of BIS and frontal channel EEG monitoring in the cardiac surgical population. PMID:22253267

Simultaneous scalp electroencephalography (EEG), electromyography (EMG), and whole-body segmental inertial recording for multi-modal neural decoding.

PubMed

Bulea, Thomas C; Kilicarslan, Atilla; Ozdemir, Recep; Paloski, William H; Contreras-Vidal, Jose L

2013-07-26

Recent studies support the involvement of supraspinal networks in control of bipedal human walking. Part of this evidence encompasses studies, including our previous work, demonstrating that gait kinematics and limb coordination during treadmill walking can be inferred from the scalp electroencephalogram (EEG) with reasonably high decoding accuracies. These results provide impetus for development of non-invasive brain-machine-interface (BMI) systems for use in restoration and/or augmentation of gait- a primary goal of rehabilitation research. To date, studies examining EEG decoding of activity during gait have been limited to treadmill walking in a controlled environment. However, to be practically viable a BMI system must be applicable for use in everyday locomotor tasks such as over ground walking and turning. Here, we present a novel protocol for non-invasive collection of brain activity (EEG), muscle activity (electromyography (EMG)), and whole-body kinematic data (head, torso, and limb trajectories) during both treadmill and over ground walking tasks. By collecting these data in the uncontrolled environment insight can be gained regarding the feasibility of decoding unconstrained gait and surface EMG from scalp EEG.

Quantitative EEG analysis in minimally conscious state patients during postural changes.

PubMed

Greco, A; Carboncini, M C; Virgillito, A; Lanata, A; Valenza, G; Scilingo, E P

2013-01-01

Mobilization and postural changes of patients with cognitive impairment are standard clinical practices useful for both psychic and physical rehabilitation process. During this process, several physiological signals, such as Electroen-cephalogram (EEG), Electrocardiogram (ECG), Photopletysmography (PPG), Respiration activity (RESP), Electrodermal activity (EDA), are monitored and processed. In this paper we investigated how quantitative EEG (qEEG) changes with postural modifications in minimally conscious state patients. This study is quite novel and no similar experimental data can be found in the current literature, therefore, although results are very encouraging, a quantitative analysis of the cortical area activated in such postural changes still needs to be deeply investigated. More specifically, this paper shows EEG power spectra and brain symmetry index modifications during a verticalization procedure, from 0 to 60 degrees, of three patients in Minimally Consciousness State (MCS) with focused region of impairment. Experimental results show a significant increase of the power in β band (12 - 30 Hz), commonly associated to human alertness process, thus suggesting that mobilization and postural changes can have beneficial effects in MCS patients.

Reproducibility of the spectral components of the electroencephalogram during driver fatigue.

PubMed

Lal, Saroj K L; Craig, Ashley

2005-02-01

To date, no study has tested the reproducibility of EEG changes that occur during driver fatigue. For the EEG changes to be useful in the development of a fatigue countermeasure device the EEG response during each onset period of fatigue in individuals needs to be reproducible. It should be noted that fatigue during driving is not a continuous process but consists of successive episodes of 'microsleeps' where the subject may go in and out of a fatigue state. The aim of the present study was to investigate the reproducibility of fatigue during driving in both professional and non-professional drivers. Thirty five non-professional drivers and twenty professional drivers were tested during two separate sessions of a driver simulator task. EEG, EOG and behavioural measurements of fatigue were obtained during the driving task. The results showed high reproducibility for the delta and theta bands (r>0.95) in both groups of drivers. The results are discussed in light of implications for future studies and for the development of an EEG based fatigue countermeasure device.

Quantitative EEG of Rapid-Eye-Movement Sleep: A Marker of Amnestic Mild Cognitive Impairment.

PubMed

Brayet, Pauline; Petit, Dominique; Frauscher, Birgit; Gagnon, Jean-François; Gosselin, Nadia; Gagnon, Katia; Rouleau, Isabelle; Montplaisir, Jacques

2016-04-01

The basal forebrain cholinergic system, which is impaired in early Alzheimer's disease, is more crucial for the activation of rapid-eye-movement (REM) sleep electroencephalogram (EEG) than it is for wakefulness. Quantitative EEG from REM sleep might thus provide an earlier and more accurate marker of the development of Alzheimer's disease in subjects with mild cognitive impairment (MCI) subjects than that from wakefulness. To assess the superiority of the REM sleep EEG as a screening tool for preclinical Alzheimer's disease, 22 subjects with amnestic MCI (a-MCI; 63.9±7.7 years), 10 subjects with nonamnestic MCI (na-MCI; 64.1±4.5 years) and 32 controls (63.7±6.6 years) participated in the study. Spectral analyses of the waking EEG and REM sleep EEG were performed and the [(delta+theta)/(alpha+beta)] ratio was used to assess between-group differences in EEG slowing. The a-MCI subgroup showed EEG slowing in frontal lateral regions compared to both na-MCI and control groups. This EEG slowing was present in wakefulness (compared to controls) but was much more prominent in REM sleep. Moreover, the comparison between amnestic and nonamnestic subjects was found significant only for the REM sleep EEG. There was no difference in EEG power ratio between na-MCI and controls for any of the 7 cortical regions studied. These findings demonstrate the superiority of the REM sleep EEG in the discrimination between a-MCI and both na-MCI and control subjects. © EEG and Clinical Neuroscience Society (ECNS) 2015.

Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering

PubMed Central

Huang, Chih-Sheng; Yang, Wen-Yu; Chuang, Chun-Hsiang; Wang, Yu-Kai

2018-01-01

Electroencephalogram (EEG) signals are usually contaminated with various artifacts, such as signal associated with muscle activity, eye movement, and body motion, which have a noncerebral origin. The amplitude of such artifacts is larger than that of the electrical activity of the brain, so they mask the cortical signals of interest, resulting in biased analysis and interpretation. Several blind source separation methods have been developed to remove artifacts from the EEG recordings. However, the iterative process for measuring separation within multichannel recordings is computationally intractable. Moreover, manually excluding the artifact components requires a time-consuming offline process. This work proposes a real-time artifact removal algorithm that is based on canonical correlation analysis (CCA), feature extraction, and the Gaussian mixture model (GMM) to improve the quality of EEG signals. The CCA was used to decompose EEG signals into components followed by feature extraction to extract representative features and GMM to cluster these features into groups to recognize and remove artifacts. The feasibility of the proposed algorithm was demonstrated by effectively removing artifacts caused by blinks, head/body movement, and chewing from EEG recordings while preserving the temporal and spectral characteristics of the signals that are important to cognitive research. PMID:29599950

Highly Efficient Compression Algorithms for Multichannel EEG.

PubMed

Shaw, Laxmi; Rahman, Daleef; Routray, Aurobinda

2018-05-01

The difficulty associated with processing and understanding the high dimensionality of electroencephalogram (EEG) data requires developing efficient and robust compression algorithms. In this paper, different lossless compression techniques of single and multichannel EEG data, including Huffman coding, arithmetic coding, Markov predictor, linear predictor, context-based error modeling, multivariate autoregression (MVAR), and a low complexity bivariate model have been examined and their performances have been compared. Furthermore, a high compression algorithm named general MVAR and a modified context-based error modeling for multichannel EEG have been proposed. The resulting compression algorithm produces a higher relative compression ratio of 70.64% on average compared with the existing methods, and in some cases, it goes up to 83.06%. The proposed methods are designed to compress a large amount of multichannel EEG data efficiently so that the data storage and transmission bandwidth can be effectively used. These methods have been validated using several experimental multichannel EEG recordings of different subjects and publicly available standard databases. The satisfactory parametric measures of these methods, namely percent-root-mean square distortion, peak signal-to-noise ratio, root-mean-square error, and cross correlation, show their superiority over the state-of-the-art compression methods.

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.

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.

An artificial intelligence approach to classify and analyse EEG traces.

PubMed

Castellaro, C; Favaro, G; Castellaro, A; Casagrande, A; Castellaro, S; Puthenparampil, D V; Salimbeni, C Fattorello

2002-06-01

We present a fully automatic system for the classification and analysis of adult electroencephalograms (EEGs). The system is based on an artificial neural network which classifies the single epochs of trace, and on an Expert System (ES) which studies the time and space correlation among the outputs of the neural network; compiling a final report. On the last 2000 EEGs representing different kinds of alterations according to clinical occurrences, the system was able to produce 80% good or very good final comments and 18% sufficient comments, which represent the documents delivered to the patient. In the remaining 2% the automatic comment needed some modifications prior to be presented to the patient. No clinical false-negative classifications did arise, i.e. no altered traces were classified as 'normal' by the neural network. The analysis method we describe is based on the interpretation of objective measures performed on the trace. It can improve the quality and reliability of the EEG exam and appears useful for the EEG medical reports although it cannot totally substitute the medical doctor who should now read the automatic EEG analysis in light of the patient's history and age.

Assessment of the suitability of using a forehead EEG electrode set and chin EMG electrodes for sleep staging in polysomnography.

PubMed

Myllymaa, Sami; Muraja-Murro, Anu; Westeren-Punnonen, Susanna; Hukkanen, Taina; Lappalainen, Reijo; Mervaala, Esa; Töyräs, Juha; Sipilä, Kirsi; Myllymaa, Katja

2016-12-01

Recently, a number of portable devices designed for full polysomnography at home have appeared. However, current scalp electrodes used for electroencephalograms are not practical for patient self-application. The aim of this study was to evaluate the suitability of recently introduced forehead electroencephalogram electrode set and supplementary chin electromyogram electrodes for sleep staging. From 31 subjects (10 male, 21 female; age 31.3 ± 11.8 years), sleep was recorded simultaneously with a forehead electroencephalogram electrode set and with a standard polysomnography setup consisting of six recommended electroencephalogram channels, two electrooculogram channels and chin electromyogram. Thereafter, two experienced specialists scored each recording twice, based on either standard polysomnography or forehead recordings. Sleep variables recorded with the forehead electroencephalogram electrode set and separate chin electromyogram electrodes were highly consistent with those obtained with the standard polysomnography. There were no statistically significant differences in total sleep time, sleep efficiency or sleep latencies. However, compared with the standard polysomnography, there was a significant increase in the amount of stage N1 and N2, and a significant reduction in stage N3 and rapid eye movement sleep. Overall, epoch-by-epoch agreement between the methods was 79.5%. Inter-scorer agreement for the forehead electroencephalogram was only slightly lower than that for standard polysomnography (76.1% versus 83.2%). Forehead electroencephalogram electrode set as supplemented with chin electromyogram electrodes may serve as a reliable and simple solution for recording total sleep time, and may be adequate for measuring sleep architecture. Because this electrode concept is well suited for patient's self-application, it may offer a significant advancement in home polysomnography. © 2016 European Sleep Research Society.

Umbilical cord mesenchymal stem cell (UC-MSC) transplantations for cerebral palsy

PubMed Central

Dong, Huajiang; Li, Gang; Shang, Chongzhi; Yin, Huijuan; Luo, Yuechen; Meng, Huipeng; Li, Xiaohong; Wang, Yali; Lin, Ling; Zhao, Mingliang

2018-01-01

This study reports a case of a 4-year-old boy patient with abnormalities of muscle tone, movement and motor skills, as well as unstable gait leading to frequent falls. The results of the electroencephalogram (EEG) indicate moderately abnormal EEG, accompanied by irregular seizures. Based on these clinical characteristics, the patient was diagnosed with cerebral palsy (CP) in our hospital. In this study, the patient was treated with umbilical cord mesenchymal stem cell (UC-MSC) transplantation therapy. This patient received UC-MSC transplantation 3 times (5.3*107) in total. After three successive cell transplantations, the patient recovered well and showed obvious improvements in EEG and limb strength, motor function, and language expression. However, the improvement in intelligence quotient (IQ) was less obvious. These results indicate that UC-MSC transplantation is a promising treatment for cerebral palsy. PMID:29636880

Electroencephalography Based Fusion Two-Dimensional (2D)-Convolution Neural Networks (CNN) Model for Emotion Recognition System.

