Regional reductions in sleep electroencephalography power in obstructive sleep apnea: a high-density EEG study.

PubMed

Jones, Stephanie G; Riedner, Brady A; Smith, Richard F; Ferrarelli, Fabio; Tononi, Giulio; Davidson, Richard J; Benca, Ruth M

2014-02-01

Obstructive sleep apnea (OSA) is associated with significant alterations in neuronal integrity resulting from either hypoxemia and/or sleep loss. A large body of imaging research supports reductions in gray matter volume, alterations in white matter integrity and resting state activity, and functional abnormalities in response to cognitive challenge in various brain regions in patients with OSA. In this study, we used high-density electroencephalography (hdEEG), a functional imaging tool that could potentially be used during routine clinical care, to examine the regional distribution of neural activity in a non-clinical sample of untreated men and women with moderate/severe OSA. Sleep was recorded with 256-channel EEG in relatively healthy subjects with apnea-hypopnea index (AHI) > 10, as well as age-, sex-, and body mass index-matched controls selected from a research population initially recruited for a study on sleep and meditation. Sleep laboratory. Nine subjects with AHI > 10 and nine matched controls. N/A. Topographic analysis of hdEEG data revealed a broadband reduction in EEG power in a circumscribed region overlying the parietal cortex in OSA subjects. This parietal reduction in neural activity was present, to some extent, across all frequency bands in all stages and episodes of nonrapid eye movement sleep. This investigation suggests that regional deficits in electroencephalography (EEG) power generation may be a useful clinical marker for neural disruption in obstructive sleep apnea, and that high-density EEG may have the sensitivity to detect pathological cortical changes early in the disease process.

Electroencephalography and analgesics.

PubMed

Malver, Lasse Paludan; Brokjaer, Anne; Staahl, Camilla; Graversen, Carina; Andresen, Trine; Drewes, Asbjørn Mohr

2014-01-01

To assess centrally mediated analgesic mechanisms in clinical trials with pain patients, objective standardized methods such as electroencephalography (EEG) has many advantages. The aim of this review is to provide the reader with an overview of present findings in analgesics assessed with spontaneous EEG and evoked brain potentials (EPs) in humans. Furthermore, EEG methodologies will be discussed with respect to translation from animals to humans and future perspectives in predicting analgesic efficacy. We searched PubMed with MeSH terms 'analgesics', 'electroencephalography' and 'evoked potentials' for relevant articles. Combined with a search in their reference lists 15 articles on spontaneous EEG and 55 papers on EPs were identified. Overall, opioids produced increased activity in the delta band in the spontaneous EEG, but increases in higher frequency bands were also seen. The EP amplitudes decreased in the majority of studies. Anticonvulsants used as analgesics showed inconsistent results. The N-methyl-D-aspartate receptor antagonist ketamine showed an increase in the theta band in spontaneous EEG and decreases in EP amplitudes. Tricyclic antidepressants increased the activity in the delta, theta and beta bands in the spontaneous EEG while EPs were inconsistently affected. Weak analgesics were mainly investigated with EPs and a decrease in amplitudes was generally observed. This review reveals that both spontaneous EEG and EPs are widely used as biomarkers for analgesic drug effects. Methodological differences are common and a more uniform approach will further enhance the value of such biomarkers for drug development and prediction of treatment response in individual patients. © 2013 The British Pharmacological Society.

Effects of Cable Sway, Electrode Surface Area, and Electrode Mass on Electroencephalography Signal Quality during Motion.

PubMed

Symeonidou, Evangelia-Regkina; Nordin, Andrew D; Hairston, W David; Ferris, Daniel P

2018-04-03

More neuroscience researchers are using scalp electroencephalography (EEG) to measure electrocortical dynamics during human locomotion and other types of movement. Motion artifacts corrupt the EEG and mask underlying neural signals of interest. The cause of motion artifacts in EEG is often attributed to electrode motion relative to the skin, but few studies have examined EEG signals under head motion. In the current study, we tested how motion artifacts are affected by the overall mass and surface area of commercially available electrodes, as well as how cable sway contributes to motion artifacts. To provide a ground-truth signal, we used a gelatin head phantom with embedded antennas broadcasting electrical signals, and recorded EEG with a commercially available electrode system. A robotic platform moved the phantom head through sinusoidal displacements at different frequencies (0-2 Hz). Results showed that a larger electrode surface area can have a small but significant effect on improving EEG signal quality during motion and that cable sway is a major contributor to motion artifacts. These results have implications in the development of future hardware for mobile brain imaging with EEG.

Correspondence of electroencephalography and near-infrared spectroscopy sensitivities to the cerebral cortex using a high-density layout

PubMed Central

Giacometti, Paolo; Diamond, Solomon G.

2014-01-01

Abstract. This study investigates the correspondence of the cortical sensitivity of electroencephalography (EEG) and near-infrared spectroscopy (NIRS). EEG forward model sensitivity to the cerebral cortex was calculated for 329 EEG electrodes following the 10-5 EEG positioning system using a segmented structural magnetic resonance imaging scan of a human subject. NIRS forward model sensitivity was calculated for the same subject using 156 NIRS source-detector pairs selected from 32 source and 32 detector optodes positioned on the scalp using a subset of the 10-5 EEG positioning system. Sensitivity correlations between colocalized NIRS source-detector pair groups and EEG channels yielded R=0.46±0.08. Groups of NIRS source-detector pairs with maximum correlations to EEG electrode sensitivities are tabulated. The mean correlation between the point spread functions for EEG and NIRS regions of interest (ROI) was R=0.43±0.07. Spherical ROIs with radii of 26 mm yielded the maximum correlation between EEG and NIRS averaged across all cortical mesh nodes. These sensitivity correlations between EEG and NIRS should be taken into account when designing multimodal studies of neurovascular coupling and when using NIRS as a statistical prior for EEG source localization. PMID:25558462

Using Electroencephalography for Treatment Guidance in Major Depressive Disorder.

PubMed

Wade, Elizabeth C; Iosifescu, Dan V

2016-09-01

Given the high prevalence of treatment-resistant depression and the long delays in finding effective treatments via trial and error, valid biomarkers of treatment outcome with the ability to guide treatment selection represent one of the most important unmet needs in mood disorders. A large body of research has investigated, for this purpose, biomarkers derived from electroencephalography (EEG), using resting state EEG or evoked potentials. Most studies have focused on specific EEG features (or combinations thereof), whereas more recently machine-learning approaches have been used to define the EEG features with the best predictive abilities without a priori hypotheses. While reviewing these different approaches, we have focused on the predictor characteristics and the quality of the supporting evidence. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

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

PubMed

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

2017-01-01

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

Seizures and electroencephalography findings in 61 patients with fetal alcohol spectrum disorders.

PubMed

Boronat, S; Vicente, M; Lainez, E; Sánchez-Montañez, A; Vázquez, E; Mangado, L; Martínez-Ribot, L; Del Campo, M

2017-01-01

Fetal alcohol spectrum disorders (FASD) cause neurodevelopmental abnormalities. However, publications about epilepsy and electroencephalographic features are scarce. In this study, we prospectively performed electroencephalography (EEG) and brain magnetic resonance (MR) imaging in 61 patients with diagnosis of FASD. One patient had multiple febrile seizures with normal EEGs. Fourteen children showed EEG anomalies, including slow background activity and interictal epileptiform discharges, focal and/or generalized, and 3 of them had epilepsy. In one patient, seizures were first detected during the EEG recording and one case had an encephalopathy with electrical status epilepticus during slow sleep (ESES). Focal interictal discharges in our patients did not imply the presence of underlying visible focal brain lesions in the neuroimaging studies, such as cortical dysplasia or polymicrogyria. However, they had nonspecific brain MR abnormalities, including corpus callosum hypoplasia, vermis hypoplasia or cavum septum pellucidum. The latter was significantly more frequent in the group with EEG abnormal findings (p < 0.01). Copyright © 2016 Elsevier Masson SAS. All rights reserved.

“The wondrous eyes of a new technology”—a history of the early electroencephalography (EEG) of psychopathy, delinquency, and immorality

PubMed Central

Schirmann, Felix

2014-01-01

This article presents a history of the early electroencephalography (EEG) of psychopathy, delinquency, and immorality in Great Britain and the United States in the 1940s and 1950s. Then, EEG was a novel research tool that promised ground-breaking insights in psychiatry and criminology. Experts explored its potential regarding the diagnosis, classification, etiology, and treatment of unethical and unlawful persons. This line of research yielded tentative and inconsistent findings, which the experts attributed to methodological and theoretical shortcomings. Accordingly, the scientific community discussed the reliability, validity, and utility of EEG, and launched initiatives to calibrate and standardize the novel tool. The analysis shows that knowledge production, gauging of the research tool, and attempts to establish credibility for EEG in the study of immoral persons occurred simultaneously. The paper concludes with a reflection on the similarities between EEG and neuroimaging—the prime research tool in the current neuroscience of morality—and calls for a critical assessment of their potentials and limitations in the study of immorality and crime. PMID:24860464

"The wondrous eyes of a new technology"-a history of the early electroencephalography (EEG) of psychopathy, delinquency, and immorality.

PubMed

Schirmann, Felix

2014-01-01

This article presents a history of the early electroencephalography (EEG) of psychopathy, delinquency, and immorality in Great Britain and the United States in the 1940s and 1950s. Then, EEG was a novel research tool that promised ground-breaking insights in psychiatry and criminology. Experts explored its potential regarding the diagnosis, classification, etiology, and treatment of unethical and unlawful persons. This line of research yielded tentative and inconsistent findings, which the experts attributed to methodological and theoretical shortcomings. Accordingly, the scientific community discussed the reliability, validity, and utility of EEG, and launched initiatives to calibrate and standardize the novel tool. The analysis shows that knowledge production, gauging of the research tool, and attempts to establish credibility for EEG in the study of immoral persons occurred simultaneously. The paper concludes with a reflection on the similarities between EEG and neuroimaging-the prime research tool in the current neuroscience of morality-and calls for a critical assessment of their potentials and limitations in the study of immorality and crime.

Effects of Cable Sway, Electrode Surface Area, and Electrode Mass on Electroencephalography Signal Quality during Motion

PubMed Central

Symeonidou, Evangelia-Regkina; Nordin, Andrew D.; Hairston, W. David

2018-01-01

More neuroscience researchers are using scalp electroencephalography (EEG) to measure electrocortical dynamics during human locomotion and other types of movement. Motion artifacts corrupt the EEG and mask underlying neural signals of interest. The cause of motion artifacts in EEG is often attributed to electrode motion relative to the skin, but few studies have examined EEG signals under head motion. In the current study, we tested how motion artifacts are affected by the overall mass and surface area of commercially available electrodes, as well as how cable sway contributes to motion artifacts. To provide a ground-truth signal, we used a gelatin head phantom with embedded antennas broadcasting electrical signals, and recorded EEG with a commercially available electrode system. A robotic platform moved the phantom head through sinusoidal displacements at different frequencies (0–2 Hz). Results showed that a larger electrode surface area can have a small but significant effect on improving EEG signal quality during motion and that cable sway is a major contributor to motion artifacts. These results have implications in the development of future hardware for mobile brain imaging with EEG. PMID:29614020

Discovering the Neural Nature of Moral Cognition? Empirical, Theoretical, and Practical Challenges in Bioethical Research with Electroencephalography (EEG).

PubMed

Wagner, Nils-Frederic; Chaves, Pedro; Wolff, Annemarie

2017-06-01

In this article we critically review the neural mechanisms of moral cognition that have recently been studied via electroencephalography (EEG). Such studies promise to shed new light on traditional moral questions by helping us to understand how effective moral cognition is embodied in the brain. It has been argued that conflicting normative ethical theories require different cognitive features and can, accordingly, in a broadly conceived naturalistic attempt, be associated with different brain processes that are rooted in different brain networks and regions. This potentially morally relevant brain activity has been empirically investigated through EEG-based studies on moral cognition. From neuroscientific evidence gathered in these studies, a variety of normative conclusions have been drawn and bioethical applications have been suggested. We discuss methodological and theoretical merits and demerits of the attempt to use EEG techniques in a morally significant way, point to legal challenges and policy implications, indicate the potential to reveal biomarkers of psychopathological conditions, and consider issues that might inform future bioethical work.