PubMed

Kwon, Yea-Hoon; Shin, Sae-Byuk; Kim, Shin-Dug

2018-04-30

The purpose of this study is to improve human emotional classification accuracy using a convolution neural networks (CNN) model and to suggest an overall method to classify emotion based on multimodal data. We improved classification performance by combining electroencephalogram (EEG) and galvanic skin response (GSR) signals. GSR signals are preprocessed using by the zero-crossing rate. Sufficient EEG feature extraction can be obtained through CNN. Therefore, we propose a suitable CNN model for feature extraction by tuning hyper parameters in convolution filters. The EEG signal is preprocessed prior to convolution by a wavelet transform while considering time and frequency simultaneously. We use a database for emotion analysis using the physiological signals open dataset to verify the proposed process, achieving 73.4% accuracy, showing significant performance improvement over the current best practice models.

Application of tripolar concentric electrodes and prefeature selection algorithm for brain-computer interface.

PubMed

Besio, Walter G; Cao, Hongbao; Zhou, Peng

2008-04-01

For persons with severe disabilities, a brain-computer interface (BCI) may be a viable means of communication. Lapalacian electroencephalogram (EEG) has been shown to improve classification in EEG recognition. In this work, the effectiveness of signals from tripolar concentric electrodes and disc electrodes were compared for use as a BCI. Two sets of left/right hand motor imagery EEG signals were acquired. An autoregressive (AR) model was developed for feature extraction with a Mahalanobis distance based linear classifier for classification. An exhaust selection algorithm was employed to analyze three factors before feature extraction. The factors analyzed were 1) length of data in each trial to be used, 2) start position of data, and 3) the order of the AR model. The results showed that tripolar concentric electrodes generated significantly higher classification accuracy than disc electrodes.

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.

Research on the Characteristics of Alzheimer's Disease Using EEG

NASA Astrophysics Data System (ADS)

Ueda, Taishi; Musha, Toshimitsu; Yagi, Tohru

In this paper, we proposed a new method for diagnosing Alzheimer's disease (AD) on the basis of electroencephalograms (EEG). The method, which is termed Power Variance Function (PVF) method, indicates the variance of the power at each frequency. By using the proposed method, the power of EEG at each frequency was calculated using Wavelet transform, and the corresponding variances were defined as PVF. After the PVF histogram of 55 healthy people was approximated as a Generalized Extreme Value (GEV) distribution, we evaluated the PVF of 22 patients with AD and 25 patients with mild cognitive impairment (MCI). As a result, the values for all AD and MCI subjects were abnormal. In particular, the PVF in the θ band for MCI patients was abnormally high, and the PVF in the α band for AD patients was low.

Empirical Analysis of EEG and ERPs for Psychophysiological Adaptive Task Allocation

NASA Technical Reports Server (NTRS)

Prinzel, Lawrence J., III; Pope, Alan T.; Freeman, Frederick G.; Scerbo, Mark W.; Mikulka, Peter J.

2001-01-01

The present study was designed to test the efficacy of using Electroencephalogram (EEG) and Event-Related Potentials (ERPs) for making task allocation decisions. Thirty-six participants were randomly assigned to an experimental, yoked, or control group condition. Under the experimental condition, a tracking task was switched between task modes based upon the participant's EEG. The results showed that the use of adaptive aiding improved performance and lowered subjective workload under negative feedback as predicted. Additionally, participants in the adaptive group had significantly lower RMSE and NASA-TLX ratings than participants in either the yoked or control group conditions. Furthermore, the amplitudes of the N1 and P3 ERP components were significantly larger under the experimental group condition than under either the yoked or control group conditions. These results are discussed in terms of the implications for adaptive automation design.

Masturbation mimicking seizure in an infant.

PubMed

Deda, G; Caksen, H; Suskan, E; Gümüs, D

2001-08-01

A 3.5-month-old boy was referred to our hospital with the diagnosis of infantile spasm. His developmental milestones and physical examination were normal. During the follow-up we recorded about six to nine attacks a day and the duration of attacks was changed between 15 seconds-1.5 minutes. During the episodic attacks he was flushed and had tonic posturing associated with crossing of thighs, without loss of consciousness and his eye movements were normal. Routine and long-term electroencephalogram (EEG) were normal during attack. The patient was diagnosed as masturbation according to the clinical and EEG findings. In conclusion, we would like to stress that masturbation should also be considered in infants who were admitted with complaint of seizure, and aside from EEG monitoring a detailed history and careful observation are very important factors in differential diagnosis of these two different conditions.

Intellectual Disabilities and Power Spectra Analysis during Sleep: A New Perspective on Borderline Intellectual Functioning

ERIC Educational Resources Information Center

Esposito, M.; Carotenuto, M.

2014-01-01

Background: The role of sleep in cognitive processes has been confirmed by a growing number of reports for all ages of life. Analysing sleep electroencephalogram (EEG) spectra may be useful to study cortical organisation in individuals with Borderline Intellectual Functioning (BIF), as seen in other disturbances even if it is not considered a…

Interpretation of the auto-mutual information rate of decrease in the context of biomedical signal analysis. Application to electroencephalogram recordings.

PubMed

Escudero, Javier; Hornero, Roberto; Abásolo, Daniel

2009-02-01

The mutual information (MI) is a measure of both linear and nonlinear dependences. It can be applied to a time series and a time-delayed version of the same sequence to compute the auto-mutual information function (AMIF). Moreover, the AMIF rate of decrease (AMIFRD) with increasing time delay in a signal is correlated with its entropy and has been used to characterize biomedical data. In this paper, we aimed at gaining insight into the dependence of the AMIFRD on several signal processing concepts and at illustrating its application to biomedical time series analysis. Thus, we have analysed a set of synthetic sequences with the AMIFRD. The results show that the AMIF decreases more quickly as bandwidth increases and that the AMIFRD becomes more negative as there is more white noise contaminating the time series. Additionally, this metric detected changes in the nonlinear dynamics of a signal. Finally, in order to illustrate the analysis of real biomedical signals with the AMIFRD, this metric was applied to electroencephalogram (EEG) signals acquired with eyes open and closed and to ictal and non-ictal intracranial EEG recordings.

Objective response detection in an electroencephalogram during somatosensory stimulation.

PubMed

Simpson, D M; Tierra-Criollo, C J; Leite, R T; Zayen, E J; Infantosi, A F

2000-06-01

Techniques for objective response detection aim to identify the presence of evoked potentials based purely on statistical principles. They have been shown to be potentially more sensitive than the conventional approach of subjective evaluation by experienced clinicians and could be of great clinical use. Three such techniques to detect changes in an electroencephalogram (EEG) synchronous with the stimuli, namely, magnitude-squared coherence (MSC), the phase-synchrony measure (PSM) and the spectral F test (SFT) were applied to EEG signals of 12 normal subjects under conventional somatosensory pulse stimulation to the tibial nerve. The SFT, which uses only the power spectrum, showed the poorest performance, while the PSM, based only on the phase spectrum, gave results almost as good as those of the MSC, which uses both phase and power spectra. With the latter two techniques, stimulus responses were evident in the frequency range of 20-80 Hz in all subjects after 200 stimuli (5 Hz stimulus frequency), whereas for visual recognition at least 500 stimuli are usually applied. Based on these results and on simulations, the phase-based techniques appear promising for the automated detection and monitoring of somatosensory evoked potentials.

Rett syndrome: EEG presentation.

PubMed

Robertson, R; Langill, L; Wong, P K; Ho, H H

1988-11-01

Rett syndrome, a degenerative neurological disorder of girls, has a classical presentation and typical EEG findings. The electroencephalograms (EEGs) of 7 girls whose records have been followed from the onset of symptoms to the age of 5 or more are presented. These findings are tabulated with the Clinical Staging System of Hagberg and Witt-Engerström (1986). The records show a progressive deterioration in background rhythms in waking and sleep. The abnormalities of the background activity may only become evident at 4-5 years of age or during stage 2--the Rapid Destructive Stage. The marked contrast between waking and sleep background may not occur until stage 3--the Pseudostationary Stage. In essence EEG changes appear to lag behind clinical symptomatology by 1-3 years. An unexpected, but frequent, abnormality was central spikes seen in 5 of 7 girls. They appeared to be age related and could be evoked by tactile stimulation in 2 patients. We hypothesize that the prominent 'hand washing' mannerism may be self-stimulating and related to the appearance of central spike discharges.

Causal Neuro-immune Relationships at Patients with Chronic Pyelonephritis and Cholecystitis. Correlations between Parameters EEG, HRV and White Blood Cell Count.

PubMed

Kul'chyns'kyi, Andriy B; Kyjenko, Valeriy M; Zukow, Walery; Popovych, Igor L

2017-01-01

We aim to analyze in bounds KJ Tracey's immunological homunculus conception the relationships between parameters of electroencephalogram (EEG) and heart rate variability (HRV), on the one hand, and the parameters of bhite blood cell count, on the other hand. In basal conditions in 23 men, patients with chronic pyelonephritis and cholecystitis in remission, recorded EEG ("NeuroCom Standard", KhAI Medica, Ukraine) and HRV ("Cardiolab+VSR", KhAI Medica, Ukraine). In portion of blood counted up white blood cell count. Revealed that canonical correlation between constellation EEG and HRV parameters form with blood level of leukocytes 0.92 (p<10-5), with relative content in white blood cell count stubnuclear neutrophiles 0.93 (p<10-5), segmentonucleary neutrophiles 0.89 (p<10-3), eosinophiles 0.87 (p=0.003), lymphocytes 0.77 (p<10-3) and with monocytes 0.75 (p=0.003). Parameters of white blood cell count significantly modulated by electrical activity some structures of central and autonomic nervous systems.

Association of autonomic nervous system and EEG scalp potential during playing 2D Grand Turismo 5.

PubMed

Subhani, Ahmad Rauf; Likun, Xia; Saeed Malik, Aamir

2012-01-01

Cerebral activation and autonomic nervous system have importance in studies such as mental stress. The aim of this study is to analyze variations in EEG scalp potential which may influence autonomic activation of heart while playing video games. Ten healthy participants were recruited in this study. Electroencephalogram (EEG) and electrocardiogram (ECG) signals were measured simultaneously during playing video game and rest conditions. Sympathetic and parasympathetic innervations of heart were evaluated from heart rate variability (HRV), derived from the ECG. Scalp potential was measured by the EEG. The results showed a significant upsurge in the value theta Fz/alpha Pz (p<0.001) while playing game. The results also showed tachycardia while playing video game as compared to rest condition (p<0.005). Normalized low frequency power and ratio of low frequency/high frequency power were significantly increased while playing video game and normalized high frequency power sank during video games. Results showed synchronized activity of cerebellum and sympathetic and parasympathetic innervation of heart.

Utilizing gamma band to improve mental task based brain-computer interface design.

PubMed

Palaniappan, Ramaswamy

2006-09-01

A common method for designing brain-computer Interface (BCI) is to use electroencephalogram (EEG) signals extracted during mental tasks. In these BCI designs, features from EEG such as power and asymmetry ratios from delta, theta, alpha, and beta bands have been used in classifying different mental tasks. In this paper, the performance of the mental task based BCI design is improved by using spectral power and asymmetry ratios from gamma (24-37 Hz) band in addition to the lower frequency bands. In the experimental study, EEG signals extracted during five mental tasks from four subjects were used. Elman neural network (ENN) trained by the resilient backpropagation algorithm was used to classify the power and asymmetry ratios from EEG into different combinations of two mental tasks. The results indicated that ((1) the classification performance and training time of the BCI design were improved through the use of additional gamma band features; (2) classification performances were nearly invariant to the number of ENN hidden units or feature extraction method.

Dynamical complexity in a mean-field model of human EEG

NASA Astrophysics Data System (ADS)

Frascoli, Federico; Dafilis, Mathew P.; van Veen, Lennaert; Bojak, Ingo; Liley, David T. J.

2008-12-01

A recently proposed mean-field theory of mammalian cortex rhythmogenesis describes the salient features of electrical activity in the cerebral macrocolumn, with the use of inhibitory and excitatory neuronal populations (Liley et al 2002). This model is capable of producing a range of important human EEG (electroencephalogram) features such as the alpha rhythm, the 40 Hz activity thought to be associated with conscious awareness (Bojak & Liley 2007) and the changes in EEG spectral power associated with general anesthetic effect (Bojak & Liley 2005). From the point of view of nonlinear dynamics, the model entails a vast parameter space within which multistability, pseudoperiodic regimes, various routes to chaos, fat fractals and rich bifurcation scenarios occur for physiologically relevant parameter values (van Veen & Liley 2006). The origin and the character of this complex behaviour, and its relevance for EEG activity will be illustrated. The existence of short-lived unstable brain states will also be discussed in terms of the available theoretical and experimental results. A perspective on future analysis will conclude the presentation.