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

PubMed

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

2016-06-01

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

EEG Estimates of Cognitive Workload and Engagement Predict Math Problem Solving Outcomes

ERIC Educational Resources Information Center

Beal, Carole R.; Galan, Federico Cirett

2012-01-01

In the present study, the authors focused on the use of electroencephalography (EEG) data about cognitive workload and sustained attention to predict math problem solving outcomes. EEG data were recorded as students solved a series of easy and difficult math problems. Sequences of attention and cognitive workload estimates derived from the EEG…

Role of ictal baseline shifts and ictal high-frequency oscillations in stereo-electroencephalography analysis of mesial temporal lobe seizures.

PubMed

Wu, Shasha; Kunhi Veedu, Hari Prasad; Lhatoo, Samden D; Koubeissi, Mohamad Z; Miller, Jonathan P; Lüders, Hans O

2014-05-01

To assess the role of ictal baseline shifts (IBS) and ictal high-frequency oscillations (iHFOs) in intracranial electroencephalography (EEG) presurgical evaluation by analysis of the spatial and temporal relationship of IBS, iHFOs with ictal conventional stereo-electroencephalography (icEEG) in mesial temporal lobe seizures (MTLS). We studied 15 adult patients with medically refractory MTLS who underwent monitoring with depth electrodes. Seventy-five ictal EEG recordings at 1,000 Hz sampling rate were studied. Visual comparison of icEEG, IBS, and iHFOs were performed using Nihon-Kohden Neurofax systems (acquisition range 0.016-300 Hz). Each recorded ictal EEG was analyzed with settings appropriate for displaying icEEG, IBS, and iHFOs. IBS and iHFOs were observed in all patients and in 91% and 81% of intracranial seizures, respectively. IBS occurred before (22%), at (57%), or after (21%) icEEG onset. In contrast, iHFOs occurred at (30%) or after (70%) icEEG onset. The onset of iHFOs was 11.5 s later than IBS onset (p < 0.0001). All of the earliest onset of IBS and 70% of the onset of iHFOs overlapped with the ictal onset zone (IOZ). Compared with iHFOs, interictal HFOs (itHFOs) were less correlated with IOZ. In contrast to icEEG, IBS and iHFOs had smaller spatial distributions in 70% and 100% of the seizures, respectively. An IBS dipole was observed in 66% of the seizures. Eighty-seven percent of the dipoles had a negative pole at the anterior/medial part of amygdala/hippocampus complex (A-H complex) and a positive pole at the posterior/lateral part of the A-H complex. The results suggest that evaluation of IBS and iHFOs, in addition to routine icEEG, helps in more accurately defining the IOZ. This study also shows that the onset and the spatial distribution of icEEG, IBS, and iHFOs do not overlap, suggesting that they reflect different cellular or network dynamics. Wiley Periodicals, Inc. © 2014 International League Against Epilepsy.

Automated electroencephalography system and electroencephalographic coordinates of space motion sickness, part 1

NASA Technical Reports Server (NTRS)

Frost, J. D., Jr.

1976-01-01

A self-contained and portable device which permits clinical electroencephalography (EEG) to be conducted in remote locations by minimally trained, nontechnical personnel was developed and tested. The unit accomplishes semiautomatic acquisition of EEG data from the patient, simultaneous transmission of eight data channels to a central hospital facility over conventional telephone equipment, and automatic printing (at the remote site) of the EEG report generated at the central location. Consequently, this system enables the delivery of high-quality EEG diagnostic services in a geographically remote site with the accuracy and speed formerly possible only in certain large medical centers. Beside obvious potential clinical applications, this system serves as an initial prototype of a unit which could provide inflight EEG during future space missions.

Active Deep Learning-Based Annotation of Electroencephalography Reports for Cohort Identification

PubMed Central

Maldonado, Ramon; Goodwin, Travis R; Harabagiu, Sanda M

2017-01-01

The annotation of a large corpus of Electroencephalography (EEG) reports is a crucial step in the development of an EEG-specific patient cohort retrieval system. The annotation of multiple types of EEG-specific medical concepts, along with their polarity and modality, is challenging, especially when automatically performed on Big Data. To address this challenge, we present a novel framework which combines the advantages of active and deep learning while producing annotations that capture a variety of attributes of medical concepts. Results obtained through our novel framework show great promise. PMID:28815135

Assessment of the QT interval in the electroencephalography (EEG) of children with syncope, epilepsy, and attention-deficit hyperactivity disorder (ADHD).

PubMed

Jha, Om P; Khurana, Divya S; Carvalho, Karen S; Melvin, Joseph J; Legido, Agustin; O'Riordan, Anna C; Valencia, Ignacio

2010-03-01

The interpretation of QT interval is often neglected during electroencephalography (EEG) reading. We compared the incidence of prolonged QT interval, as seen in the electrocardiography (ECG) recording lead of the EEG, in children presenting with seizure, syncope, or attention-deficit hyperactivity disorder (ADHD). Abnormal QT was defined as >460 ms. The incidence of prolonged QT in the seizure, syncope, and ADHD groups was 1/50 (2%), 7/50 (14%), and 2/50 (4%), respectively (P = .036, chi-square). The mean +/- SD of QT were 405 +/- 34, 424 +/- 39, and 414 +/- 36, respectively (P = .035, analysis of variance [ANOVA], syncope group, compared with seizure group). The incidence of prolonged QT as measured in the EEG was unexpectedly high in children presenting with seizure, syncope, or ADHD. These data support the concept that QT evaluation should be emphasized during routine EEG reading, as it may aid in identifying cases of undiagnosed cardiac conduction abnormalities. Prospective studies comparing EEG-ECG tracings with 12-lead ECG are warranted.

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

NASA Astrophysics Data System (ADS)

Muzafar Shah, Mazlina; Fatah Wahab, Abdul

2017-09-01

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

Decoding human swallowing via electroencephalography: a state-of-the-art review

PubMed Central

Jestrović, Iva; Coyle, James L.

2015-01-01

Swallowing and swallowing disorders have garnered continuing interest over the past several decades. Electroencephalography (EEG) is an inexpensive and non-invasive procedure with very high temporal resolution which enables analysis of short and fast swallowing events, as well as an analysis of the organizational and behavioral aspects of cortical motor preparation, swallowing execution and swallowing regulation. EEG is a powerful technique which can be used alone or in combination with other techniques for monitoring swallowing, detection of swallowing motor imagery for diagnostic or biofeedback purposes, or to modulate and measure the effects of swallowing rehabilitation. This paper provides a review of the existing literature which has deployed EEG in the investigation of oropharyngeal swallowing, smell, taste and texture related to swallowing, cortical pre-motor activation in swallowing, and swallowing motor imagery detection. Furthermore, this paper provides a brief review of the different modalities of brain imaging techniques used to study swallowing brain activities, as well as the EEG components of interest for studies on swallowing and on swallowing motor imagery. Lastly, this paper provides directions for future swallowing investigations using EEG. PMID:26372528

Association of Electroencephalography (EEG) Power Spectra with Corneal Nerve Fiber Injury in Retinoblastoma Patients.

PubMed

Liu, Jianliang; Sun, Juanjuan; Diao, Yumei; Deng, Aijun

2016-09-04

BACKGROUND In our clinical experience we discovered that EEG band power may be correlated with corneal nerve injury in retinoblastoma patients. This study aimed to investigate biomarkers obtained from electroencephalography (EEG) recordings to reflect corneal nerve injury in retinoblastoma patients. MATERIAL AND METHODS Our study included 20 retinoblastoma patients treated at the Department of Ophthalmology, Affiliated Hospital of Weifang Medical University between 2010 and 2014. Twenty normal individuals were included in the control group. EEG activity was recorded continuously with 32 electrodes using standard EEG electrode placement for detecting EEG power. A cornea confocal microscope was used to examine corneal nerve injury in retinoblastoma patients and normal individuals. Spearman rank correlation analysis was used to analyze the correlation between corneal nerve injury and EEG power changes. The sensitivity and specificity of changed EEG power in diagnosis of corneal nerve injury were also analyzed. RESULTS The predominantly slow EEG oscillations changed gradually into faster waves in retinoblastoma patients. The EEG pattern in retinoblastoma patients was characterized by a distinct increase of delta (P<0.01) and significant decrease of theta power P<0.05). Corneal nerves were damaged in corneas of retinoblastoma patients. Corneal nerve injury was positively correlated with delta EEG spectra power and negatively correlated with theta EEG spectra power. The diagnostic sensitivity and specificity by compounding in the series were 60% and 67%, respectively. CONCLUSIONS Changes in delta and theta of EEG appear to be associated with occurrence of corneal nerve injury. Useful information can be provided for evaluating corneal nerve damage in retinoblastoma patients through analyzing EEG power bands.

Temporal lobe deficits in murderers: EEG findings undetected by PET.

PubMed

Gatzke-Kopp, L M; Raine, A; Buchsbaum, M; LaCasse, L

2001-01-01

This study evaluates electroencephalography (EEG) and positron emission tomography (PET) in the same subjects. Fourteen murderers were assessed by using both PET (while they were performing the continuous performance task) and EEG during a resting state. EEG revealed significant increases in slow-wave activity in the temporal, but not frontal, lobe in murderers, in contrast to prior PET findings that showed reduced prefrontal, but not temporal, glucose metabolism. Results suggest that resting EEG shows empirical utility distinct from PET activation findings.

Electroencephalography in premature and full-term infants. Developmental features and glossary.

PubMed

André, M; Lamblin, M-D; d'Allest, A M; Curzi-Dascalova, L; Moussalli-Salefranque, F; S Nguyen The, Tich; Vecchierini-Blineau, M-F; Wallois, F; Walls-Esquivel, E; Plouin, P

2010-05-01

Following the pioneering work of C. Dreyfus-Brisac and N. Monod, research into neonatal electroencephalography (EEG) has developed tremendously in France. French neurophysiologists who had been trained in Paris (France) collaborated on a joint project on the introduction, development, and currently available neonatal EEG recording techniques. They assessed the analytical criteria for the different maturational stages and standardized neonatal EEG terminology on the basis of the large amount of data available in the French and the English literature. The results of their work were presented in 1999. Since the first edition, technology has moved towards the widespread use of digitized recordings. Although the data obtained with analog recordings can be applied to digitized EEG tracings, the present edition, including new published data, is illustrated with digitized recordings. Herein, the reader can find a comprehensive description of EEG features and neonatal behavioural states at different gestational ages, and also a definition of the main aspects and patterns of both pathological and normal EEGs, presented in glossary form. In both sections, numerous illustrations have been provided. This precise neonatal EEG terminology should improve homogeneity in the analysis of neonatal EEG recordings, and facilitate the setting up of multicentric studies on certain aspects of normal EEG recordings and various pathological patterns. Copyright 2010 Elsevier Masson SAS. All rights reserved.

Electroencephalography and Brain MRI Patterns in Encephalopathy.

PubMed

Wabulya, Angela; Lesser, Ronald P; Llinas, Rafael; Kaplan, Peter W

2016-04-01

Using electroencephalography (EEG) and histology in patients with diffuse encephalopathy, Gloor et al reported that paroxysmal synchronous discharges (PSDs) on EEG required combined cortical gray (CG) and "subcortical" gray (SCG) matter pathology, while polymorphic delta activity (PDA) occurred in patients with white matter pathology. In patients with encephalopathy, we compared EEG findings and magnetic resonance imaging (MRI) to determine if MRI reflected similar pathological EEG correlations. Retrospective case control study of 52 cases with EEG evidence of encephalopathy and 50 controls without evidence of encephalopathy. Review of clinical, EEG and MRI data acquired within 4 days of each other. The most common EEG finding in encephalopathy was background slowing, in 96.1%. We found PSDs in 0% of cases with the combination of CG and SCG abnormalities. Although 13.5% (n=7) had PSDs on EEG; 3 of these had CG and 4 had SCG abnormalities. A total of 73.1% (38/52) had white matter abnormalities-of these 28.9% (11/38) had PDA. PSDs were found with either CG or "SCG" MRI abnormalities and did not require a combination of the two. In agreement with Gloor et al, PDA occurred with white matter MRI abnormalities in the absence of gray matter abnormalities. © EEG and Clinical Neuroscience Society (ECNS) 2015.

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

DTIC Science & Technology

2016-12-01

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

Human electroencephalography and the tobacco industry: a review of internal documents.

PubMed

Panzano, Vincent C; Wayne, Geoffrey Ferris; Pickworth, Wallace B; Connolly, Gregory N

2010-04-01

To determine the extent and implications of internal human electroencephalography (EEG) research conducted by the tobacco industry. This study analysed internal documents that describe the results of human EEG studies conducted by tobacco manufacturers. Emphasis was placed on documents that pertain to the application of EEG to product evaluation efforts. Internal EEG research was used to determine dose-response relations and effective threshold levels for nicotine, emphasising the importance of form and mechanism of nicotine delivery for initiating robust central nervous system (CNS) effects. Internal studies also highlight the importance of human behaviour during naturalistic smoking, revealing neurophysiological markers of compensation during smoking of reduced nicotine cigarettes. Finally, internal research demonstrates the effectiveness of EEG for the evaluation of non-nicotine phenomena including smoke-component discrimination by smokers, classification of sensory characteristics and measurement of hedonics and other subjective effects. Tobacco manufacturers successfully developed objective, EEG-based techniques to evaluate the influence of product characteristics on acceptance and use. Internal results suggest that complex interactions between pharmacological, sensory and behavioural factors mediate the brain changes that occur with smoking. These findings have implications for current proposals regarding the regulation of tobacco products and argue for the incorporation of objective measures of product effects when evaluating the health risks of new and existing tobacco products.

Sedation for electroencephalography with dexmedetomidine or chloral hydrate: a comparative study on the qualitative and quantitative electroencephalogram pattern.

PubMed

Fernandes, Magda L; Oliveira, Welser Machado de; Santos, Maria do Carmo Vasconcellos; Gomez, Renato S

2015-01-01

Sedation for electroencephalography in uncooperative patients is a controversial issue because majority of sedatives, hypnotics, and general anesthetics interfere with the brain's electrical activity. Chloral hydrate (CH) is typically used for this sedation, and dexmedetomidine (DEX) was recently tested because preliminary data suggest that this drug does not affect the electroencephalogram (EEG). The aim of the present study was to compare the EEG pattern during DEX or CH sedation to test the hypothesis that both drugs exert similar effects on the EEG. A total of 17 patients underwent 2 EEGs on 2 separate occasions, one with DEX and the other with CH. The EEG qualitative variables included the phases of sleep and the background activity. The EEG quantitative analysis was performed during the first 2 minutes of the second stage of sleep. The EEG quantitative variables included density, duration, and amplitude of the sleep spindles and absolute spectral power. The results showed that the qualitative analysis, density, duration, and amplitude of sleep spindles did not differ between DEX and CH sedation. The power of the slow-frequency bands (δ and θ) was higher with DEX, but the power of the faster-frequency bands (α and β) was higher with CH. The total power was lower with DEX than with CH. The differences of DEX and CH in EEG power did not change the EEG qualitative interpretation, which was similar with the 2 drugs. Other studies comparing natural sleep and sleep induced by these drugs are needed to clarify the clinical relevance of the observed EEG quantitative differences.

Hemispheric asymmetry of electroencephalography-based functional brain networks.

PubMed

Jalili, Mahdi

2014-11-12

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

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

PubMed

Beres, Anna M

2017-12-01

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

Evidence of a Faster Posterior Dominant EEG Rhythm in Children with Autism

ERIC Educational Resources Information Center

Gregory, Michael D.; Mandelbaum, David E.

2012-01-01

Multiple electroencephalography (EEG) abnormalities have been associated with autism. In the course of clinical work, we have observed a posterior dominant EEG rhythm at higher frequency in children with autism. To test this observation, 56 EEG tracings of children with autism were compared to the EEGs of age-matched controls. Children with autism…

Cerebrospinal Fluid Biomarkers of Alzheimer's Disease Correlate With Electroencephalography Parameters Assessed by Exact Low-Resolution Electromagnetic Tomography (eLORETA).

PubMed

Hata, Masahiro; Tanaka, Toshihisa; Kazui, Hiroaki; Ishii, Ryouhei; Canuet, Leonides; Pascual-Marqui, Roberto D; Aoki, Yasunori; Ikeda, Shunichiro; Sato, Shunsuke; Suzuki, Yukiko; Kanemoto, Hideki; Yoshiyama, Kenji; Iwase, Masao

2017-09-01

Recently, cerebrospinal fluid (CSF) biomarkers related to Alzheimer's disease (AD) have garnered a lot of clinical attention. To explore neurophysiological traits of AD and parameters for its clinical diagnosis, we examined the association between CSF biomarkers and electroencephalography (EEG) parameters in 14 probable AD patients. Using exact low-resolution electromagnetic tomography (eLORETA), artifact-free 40-sesond EEG data were estimated with current source density (CSD) and lagged phase synchronization (LPS) as the EEG parameters. Correlations between CSF biomarkers and the EEG parameters were assessed. Patients with AD showed significant negative correlation between CSF beta-amyloid (Aβ)-42 concentration and the logarithms of CSD over the right temporal area in the theta band. Total tau concentration was negatively correlated with the LPS between the left frontal eye field and the right auditory area in the alpha-2 band in patients with AD. Our study results suggest that AD biomarkers, in particular CSF Aβ42 and total tau concentrations are associated with the EEG parameters CSD and LPS, respectively. Our results could yield more insights into the complicated pathology of AD.

Probing interval timing with scalp-recorded electroencephalography (EEG).

PubMed

Ng, Kwun Kei; Penney, Trevor B

2014-01-01

Humans, and other animals, are able to easily learn the durations of events and the temporal relationships among them in spite of the absence of a dedicated sensory organ for time. This chapter summarizes the investigation of timing and time perception using scalp-recorded electroencephalography (EEG), a non-invasive technique that measures brain electrical potentials on a millisecond time scale. Over the past several decades, much has been learned about interval timing through the examination of the characteristic features of averaged EEG signals (i.e., event-related potentials, ERPs) elicited in timing paradigms. For example, the mismatch negativity (MMN) and omission potential (OP) have been used to study implicit and explicit timing, respectively, the P300 has been used to investigate temporal memory updating, and the contingent negative variation (CNV) has been used as an index of temporal decision making. In sum, EEG measures provide biomarkers of temporal processing that allow researchers to probe the cognitive and neural substrates underlying time perception.

Clonidine Sedation Effects in Children During Electroencephalography.

PubMed

Barzegar, Mohammad; Piri, Reza; Naghavi-Behzad, Mohammad; Ghasempour, Masoumeh

2017-09-01

It is very important to have proper management in children with Seizure. Electroencephalography (EEG) as a diagnostic instrument has a key role in determining the management method of seizure in children. Because of poor cooperation of some children (especially children with attention deficit hyperactivity disorders and developmental disorders) in performing EEG, it is the best choice to sedate children before EEG. The aim of present study is to evaluate the sedation efficacy of clonidine in children before EEG. In a randomized clinical trial, 45 children age 2 to 12 with seizure, who referred to Children Hospital of Tabriz University of Medical Sciences and candidate for EEG, were studied. Sedation before EEG induced by 0.5 to 2.0 mg clonidine orally. Sedation score (0 to 5) measured by using eyes condition, response to voice, and response to touch. Successful sedation, EEG performing, and hemodynamic stability were evaluated during sedation. Of all patients, 40 patients (88.88%) were sedated successfully, and EEG was performed for all of the children. Mean onset time of clonidine effect was 35.47±13.56 minutes and mean time of that the patients' level of consciousness back to the level before administrating of clonidine was 77.55±26.87 minutes. Hemodynamic states of all patients were stable during the study, and there were no significant changes in vital sign of patients. In conclusion, clonidine can be considered as a safe alternative medication for sedation for EEG, which is fortunately associated with no significant change in vital signs, which may complicate overall status of patients.

Electroencephalography signatures of attention-deficit/hyperactivity disorder: clinical utility.

PubMed

Alba, Guzmán; Pereda, Ernesto; Mañas, Soledad; Méndez, Leopoldo D; González, Almudena; González, Julián J

2015-01-01

The techniques and the most important results on the use of electroencephalography (EEG) to extract different measures are reviewed in this work, which can be clinically useful to study subjects with attention-deficit/hyperactivity disorder (ADHD). First, we discuss briefly and in simple terms the EEG analysis and processing techniques most used in the context of ADHD. We review techniques that both analyze individual EEG channels (univariate measures) and study the statistical interdependence between different EEG channels (multivariate measures), the so-called functional brain connectivity. Among the former ones, we review the classical indices of absolute and relative spectral power and estimations of the complexity of the channels, such as the approximate entropy and the Lempel-Ziv complexity. Among the latter ones, we focus on the magnitude square coherence and on different measures based on the concept of generalized synchronization and its estimation in the state space. Second, from a historical point of view, we present the most important results achieved with these techniques and their clinical utility (sensitivity, specificity, and accuracy) to diagnose ADHD. Finally, we propose future research lines based on these results.

Diagnostic Utility of Wireless Video-Electroencephalography in Unsedated Dogs.

PubMed

James, F M K; Cortez, M A; Monteith, G; Jokinen, T S; Sanders, S; Wielaender, F; Fischer, A; Lohi, H

2017-09-01

Poor agreement between observers on whether an unusual event is a seizure drives the need for a specific diagnostic tool provided by video-electroencephalography (video-EEG) in human pediatric epileptology. That successful classification of events would be positively associated with increasing EEG recording length and higher event frequency reported before video-EEG evaluation; that a novel wireless video-EEG technique would clarify whether unusual behavioral events were seizures in unsedated dogs. Eighty-one client-owned dogs of various breeds undergoing investigation of unusual behavioral events at 4 institutions. Retrospective case series: evaluation of wireless video-EEG recordings in unsedated dogs performed at 4 institutions. Electroencephalography achieved/excluded diagnosis of epilepsy in 58 dogs (72%); 25 dogs confirmed with epileptic seizures based on ictal/interictal epileptiform discharges, and 33 dogs with no EEG abnormalities associated with their target events. As reported frequency of the target events decreased (annually, monthly, weekly, daily, hourly, minutes, seconds), EEG was less likely to achieve diagnosis (P < 0.001). Every increase in event frequency increased the odds of achieving diagnosis by 2.315 (95% confidence interval: 1.36-4.34). EEG recording length (mean = 3.69 hours, range: 0.17-22.5) was not associated (P = 0.2) with the likelihood of achieving a diagnosis. Wireless video-EEG in unsedated dogs had a high success for diagnosis of unusual behavioral events. This technique offered a reliable clinical tool to investigate the epileptic origin of behavioral events in dogs. Copyright © 2017 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine.

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

PubMed

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

2016-05-01

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

Cot-side electroencephalography monitoring is not clinically useful in the detection of mild neonatal hypoglycemia.

PubMed

Harris, Deborah L; Weston, Philip J; Williams, Christopher E; Pleasants, Anthony B; Battin, Malcolm R; Spooner, Claire G; Harding, Jane E

2011-11-01

To determine whether there is a relationship between electroencephalography patterns and hypoglycemia, by using simultaneous cot-side amplitude integrated electroencephalography (aEEG) and continuous interstitial glucose monitoring, and whether non-glucose cerebral fuels modified these patterns. Eligible babies were ≥ 32 weeks gestation, at risk for hypoglycemia, and admitted to the neonatal intensive care unit. Electrodes were placed in C3-P3, C4-P4 O1-O2 montages. A continuous interstitial glucose sensor was placed subcutaneously, and blood glucose was measured by using the glucose oxidase method. Non-glucose cerebral fuels were measured at study entry, exit, and during recognized hypoglycemia. A total of 101 babies were enrolled, with a median weight of 2179 g and gestation of 35 weeks. Twenty-four of the babies had aEEG recordings, and glucose concentrations were low (< 2.6 mM). There were 103 episodes of low glucose concentrations lasting 5 to 475 minutes, but no observable changes in aEEG variables. Plasma concentrations of lactate, beta-hydroxybutyrate, and glycerol were low and did not alter during hypoglycemia. Cot-side aEEG was not useful for the detection of neurological changes during mild hypoglycemia. Plasma concentrations of non-glucose cerebral fuels were low and unlikely to provide substantial neuroprotection. Copyright © 2011 Mosby, Inc. All rights reserved.