Noise Reduction in Brainwaves by Using Both EEG Signals and Frontal Viewing Camera Images

PubMed Central

Bang, Jae Won; Choi, Jong-Suk; Park, Kang Ryoung

2013-01-01

Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have been used in various applications, including human–computer interfaces, diagnosis of brain diseases, and measurement of cognitive status. However, EEG signals can be contaminated with noise caused by user's head movements. Therefore, we propose a new method that combines an EEG acquisition device and a frontal viewing camera to isolate and exclude the sections of EEG data containing these noises. This method is novel in the following three ways. First, we compare the accuracies of detecting head movements based on the features of EEG signals in the frequency and time domains and on the motion features of images captured by the frontal viewing camera. Second, the features of EEG signals in the frequency domain and the motion features captured by the frontal viewing camera are selected as optimal ones. The dimension reduction of the features and feature selection are performed using linear discriminant analysis. Third, the combined features are used as inputs to support vector machine (SVM), which improves the accuracy in detecting head movements. The experimental results show that the proposed method can detect head movements with an average error rate of approximately 3.22%, which is smaller than that of other methods. PMID:23669713

A Novel EEG Based Spectral Analysis of Persistent Brain Function Alteration in Athletes with Concussion History.

PubMed

Munia, Tamanna T K; Haider, Ali; Schneider, Charles; Romanick, Mark; Fazel-Rezai, Reza

2017-12-08

The neurocognitive sequelae of a sport-related concussion and its management are poorly defined. Detecting deficits are vital in making a decision about the treatment plan as it can persist one year or more following a brain injury. The reliability of traditional cognitive assessment tools is debatable, and thus attention has turned to assessments based on electroencephalogram (EEG) to evaluate subtle post-concussive alterations. In this study, we calculated neurocognitive deficits combining EEG analysis with three standard post-concussive assessment tools. Data were collected for all testing modalities from 21 adolescent athletes (seven concussive and fourteen healthy) in three different trials. For EEG assessment, along with linear frequency-based features, we introduced a set of time-frequency (Hjorth Parameters) and nonlinear features (approximate entropy and Hurst exponent) for the first time to explore post-concussive deficits. Besides traditional frequency-band analysis, we also presented a new individual frequency-based approach for EEG assessment. While EEG analysis exhibited significant discrepancies between the groups, none of the cognitive assessment resulted in significant deficits. Therefore, the evidence from the study highlights that our proposed EEG analysis and markers are more efficient at deciphering post-concussion residual neurocognitive deficits and thus has a potential clinical utility of proper concussion assessment and management.

EEG dynamical correlates of focal and diffuse causes of coma.

PubMed

Kafashan, MohammadMehdi; Ryu, Shoko; Hargis, Mitchell J; Laurido-Soto, Osvaldo; Roberts, Debra E; Thontakudi, Akshay; Eisenman, Lawrence; Kummer, Terrance T; Ching, ShiNung

2017-11-15

Rapidly determining the causes of a depressed level of consciousness (DLOC) including coma is a common clinical challenge. Quantitative analysis of the electroencephalogram (EEG) has the potential to improve DLOC assessment by providing readily deployable, temporally detailed characterization of brain activity in such patients. While used commonly for seizure detection, EEG-based assessment of DLOC etiology is less well-established. As a first step towards etiological diagnosis, we sought to distinguish focal and diffuse causes of DLOC through assessment of temporal dynamics within EEG signals. We retrospectively analyzed EEG recordings from 40 patients with DLOC with consensus focal or diffuse culprit pathology. For each recording, we performed a suite of time-series analyses, then used a statistical framework to identify which analyses (features) could be used to distinguish between focal and diffuse cases. Using cross-validation approaches, we identified several spectral and non-spectral EEG features that were significantly different between DLOC patients with focal vs. diffuse etiologies, enabling EEG-based classification with an accuracy of 76%. Our findings suggest that DLOC due to focal vs. diffuse injuries differ along several electrophysiological parameters. These results may form the basis of future classification strategies for DLOC and coma that are more etiologically-specific and therefore therapeutically-relevant.

Discriminative Ocular Artifact Correction for Feature Learning in EEG Analysis.

PubMed

Xinyang Li; Cuntai Guan; Haihong Zhang; Kai Keng Ang

2017-08-01

Electrooculogram (EOG) artifact contamination is a common critical issue in general electroencephalogram (EEG) studies as well as in brain-computer interface (BCI) research. It is especially challenging when dedicated EOG channels are unavailable or when there are very few EEG channels available for independent component analysis based ocular artifact removal. It is even more challenging to avoid loss of the signal of interest during the artifact correction process, where the signal of interest can be multiple magnitudes weaker than the artifact. To address these issues, we propose a novel discriminative ocular artifact correction approach for feature learning in EEG analysis. Without extra ocular movement measurements, the artifact is extracted from raw EEG data, which is totally automatic and requires no visual inspection of artifacts. Then, artifact correction is optimized jointly with feature extraction by maximizing oscillatory correlations between trials from the same class and minimizing them between trials from different classes. We evaluate this approach on a real-world EEG dataset comprising 68 subjects performing cognitive tasks. The results showed that the approach is capable of not only suppressing the artifact components but also improving the discriminative power of a classifier with statistical significance. We also demonstrate that the proposed method addresses the confounding issues induced by ocular movements in cognitive EEG study.

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.

Deep Neural Architectures for Mapping Scalp to Intracranial EEG.

PubMed

Antoniades, Andreas; Spyrou, Loukianos; Martin-Lopez, David; Valentin, Antonio; Alarcon, Gonzalo; Sanei, Saeid; Took, Clive Cheong

2018-03-19

Data is often plagued by noise which encumbers machine learning of clinically useful biomarkers and electroencephalogram (EEG) data is no exemption. Intracranial EEG (iEEG) data enhances the training of deep learning models of the human brain, yet is often prohibitive due to the invasive recording process. A more convenient alternative is to record brain activity using scalp electrodes. However, the inherent noise associated with scalp EEG data often impedes the learning process of neural models, achieving substandard performance. Here, an ensemble deep learning architecture for nonlinearly mapping scalp to iEEG data is proposed. The proposed architecture exploits the information from a limited number of joint scalp-intracranial recording to establish a novel methodology for detecting the epileptic discharges from the sEEG of a general population of subjects. Statistical tests and qualitative analysis have revealed that the generated pseudo-intracranial data are highly correlated with the true intracranial data. This facilitated the detection of IEDs from the scalp recordings where such waveforms are not often visible. As a real-world clinical application, these pseudo-iEEGs are then used by a convolutional neural network for the automated classification of intracranial epileptic discharges (IEDs) and non-IED of trials in the context of epilepsy analysis. Although the aim of this work was to circumvent the unavailability of iEEG and the limitations of sEEG, we have achieved a classification accuracy of 68% an increase of 6% over the previously proposed linear regression mapping.

Temporal profile of prolonged, night-time driving performance: breaks from driving temporarily reduce time-on-task fatigue but not sleepiness.

PubMed

Phipps-Nelson, Jo; Redman, Jennifer R; Rajaratnam, Shantha M W

2011-09-01

Breaks are often used by drivers to counteract sleepiness and time-on-task fatigue during prolonged driving. We examined the temporal profile of changes in driving performance, electroencephalogram (EEG) activity and subjective measures of sleepiness and fatigue during prolonged nocturnal driving in a car simulator. In addition, the study examined the impact of regular breaks from driving on performance, sleepiness and fatigue. Healthy volunteers (n=12, 23-45 years) maintained a regular sleep-wake pattern for 14 days and were then in a laboratory from 21:00 to 08:30 hours. The driving simulator scene was designed to simulate monotonous night-time rural driving. Participants drove 4 × 2-h test sessions, with a break from driving of 1 h between each session. During the break participants performed tests assessing sleepiness and fatigue, and psychomotor performance (~30 mins), and then were permitted to sit quietly. They were monitored for wakefulness, and not permitted to nap or ingest caffeine. EEG was recorded during the driving task, and subjective assessments of sleepiness and fatigue were obtained at the start and completion of each session. We found that driving performance deteriorated (2.5-fold), EEG delta, theta and alpha activity increased, and subjective sleepiness and fatigue ratings increased across the testing period. Driving performance and fatigue ratings improved following the scheduled breaks from driving, while the breaks did not affect EEG activity and subjective sleepiness. Time-on-task effects increased through the testing period, indicating that these effects are exacerbated by increasing sleepiness. Breaks from driving without sleep temporarily ameliorate time-on-task fatigue, but provide little benefit to the sleepy driver. © 2010 European Sleep Research Society.

EEG Brain Activity in Dynamic Health Qigong Training: Same Effects for Mental Practice and Physical Training?

PubMed

Henz, Diana; Schöllhorn, Wolfgang I

2017-01-01

In recent years, there has been significant uptake of meditation and related relaxation techniques, as a means of alleviating stress and fostering an attentive mind. Several electroencephalogram (EEG) studies have reported changes in spectral band frequencies during Qigong meditation indicating a relaxed state. Much less is reported on effects of brain activation patterns induced by Qigong techniques involving bodily movement. In this study, we tested whether (1) physical Qigong training alters EEG theta and alpha activation, and (2) mental practice induces the same effect as a physical Qigong training. Subjects performed the dynamic Health Qigong technique Wu Qin Xi (five animals) physically and by mental practice in a within-subjects design. Experimental conditions were randomized. Two 2-min (eyes-open, eyes-closed) EEG sequences under resting conditions were recorded before and immediately after each 15-min exercise. Analyses of variance were performed for spectral power density data. Increased alpha power was found in posterior regions in mental practice and physical training for eyes-open and eyes-closed conditions. Theta power was increased after mental practice in central areas in eyes-open conditions, decreased in fronto-central areas in eyes-closed conditions. Results suggest that mental, as well as physical Qigong training, increases alpha activity and therefore induces a relaxed state of mind. The observed differences in theta activity indicate different attentional processes in physical and mental Qigong training. No difference in theta activity was obtained in physical and mental Qigong training for eyes-open and eyes-closed resting state. In contrast, mental practice of Qigong entails a high degree of internalized attention that correlates with theta activity, and that is dependent on eyes-open and eyes-closed resting state.

Hyperventilation in Patients With Focal Epilepsy: Electromagnetic Tomography, Functional Connectivity and Graph Theory - A Possible Tool in Epilepsy Diagnosis?

PubMed

Mazzucchi, Edoardo; Vollono, Catello; Losurdo, Anna; Testani, Elisa; Gnoni, Valentina; Di Blasi, Chiara; Giannantoni, Nadia M; Lapenta, Leonardo; Brunetti, Valerio; Della Marca, Giacomo

2017-01-01

Hyperventilation (HV) is a commonly used electroencephalogram activation method. We analyzed EEG recordings in 22 normal subjects and 22 patients with focal epilepsy of unknown cause. We selected segments before (PRE), during (HYPER), and 5 minutes after (POST) HV. To analyze the neural generators of EEG signal, we used standard low-resolution electromagnetic tomography (sLORETA software). We then computed EEG lagged coherence, an index of functional connectivity, between 19 regions of interest. A weighted graph was built for each band in every subject, and characteristic path length (L) and clustering coefficient (C) have been computed. Statistical comparisons were performed by means of analysis of variance (Group X Condition X Band) for mean lagged coherence, L and C. Hyperventilation significantly increases EEG neural generators (P < 0.001); the effect is particularly evident in cingulate cortex. Functional connectivity was increased by HV in delta, theta, alpha, and beta bands in the Epileptic group (P < 0.01) and only in theta band in Control group. Intergroup analysis of mean lagged coherence, C and L, showed significant differences for Group (P < 0.001), Condition (P < 0.001), and Band (P < 0.001). Analysis of variance for L also showed significant interactions: Group X Condition (P = 0.003) and Group X Band (P < 0.001). In our relatively small group of epileptic patients, HV is associated with activation of cingulate cortex; moreover, it modifies brain connectivity. The significant differences in mean lagged coherence, path length, and clustering coefficient permit to hypothesize that this activation method leads to different brain connectivity patterns in patients with epilepsy when compared with normal subjects. If confirmed by other studies involving larger populations, this analysis could become a diagnostic tool in epilepsy.