Open Ephys electroencephalography (Open Ephys  +  EEG): a modular, low-cost, open-source solution to human neural recording

NASA Astrophysics Data System (ADS)

Black, Christopher; Voigts, Jakob; Agrawal, Uday; Ladow, Max; Santoyo, Juan; Moore, Christopher; Jones, Stephanie

2017-06-01

Objective. Electroencephalography (EEG) offers a unique opportunity to study human neural activity non-invasively with millisecond resolution using minimal equipment in or outside of a lab setting. EEG can be combined with a number of techniques for closed-loop experiments, where external devices are driven by specific neural signals. However, reliable, commercially available EEG systems are expensive, often making them impractical for individual use and research development. Moreover, by design, a majority of these systems cannot be easily altered to the specification needed by the end user. We focused on mitigating these issues by implementing open-source tools to develop a new EEG platform to drive down research costs and promote collaboration and innovation. Approach. Here, we present methods to expand the open-source electrophysiology system, Open Ephys (www.openephys.org), to include human EEG recordings. We describe the equipment and protocol necessary to interface various EEG caps with the Open Ephys acquisition board, and detail methods for processing data. We present applications of Open Ephys  +  EEG as a research tool and discuss how this innovative EEG technology lays a framework for improved closed-loop paradigms and novel brain-computer interface experiments. Main results. The Open Ephys  +  EEG system can record reliable human EEG data, as well as human EMG data. A side-by-side comparison of eyes closed 8-14 Hz activity between the Open Ephys  +  EEG system and the Brainvision ActiCHamp EEG system showed similar average power and signal to noise. Significance. Open Ephys  +  EEG enables users to acquire high-quality human EEG data comparable to that of commercially available systems, while maintaining the price point and extensibility inherent to open-source systems.

Open Ephys electroencephalography (Open Ephys  +  EEG): a modular, low-cost, open-source solution to human neural recording.

PubMed

Black, Christopher; Voigts, Jakob; Agrawal, Uday; Ladow, Max; Santoyo, Juan; Moore, Christopher; Jones, Stephanie

2017-06-01

Electroencephalography (EEG) offers a unique opportunity to study human neural activity non-invasively with millisecond resolution using minimal equipment in or outside of a lab setting. EEG can be combined with a number of techniques for closed-loop experiments, where external devices are driven by specific neural signals. However, reliable, commercially available EEG systems are expensive, often making them impractical for individual use and research development. Moreover, by design, a majority of these systems cannot be easily altered to the specification needed by the end user. We focused on mitigating these issues by implementing open-source tools to develop a new EEG platform to drive down research costs and promote collaboration and innovation. Here, we present methods to expand the open-source electrophysiology system, Open Ephys (www.openephys.org), to include human EEG recordings. We describe the equipment and protocol necessary to interface various EEG caps with the Open Ephys acquisition board, and detail methods for processing data. We present applications of Open Ephys  +  EEG as a research tool and discuss how this innovative EEG technology lays a framework for improved closed-loop paradigms and novel brain-computer interface experiments. The Open Ephys  +  EEG system can record reliable human EEG data, as well as human EMG data. A side-by-side comparison of eyes closed 8-14 Hz activity between the Open Ephys  +  EEG system and the Brainvision ActiCHamp EEG system showed similar average power and signal to noise. Open Ephys  +  EEG enables users to acquire high-quality human EEG data comparable to that of commercially available systems, while maintaining the price point and extensibility inherent to open-source systems.

Unfavorable surgical outcomes in partial epilepsy with secondary bilateral synchrony: Intracranial electroencephalography study.

PubMed

Sunwoo, Jun-Sang; Byun, Jung-Ick; Moon, Jangsup; Lim, Jung-Ah; Kim, Tae-Joon; Lee, Soon-Tae; Jung, Keun-Hwa; Park, Kyung-Il; Chu, Kon; Kim, Manho; Chung, Chun-Kee; Jung, Ki-Young; Lee, Sang Kun

2016-05-01

Secondary bilateral synchrony (SBS) indicates bilaterally synchronous epileptiform discharges arising from a focal cortical origin. The present study aims to investigate SBS in partial epilepsy with regard to surgical outcomes and intracranial EEG findings. We retrospectively reviewed consecutive patients who underwent epilepsy surgery following extraoperative intracranial electroencephalography (EEG) study from 2008 to 2012. The presence of SBS was determined based upon the results of scalp EEG monitoring performed for presurgical evaluations. We reviewed scalp EEG, neuroimaging, intracranial EEG findings, and surgical outcomes in patients with SBS. We found 12 patients with SBS who were surgically treated for intractable partial epilepsy. Nine (75%) patients had lateralized ictal semiology and only two (16.6%) patients showed localized ictal onset in scalp EEG. Brain MRI showed epileptogenic lesion in three (25%) patients. Intracranial EEG demonstrated that ictal onset zone was widespread or non-localized in six (50%) patients. Low-voltage fast activity was the most common ictal onset EEG pattern. Rapid propagation of ictal onset was noted in 10 (83.3%) patients. Eleven patients underwent resective epilepsy surgery and only two patients (18.2%) achieved seizure-freedom (median follow-up 56 months). MRI-visible brain lesions were associated with favorable outcomes (p=0.024). Patients with SBS, compared to frontal lobe epilepsy without SBS, showed lesser localization in ictal onset EEG (p=0.029) and more rapid propagation during evolution of ictal rhythm (p=0.015). The present results suggested that resective surgery for partial epilepsy with SBS should be decided carefully, especially in case of nonlesional epilepsy. Poor localization and rapid spread of ictal onset were prominent in intracranial EEG, which might contribute to incomplete resection of the epileptogenic zone and poor surgical outcomes. Copyright © 2016 Elsevier B.V. All rights reserved.

Effect of intravenous sodium salicylate administration prior to castration on plasma cortisol and electroencephalography parameters in calves.

PubMed

Bergamasco, L; Coetzee, J F; Gehring, R; Murray, L; Song, T; Mosher, R A

2011-12-01

Nociception is an unavoidable consequence of many routine management procedures such as castration in cattle. This study investigated electroencephalography (EEG) parameters and cortisol levels in calves receiving intravenous sodium salicylate in response to a castration model. Twelve Holstein calves were randomly assigned to the following groups: (i) castrated, untreated controls, (ii) 50 mg/kg sodium salicylate IV precastration, were blood sampled at 0, 5, 10, 20, 30, 45, 60, 90, 120, 150, 180, 240, 360, and 480 min postcastration. The EEG recording included baseline, castration, immediate recovery (0-5 min after castration), middle recovery (5-10 min after castration), and late recovery (10-20 min after castration). Samples were analyzed by competitive chemiluminescent immunoassay and fluorescence polarization immunoassay for cortisol and salicylate, respectively. EEG visual inspection and spectral analysis were performed. Statistical analyses included anova repeated measures and correlations between response variable. No treatment effect was noted between the two groups for cortisol and EEG measurements, namely an attenuation of acute cortisol response and EEG desynchronization in sodium salicylate group. Time effects were noted for EEG measurements, cortisol and salicylates levels. Significant correlations between cortisol and EEG parameters were noted. These findings have implications for designing effective analgesic regimens, and they suggest that EEG can be useful to monitor pain attributable to castration. © 2011 Blackwell Publishing Ltd.

Combined process automation for large-scale EEG analysis.

PubMed

Sfondouris, John L; Quebedeaux, Tabitha M; Holdgraf, Chris; Musto, Alberto E

2012-01-01

Epileptogenesis is a dynamic process producing increased seizure susceptibility. Electroencephalography (EEG) data provides information critical in understanding the evolution of epileptiform changes throughout epileptic foci. We designed an algorithm to facilitate efficient large-scale EEG analysis via linked automation of multiple data processing steps. Using EEG recordings obtained from electrical stimulation studies, the following steps of EEG analysis were automated: (1) alignment and isolation of pre- and post-stimulation intervals, (2) generation of user-defined band frequency waveforms, (3) spike-sorting, (4) quantification of spike and burst data and (5) power spectral density analysis. This algorithm allows for quicker, more efficient EEG analysis. Copyright © 2011 Elsevier Ltd. All rights reserved.

Methodological standards and interpretation of video-electroencephalography in adult control rodents. A TASK1-WG1 report of the AES/ILAE Translational Task Force of the ILAE.

PubMed

Kadam, Shilpa D; D'Ambrosio, Raimondo; Duveau, Venceslas; Roucard, Corinne; Garcia-Cairasco, Norberto; Ikeda, Akio; de Curtis, Marco; Galanopoulou, Aristea S; Kelly, Kevin M

2017-11-01

In vivo electrophysiological recordings are widely used in neuroscience research, and video-electroencephalography (vEEG) has become a mainstay of preclinical neuroscience research, including studies of epilepsy and cognition. Studies utilizing vEEG typically involve comparison of measurements obtained from different experimental groups, or from the same experimental group at different times, in which one set of measurements serves as "control" and the others as "test" of the variables of interest. Thus, controls provide mainly a reference measurement for the experimental test. Control rodents represent an undiagnosed population, and cannot be assumed to be "normal" in the sense of being "healthy." Certain physiological EEG patterns seen in humans are also seen in control rodents. However, interpretation of rodent vEEG studies relies on documented differences in frequency, morphology, type, location, behavioral state dependence, reactivity, and functional or structural correlates of specific EEG patterns and features between control and test groups. This paper will focus on the vEEG of standard laboratory rodent strains with the aim of developing a small set of practical guidelines that can assist researchers in the design, reporting, and interpretation of future vEEG studies. To this end, we will: (1) discuss advantages and pitfalls of common vEEG techniques in rodents and propose a set of recommended practices and (2) present EEG patterns and associated behaviors recorded from adult rats of a variety of strains. We will describe the defining features of selected vEEG patterns (brain-generated or artifactual) and note similarities to vEEG patterns seen in adult humans. We will note similarities to normal variants or pathological human EEG patterns and defer their interpretation to a future report focusing on rodent seizure patterns. Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.

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

ERIC Educational Resources Information Center

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

2013-01-01

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

Prognostic value of continuous electroencephalography monitoring in children with severe brain damage.

PubMed

Lan, Yan-huai; Zhu, Xiao-mei; Zhou, Yuan-feng; Qiu, Peng-ling; Lu, Guo-ping; Sun, Dao-kai; Wang, Yi

2015-06-01

The purpose of this study is to determine whether there is a relationship between continuous electroencephalography (EEG) monitoring patterns and prognosis for children with severe brain damage. Patients and The different patterns of EEG were analyzed for 103 children (Glasgow Coma Scale [GCS] score < 8) who were monitored with continuous video-EEG (CVEEG) within 72 hours after the onset of coma. The clinical outcomes were scored and evaluated at hospital discharge by the modified Pediatric Cerebral and Overall Performance Category Scale (PCOPCS). EEG parameters of the different prognosis groups were compared and risk factors for prognosis were identified. Of the 103 children, 36 were in the good prognosis group (PCOPCS scores 1 and 2) and 67 were in the poor prognosis group (PCOPCS scores 3-6). The poor prognosis group had the lower proportion of events in reactive EEG patterns and sleep architecture, and a higher proportion of low-voltage events. Multivariate analyses showed that the lower GCS score and no sleep architecture were significantly associated with poor prognosis. Comatose children with higher GCS score and sleep architecture have better clinical outcomes in terms of morbidity and mortality. Georg Thieme Verlag KG Stuttgart · New York.

Inter-hemispheric electroencephalography coherence analysis: assessing brain activity during monotonous driving.

PubMed

Jap, Budi Thomas; Lal, Sara; Fischer, Peter

2010-06-01

The current study investigated the effect of monotonous driving on inter-hemispheric electroencephalography (EEG) coherence. Twenty-four non-professional drivers were recruited to perform a fatigue instigating monotonous driving task while 30 channels of EEG were simultaneously recorded. The EEG recordings were then divided into 5 equal sections over the entire driving period for analysis. Inter-hemispheric coherence was computed from 5 homologous EEG electrode pairs (FP1-FP2, C3-C4, T7-T8, P7-P8, and O1-O2) for delta, theta, alpha and beta frequency bands. Results showed that frontal and occipital inter-hemispheric coherence values were significantly higher than central, parietal, and temporal sites for all four frequency bands (p<0.0001). In the alpha frequency band, significant difference was found between earlier and later driving sections (p=0.02). The coherence values in all EEG frequency bands were slightly increased at the end of the driving session, except for FP1-FP2 electrode pair, which showed no significant change in coherence in the beta frequency band at the end of the driving session. Copyright 2010 Elsevier B.V. All rights reserved.

Transcranial magnetic stimulation (TMS) coupled with electroencephalography (EEG): Biomarker of the future.