Mixture of autoregressive modeling orders and its implication on single trial EEG classification

PubMed Central

Atyabi, Adham; Shic, Frederick; Naples, Adam

2016-01-01

Autoregressive (AR) models are of commonly utilized feature types in Electroencephalogram (EEG) studies due to offering better resolution, smoother spectra and being applicable to short segments of data. Identifying correct AR’s modeling order is an open challenge. Lower model orders poorly represent the signal while higher orders increase noise. Conventional methods for estimating modeling order includes Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Final Prediction Error (FPE). This article assesses the hypothesis that appropriate mixture of multiple AR orders is likely to better represent the true signal compared to any single order. Better spectral representation of underlying EEG patterns can increase utility of AR features in Brain Computer Interface (BCI) systems by increasing timely & correctly responsiveness of such systems to operator’s thoughts. Two mechanisms of Evolutionary-based fusion and Ensemble-based mixture are utilized for identifying such appropriate mixture of modeling orders. The classification performance of the resultant AR-mixtures are assessed against several conventional methods utilized by the community including 1) A well-known set of commonly used orders suggested by the literature, 2) conventional order estimation approaches (e.g., AIC, BIC and FPE), 3) blind mixture of AR features originated from a range of well-known orders. Five datasets from BCI competition III that contain 2, 3 and 4 motor imagery tasks are considered for the assessment. The results indicate superiority of Ensemble-based modeling order mixture and evolutionary-based order fusion methods within all datasets. PMID:28740331

A continuous mapping of sleep states through association of EEG with a mesoscale cortical model.

PubMed

Lopour, Beth A; Tasoglu, Savas; Kirsch, Heidi E; Sleigh, James W; Szeri, Andrew J

2011-04-01

Here we show that a mathematical model of the human sleep cycle can be used to obtain a detailed description of electroencephalogram (EEG) sleep stages, and we discuss how this analysis may aid in the prediction and prevention of seizures during sleep. The association between EEG data and the cortical model is found via locally linear embedding (LLE), a method of dimensionality reduction. We first show that LLE can distinguish between traditional sleep stages when applied to EEG data. It reliably separates REM and non-REM sleep and maps the EEG data to a low-dimensional output space where the sleep state changes smoothly over time. We also incorporate the concept of strongly connected components and use this as a method of automatic outlier rejection for EEG data. Then, by using LLE on a hybrid data set containing both sleep EEG and signals generated from the mesoscale cortical model, we quantify the relationship between the data and the mathematical model. This enables us to take any sample of sleep EEG data and associate it with a position among the continuous range of sleep states provided by the model; we can thus infer a trajectory of states as the subject sleeps. Lastly, we show that this method gives consistent results for various subjects over a full night of sleep and can be done in real time.

Directed Motor-Auditory EEG Connectivity Is Modulated by Music Tempo.

PubMed

Nicolaou, Nicoletta; Malik, Asad; Daly, Ian; Weaver, James; Hwang, Faustina; Kirke, Alexis; Roesch, Etienne B; Williams, Duncan; Miranda, Eduardo R; Nasuto, Slawomir J

2017-01-01

Beat perception is fundamental to how we experience music, and yet the mechanism behind this spontaneous building of the internal beat representation is largely unknown. Existing findings support links between the tempo (speed) of the beat and enhancement of electroencephalogram (EEG) activity at tempo-related frequencies, but there are no studies looking at how tempo may affect the underlying long-range interactions between EEG activity at different electrodes. The present study investigates these long-range interactions using EEG activity recorded from 21 volunteers listening to music stimuli played at 4 different tempi (50, 100, 150 and 200 beats per minute). The music stimuli consisted of piano excerpts designed to convey the emotion of "peacefulness". Noise stimuli with an identical acoustic content to the music excerpts were also presented for comparison purposes. The brain activity interactions were characterized with the imaginary part of coherence (iCOH) in the frequency range 1.5-18 Hz (δ, θ, α and lower β) between all pairs of EEG electrodes for the four tempi and the music/noise conditions, as well as a baseline resting state (RS) condition obtained at the start of the experimental task. Our findings can be summarized as follows: (a) there was an ongoing long-range interaction in the RS engaging fronto-posterior areas; (b) this interaction was maintained in both music and noise, but its strength and directionality were modulated as a result of acoustic stimulation; (c) the topological patterns of iCOH were similar for music, noise and RS, however statistically significant differences in strength and direction of iCOH were identified; and (d) tempo had an effect on the direction and strength of motor-auditory interactions. Our findings are in line with existing literature and illustrate a part of the mechanism by which musical stimuli with different tempi can entrain changes in cortical activity.

Comparison of intraosseous pentobarbital administration and thoracic compression for euthanasia of anesthetized sparrows (Passer domesticus) and starlings (Sturnus vulgaris).

PubMed

Paul-Murphy, Joanne R; Engilis, Andrew; Pascoe, Peter J; Williams, D Colette; Gustavsen, Kate A; Drazenovich, Tracy L; Keel, M Kevin; Polley, Tamsen M; Engilis, Irene E

2017-08-01

OBJECTIVE To compare intraosseous pentobarbital treatment (IPT) and thoracic compression (TC) on time to circulatory arrest and an isoelectric electroencephalogram (EEG) in anesthetized passerine birds. ANIMALS 30 wild-caught adult birds (17 house sparrows [Passer domesticus] and 13 European starlings [Sturnus vulgaris]). PROCEDURES Birds were assigned to receive IPT or TC (n = 6/species/group). Birds were anesthetized, and carotid arterial pulses were monitored by Doppler methodology. Five subdermal braided-wire electrodes were used for EEG. Anesthetic depth was adjusted until a continuous EEG pattern was maintained, then euthanasia was performed. Times from initiation of euthanasia to cessation of carotid pulse and irreversible isoelectric EEG (indicators of death) were measured. Data (medians and first to third quartiles) were summarized and compared between groups within species. Necropsies were performed for all birds included in experiments and for another 6 birds euthanized under anesthesia by TC (4 sparrows and 1 starling) or IPT (1 sparrow). RESULTS Median time to isoelectric EEG did not differ significantly between treatment groups for sparrows (19.0 and 6.0 seconds for TC and IPT, respectively) or starlings (88.5 and 77.5 seconds for TC and IPT, respectively). Median times to cessation of pulse were significantly shorter for TC than for IPT in sparrows (0.0 vs 18.5 seconds) and starlings (9.5 vs 151.0 seconds). On necropsy, most (14/17) birds that underwent TC had grossly visible coelomic, pericardial, or perihepatic hemorrhage. CONCLUSIONS AND CLINICAL RELEVANCE Results suggested that TC might be an efficient euthanasia method for small birds. Digital pressure directly over the heart during TC obstructed venous return, causing rapid circulatory arrest, with rupture of the atria or vena cava in several birds. The authors propose that cardiac compression is a more accurate description than TC for this procedure.

Directed Motor-Auditory EEG Connectivity Is Modulated by Music Tempo

PubMed Central

Nicolaou, Nicoletta; Malik, Asad; Daly, Ian; Weaver, James; Hwang, Faustina; Kirke, Alexis; Roesch, Etienne B.; Williams, Duncan; Miranda, Eduardo R.; Nasuto, Slawomir J.

2017-01-01

Beat perception is fundamental to how we experience music, and yet the mechanism behind this spontaneous building of the internal beat representation is largely unknown. Existing findings support links between the tempo (speed) of the beat and enhancement of electroencephalogram (EEG) activity at tempo-related frequencies, but there are no studies looking at how tempo may affect the underlying long-range interactions between EEG activity at different electrodes. The present study investigates these long-range interactions using EEG activity recorded from 21 volunteers listening to music stimuli played at 4 different tempi (50, 100, 150 and 200 beats per minute). The music stimuli consisted of piano excerpts designed to convey the emotion of “peacefulness”. Noise stimuli with an identical acoustic content to the music excerpts were also presented for comparison purposes. The brain activity interactions were characterized with the imaginary part of coherence (iCOH) in the frequency range 1.5–18 Hz (δ, θ, α and lower β) between all pairs of EEG electrodes for the four tempi and the music/noise conditions, as well as a baseline resting state (RS) condition obtained at the start of the experimental task. Our findings can be summarized as follows: (a) there was an ongoing long-range interaction in the RS engaging fronto-posterior areas; (b) this interaction was maintained in both music and noise, but its strength and directionality were modulated as a result of acoustic stimulation; (c) the topological patterns of iCOH were similar for music, noise and RS, however statistically significant differences in strength and direction of iCOH were identified; and (d) tempo had an effect on the direction and strength of motor-auditory interactions. Our findings are in line with existing literature and illustrate a part of the mechanism by which musical stimuli with different tempi can entrain changes in cortical activity. PMID:29093672

Authentication, privacy, security can exploit brainwave by biomarker

NASA Astrophysics Data System (ADS)

Jenkins, Jeffrey; Sweet, Charles; Sweet, James; Noel, Steven; Szu, Harold

2014-05-01

We seek to augment the current Common Access Control (CAC) card and Personal Identification Number (PIN) verification systems with an additional layer of classified access biometrics. Among proven devices such as fingerprint readers and cameras that can sense the human eye's iris pattern, we introduced a number of users to a sequence of 'grandmother images', or emotionally evoked stimuli response images from other users, as well as one of their own, for the purpose of authentication. We performed testing and evaluation of the Authenticity Privacy and Security (APS) brainwave biometrics, similar to the internal organ of the human eye's iris which cannot easily be altered. `Aha' recognition through stimulus-response habituation can serve as a biomarker, similar to keystroke dynamics analysis for inter and intra key fluctuation time of a memorized PIN number (FIST). Using a non-tethered Electroencephalogram (EEG) wireless smartphone/pc monitor interface, we explore the appropriate stimuli-response biomarker present in DTAB low frequency group waves. Prior to login, the user is shown a series of images on a computer display. They have been primed to click their mouse when the image is presented. DTAB waves are collected with a wireless EEG and are sent via Smartphone to a cloud based processing infrastructure. There, we measure fluctuations in DTAB waves from a wireless, non-tethered, single node EEG device between the Personal Graphic Image Number (PGIN) stimulus image and the response time from an individual's mental performance baseline. Towards that goal, we describe an infrastructure that supports distributed verification for web-based EEG authentication. The performance of machine learning on the relative Power Spectral Density EEG data may uncover features required for subsequent access to web or media content. Our approach provides a scalable framework wrapped into a robust Neuro-Informatics toolkit, viable for use in the Biomedical and mental health communities, as well as numerous consumer applications.

Application of recurrence quantification analysis for the automated identification of epileptic EEG signals.

PubMed

Acharya, U Rajendra; Sree, S Vinitha; Chattopadhyay, Subhagata; Yu, Wenwei; Ang, Peng Chuan Alvin

2011-06-01

Epilepsy is a common neurological disorder that is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals are widely used to diagnose seizures. Because of the non-linear and dynamic nature of the EEG signals, it is difficult to effectively decipher the subtle changes in these signals by visual inspection and by using linear techniques. Therefore, non-linear methods are being researched to analyze the EEG signals. In this work, we use the recorded EEG signals in Recurrence Plots (RP), and extract Recurrence Quantification Analysis (RQA) parameters from the RP in order to classify the EEG signals into normal, ictal, and interictal classes. Recurrence Plot (RP) is a graph that shows all the times at which a state of the dynamical system recurs. Studies have reported significantly different RQA parameters for the three classes. However, more studies are needed to develop classifiers that use these promising features and present good classification accuracy in differentiating the three types of EEG segments. Therefore, in this work, we have used ten RQA parameters to quantify the important features in the EEG signals.These features were fed to seven different classifiers: Support vector machine (SVM), Gaussian Mixture Model (GMM), Fuzzy Sugeno Classifier, K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC), Decision Tree (DT), and Radial Basis Probabilistic Neural Network (RBPNN). Our results show that the SVM classifier was able to identify the EEG class with an average efficiency of 95.6%, sensitivity and specificity of 98.9% and 97.8%, respectively.

A Comparative Study of Different EEG Reference Choices for Diagnosing Unipolar Depression.

PubMed

Mumtaz, Wajid; Malik, Aamir Saeed

2018-06-02

The choice of an electroencephalogram (EEG) reference has fundamental importance and could be critical during clinical decision-making because an impure EEG reference could falsify the clinical measurements and subsequent inferences. In this research, the suitability of three EEG references was compared while classifying depressed and healthy brains using a machine-learning (ML)-based validation method. In this research, the EEG data of 30 unipolar depressed subjects and 30 age-matched healthy controls were recorded. The EEG data were analyzed in three different EEG references, the link-ear reference (LE), average reference (AR), and reference electrode standardization technique (REST). The EEG-based functional connectivity (FC) was computed. Also, the graph-based measures, such as the distances between nodes, minimum spanning tree, and maximum flow between the nodes for each channel pair, were calculated. An ML scheme provided a mechanism to compare the performances of the extracted features that involved a general framework such as the feature extraction (graph-based theoretic measures), feature selection, classification, and validation. For comparison purposes, the performance metrics such as the classification accuracies, sensitivities, specificities, and F scores were computed. When comparing the three references, the diagnostic accuracy showed better performances during the REST, while the LE and AR showed less discrimination between the two groups. Based on the results, it can be concluded that the choice of appropriate reference is critical during the clinical scenario. The REST reference is recommended for future applications of EEG-based diagnosis of mental illnesses.

Diagnostic Role of ECG Recording Simultaneously With EEG Testing.

PubMed

Kendirli, Mustafa Tansel; Aparci, Mustafa; Kendirli, Nurten; Tekeli, Hakan; Karaoglan, Mustafa; Senol, Mehmet Guney; Togrol, Erdem

2015-07-01

Arrhythmia is not uncommon in the etiology of syncope which mimics epilepsy. Data about the epilepsy induced vagal tonus abnormalities have being increasingly reported. So we aimed to evaluate what a neurologist may gain by a simultaneous electrocardiogram (ECG) and electroencephalogram (EEG) recording in the patients who underwent EEG testing due to prediagnosis of epilepsy. We retrospectively evaluated and detected ECG abnormalities in 68 (18%) of 376 patients who underwent EEG testing. A minimum of 20 of minutes artifact-free recording were required for each patient. Standard 1-channel ECG was simultaneously recorded in conjunction with the EEG. In all, 28% of females and 14% of males had ECG abnormalities. Females (mean age 49 years, range 18-88 years) were older compared with the male group (mean age 28 years, range 16-83 years). Atrial fibrillation was more frequent in female group whereas bradycardia and respiratory sinus arrhythmia was higher in male group. One case had been detected a critical asystole indicating sick sinus syndrome in the female group and treated with a pacemaker implantation in the following period. Simultaneous ECG recording in conjunction with EEG testing is a clinical prerequisite to detect and to clarify the coexisting ECG and EEG abnormalities and their clinical relevance. Potentially rare lethal causes of syncope that mimic seizure or those that could cause resistance to antiepileptic therapy could effectively be distinguished by detecting ECG abnormalities coinciding with the signs and abnormalities during EEG recording. © EEG and Clinical Neuroscience Society (ECNS) 2014.

Directed differential connectivity graph of interictal epileptiform discharges

PubMed Central

Amini, Ladan; Jutten, Christian; Achard, Sophie; David, Olivier; Soltanian-Zadeh, Hamid; Hossein-Zadeh, Gh. Ali; Kahane, Philippe; Minotti, Lorella; Vercueil, Laurent

2011-01-01

In this paper, we study temporal couplings between interictal events of spatially remote regions in order to localize the leading epileptic regions from intracerebral electroencephalogram (iEEG). We aim to assess whether quantitative epileptic graph analysis during interictal period may be helpful to predict the seizure onset zone of ictal iEEG. Using wavelet transform, cross-correlation coefficient, and multiple hypothesis test, we propose a differential connectivity graph (DCG) to represent the connections that change significantly between epileptic and non-epileptic states as defined by the interictal events. Post-processings based on mutual information and multi-objective optimization are proposed to localize the leading epileptic regions through DCG. The suggested approach is applied on iEEG recordings of five patients suffering from focal epilepsy. Quantitative comparisons of the proposed epileptic regions within ictal onset zones detected by visual inspection and using electrically stimulated seizures, reveal good performance of the present method. PMID:21156385

ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features.

PubMed

Mognon, Andrea; Jovicich, Jorge; Bruzzone, Lorenzo; Buiatti, Marco

2011-02-01

A successful method for removing artifacts from electroencephalogram (EEG) recordings is Independent Component Analysis (ICA), but its implementation remains largely user-dependent. Here, we propose a completely automatic algorithm (ADJUST) that identifies artifacted independent components by combining stereotyped artifact-specific spatial and temporal features. Features were optimized to capture blinks, eye movements, and generic discontinuities on a feature selection dataset. Validation on a totally different EEG dataset shows that (1) ADJUST's classification of independent components largely matches a manual one by experts (agreement on 95.2% of the data variance), and (2) Removal of the artifacted components detected by ADJUST leads to neat reconstruction of visual and auditory event-related potentials from heavily artifacted data. These results demonstrate that ADJUST provides a fast, efficient, and automatic way to use ICA for artifact removal. Copyright © 2010 Society for Psychophysiological Research.

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.

Modeling and analyzing non-seizure EEG data for patients with epilepsy

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

Lawkins, W.F.; Clapp, N.E. Jr.; Daw, C.S.

1996-05-01

We present nonlinear analysis of non-seizure electroencephalogram (EEG) time series data from four epileptic patients. A non-seizure state is a period that is free of any part of an epileptic seizure, including the transition to a fully developed episode. EEG measurements are typically contaminated with a large amount of non- neurophysiological source information, generally called artifact, which arises, for example, from eye movement, muscle tension, and physical motion. The first objective of this study is to gain some insight into how much variability in analysis results to be expected from patients having similar clinical characteristics. The second objective is tomore » investigate the impact of eye movement on the analysis results. A special feature presented here is the introduction and testing of a filter for eye movement artifact. The third objective is to determine if neurophysiological activity as viewed from two adjacent channels appears dynamically to be the same.« less

Estimating Driving Performance Based on EEG Spectrum Analysis

NASA Astrophysics Data System (ADS)

Lin, Chin-Teng; Wu, Ruei-Cheng; Jung, Tzyy-Ping; Liang, Sheng-Fu; Huang, Teng-Yi

2005-12-01

The growing number of traffic accidents in recent years has become a serious concern to society. Accidents caused by driver's drowsiness behind the steering wheel have a high fatality rate because of the marked decline in the driver's abilities of perception, recognition, and vehicle control abilities while sleepy. Preventing such accidents caused by drowsiness is highly desirable but requires techniques for continuously detecting, estimating, and predicting the level of alertness of drivers and delivering effective feedbacks to maintain their maximum performance. This paper proposes an EEG-based drowsiness estimation system that combines electroencephalogram (EEG) log subband power spectrum, correlation analysis, principal component analysis, and linear regression models to indirectly estimate driver's drowsiness level in a virtual-reality-based driving simulator. Our results demonstrated that it is feasible to accurately estimate quantitatively driving performance, expressed as deviation between the center of the vehicle and the center of the cruising lane, in a realistic driving simulator.

Neurodiagnostic techniques in neonatal critical care.

PubMed

Chang, Taeun; du Plessis, Adre

2012-04-01

This article reviews recent advances in the neurodiagnostic tools available to clinicians practicing in neonatal critical care. The advent of induced mild hypothermia for acute neonatal hypoxic-ischemic encephalopathy in 2005 has been responsible for renewed urgency in the development of precise and reliable neonatal neurodiagnostic techniques. Traditional evaluations of bedside head ultrasounds, head computed tomography scans, and routine electroencephalograms (EEGs) have been upgraded in most tertiary pediatric centers to incorporate protocols for MRI, continuous EEG monitoring with remote bedside access, amplitude-integrated EEG, and near-infrared spectroscopy. Meanwhile, recent studies supporting the association between placental pathology and neonatal brain injury highlight the need for closer examination of the placenta in the neurodiagnostic evaluation of the acutely ill newborn. As the pursuit of more effective neuroprotection moves into the "hypothermia plus" era, the identification, evaluation, and treatment of the neurologically affected newborn in the neonatal intensive care unit has increasing significance.

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.

Analysis of psycho-physiological features of a subject in simple tests with the registration of electroencephalograms

NASA Astrophysics Data System (ADS)

Runnova, Anastasiya; Zhuravlev, Maxim; Kulanin, Roman; Protasov, Pavel; Efremova, Tatiana

2018-04-01

In this paper we found a correlation between the characteristics of a person revealed in classical psychological testing on the basis of Schulte tables, and its neurophysiological features of the functioning of the brain obtained from the time-frequency analysis of EEG. The results obtained are interesting from the point of view of the choice of training strategies for a particular individual. We believe that the obtained results are of interest for fundamental science and applied works of psychological testing and diagnostics. The study of such forming strategies on EEG data can be automated and do not require the work of highly skilled psychologists.

Pharmaco-electroencephalographic and clinical effects of the cholinergic substance--acetyl-L-carnitine--in patients with organic brain syndrome.

PubMed

Herrmann, W M; Dietrich, B; Hiersemenzel, R

1990-01-01

In two double-blind, placebo-controlled clinical studies of the nootropic compound acetyl-L-carnitine on the electroencephalogram (EEG) and impaired brain functions of elderly outpatients with mild to moderate cognitive decline of the organic brain syndrome, statistically significant effects could be detected after eight weeks (on the EEG), and after 12 weeks of treatment (on the physician's clinical global impression and the patient-rated level of activities of daily living). Side-effects of acetyl-L-carnitine were generally minor and overall rare. Longer treatment periods and further specifications with regard to the aetiopathology and degree of cognitive impairment are recommended for further clinical studies of this promising compound.

Left hemibody myoclonus due to anomalous right vertebral artery.

PubMed

Coelho, Miguel; Marti, Maria J; Valls-Solé, Josep; Pujol, Teresa; Tolosa, Eduardo

2005-01-01

A 43-year-old man presented with sporadic, sudden, brief, and involuntary jerks of his left limbs and trunk muscles. The electromyographic recordings showed short-lasting highly synchronized bursts, compatible with myoclonus limited to the left hemibody. Blink reflex, masseter silent period, cortical and spinal magnetic stimulation, somatosensory cortical evoked potentials, and electroencephalogram (EEG) were normal; the EEG back-averaging showed no spikes preceding the myoclonus. Magnetic resonance imaging and magnetic resonance angiography showed the presence of an anomalous nonectasic right vertebral artery compressing the right side of ventral medulla oblongata. We hypothesize that the aberrant right vertebral artery induced abnormal activation of descending motor tracts responsible for the myoclonus. (c) 2004 Movement Disorder Society.

Age-related changes in sleep-wake rhythm in dog.

PubMed

Takeuchi, Takashi; Harada, Etsumori

2002-10-17

To investigate a sleep-wake rhythm in aged dogs, a radio-telemetry monitoring was carried out for 24 h. Electrodes and telemetry device were surgically implanted in four aged dogs (16-18 years old) and four young dogs (3-4 years old). Electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG) were recorded simultaneously as parameters to determine vigilance states and an autonomic nervous function. Wakefulness, slow wave sleep (SWS) and paradoxical sleep (PS) were identified according to the EEG and EMG pattern. We also examined whether absolute powers and the low frequency-to-high frequency ratio (LF/HF) derived from the heart rate variability power spectrum could detect shifts in autonomic balance correlated with aging. The aged dogs showed a marked reduction of PS and a fragmentation of wakefulness in the daytime and a sleep disruption in the night. The pattern of 24 h sleep and waking was dramatically altered in the aged dog. It was characterized by an increase in the total amount of time spent in SWS during the daytime followed by an increasing of time spent in wakefulness during the night. Furthermore, LF/HF ratio showed a very low amplitude of variance throughout the day in the aged dog. These results suggest that the aged dog is a useful model to investigate sleep disorders in human such as daytime drowsiness, difficulties in sleep maintenance. The abnormality in sleep-wake cycle might be reflected by the altered autonomic balance in the aged dogs.

Automatic classification of 6-month-old infants at familial risk for language-based learning disorder using a support vector machine.