PubMed

Kimiskidis, V K

2016-02-01

In recent years, a number of novel brain-stimulation techniques have been developed (such as TMS-EEG, TMS-fMRI and TMS-NIRS), yet they remain underutilized in the field of epilepsy. Accumulating evidence suggests that transcranial magnetic stimulation (TMS) combined with electroencephalography (TMS-EEG) is a highly relevant technique for exploration of the pathophysiology of human epilepsies as well as a promising biomarker with diagnostic and prognostic potential. In genetic generalized epilepsies, TMS-EEG has provided pathophysiological insight by revealing quasi-stable, covert states of excitability, a subclass of which is associated with the generation of TMS-induced epileptiform discharges (EDs). In focal epilepsy, TMS-induced EDs were successfully employed to identify the epileptogenic zone. In addition, TMS trains applied during focal EDs can terminate them, and appear to restore the effective connectivity of the brain network significantly altered by EDs. This abortive effect of TMS on EDs may possibly serve as a biomarker of response to invasive neuromodulatory techniques. TMS-EEG-based stimulation paradigms can provide insight into the mechanisms underlying human epilepsies and, thus, warrant further study as diagnostic and prognostic biomarkers. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Characterization of electroencephalography signals for estimating saliency features in videos.

PubMed

Liang, Zhen; Hamada, Yasuyuki; Oba, Shigeyuki; Ishii, Shin

2018-05-12

Understanding the functions of the visual system has been one of the major targets in neuroscience formany years. However, the relation between spontaneous brain activities and visual saliency in natural stimuli has yet to be elucidated. In this study, we developed an optimized machine learning-based decoding model to explore the possible relationships between the electroencephalography (EEG) characteristics and visual saliency. The optimal features were extracted from the EEG signals and saliency map which was computed according to an unsupervised saliency model ( Tavakoli and Laaksonen, 2017). Subsequently, various unsupervised feature selection/extraction techniques were examined using different supervised regression models. The robustness of the presented model was fully verified by means of ten-fold or nested cross validation procedure, and promising results were achieved in the reconstruction of saliency features based on the selected EEG characteristics. Through the successful demonstration of using EEG characteristics to predict the real-time saliency distribution in natural videos, we suggest the feasibility of quantifying visual content through measuring brain activities (EEG signals) in real environments, which would facilitate the understanding of cortical involvement in the processing of natural visual stimuli and application developments motivated by human visual processing. Copyright © 2018 Elsevier Ltd. All rights reserved.

Association between waking electroencephalography and cognitive event-related potentials in patients with obstructive sleep apnea.

PubMed

Baril, Andrée-Ann; Gagnon, Katia; Gagnon, Jean-François; Montplaisir, Jacques; Gosselin, Nadia

2013-07-01

Sleepiness, cognitive deficits, abnormal event-related potentials (ERP), and slowing of the waking electroencephalography (EEG) activity have been reported in patients with obstructive sleep apnea (OSA). Our study aimed at evaluating if an association exists between the severity of ERP abnormalities and EEG slowing to better understand cerebral dysfunctions in OSA. Twelve OSA patients and 12 age-matched controls underwent an overnight polysomnographic recording, an EEG recording of 10 min of wakefulness, and an auditory ERP protocol known to specifically recruit attention. P300 and P3a ERP components were measured as well as the spectral power in each frequency band of the waking EEG. Pearson product moment correlations were used to measure associations between ERP characteristics and EEG spectral power in OSA patients and control subjects. A positive correlation between the late P300 amplitude and θ power in the occipital region was observed in OSA subjects (P<.01). A positive correlation was also found between P3a amplitude and β1 power in central region in OSA subjects (P<.01). No correlation was observed for control subjects. ERP abnormalities observed in an attention task are associated with a slowing of the waking EEG recorded at rest in OSA. Copyright © 2013 Elsevier B.V. All rights reserved.

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

PubMed

Rodríguez, Alejandro; Rey, Beatriz; Alcañiz, Mariano

2013-01-01

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

Design, fabrication and experimental validation of a novel dry-contact sensor for measuring electroencephalography signals without skin preparation.

PubMed

Liao, Lun-De; Wang, I-Jan; Chen, Sheng-Fu; Chang, Jyh-Yeong; Lin, Chin-Teng

2011-01-01

In the present study, novel dry-contact sensors for measuring electro-encephalography (EEG) signals without any skin preparation are designed, fabricated by an injection molding manufacturing process and experimentally validated. Conventional wet electrodes are commonly used to measure EEG signals; they provide excellent EEG signals subject to proper skin preparation and conductive gel application. However, a series of skin preparation procedures for applying the wet electrodes is always required and usually creates trouble for users. To overcome these drawbacks, novel dry-contact EEG sensors were proposed for potential operation in the presence or absence of hair and without any skin preparation or conductive gel usage. The dry EEG sensors were designed to contact the scalp surface with 17 spring contact probes. Each probe was designed to include a probe head, plunger, spring, and barrel. The 17 probes were inserted into a flexible substrate using a one-time forming process via an established injection molding procedure. With these 17 spring contact probes, the flexible substrate allows for high geometric conformity between the sensor and the irregular scalp surface to maintain low skin-sensor interface impedance. Additionally, the flexible substrate also initiates a sensor buffer effect, eliminating pain when force is applied. The proposed dry EEG sensor was reliable in measuring EEG signals without any skin preparation or conductive gel usage, as compared with the conventional wet electrodes.

Design, Fabrication and Experimental Validation of a Novel Dry-Contact Sensor for Measuring Electroencephalography Signals without Skin Preparation

PubMed Central

Liao, Lun-De; Wang, I-Jan; Chen, Sheng-Fu; Chang, Jyh-Yeong; Lin, Chin-Teng

2011-01-01

In the present study, novel dry-contact sensors for measuring electro-encephalography (EEG) signals without any skin preparation are designed, fabricated by an injection molding manufacturing process and experimentally validated. Conventional wet electrodes are commonly used to measure EEG signals; they provide excellent EEG signals subject to proper skin preparation and conductive gel application. However, a series of skin preparation procedures for applying the wet electrodes is always required and usually creates trouble for users. To overcome these drawbacks, novel dry-contact EEG sensors were proposed for potential operation in the presence or absence of hair and without any skin preparation or conductive gel usage. The dry EEG sensors were designed to contact the scalp surface with 17 spring contact probes. Each probe was designed to include a probe head, plunger, spring, and barrel. The 17 probes were inserted into a flexible substrate using a one-time forming process via an established injection molding procedure. With these 17 spring contact probes, the flexible substrate allows for high geometric conformity between the sensor and the irregular scalp surface to maintain low skin-sensor interface impedance. Additionally, the flexible substrate also initiates a sensor buffer effect, eliminating pain when force is applied. The proposed dry EEG sensor was reliable in measuring EEG signals without any skin preparation or conductive gel usage, as compared with the conventional wet electrodes. PMID:22163929

Design, Fabrication, and Experimental Validation of Novel Flexible Silicon-Based Dry Sensors for Electroencephalography Signal Measurements.

PubMed

Yu, Yi-Hsin; Lu, Shao-Wei; Liao, Lun-De; Lin, Chin-Teng

2014-01-01

Many commercially available electroencephalography (EEG) sensors, including conventional wet and dry sensors, can cause skin irritation and user discomfort owing to the foreign material. The EEG products, especially sensors, highly prioritize the comfort level during devices wear. To overcome these drawbacks for EEG sensors, this paper designs Societe Generale de Surveillance S [Formula: see text] A [Formula: see text] (SGS)-certified, silicon-based dry-contact EEG sensors (SBDSs) for EEG signal measurements. According to the SGS testing report, SBDSs extract does not irritate skin or induce noncytotoxic effects on L929 cells according to ISO10993-5. The SBDS is also lightweight, flexible, and nonirritating to the skin, as well as capable of easily fitting to scalps without any skin preparation or use of a conductive gel. For forehead and hairy sites, EEG signals can be measured reliably with the designed SBDSs. In particular, for EEG signal measurements at hairy sites, the acicular and flexible design of SBDS can push the hair aside to achieve satisfactory scalp contact, as well as maintain low skin-electrode interface impedance. Results of this paper demonstrate that the proposed sensors perform well in the EEG measurements and are feasible for practical applications.

The inverse electroencephalography pipeline

NASA Astrophysics Data System (ADS)

Weinstein, David Michael

The inverse electroencephalography (EEG) problem is defined as determining which regions of the brain are active based on remote measurements recorded with scalp EEG electrodes. An accurate solution to this problem would benefit both fundamental neuroscience research and clinical neuroscience applications. However, constructing accurate patient-specific inverse EEG solutions requires complex modeling, simulation, and visualization algorithms, and to date only a few systems have been developed that provide such capabilities. In this dissertation, a computational system for generating and investigating patient-specific inverse EEG solutions is introduced, and the requirements for each stage of this Inverse EEG Pipeline are defined and discussed. While the requirements of many of the stages are satisfied with existing algorithms, others have motivated research into novel modeling and simulation methods. The principal technical results of this work include novel surface-based volume modeling techniques, an efficient construction for the EEG lead field, and the Open Source release of the Inverse EEG Pipeline software for use by the bioelectric field research community. In this work, the Inverse EEG Pipeline is applied to three research problems in neurology: comparing focal and distributed source imaging algorithms; separating measurements into independent activation components for multifocal epilepsy; and localizing the cortical activity that produces the P300 effect in schizophrenia.

How Useful Is Electroencephalography in the Diagnosis of Autism Spectrum Disorders and the Delineation of Subtypes: A Systematic Review

PubMed Central

Gurau, Oana; Bosl, William J.; Newton, Charles R.

2017-01-01

Autism spectrum disorders (ASD) are thought to be associated with abnormal neural connectivity. Presently, neural connectivity is a theoretical construct that cannot be easily measured. Research in network science and time series analysis suggests that neural network structure, a marker of neural activity, can be measured with electroencephalography (EEG). EEG can be quantified by different methods of analysis to potentially detect brain abnormalities. The aim of this review is to examine evidence for the utility of three methods of EEG signal analysis in the ASD diagnosis and subtype delineation. We conducted a review of literature in which 40 studies were identified and classified according to the principal method of EEG analysis in three categories: functional connectivity analysis, spectral power analysis, and information dynamics. All studies identified significant differences between ASD patients and non-ASD subjects. However, due to high heterogeneity in the results, generalizations could not be inferred and none of the methods alone are currently useful as a new diagnostic tool. The lack of studies prevented the analysis of these methods as tools for ASD subtypes delineation. These results confirm EEG abnormalities in ASD, but as yet not sufficient to help in the diagnosis. Future research with larger samples and more robust study designs could allow for higher sensitivity and consistency in characterizing ASD, paving the way for developing new means of diagnosis. PMID:28747892

Sex differences in a chronometric mental rotation test with cube figures: a behavioral, electroencephalography, and eye-tracking pilot study.

PubMed

Scheer, Clara; Mattioni Maturana, Felipe; Jansen, Petra

2018-05-07

In chronometric mental rotation tasks, sex differences are widely discussed. Most studies find men to be more skilled in mental rotation than women, which can be explained by the holistic strategy that they use to rotate stimuli. Women are believed to apply a piecemeal strategy. So far, there have been no studies investigating this phenomenon using eye-tacking methods in combination with electroencephalography (EEG) analysis: the present study compared behavioral responses, EEG activity, and eye movements of 15 men and 15 women while solving a three-dimensional chronometric mental rotation test. The behavioral analysis showed neither differences in reaction time nor in the accuracy rate between men and women. The EEG data showed a higher right activation on parietal electrodes for women and the eye-tracking results indicated a longer fixation in a higher number of areas of interest at 0° for women. Men and women are likely to possess different perceptual (visual search) and decision-making mechanisms, but similar mental rotation processes. Furthermore, men presented a longer visual search processing, characterized by the greater saccade latency of 0°-135°. Generally, this study could be considered a pilot study to investigate sex differences in mental rotation tasks while combining eye-tracking and EEG methods.