PubMed

Zare, Marzieh; Rezvani, Zahra; Benasich, April A

2016-07-01

This study assesses the ability of a novel, "automatic classification" approach to facilitate identification of infants at highest familial risk for language-learning disorders (LLD) and to provide converging assessments to enable earlier detection of developmental disorders that disrupt language acquisition. Network connectivity measures derived from 62-channel electroencephalogram (EEG) recording were used to identify selected features within two infant groups who differed on LLD risk: infants with a family history of LLD (FH+) and typically-developing infants without such a history (FH-). A support vector machine was deployed; global efficiency and global and local clustering coefficients were computed. A novel minimum spanning tree (MST) approach was also applied. Cross-validation was employed to assess the resultant classification. Infants were classified with about 80% accuracy into FH+ and FH- groups with 89% specificity and precision of 92%. Clustering patterns differed by risk group and MST network analysis suggests that FH+ infants' EEG complexity patterns were significantly different from FH- infants. The automatic classification techniques used here were shown to be both robust and reliable and should provide valuable information when applied to early identification of risk or clinical groups. The ability to identify infants at highest risk for LLD using "automatic classification" strategies is a novel convergent approach that may facilitate earlier diagnosis and remediation. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Bezafibrate, a peroxisome proliferator-activated receptors agonist, decreases body temperature and enhances electroencephalogram delta-oscillation during sleep in mice.

PubMed

Chikahisa, Sachiko; Tominaga, Kumiko; Kawai, Tomoko; Kitaoka, Kazuyoshi; Oishi, Katsutaka; Ishida, Norio; Rokutan, Kazuhito; Séi, Hiroyoshi

2008-10-01

Peroxisome proliferator-activated receptors (PPARs) are ligand-activated transcription factors belonging to the nuclear receptor family. PPARs play a critical role in lipid and glucose metabolism. We examined whether chronic treatment with bezafibrate, a PPAR agonist, would alter sleep and body temperature (BT). Mice fed with a control diet were monitored for BT, electroencephalogram (EEG), and electromyogram for 48 h under light-dark conditions. After obtaining the baseline recording, the mice were provided with bezafibrate-supplemented food for 2 wk, after which the same recordings were performed. Two-week feeding of bezafibrate decreased BT, especially during the latter half of the dark period. BT rhythm and sleep/wake rhythm were phase advanced about 2-3 h by bezafibrate treatment. Bezafibrate treatment also increased the EEG delta-power in nonrapid eye movement sleep compared with the control diet attenuating its daily amplitude. Furthermore, bezafibrate-treated mice showed no rebound of EEG delta-power in nonrapid eye movement sleep after 6 h sleep deprivation, whereas values in control mice largely increased relative to baseline. DNA microarray, and real-time RT-PCR analysis showed that bezafibrate treatment increased levels of Neuropeptide Y mRNA in the hypothalamus at both Zeitgeber time (ZT) 10 and ZT22, and decreased proopiomelanocortin-alpha mRNA in the hypothalamus at ZT10. These findings demonstrate that PPARs participate in the control of both BT and sleep regulation, which accompanied changes in gene expression in the hypothalamus. Activation of PPARs may enhance deep sleep and improve resistance to sleep loss.

Sleep/Wake Physiology and Quantitative Electroencephalogram Analysis of the Neuroligin-3 Knockout Rat Model of Autism Spectrum Disorder.

PubMed

Thomas, Alexia M; Schwartz, Michael D; Saxe, Michael D; Kilduff, Thomas S

2017-10-01

Neuroligin-3 (NLGN3) is one of the many genes associated with autism spectrum disorder (ASD). Sleep dysfunction is highly prevalent in ASD, but has not been rigorously examined in ASD models. Here, we evaluated sleep/wake physiology and behavioral phenotypes of rats with genetic ablation of Nlgn3. Male Nlgn3 knockout (KO) and wild-type (WT) rats were assessed using a test battery for ASD-related behaviors and also implanted with telemeters to record the electroencephalogram (EEG), electromyogram, body temperature, and locomotor activity. 24-h EEG recordings were analyzed for sleep/wake states and spectral composition. Nlgn3 KO rats were hyperactive, exhibited excessive chewing behavior, and had impaired prepulse inhibition to an auditory startle stimulus. KO rats also spent less time in non-rapid eye movement (NREM) sleep, more time in rapid eye movement (REM) sleep, exhibited elevated theta power (4-9 Hz) during wakefulness and REM, and elevated delta power (0.5-4 Hz) during NREM. Beta (12-30 Hz) power and gamma (30-50 Hz) power were suppressed across all vigilance states. The sleep disruptions in Nlgn3 KO rats are consistent with observations of sleep disturbances in ASD patients. The EEG provides objective measures of brain function to complement rodent behavioral analyses and therefore may be a useful tool to study ASD. © 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.

Power spectral analysis of the sleep electroencephalogram in heartburn patients with or without gastroesophageal reflux disease: a feasibility study.

PubMed

Budhiraja, Rohit; Quan, Stuart F; Punjabi, Naresh M; Drake, Christopher L; Dickman, Ram; Fass, Ronnie

2010-02-01

Determine the feasibility of using power spectrum of the sleep electroencephalogram (EEG) as a more sensitive tool than sleep architecture to evaluate the relationship between gastroesophageal reflux disease (GERD) and sleep. GERD has been shown to adversely affect subjective sleep reports but not necessarily objective sleep parameters. Data were prospectively collected from symptomatic patients with heartburn. All symptomatic patients underwent upper endoscopy. Patients without erosive esophagitis underwent pH testing. Sleep was polygraphically recorded in the laboratory. Spectral analysis was performed to determine the power spectrum in 4 bandwidths: delta (0.8 to 4.0 Hz), theta (4.1 to 8.0 Hz), alpha (8.1 to 13.0 Hz), and beta (13.1 to 20.0 Hz). Eleven heartburn patients were included in the GERD group (erosive esophagitis) and 6 heartburn patients in the functional heartburn group (negative endoscopy, pH test, response to proton pump inhibitors). The GERD patients had evidence of lower average delta-power than functional heartburn patients. Patients with GERD had greater overall alpha-power in the latter half of the night (3 hours after sleep onset) than functional heartburn patients. No significant differences were noted in conventional sleep stage summaries between the 2 groups. Among heartburn patients with GERD, EEG spectral power during sleep is shifted towards higher frequencies compared with heartburn patients without GERD despite similar sleep architecture. This feasibility study demonstrated that EEG spectral power during sleep might be the preferred tool to provide an objective analysis about the effect of GERD on sleep.

Hypoglycemia-Associated EEG Changes Following Antecedent Hypoglycemia in Type 1 Diabetes Mellitus.

PubMed

Sejling, Anne-Sophie; Kjaer, Troels W; Pedersen-Bjergaard, Ulrik; Remvig, Line S; Frandsen, Christian S; Hilsted, Linda; Faber, Jens; Holst, Jens Juul; Tarnow, Lise; Møller, Jakob Skadkær; Nielsen, Martin N; Thorsteinsson, Birger; Juhl, Claus B

2017-02-01

Recurrent hypoglycemia has been shown to blunt hypoglycemia symptom scores and counterregulatory hormonal responses during subsequent hypoglycemia. We therefore studied whether hypoglycemia-associated electroencephalogram (EEG) changes are affected by an antecedent episode of hypoglycemia. Twenty-four patients with type 1 diabetes mellitus (10 with normal hypoglycemia awareness, 14 with hypoglycemia unawareness) were studied on 2 consecutive days by hyperinsulinemic glucose clamp at hypoglycemia (2.0-2.5 mmol/L) during a 1-h period. EEG was recorded, cognitive function assessed, and hypoglycemia symptom scores and counterregulatory hormonal responses were obtained. Twenty-one patients completed the study. Hypoglycemia-associated EEG changes were identified on both days with no differences in power or frequency distribution in the theta, alpha, or the combined theta-alpha band during hypoglycemia on the 2 days. Similar degree of cognitive dysfunction was also present during hypoglycemia on both days. When comparing the aware and unaware group, there were no differences in the hypoglycemia-associated EEG changes. There were very subtle differences in cognitive function between the two groups on day 2. The symptom response was higher in the aware group on both days, while only subtle differences were seen in the counterregulatory hormonal response. Antecedent hypoglycemia does not affect hypoglycemia-associated EEG changes in patients with type 1 diabetes mellitus.

EEG amplitude modulation analysis for semi-automated diagnosis of Alzheimer's disease

NASA Astrophysics Data System (ADS)

Falk, Tiago H.; Fraga, Francisco J.; Trambaiolli, Lucas; Anghinah, Renato

2012-12-01

Recent experimental evidence has suggested a neuromodulatory deficit in Alzheimer's disease (AD). In this paper, we present a new electroencephalogram (EEG) based metric to quantitatively characterize neuromodulatory activity. More specifically, the short-term EEG amplitude modulation rate-of-change (i.e., modulation frequency) is computed for five EEG subband signals. To test the performance of the proposed metric, a classification task was performed on a database of 32 participants partitioned into three groups of approximately equal size: healthy controls, patients diagnosed with mild AD, and those with moderate-to-severe AD. To gauge the benefits of the proposed metric, performance results were compared with those obtained using EEG spectral peak parameters which were recently shown to outperform other conventional EEG measures. Using a simple feature selection algorithm based on area-under-the-curve maximization and a support vector machine classifier, the proposed parameters resulted in accuracy gains, relative to spectral peak parameters, of 21.3% when discriminating between the three groups and by 50% when mild and moderate-to-severe groups were merged into one. The preliminary findings reported herein provide promising insights that automated tools may be developed to assist physicians in very early diagnosis of AD as well as provide researchers with a tool to automatically characterize cross-frequency interactions and their changes with disease.

Electroencephalographic abnormalities during sleep in children with developmental speech-language disorders: a case-control study.

PubMed

Parry-Fielder, Bronwyn; Collins, Kevin; Fisher, John; Keir, Eddie; Anderson, Vicki; Jacobs, Rani; Scheffer, Ingrid E; Nolan, Terry

2009-03-01

Earlier research has suggested a link between epileptiform activity in the electroencephalogram (EEG) and developmental speech-language disorder (DSLD). This study investigated the strength of this association by comparing the frequency of EEG abnormalities in 45 language-normal children (29 males, 16 females; mean age 6y 11mo, SD 1y 10mo, range 4y-9y 10mo) and 54 community-ascertained children (35 males, 19 females; mean age 5y 7mo, SD 1y 6mo, range 4y-9y 11mo) with a diagnosis of severe DSLD, defined as a score at least 2 SD below the mean on at least one speech-language measure, and a performance IQ of at least 80 points. All participants underwent sleep EEGs after sedation. Children with DSLD also had detailed speech-language, hearing, and psychological assessments. Results failed to support the previously identified strong association between abnormal EEG and DSLD. There was a weak, non-significant relationship between DSLD and epileptiform EEG. Epileptiform EEG was significantly associated with low performance IQ (p=0.04). This study draws into question previously reported associations between epileptiform activity and DSLD probably because it examined a purer cohort of children with more severe language difficulties who did not have seizures.

Negligible Motion Artifacts in Scalp Electroencephalography (EEG) During Treadmill Walking.

PubMed

Nathan, Kevin; Contreras-Vidal, Jose L

2015-01-01

Recent mobile brain/body imaging (MoBI) techniques based on active electrode scalp electroencephalogram (EEG) allow the acquisition and real-time analysis of brain dynamics during active unrestrained motor behavior involving whole body movements such as treadmill walking, over-ground walking and other locomotive and non-locomotive tasks. Unfortunately, MoBI protocols are prone to physiological and non-physiological artifacts, including motion artifacts that may contaminate the EEG recordings. A few attempts have been made to quantify these artifacts during locomotion tasks but with inconclusive results due in part to methodological pitfalls. In this paper, we investigate the potential contributions of motion artifacts in scalp EEG during treadmill walking at three different speeds (1.5, 3.0, and 4.5 km/h) using a wireless 64 channel active EEG system and a wireless inertial sensor attached to the subject's head. The experimental setup was designed according to good measurement practices using state-of-the-art commercially available instruments, and the measurements were analyzed using Fourier analysis and wavelet coherence approaches. Contrary to prior claims, the subjects' motion did not significantly affect their EEG during treadmill walking although precaution should be taken when gait speeds approach 4.5 km/h. Overall, these findings suggest how MoBI methods may be safely deployed in neural, cognitive, and rehabilitation engineering applications.