Electroencephalography and analgesics

PubMed Central

Malver, Lasse Paludan; Brokjær, Anne; Staahl, Camilla; Graversen, Carina; Andresen, Trine; Drewes, Asbjørn Mohr

2014-01-01

To assess centrally mediated analgesic mechanisms in clinical trials with pain patients, objective standardized methods such as electroencephalography (EEG) has many advantages. The aim of this review is to provide the reader with an overview of present findings in analgesics assessed with spontaneous EEG and evoked brain potentials (EPs) in humans. Furthermore, EEG methodologies will be discussed with respect to translation from animals to humans and future perspectives in predicting analgesic efficacy. We searched PubMed with MeSH terms ‘analgesics’, ‘electroencephalography’ and ‘evoked potentials’ for relevant articles. Combined with a search in their reference lists 15 articles on spontaneous EEG and 55 papers on EPs were identified. Overall, opioids produced increased activity in the delta band in the spontaneous EEG, but increases in higher frequency bands were also seen. The EP amplitudes decreased in the majority of studies. Anticonvulsants used as analgesics showed inconsistent results. The N-methyl-D-aspartate receptor antagonist ketamine showed an increase in the theta band in spontaneous EEG and decreases in EP amplitudes. Tricyclic antidepressants increased the activity in the delta, theta and beta bands in the spontaneous EEG while EPs were inconsistently affected. Weak analgesics were mainly investigated with EPs and a decrease in amplitudes was generally observed. This review reveals that both spontaneous EEG and EPs are widely used as biomarkers for analgesic drug effects. Methodological differences are common and a more uniform approach will further enhance the value of such biomarkers for drug development and prediction of treatment response in individual patients. PMID:23593934

Intra- and Interindividual Differences in Lateralized Cognitive Performance and Asymmetrical EEG Activity in the Frontal Cortex

ERIC Educational Resources Information Center

Papousek, Ilona; Murhammer, Daniela; Schulter, Gunter

2011-01-01

The study shows that changes in relative verbal vs. figural working memory and fluency performance from one session to a second session two to 3 weeks apart covary with spontaneously occurring changes of cortical asymmetry in the lateral frontal and central cortex, measured by electroencephalography (EEG) in resting conditions before the execution…

Multimodal 2D Brain Computer Interface.

PubMed

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

2015-08-01

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

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

PubMed

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

2012-01-01

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

Memory-Augmented Active Deep Learning for Identifying Relations Between Distant Medical Concepts in Electroencephalography Reports.

PubMed

Maldonado, Ramon; Goodwin, Travis R; Harabagiu, Sanda M

2018-01-01

The automatic identification of relations between medical concepts in a large corpus of Electroencephalography (EEG) reports is an important step in the development of an EEG-specific patient cohort retrieval system as well as in the acquisition of EEG-specific knowledge from this corpus. EEG-specific relations involve medical concepts that are not typically mentioned in the same sentence or even the same section of a report, thus requiring extraction techniques that can handle such long-distance dependencies. To address this challenge, we present a novel frame work which combines the advantages of a deep learning framework employing Dynamic Relational Memory (DRM) with active learning. While DRM enables the prediction of long-distance relations, active learning provides a mechanism for accurately identifying relations with minimal training data, obtaining an 5-fold cross validationF1 score of 0.7475 on a set of 140 EEG reports selected with active learning. The results obtained with our novel framework show great promise.

Is routine electroencephalography (EEG) a useful biomarker for pharmacoresistant epilepsy?

PubMed

Steinhoff, Bernhard J; Scholly, Julia; Dentel, Christel; Staack, Anke Maren

2013-05-01

People with seizure disorders who have been treated at the Kork Epilepsy Center over a prolonged time period and who thus provide data concerning the chronic course of epilepsy were investigated in order to address the potential role of electroencephalography (EEG) as a biomarker for pharmacoresistant epilepsy. Clinical course and the corresponding findings from their first recorded EEG, their first EEG following appropriate treatment, and their last EEG were compared. Furthermore, we investigated if interictal epileptiform discharges (IEDs) differ in amplitude and morphology if recorded in long-term seizure-free patients. The early cessation of IEDs was a relatively good marker for a good prognosis, especially in idiopathic generalized epilepsies. However, persistent IEDs had no major impact on the long-term prognosis. We found no differences between IEDs in seizure-free patients or patients with ongoing seizures. Therefore, in our hands, routine EEG was not an appropriate biomarker for the prediction of pharmacoresistant epilepsy. Additional factors such as etiology and pathophysiology also need to be considered. Wiley Periodicals, Inc. © 2013 International League Against Epilepsy.

Electroencephalography for diagnosis and prognosis of acute encephalitis.

PubMed

Sutter, Raoul; Kaplan, Peter W; Cervenka, Mackenzie C; Thakur, Kiran T; Asemota, Anthony O; Venkatesan, Arun; Geocadin, Romergryko G

2015-08-01

To confirm the previously identified EEG characteristics for HSV encephalitis and to determine the diagnostic and predictive value of electroencephalography (EEG) features for etiology and outcome of acute encephalitis in adults. In addition, we sought to investigate their independence from possible clinical confounders. This study was performed in the Intensive Care Units of two academic tertiary care centers. From 1997 to 2011, all consecutive patients with acute encephalitis who received one or more EEGs were included. Examination of the diagnostic and predictive value of EEG patterns regarding etiology, clinical conditions, and survival was performed. The main outcome measure was in-hospital death. Of 103 patients with encephalitis, EEGs were performed in 76 within a median of 1 day (inter quartile range 0.5-3) after admission. Mortality was 19.7%. Higher proportions of periodic discharges (PDs) (p=0.029) and focal slowing (p=0.017) were detected in Herpes Simplex virus (HSV) encephalitis as compared to non-HSV encephalitis, while clinical characteristics did not differ. Normal EEG remained the strongest association with a low relative risk for death in multivariable analyses (RR<0.001, p<0.001) adjusting for confounders as coma, global cerebral edema and mechanical ventilation. None of the patients with a normal EEG had a GCS of 15. Normal EEG predicted survival independently from possible confounders, highlighting the prognostic value of EEG in evaluating patients with encephalitis. EEG revealed higher proportions of PDs along with focal slowing in HSV encephalitis as compared to other etiologies. EEG significantly adds to clinical, diagnostic and prognostic information in patients with acute encephalitis. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Electroencephalography as a tool for evidence-based diagnosis and improved outcomes in children with epilepsy in a resource-poor setting.

PubMed

Lagunju, Ike Oluwa Abiola; Oyinlade, Alexander Opebiyi; Atalabi, Omolola Mojisola; Ogbole, Godwin; Tedimola, Olushola; Famosaya, Abimbola; Ogunniyi, Adesola; Ogunseyinde, Ayotunde Oluremi; Ragin, Ann

2015-01-01

Electroencephalography (EEG) remains the most important investigative modality in the diagnostic evaluation of individuals with epilepsy. Children living with epilepsy in the developing world are faced with challenges of lack of access to appropriate diagnostic evaluation and a high risk of misdiagnosis and inappropriate therapy. We appraised EEG studies in a cohort of Nigerian children with epilepsy seen in a tertiary center in order to evaluate access to and the impact of EEG in the diagnostic evaluation of the cases. Inter-ictal EEG was requested in all cases of pediatric epilepsy seen at the pediatric neurology clinic of the University College Hospital, Ibadan, Nigeria over a period of 18 months. Clinical diagnosis without EEG evaluation was compared with the final diagnosis post- EEG evaluation. A total of 329 EEGs were recorded in 329 children, aged 3 months to 16 years, median 61.0 months. Clinical evaluation pre-EEG classified 69.3% of the epilepsies as generalized. The a posteriori EEG evaluations showed a considerably higher proportion of localization-related epilepsies (33.6%). The final evaluation post EEG showed a 21% reduction in the proportion of cases labeled as generalized epilepsy and a 55% increase in cases of localization-related epilepsy(p<0.001). Here we show that there is a high risk of misdiagnosis and therefore the use of inappropriate therapies in children with epilepsy in the absence of EEG evaluation. The implications of our findings in the resource-poor country scenario are key for reducing the burden of care and cost of epilepsy treatment on both the caregivers and the already overloaded tertiary care services.

Protocol Based Real-Time Continuous Electroencephalography for Detecting Vasospasm in Subarachnoid Hemorrhage.

PubMed

Hong, Jeong-Ho; Bang, Jae Seung; Chung, Jin-Heon; Han, Moon-Ku

2016-03-01

A continuous electroencephalography (cEEG) can be helpful in detecting vasospasm and delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage (SAH). We describe a patient with an aneurysmal SAH whose symptomatic vasospasm was detected promptly by using a real-time cEEG. Patient was immediately treated by intraarterial vasodilator therapy. A 50-year-old woman without any significant medical history presented with a severe bifrontal headache due to acute SAH with a ruptured aneurysm on the anterior communicating artery (Fisher grade 3). On bleed day 6, she developed a sudden onset of global aphasia and left hemiparesis preceded by cEEG changes consistent with vasospasm. A stat chemical dilator therapy was performed and she recovered without significant neurological deficits. A real-time and protocol-based cEEG can be utilized in order to avoid any delay in detection of vasospasm in aneurysmal SAH and thereby improve clinical outcomes.

Electroencephalography in Mesial Temporal Lobe Epilepsy: A Review

PubMed Central

Javidan, Manouchehr

2012-01-01

Electroencephalography (EEG) has an important role in the diagnosis and classification of epilepsy. It can provide information for predicting the response to antiseizure drugs and to identify the surgically remediable epilepsies. In temporal lobe epilepsy (TLE) seizures could originate in the medial or lateral neocortical temporal region, and many of these patients are refractory to medical treatment. However, majority of patients have had excellent results after surgery and this often relies on the EEG and magnetic resonance imaging (MRI) data in presurgical evaluation. If the scalp EEG data is insufficient or discordant, invasive EEG recording with placement of intracranial electrodes could identify the seizure focus prior to surgery. This paper highlights the general information regarding the use of EEG in epilepsy, EEG patterns resembling epileptiform discharges, and the interictal, ictal and postictal findings in mesial temporal lobe epilepsy using scalp and intracranial recordings prior to surgery. The utility of the automated seizure detection and computerized mathematical models for increasing yield of non-invasive localization is discussed. This paper also describes the sensitivity, specificity, and predictive value of EEG for seizure recurrence after withdrawal of medications following seizure freedom with medical and surgical therapy. PMID:22957235

[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.

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.

Electroencephalography (EEG) in the Study of Equivalence Class Formation. An Explorative Study.

PubMed

Arntzen, Erik; Steingrimsdottir, Hanna S

2017-01-01

Teaching arbitrary conditional discriminations and testing for derived relations may be essential for understanding changes in cognitive skills. Such conditional discrimination procedures are often used within stimulus equivalence research. For example, the participant is taught AB and BC relations and tested if emergent relations as BA, CB, AC and CA occur. The purpose of the current explorative experiment was to study stimulus equivalence class formation in older adults with electroencephalography (EEG) recordings as an additional measure. The EEG was used to learn about whether there was an indication of cognitive changes such as those observed in neurocognitive disorders (NCD). The present study included four participants who did conditional discrimination training and testing. The experimental design employed pre-class formation sorting and post-class formation sorting of the stimuli used in the experiment. EEG recordings were conducted before training, after training and after testing. The results showed that two participants formed equivalence classes, one participant failed in one of the three test relations, and one participant failed in two of the three test relations. This fourth participant also failed to sort the stimuli in accordance with the experimenter-defined stimulus equivalence classes during post-class formation sorting. The EEG indicated no cognitive decline in the first three participants but possible mild cognitive impairment (MCI) in the fourth participant. The results suggest that equivalence class formation may provide information about cognitive impairments such as those that are likely to occur in the early stages of NCD. The study recommends replications with broader samples.

Can We Predict Functional Outcome in Neonates with Hypoxic Ischemic Encephalopathy by the Combination of Neuroimaging and Electroencephalography?