Epileptic Seizure Detection Based on Time-Frequency Images of EEG Signals using Gaussian Mixture Model and Gray Level Co-Occurrence Matrix Features.

PubMed

Li, Yang; Cui, Weigang; Luo, Meilin; Li, Ke; Wang, Lina

2018-01-25

The electroencephalogram (EEG) signal analysis is a valuable tool in the evaluation of neurological disorders, which is commonly used for the diagnosis of epileptic seizures. This paper presents a novel automatic EEG signal classification method for epileptic seizure detection. The proposed method first employs a continuous wavelet transform (CWT) method for obtaining the time-frequency images (TFI) of EEG signals. The processed EEG signals are then decomposed into five sub-band frequency components of clinical interest since these sub-band frequency components indicate much better discriminative characteristics. Both Gaussian Mixture Model (GMM) features and Gray Level Co-occurrence Matrix (GLCM) descriptors are then extracted from these sub-band TFI. Additionally, in order to improve classification accuracy, a compact feature selection method by combining the ReliefF and the support vector machine-based recursive feature elimination (RFE-SVM) algorithm is adopted to select the most discriminative feature subset, which is an input to the SVM with the radial basis function (RBF) for classifying epileptic seizure EEG signals. The experimental results from a publicly available benchmark database demonstrate that the proposed approach provides better classification accuracy than the recently proposed methods in the literature, indicating the effectiveness of the proposed method in the detection of epileptic seizures.

Effects of Neurofeekback Training on EEG, Continuous Performance Task (CPT), and ADHD Symptoms in ADHD-prone College Students.

PubMed

Ryoo, Manhee; Son, Chongnak

2015-12-01

This study explored the effects of neurofeedback training on Electroencephalogram (EEG), Continuous Performance Task (CPT) and ADHD symptoms in ADHD prone college students. Two hundred forty seven college students completed Korean Version of Conners' Adult ADHD Rating Scales (CAARS-K) and Korean Version of Beck Depression Inventory (K-BDI). The 16 participants who ranked in the top 25% of CAARS-K score and had 16 less of K-BDI score participated in this study. Among them, 8 participants who are fit for the research schedule were assigned to neurofeedback training group and 8 not fit for the research schedule to the control group. All participants completed Adult Attention Deficiency Questionnaire, CPT and EEG measurement at pretest. The neurofeedback group received 15 neurofeedback training sessions (5 weeks, 3 sessions per week). The control group did not receive any treatment. Four weeks after completion of the program, all participants completed CAARS-K, Adult Attention Deficiency Questionnaire, CPT and EEG measurement for post-test. The neurofeedback group showed more significant improvement in EEG, CPT performance and ADHD symptoms than the control group. The improvements were maintained at follow up. Neurofeedback training adjusted abnormal EEG and was effective in improving objective and subjective ADHD symptoms in ADHD prone college students.

Changes of EEG Spectra and Functional Connectivity during an Object-Location Memory Task in Alzheimer's Disease.

PubMed

Han, Yuliang; Wang, Kai; Jia, Jianjun; Wu, Weiping

2017-01-01

Object-location memory is particularly fragile and specifically impaired in Alzheimer's disease (AD) patients. Electroencephalogram (EEG) was utilized to objectively measure memory impairment for memory formation correlates of EEG oscillatory activities. We aimed to construct an object-location memory paradigm and explore EEG signs of it. Two groups of 20 probable mild AD patients and 19 healthy older adults were included in a cross-sectional analysis. All subjects took an object-location memory task. EEG recordings performed during object-location memory tasks were compared between the two groups in the two EEG parameters (spectral parameters and phase synchronization). The memory performance of AD patients was worse than that of healthy elderly adults The power of object-location memory of the AD group was significantly higher than the NC group (healthy elderly adults) in the alpha band in the encoding session, and alpha and theta bands in the retrieval session. The channels-pairs the phase lag index value of object-location memory in the AD group was clearly higher than the NC group in the delta, theta, and alpha bands in encoding sessions and delta and theta bands in retrieval sessions. The results provide support for the hypothesis that the AD patients may use compensation mechanisms to remember the items and episode.

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

[Wavelet packet extraction and entropy analysis of telemetry EEG from the prelimbic cortex of medial prefrontal cortex in morphine-induced CPP rats].

PubMed

Bai, Yu; Bai, Jia-Ming; Li, Jing; Li, Min; Yu, Ran; Pan, Qun-Wan

2014-12-25

The purpose of the present study is to analyze the relationship between the telemetry electroencephalogram (EEG) changes of the prelimbic (PL) cortex and the drug-seeking behavior of morphine-induced conditioned place preference (CPP) rats by using the wavelet packet extraction and entropy measurement. The recording electrode was stereotactically implanted into the PL cortex of rats. The animals were then divided randomly into operation-only control and morphine-induced CPP groups, respectively. A CPP video system in combination with an EEG wireless telemetry device was used for recording EEG of PL cortex when the rats shuttled between black-white or white-black chambers. The telemetry recorded EEGs were analyzed by wavelet packet extraction, Welch power spectrum estimate, normalized amplitude and Shannon entropy algorithm. The results showed that, compared with operation-only control group, the left PL cortex's EEG of morphine-induced CPP group during black-white chamber shuttling exhibited the following changes: (1) the amplitude of average EEG for each frequency bands extracted by wavelet packet was reduced; (2) the Welch power intensity was increased significantly in 10-50 Hz EEG band (P < 0.01 or P < 0.05); (3) Shannon entropy was increased in β, γ₁, and γ₂waves of the EEG (P < 0.01 or P < 0.05); and (4) the average information entropy was reduced (P < 0.01). The results suggest that above mentioned EEG changes in morphine-induced CPP group rat may be related to animals' drug-seeking motivation and behavior launching.

Wireless EEG System Achieving High Throughput and Reduced Energy Consumption Through Lossless and Near-Lossless Compression.

PubMed

Alvarez, Guillermo Dufort Y; Favaro, Federico; Lecumberry, Federico; Martin, Alvaro; Oliver, Juan P; Oreggioni, Julian; Ramirez, Ignacio; Seroussi, Gadiel; Steinfeld, Leonardo

2018-02-01

This work presents a wireless multichannel electroencephalogram (EEG) recording system featuring lossless and near-lossless compression of the digitized EEG signal. Two novel, low-complexity, efficient compression algorithms were developed and tested in a low-power platform. The algorithms were tested on six public EEG databases comparing favorably with the best compression rates reported up to date in the literature. In its lossless mode, the platform is capable of encoding and transmitting 59-channel EEG signals, sampled at 500 Hz and 16 bits per sample, at a current consumption of 337 A per channel; this comes with a guarantee that the decompressed signal is identical to the sampled one. The near-lossless mode allows for significant energy savings and/or higher throughputs in exchange for a small guaranteed maximum per-sample distortion in the recovered signal. Finally, we address the tradeoff between computation cost and transmission savings by evaluating three alternatives: sending raw data, or encoding with one of two compression algorithms that differ in complexity and compression performance. We observe that the higher the throughput (number of channels and sampling rate) the larger the benefits obtained from compression.

Human EEG responses to controlled alterations of the Earth's magnetic field.

PubMed

Sastre, Antonio; Graham, Charles; Cook, Mary R; Gerkovich, Mary M; Gailey, Paul

2002-09-01

Examine the effects of controlled changes in the Earth's magnetic field on electroencephalogram (EEG) and subjective report. Fifty volunteers were exposed double-blind to changes in field magnitude, angle of inclination, and angle of deviation. Volunteers were also exposed to magnetic field conditions found near the North and South Pole. EEG recorded over temporal and occipital sites was compared across 4s baseline, field exposure, and no-change control trials. No EEG spectral differences as a function of gender or recording site were found. Geomagnetic field alterations had no effect on total energy (0.5-42 Hz), energy within traditional EEG analysis bands, or on the 95% spectral edge. Most volunteers reported no sensations; others reported non-specific symptoms unrelated to type of field change. Three hypothesized field detection mechanisms were not supported: (1) mechanical reception through torque exerted on the ferromagnetic material magnetite; (2) movement-induced induction of an electric field in the body; and (3) enhanced sensitivity due to alterations in the rates of chemical reactions involving electron spin states. Humans have little ability to detect brief alterations in the geomagnetic field, even if these alteration are of a large magnitude.

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

PubMed Central

Bulea, Thomas C.; Kilicarslan, Atilla; Ozdemir, Recep; Paloski, William H.; Contreras-Vidal, Jose L.

2013-01-01

Recent studies support the involvement of supraspinal networks in control of bipedal human walking. Part of this evidence encompasses studies, including our previous work, demonstrating that gait kinematics and limb coordination during treadmill walking can be inferred from the scalp electroencephalogram (EEG) with reasonably high decoding accuracies. These results provide impetus for development of non-invasive brain-machine-interface (BMI) systems for use in restoration and/or augmentation of gait- a primary goal of rehabilitation research. To date, studies examining EEG decoding of activity during gait have been limited to treadmill walking in a controlled environment. However, to be practically viable a BMI system must be applicable for use in everyday locomotor tasks such as over ground walking and turning. Here, we present a novel protocol for non-invasive collection of brain activity (EEG), muscle activity (electromyography (EMG)), and whole-body kinematic data (head, torso, and limb trajectories) during both treadmill and over ground walking tasks. By collecting these data in the uncontrolled environment insight can be gained regarding the feasibility of decoding unconstrained gait and surface EMG from scalp EEG. PMID:23912203

Smart Helmet: Wearable Multichannel ECG and EEG

PubMed Central

Chanwimalueang, Theerasak; Goverdovsky, Valentin; Looney, David; Sharp, David; Mandic, Danilo P.

2016-01-01

Modern wearable technologies have enabled continuous recording of vital signs, however, for activities such as cycling, motor-racing, or military engagement, a helmet with embedded sensors would provide maximum convenience and the opportunity to monitor simultaneously both the vital signs and the electroencephalogram (EEG). To this end, we investigate the feasibility of recording the electrocardiogram (ECG), respiration, and EEG from face-lead locations, by embedding multiple electrodes within a standard helmet. The electrode positions are at the lower jaw, mastoids, and forehead, while for validation purposes a respiration belt around the thorax and a reference ECG from the chest serve as ground truth to assess the performance. The within-helmet EEG is verified by exposing the subjects to periodic visual and auditory stimuli and screening the recordings for the steady-state evoked potentials in response to these stimuli. Cycling and walking are chosen as real-world activities to illustrate how to deal with the so-induced irregular motion artifacts, which contaminate the recordings. We also propose a multivariate R-peak detection algorithm suitable for such noisy environments. Recordings in real-world scenarios support a proof of concept of the feasibility of recording vital signs and EEG from the proposed smart helmet. PMID:27957405

LMD Based Features for the Automatic Seizure Detection of EEG Signals Using SVM.

PubMed

Zhang, Tao; Chen, Wanzhong

2017-08-01

Achieving the goal of detecting seizure activity automatically using electroencephalogram (EEG) signals is of great importance and significance for the treatment of epileptic seizures. To realize this aim, a newly-developed time-frequency analytical algorithm, namely local mean decomposition (LMD), is employed in the presented study. LMD is able to decompose an arbitrary signal into a series of product functions (PFs). Primarily, the raw EEG signal is decomposed into several PFs, and then the temporal statistical and non-linear features of the first five PFs are calculated. The features of each PF are fed into five classifiers, including back propagation neural network (BPNN), K-nearest neighbor (KNN), linear discriminant analysis (LDA), un-optimized support vector machine (SVM) and SVM optimized by genetic algorithm (GA-SVM), for five classification cases, respectively. Confluent features of all PFs and raw EEG are further passed into the high-performance GA-SVM for the same classification tasks. Experimental results on the international public Bonn epilepsy EEG dataset show that the average classification accuracy of the presented approach are equal to or higher than 98.10% in all the five cases, and this indicates the effectiveness of the proposed approach for automated seizure detection.

Bispectral index monitoring during electroconvulsive therapy under propofol anaesthesia.

PubMed

Gunawardane, P O; Murphy, P A; Sleigh, J W

2002-02-01

The accuracy of the bispectral index (BIS) as a monitor of consciousness has not been well studied in patients who have abnormal electroencephalograms (EEG). We studied the changes in BIS, its subparameters, and spectral entropy of the EEG during 18 electroconvulsive treatments under propofol and succinylcholine anaesthesia. A single bifrontal EEG, and second subocular channel (for eye movement estimation) was recorded. The median (interquartile range) BIS value at re-awakening was only 57 (47-78)--thus more than a quarter of the patients woke at BIS values of less than 50. The changes in spectral entropy values were similar: 0.84 (0.68-0.99) at the start, 0.65 (0.42-0.88) at the point of loss-of-consciousness, 0.63 (0.47-0.79) during the seizures, and 0.58 (0.31-0.85) at awakening. Post-ictal slow-wave activity in the EEG (acting via the SynchFastSlow subparameter) may cause low BIS values that do not correspond to the patient's clinical level of consciousness. This may be important in the interpretation of the BIS in other groups of patients who have increased delta-band power in their EEG.

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.

EEG seizure detection and prediction algorithms: a survey

NASA Astrophysics Data System (ADS)

Alotaiby, Turkey N.; Alshebeili, Saleh A.; Alshawi, Tariq; Ahmad, Ishtiaq; Abd El-Samie, Fathi E.

2014-12-01

Epilepsy patients experience challenges in daily life due to precautions they have to take in order to cope with this condition. When a seizure occurs, it might cause injuries or endanger the life of the patients or others, especially when they are using heavy machinery, e.g., deriving cars. Studies of epilepsy often rely on electroencephalogram (EEG) signals in order to analyze the behavior of the brain during seizures. Locating the seizure period in EEG recordings manually is difficult and time consuming; one often needs to skim through tens or even hundreds of hours of EEG recordings. Therefore, automatic detection of such an activity is of great importance. Another potential usage of EEG signal analysis is in the prediction of epileptic activities before they occur, as this will enable the patients (and caregivers) to take appropriate precautions. In this paper, we first present an overview of seizure detection and prediction problem and provide insights on the challenges in this area. Second, we cover some of the state-of-the-art seizure detection and prediction algorithms and provide comparison between these algorithms. Finally, we conclude with future research directions and open problems in this topic.

Automatic detection of sleep apnea based on EEG detrended fluctuation analysis and support vector machine.

PubMed

Zhou, Jing; Wu, Xiao-ming; Zeng, Wei-jie

2015-12-01

Sleep apnea syndrome (SAS) is prevalent in individuals and recently, there are many studies focus on using simple and efficient methods for SAS detection instead of polysomnography. However, not much work has been done on using nonlinear behavior of the electroencephalogram (EEG) signals. The purpose of this study is to find a novel and simpler method for detecting apnea patients and to quantify nonlinear characteristics of the sleep apnea. 30 min EEG scaling exponents that quantify power-law correlations were computed using detrended fluctuation analysis (DFA) and compared between six SAS and six healthy subjects during sleep. The mean scaling exponents were calculated every 30 s and 360 control values and 360 apnea values were obtained. These values were compared between the two groups and support vector machine (SVM) was used to classify apnea patients. Significant difference was found between EEG scaling exponents of the two groups (p < 0.001). SVM was used and obtained high and consistent recognition rate: average classification accuracy reached 95.1% corresponding to the sensitivity 93.2% and specificity 98.6%. DFA of EEG is an efficient and practicable method and is helpful clinically in diagnosis of sleep apnea.

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.

Linking Gene, Brain, and Behavior

PubMed Central

Schmidt, Louis A.; Fox, Nathan A.; Perez-Edgar, Koraly; Hamer, Dean H.

2009-01-01

Gene-environment interactions involving exogenous environmental factors are known to shape behavior and personality development. Although gene-environment interactions involving endogenous environmental factors are hypothesized to play an equally important role, this conceptual approach has not been empirically applied in the study of early-developing temperament in humans. Here we report evidence for a gene-endoenvironment (i.e., resting frontal brain electroencephalogram, EEG, asymmetry) interaction in predicting child temperament. The DRD4 gene (long allele vs. short allele) moderated the relation between resting frontal EEG asymmetry (left vs. right) at 9 months and temperament at 48 months. Children who exhibited left frontal EEG asymmetry at 9 months and who possessed the DRD4 long allele were significantly more soothable at 48 months than other children. Among children with right frontal EEG asymmetry at 9 months, those with the DRD4 long allele had significantly more difficulties focusing and sustaining attention at 48 months than those with the DRD4 short allele. Resting frontal EEG asymmetry did not influence temperament in the absence of the DRD4 long allele. We discuss how the interaction of genetic and endoenvironment factors may confer risk and protection for different behavioral styles in children. PMID:19493320

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.

[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

Tracking variations in the alpha activity in an electroencephalogram

NASA Technical Reports Server (NTRS)

Prabhu, K. S.

1971-01-01

The problem of tracking Alpha voltage variations in an electroencephalogram is discussed. This problem is important in encephalographic studies of sleep and effects of different stimuli on the brain. Very often the Alpha voltage is tracked by passing the EEG signal through a bandpass filter centered at the Alpha frequency, which hopefully will filter out unwanted noise from the Alpha activity. Some alternative digital techniques are suggested and their performance is compared with the standard technique. These digital techniques can be used in an environment where an electroencephalograph is interfaced with a small digital computer via an A/D convertor. They have the advantage that statistical statements about their variability can sometimes be made so that the effect sought can be assessed correctly in the presence of random fluctuations.

Automatic identification and removal of ocular artifacts in EEG--improved adaptive predictor filtering for portable applications.

PubMed

Zhao, Qinglin; Hu, Bin; Shi, Yujun; Li, Yang; Moore, Philip; Sun, Minghou; Peng, Hong

2014-06-01

Electroencephalogram (EEG) signals have a long history of use as a noninvasive approach to measure brain function. An essential component in EEG-based applications is the removal of Ocular Artifacts (OA) from the EEG signals. In this paper we propose a hybrid de-noising method combining Discrete Wavelet Transformation (DWT) and an Adaptive Predictor Filter (APF). A particularly novel feature of the proposed method is the use of the APF based on an adaptive autoregressive model for prediction of the waveform of signals in the ocular artifact zones. In our test, based on simulated data, the accuracy of noise removal in the proposed model was significantly increased when compared to existing methods including: Wavelet Packet Transform (WPT) and Independent Component Analysis (ICA), Discrete Wavelet Transform (DWT) and Adaptive Noise Cancellation (ANC). The results demonstrate that the proposed method achieved a lower mean square error and higher correlation between the original and corrected EEG. The proposed method has also been evaluated using data from calibration trials for the Online Predictive Tools for Intervention in Mental Illness (OPTIMI) project. The results of this evaluation indicate an improvement in performance in terms of the recovery of true EEG signals with EEG tracking and computational speed in the analysis. The proposed method is well suited to applications in portable environments where the constraints with respect to acceptable wearable sensor attachments usually dictate single channel devices.

Can arousing feedback rectify lapses in driving? Prediction from EEG power spectra.

PubMed

Lin, Chin-Teng; Huang, Kuan-Chih; Chuang, Chun-Hsiang; Ko, Li-Wei; Jung, Tzyy-Ping

2013-10-01

This study explores the neurophysiological changes, measured using an electroencephalogram (EEG), in response to an arousing warning signal delivered to drowsy drivers, and predicts the efficacy of the feedback based on changes in the EEG. Eleven healthy subjects participated in sustained-attention driving experiments. The driving task required participants to maintain their cruising position and compensate for randomly induced lane deviations using the steering wheel, while their EEG and driving performance were continuously monitored. The arousing warning signal was delivered to participants who experienced momentary behavioral lapses, failing to respond rapidly to lane-departure events (specifically the reaction time exceeded three times the alert reaction time). The results of our previous studies revealed that arousing feedback immediately reversed deteriorating driving performance, which was accompanied by concurrent EEG theta- and alpha-power suppression in the bilateral occipital areas. This study further proposes a feedback efficacy assessment system to accurately estimate the efficacy of arousing warning signals delivered to drowsy participants by monitoring the changes in their EEG power spectra immediately thereafter. The classification accuracy was up 77.8% for determining the need for triggering additional warning signals. The findings of this study, in conjunction with previous studies on EEG correlates of behavioral lapses, might lead to a practical closed-loop system to predict, monitor and rectify behavioral lapses of human operators in attention-critical settings.

A statistically robust EEG re-referencing procedure to mitigate reference effect

PubMed Central

Lepage, Kyle Q.; Kramer, Mark A.; Chu, Catherine J.

2014-01-01

Background The electroencephalogram (EEG) remains the primary tool for diagnosis of abnormal brain activity in clinical neurology and for in vivo recordings of human neurophysiology in neuroscience research. In EEG data acquisition, voltage is measured at positions on the scalp with respect to a reference electrode. When this reference electrode responds to electrical activity or artifact all electrodes are affected. Successful analysis of EEG data often involves re-referencing procedures that modify the recorded traces and seek to minimize the impact of reference electrode activity upon functions of the original EEG recordings. New method We provide a novel, statistically robust procedure that adapts a robust maximum-likelihood type estimator to the problem of reference estimation, reduces the influence of neural activity from the re-referencing operation, and maintains good performance in a wide variety of empirical scenarios. Results The performance of the proposed and existing re-referencing procedures are validated in simulation and with examples of EEG recordings. To facilitate this comparison, channel-to-channel correlations are investigated theoretically and in simulation. Comparison with existing methods The proposed procedure avoids using data contaminated by neural signal and remains unbiased in recording scenarios where physical references, the common average reference (CAR) and the reference estimation standardization technique (REST) are not optimal. Conclusion The proposed procedure is simple, fast, and avoids the potential for substantial bias when analyzing low-density EEG data. PMID:24975291

What does the electroencephalogram tell us about the mechanisms of action of ECT in major depressive disorders?

PubMed

Farzan, Faranak; Boutros, Nash N; Blumberger, Daniel M; Daskalakis, Zafiris J

2014-06-01

Electroconvulsive therapy (ECT) remains to be one of the most effective treatment options in treatment-resistant major depressive disorder (MDD). From the early days, researchers have embarked on extracting information from electroencephalography (EEG) recordings before, during, and after ECT to identify neurophysiological targets of ECT and discover EEG predictors of response to ECT in patients with MDD. In this article, we provide an overview of visually detected and quantitative EEG features that could help in furthering our understanding of the mechanisms of action of ECT in MDD. We further discuss the EEG findings in the context of postulated hypotheses of ECT therapeutic pathways. We introduce an alternative and unifying hypothesis suggesting that ECT may exert its therapeutic efficacy through resetting the aberrant functional connectivity and promoting the generation of new and healthy connections in brain regions implicated in MDD pathophysiology, a mechanism that may be in part mediated by the ECT-induced activation of inhibitory and neuroplasticity mechanisms. We further discuss the added value of EEG markers in the larger context of ECT research and as complementary to neuroimaging and genetic markers. We conclude by drawing attention to the need for longitudinal studies in large cohort of patients and the need for standardization and validation of EEG algorithms of functional connectivity across studies to facilitate the translation of EEG correlates of ECT response in routine clinical practice.

Midfrontal Theta and Posterior Parietal Alpha Band Oscillations Support Conflict Resolution in a Masked Affective Priming Task.

PubMed

Jiang, Jun; Bailey, Kira; Xiao, Xiao

2018-01-01

Past attempts to characterize the neural mechanisms of affective priming have conceptualized it in terms of classic cognitive conflict, but have not examined the neural oscillatory mechanisms of subliminal affective priming. Using behavioral and electroencephalogram (EEG) time frequency (TF) analysis, the current study examines the oscillatory dynamics of unconsciously triggered conflict in an emotional facial expressions version of the masked affective priming task. The results demonstrate that the power dynamics of conflict are characterized by increased midfrontal theta activity and suppressed parieto-occipital alpha activity. Across-subject and within-trial correlation analyses further confirmed this pattern. Phase synchrony and Granger causality analyses (GCAs) revealed that the fronto-parietal network was involved in unconscious conflict detection and resolution. Our findings support a response conflict account of affective priming, and reveal the role of the fronto-parietal network in unconscious conflict control.