PubMed Central

Nanavati, Tania; Seemaladinne, Nirupama; Regier, Michael; Yossuck, Panitan; Pergami, Paola

2015-01-01

Background Neonatal hypoxic ischemic encephalopathy (HIE) is a major cause of mortality, morbidity, and long-term neurological deficits. Despite the availability of neuroimaging and neurophysiological testing, tools for accurate early diagnosis and prediction of developmental outcome are still lacking. The goal of this study was to determine if combined use of magnetic resonance imaging (MRI) and electroencephalography (EEG) findings could support outcome prediction. Methods We retrospectively reviewed records of 17 HIE neonates, classified brain MRI and EEG findings based on severity, and assessed clinical outcome up to 48 months. We determined the relation between MRI/EEG findings and clinical outcome. Results We demonstrated a significant relationship between MRI findings and clinical outcome (Fisher’s exact test, p = 0.017). EEG provided no additional information about the outcome beyond that contained in the MRI score. The statistical model for outcome prediction based on random forests suggested that EEG readings at 24 hours and 72 hours could be important variables for outcome prediction, but this needs to be investigated further. Conclusion Caution should be used when discussing prognosis for neonates with mild-to-moderate HIE based on early MR imaging and EEG findings. A robust, quantitative marker of HIE severity that allows for accurate prediction of long-term outcome, particularly for mild-to-moderate cases, is still needed. PMID:25862075

Prediction of Human Performance Using Electroencephalography under Different Indoor Room Temperatures

PubMed Central

Zhang, Tinghe; Mao, Zijing; Xu, Xiaojing; Zhang, Lin; Pack, Daniel J.; Dong, Bing; Huang, Yufei

2018-01-01

Varying indoor environmental conditions is known to affect office worker’s performance; wherein past research studies have reported the effects of unfavorable indoor temperature and air quality causing sick building syndrome (SBS) among office workers. Thus, investigating factors that can predict performance in changing indoor environments have become a highly important research topic bearing significant impact in our society. While past research studies have attempted to determine predictors for performance, they do not provide satisfactory prediction ability. Therefore, in this preliminary study, we attempt to predict performance during office-work tasks triggered by different indoor room temperatures (22.2 °C and 30 °C) from human brain signals recorded using electroencephalography (EEG). Seven participants were recruited, from whom EEG, skin temperature, heart rate and thermal survey questionnaires were collected. Regression analyses were carried out to investigate the effectiveness of using EEG power spectral densities (PSD) as predictors of performance. Our results indicate EEG PSDs as predictors provide the highest R2 (> 0.70), that is 17 times higher than using other physiological signals as predictors and is more robust. Finally, the paper provides insight on the selected predictors based on brain activity patterns for low- and high-performance levels under different indoor-temperatures. PMID:29690601

Prediction of Human Performance Using Electroencephalography under Different Indoor Room Temperatures.

PubMed

Nayak, Tapsya; Zhang, Tinghe; Mao, Zijing; Xu, Xiaojing; Zhang, Lin; Pack, Daniel J; Dong, Bing; Huang, Yufei

2018-04-23

Varying indoor environmental conditions is known to affect office worker’s performance; wherein past research studies have reported the effects of unfavorable indoor temperature and air quality causing sick building syndrome (SBS) among office workers. Thus, investigating factors that can predict performance in changing indoor environments have become a highly important research topic bearing significant impact in our society. While past research studies have attempted to determine predictors for performance, they do not provide satisfactory prediction ability. Therefore, in this preliminary study, we attempt to predict performance during office-work tasks triggered by different indoor room temperatures (22.2 °C and 30 °C) from human brain signals recorded using electroencephalography (EEG). Seven participants were recruited, from whom EEG, skin temperature, heart rate and thermal survey questionnaires were collected. Regression analyses were carried out to investigate the effectiveness of using EEG power spectral densities (PSD) as predictors of performance. Our results indicate EEG PSDs as predictors provide the highest R ² (> 0.70), that is 17 times higher than using other physiological signals as predictors and is more robust. Finally, the paper provides insight on the selected predictors based on brain activity patterns for low- and high-performance levels under different indoor-temperatures.

Emotion Regulation Training for Treating Warfighters with Combat-Related PTSD Using Real-Time fMRI and EEG-Assisted Neurofeedback

DTIC Science & Technology

2014-10-01

Real-Time fMRI and EEG -Assisted Neurofeedback . PRINCIPAL INVESTIGATOR: Jerzy Bodurka RECIPIENT: Laureate Institute for Brain Research REPORT...imaging neurofeedback (rtfMRI-nf) training with concurrent electroencephalography ( EEG ) recordings to directly target and modulate the emotion...the project and are actively enrolling veterans to complete rtfMRI-nf neurofeedback training with simultaneous EEG recordings, and a pre-, post

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...

Multiscale entropy analysis of electroencephalography during sleep in patients with Parkinson disease.

PubMed

Chung, Chen-Chih; Kang, Jiunn-Horng; Yuan, Rey-Yue; Wu, Dean; Chen, Chih-Chung; Chi, Nai-Fang; Chen, Po-Chih; Hu, Chaur-Jong

2013-07-01

Sleep disorders are frequently seen in patients with Parkinson disease (PD), including rapid eye movement (REM) behavior disorder and periodic limb movement disorder. However, knowledge about changes in non-REM sleep in patients with PD is limited. This study explored the characteristics of electroencephalography (EEG) during sleep in patients with PD and non-PD controls. We further conducted multiscale entropy (MSE) analysis to evaluate and compare the complexity of sleep EEG for the 2 groups. There were 9 patients with PD (Hoehn-Yahr stage 1 or 2) and 11 non-PD controls. All participants underwent standard whole-night polysomnography (PSG), which included 23 channels, 6 of which were for EEG. The raw data of the EEG were extracted and subjected to MSE analysis. Patients with PD had a longer sleep onset time and a higher spontaneous EEG arousal index. Sleep stage-specific increased MSE was observed in patients with PD during non-REM sleep. The difference was more marked and significant at higher time scale factors (TSFs). In conclusion, increased biosignal complexity, as revealed by MSE analysis, was found in patients with PD during non-REM sleep at high TSFs. This finding might reflect a compensatory mechanism for early defects in neuronal network control machinery in PD.

Quantitative electroencephalography during rapid eye movement (REM) and non-REM sleep in combat-exposed veterans with and without post-traumatic stress disorder.

PubMed

Cohen, Daniel J; Begley, Amy; Alman, Jennie J; Cashmere, David J; Pietrone, Regina N; Seres, Robert J; Germain, Anne

2013-02-01

Sleep disturbances are a hallmark feature of post-traumatic stress disorder (PTSD), and associated with poor clinical outcomes. Few studies have examined sleep quantitative electroencephalography (qEEG), a technique able to detect subtle differences that polysomnography does not capture. We hypothesized that greater high-frequency qEEG would reflect 'hyperarousal' in combat veterans with PTSD (n = 16) compared to veterans without PTSD (n = 13). EEG power in traditional EEG frequency bands was computed for artifact-free sleep epochs across an entire night. Correlations were performed between qEEG and ratings of PTSD symptoms and combat exposure. The groups did not differ significantly in whole-night qEEG measures for either rapid eye movement (REM) or non-REM (NREM) sleep. Non-significant medium effect sizes suggest less REM beta (opposite to our hypothesis), less REM and NREM sigma and more NREM gamma in combat veterans with PTSD. Positive correlations were found between combat exposure and NREM beta (PTSD group only), and REM and NREM sigma (non-PTSD group only). Results did not support global hyperarousal in PTSD as indexed by increased beta qEEG activity. The correlation of sigma activity with combat exposure in those without PTSD and the non-significant trend towards less sigma activity during both REM and NREM sleep in combat veterans with PTSD suggests that differential information processing during sleep may characterize combat-exposed military veterans with and without PTSD. © 2012 European Sleep Research Society.

Clinical review: Continuous and simplified electroencephalography to monitor brain recovery after cardiac arrest

PubMed Central

2013-01-01

There has been a dramatic change in hospital care of cardiac arrest survivors in recent years, including the use of target temperature management (hypothermia). Clinical signs of recovery or deterioration, which previously could be observed, are now concealed by sedation, analgesia, and muscle paralysis. Seizures are common after cardiac arrest, but few centers can offer high-quality electroencephalography (EEG) monitoring around the clock. This is due primarily to its complexity and lack of resources but also to uncertainty regarding the clinical value of monitoring EEG and of treating post-ischemic electrographic seizures. Thanks to technical advances in recent years, EEG monitoring has become more available. Large amounts of EEG data can be linked within a hospital or between neighboring hospitals for expert opinion. Continuous EEG (cEEG) monitoring provides dynamic information and can be used to assess the evolution of EEG patterns and to detect seizures. cEEG can be made more simple by reducing the number of electrodes and by adding trend analysis to the original EEG curves. In our version of simplified cEEG, we combine a reduced montage, displaying two channels of the original EEG, with amplitude-integrated EEG trend curves (aEEG). This is a convenient method to monitor cerebral function in comatose patients after cardiac arrest but has yet to be validated against the gold standard, a multichannel cEEG. We recently proposed a simplified system for interpreting EEG rhythms after cardiac arrest, defining four major EEG patterns. In this topical review, we will discuss cEEG to monitor brain function after cardiac arrest in general and how a simplified cEEG, with a reduced number of electrodes and trend analysis, may facilitate and improve care. PMID:23876221

Using a Wireless Electroencephalography Device to Evaluate E-Health and E-Learning Interventions.

PubMed

Mailhot, Tanya; Lavoie, Patrick; Maheu-Cadotte, Marc-André; Fontaine, Guillaume; Cournoyer, Alexis; Côté, José; Dupuis, France; Karsenti, Thierry; Cossette, Sylvie

Measuring engagement and other reactions of patients and health professionals to e-health and e-learning interventions remains a challenge for researchers. The aim of this pilot study was to assess the feasibility and acceptability of using a wireless electroencephalography (EEG) device to measure affective (anxiety, enjoyment, relaxation) and cognitive (attention, engagement, interest) reactions of patients and healthcare professionals during e-health or e-learning interventions. Using a wireless EEG device, we measured patient (n = 6) and health professional (n = 7) reactions during a 10-minute session of an e-health or e-learning intervention. The following feasibility and acceptability indicators were assessed and compared for patients and healthcare professionals: number of eligible participants who consented to participate, reasons for refusal, time to install and calibrate the wireless EEG device, number of participants who completed the full 10-minute sessions, participant comfort when wearing the device, signal quality, and number of observations obtained for each reaction. The wireless EEG readings were compared to participant self-rating of their reactions. We obtained at least 75% of possible observations for attention, engagement, enjoyment, and interest. EEG scores were similar to self-reported scores, but they varied throughout the sessions, which gave information on participants' real-time reactions to the e-health/e-learning interventions. Results on the other indicators support the feasibility and acceptability of the wireless EEG device for both patients and professionals. Using the wireless EEG device was feasible and acceptable. Future studies must examine its use in other contexts of care and explore which components of the interventions affected participant reactions by combining wireless EEG and eye tracking.

Electroencephalography (EEG) Based Control in Assistive Mobile Robots: A Review

NASA Astrophysics Data System (ADS)

Krishnan, N. Murali; Mariappan, Muralindran; Muthukaruppan, Karthigayan; Hijazi, Mohd Hanafi Ahmad; Kitt, Wong Wei

2016-03-01

Recently, EEG based control in assistive robot usage has been gradually increasing in the area of biomedical field for giving quality and stress free life for disabled and elderly people. This study reviews the deployment of EGG based control in assistive robots, especially for those who in need and neurologically disabled. The main objective of this paper is to describe the methods used for (i) EEG data acquisition and signal preprocessing, (ii) feature extraction and (iii) signal classification methods. Besides that, this study presents the specific research challenges in the designing of these control systems and future research directions.

Long-lasting repetitive transcranial magnetic stimulation modulates electroencephalography oscillation in patients with disorders of consciousness.

PubMed

Xia, Xiaoyu; Liu, Yang; Bai, Yang; Liu, Ziyuan; Yang, Yi; Guo, Yongkun; Xu, Ruxiang; Gao, Xiaorong; Li, Xiaoli; He, Jianghong

2017-10-18

Repetitive transcranial magnetic stimulation (rTMS) has been applied for the treatment of patients with disorders of consciousness (DOC). Timely and accurate assessments of its modulation effects are very useful. This study evaluated rTMS modulation effects on electroencephalography (EEG) oscillation in patients with chronic DOC. Eighteen patients with a diagnosis of DOC lasting more than 3 months were recruited. All patients received one session of 10-Hz rTMS at the left dorsolateral prefrontal cortex and then 12 of them received consecutive rTMS treatment everyday for 20 consecutive days. Resting-state EEGs were recorded before the experiment (T0) after one session of rTMS (T1) and after the entire treatment (T2). The JFK Coma Recovery Scale-Revised scale scores were also recorded at the time points. Our data showed that application of 10-Hz rTMS to the left dorsolateral prefrontal cortex decreased low-frequency band power and increased high-frequency band power in DOC patients, especially in minimal conscious state patients. Considering the correlation of the EEG spectrum with the consciousness level of patients with DOC, quantitative EEG might be useful for assessment of the effect of rTMS in DOC patients.

Reproducibility of EEG-fMRI results in a patient with fixation-off sensitivity.

PubMed

Formaggio, Emanuela; Storti, Silvia Francesca; Galazzo, Ilaria Boscolo; Bongiovanni, Luigi Giuseppe; Cerini, Roberto; Fiaschi, Antonio; Manganotti, Paolo

2014-07-01

Blood oxygenation level-dependent (BOLD) activation associated with interictal epileptiform discharges in a patient with fixation-off sensitivity (FOS) was studied using a combined electroencephalography-functional magnetic resonance imaging (EEG-fMRI) technique. An automatic approach for combined EEG-fMRI analysis and a subject-specific hemodynamic response function was used to improve general linear model analysis of the fMRI data. The EEG showed the typical features of FOS, with continuous epileptiform discharges during elimination of central vision by eye opening and closing and fixation; modification of this pattern was clearly visible and recognizable. During all 3 recording sessions EEG-fMRI activations indicated a BOLD signal decrease related to epileptiform activity in the parietal areas. This study can further our understanding of this EEG phenomenon and can provide some insight into the reliability of the EEG-fMRI technique in localizing the irritative zone.

Identifying the effects of microsaccades in tripolar EEG signals.

PubMed

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

2017-07-01

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

Recent Advances in Resting-State Electroencephalography Biomarkers for Autism Spectrum Disorder-A Review of Methodological and Clinical Challenges.

PubMed

Heunis, Tosca-Marie; Aldrich, Chris; de Vries, Petrus J

2016-08-01

Electroencephalography (EEG) has been used for almost a century to identify seizure-related disorders in humans, typically through expert interpretation of multichannel recordings. Attempts have been made to quantify EEG through frequency analyses and graphic representations. These "traditional" quantitative EEG analysis methods were limited in their ability to analyze complex and multivariate data and have not been generally accepted in clinical settings. There has been growing interest in identification of novel EEG biomarkers to detect early risk of autism spectrum disorder, to identify clinically meaningful subgroups, and to monitor targeted intervention strategies. Most studies to date have, however, used quantitative EEG approaches, and little is known about the emerging multivariate analytical methods or the robustness of candidate biomarkers in the context of the variability of autism spectrum disorder. Here, we present a targeted review of methodological and clinical challenges in the search for novel resting-state EEG biomarkers for autism spectrum disorder. Three primary novel methodologies are discussed: (1) modified multiscale entropy, (2) coherence analysis, and (3) recurrence quantification analysis. Results suggest that these methods may be able to classify resting-state EEG as "autism spectrum disorder" or "typically developing", but many signal processing questions remain unanswered. We suggest that the move to novel EEG analysis methods is akin to the progress in neuroimaging from visual inspection, through region-of-interest analysis, to whole-brain computational analysis. Novel resting-state EEG biomarkers will have to evaluate a range of potential demographic, clinical, and technical confounders including age, gender, intellectual ability, comorbidity, and medication, before these approaches can be translated into the clinical setting. Copyright © 2016 Elsevier Inc. All rights reserved.

A Novel Analysis of Performance Classification and Workload Prediction Using Electroencephalography (EEG) Frequency Data

DTIC Science & Technology

2015-03-26

Engineering and Management Air Force Institute of Technology Air University Air Education and Training Command In Partial Fulfillment of the...Human Universal Measurement and Assessment Network (HUMAN) Lab human performance experiment trials were used to train , validate and test the...calming music to ease the individual before the start of the study [8]. EEG data contains noise ranging from muscle twitches, blinking and other functions

Electroencephalographic and electromyographic changes during the use of detomidine and detomidine-butorphanol combination in standing horses.

PubMed

Kruluc, P; Nemec, Alenka

2006-03-01

Clinically, the use of detomidine and butorphanol is suitable for sedation and deepening of analgosedation. The aim of our study was to establish the influence of detomidine used alone and a butorphanol-detomidine combination on brain activity and to evaluate and compare brain responses (using electroencephalography, EEG) by recording SEF90 (spectral edge frequency 90%), individual brain wave fractions (beta, alpha, theta and delta) and electromyographic (EMG) changes in the left temporal muscle in standing horses. Ten clinically healthy cold-blooded horses were divided into two groups of five animals each. Group I received detomidine and Group II received detomidine followed by butorphanol 10 min later. SEF90, individual brain wave fractions and EMG were recorded with a pEEG (processed EEG) monitor using computerised processed electroencephalography and electromyography. The present study found that detomidine alone and the detomidine-butorphanol combination significantly reduced SEF90 and EMG, and they caused changes in individual brain wave fractions during sedation and particularly during analgosedation. The EMG results showed that the detomidine-butorphanol combination provided greater and longer muscle relaxation. Our EEG and EMG results confirmed that the detomidine-butorphanol combination is safer and more appropriate for painless and non-painless procedures on standing horses compared to detomidine alone.

Automated Detection of Electroencephalography Artifacts in Human, Rodent and Canine Subjects using Machine Learning.

PubMed

Levitt, Joshua; Nitenson, Adam; Koyama, Suguru; Heijmans, Lonne; Curry, James; Ross, Jason T; Kamerling, Steven; Saab, Carl Y

2018-06-23

Electroencephalography (EEG) invariably contains extra-cranial artifacts that are commonly dealt with based on qualitative and subjective criteria. Failure to account for EEG artifacts compromises data interpretation. We have developed a quantitative and automated support vector machine (SVM)-based algorithm to accurately classify artifactual EEG epochs in awake rodent, canine and humans subjects. An embodiment of this method also enables the determination of 'eyes open/closed' states in human subjects. The levels of SVM accuracy for artifact classification in humans, Sprague Dawley rats and beagle dogs were 94.17%, 83.68%, and 85.37%, respectively, whereas 'eyes open/closed' states in humans were labeled with 88.60% accuracy. Each of these results was significantly higher than chance. Comparison with Existing Methods: Other existing methods, like those dependent on Independent Component Analysis, have not been tested in non-human subjects, and require full EEG montages, instead of only single channels, as this method does. We conclude that our EEG artifact detection algorithm provides a valid and practical solution to a common problem in the quantitative analysis and assessment of EEG in pre-clinical research settings across evolutionary spectra. Copyright © 2018. Published by Elsevier B.V.

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

An adaptive singular spectrum analysis method for extracting brain rhythms of electroencephalography

PubMed Central

Hu, Hai; Guo, Shengxin; Liu, Ran

2017-01-01

Artifacts removal and rhythms extraction from electroencephalography (EEG) signals are important for portable and wearable EEG recording devices. Incorporating a novel grouping rule, we proposed an adaptive singular spectrum analysis (SSA) method for artifacts removal and rhythms extraction. Based on the EEG signal amplitude, the grouping rule determines adaptively the first one or two SSA reconstructed components as artifacts and removes them. The remaining reconstructed components are then grouped based on their peak frequencies in the Fourier transform to extract the desired rhythms. The grouping rule thus enables SSA to be adaptive to EEG signals containing different levels of artifacts and rhythms. The simulated EEG data based on the Markov Process Amplitude (MPA) EEG model and the experimental EEG data in the eyes-open and eyes-closed states were used to verify the adaptive SSA method. Results showed a better performance in artifacts removal and rhythms extraction, compared with the wavelet decomposition (WDec) and another two recently reported SSA methods. Features of the extracted alpha rhythms using adaptive SSA were calculated to distinguish between the eyes-open and eyes-closed states. Results showed a higher accuracy (95.8%) than those of the WDec method (79.2%) and the infinite impulse response (IIR) filtering method (83.3%). PMID:28674650

Memories of attachment hamper EEG cortical connectivity in dissociative patients.

PubMed

Farina, Benedetto; Speranza, Anna Maria; Dittoni, Serena; Gnoni, Valentina; Trentini, Cristina; Vergano, Carola Maggiora; Liotti, Giovanni; Brunetti, Riccardo; Testani, Elisa; Della Marca, Giacomo

2014-08-01

In this study, we evaluated cortical connectivity modifications by electroencephalography (EEG) lagged coherence analysis, in subjects with dissociative disorders and in controls, after retrieval of attachment memories. We asked thirteen patients with dissociative disorders and thirteen age- and sex-matched healthy controls to retrieve personal attachment-related autobiographical memories through adult attachment interviews (AAI). EEG was recorded in the closed eyes resting state before and after the AAI. EEG lagged coherence before and after AAI was compared in all subjects. In the control group, memories of attachment promoted a widespread increase in EEG connectivity, in particular in the high-frequency EEG bands. Compared to controls, dissociative patients did not show an increase in EEG connectivity after the AAI. Conclusions: These results shed light on the neurophysiology of the disintegrative effect of retrieval of traumatic attachment memories in dissociative patients.

Outcome of intracranial electroencephalography monitoring and surgery in magnetic resonance imaging-negative temporal lobe epilepsy.

PubMed

Lee, Ricky W; Hoogs, Marietta M; Burkholder, David B; Trenerry, Max R; Drazkowski, Joseph F; Shih, Jerry J; Doll, Karey E; Tatum, William O; Cascino, Gregory D; Marsh, W Richard; Wirrell, Elaine C; Worrell, Gregory A; So, Elson L

2014-07-01

We evaluated the outcomes of intracranial electroencephalography (iEEG) recording and subsequent resective surgery in patients with magnetic resonance imaging (MRI)-negative temporal lobe epilepsy (TLE). Thirty-two patients were identified from the Mayo Clinic Epilepsy Surgery Database (Arizona, Florida, and Minnesota). Eight (25.0%) had chronic iEEG monitoring that recorded neocortical temporal seizure onsets; 12 (37.5%) had mesial temporal seizure onsets; 5 (15.6%) had independent neocortical and mesial temporal seizure onsets; and 7 (21.9%) had simultaneous neocortical and mesial seizure onsets. Neocortical temporal lobe seizure semiology was the only factor significantly associated with neocortical temporal seizure onsets on iEEG. Only 33.3% of patients who underwent lateral temporal neocorticectomy had an Engel class 1 outcome, whereas 76.5% of patients with iEEG-guided anterior temporal lobectomy that included the amygdala and the hippocampus had an Engel class 1 outcome. Limitations in cohort size precluded statistical analysis of neuropsychological test data. Copyright © 2014 Elsevier B.V. All rights reserved.

Simultaneous resting-state functional MRI and electroencephalography recordings of functional connectivity in patients with schizophrenia.

PubMed

Kirino, Eiji; Tanaka, Shoji; Fukuta, Mayuko; Inami, Rie; Arai, Heii; Inoue, Reiichi; Aoki, Shigeki

2017-04-01

It remains unclear how functional connectivity (FC) may be related to specific cognitive domains in neuropsychiatric disorders. Here we used simultaneous resting-state functional magnetic resonance imaging (rsfMRI) and electroencephalography (EEG) recording in patients with schizophrenia, to evaluate FC within and outside the default mode network (DMN). Our study population included 14 patients with schizophrenia and 15 healthy control participants. From all participants, we acquired rsfMRI data, and simultaneously recorded EEG data using an MR-compatible amplifier. We analyzed the rsfMRI-EEG data, and used the CONN toolbox to calculate the FC between regions of interest. We also performed between-group comparisons of standardized low-resolution electromagnetic tomography-based intracortical lagged coherence for each EEG frequency band. FC within the DMN, as measured by rsfMRI and EEG, did not significantly differ between groups. Analysis of rsfMRI data showed that FC between the right posterior inferior temporal gyrus and medial prefrontal cortex was stronger among patients with schizophrenia compared to control participants. Analysis of FC within the DMN using rsfMRI and EEG data revealed no significant differences between patients with schizophrenia and control participants. However, rsfMRI data revealed over-modulated FC between the medial prefrontal cortex and right posterior inferior temporal gyrus in patients with schizophrenia compared to control participants, suggesting that the patients had altered FC, with higher correlations across nodes within and outside of the DMN. Further studies using simultaneous rsfMRI and EEG are required to determine whether altered FC within the DMN is associated with schizophrenia. © 2016 The Authors. Psychiatry and Clinical Neurosciences published by John Wiley & Sons Australia, Ltd on behalf of Japanese Society of Psychiatry and Neurology.

Electroencephalography (EEG) in the Study of Equivalence Class Formation. An Explorative Study

PubMed Central

Arntzen, Erik; Steingrimsdottir, Hanna S.

2017-01-01

Teaching arbitrary conditional discriminations and testing for derived relations may be essential for understanding changes in cognitive skills. Such conditional discrimination procedures are often used within stimulus equivalence research. For example, the participant is taught AB and BC relations and tested if emergent relations as BA, CB, AC and CA occur. The purpose of the current explorative experiment was to study stimulus equivalence class formation in older adults with electroencephalography (EEG) recordings as an additional measure. The EEG was used to learn about whether there was an indication of cognitive changes such as those observed in neurocognitive disorders (NCD). The present study included four participants who did conditional discrimination training and testing. The experimental design employed pre-class formation sorting and post-class formation sorting of the stimuli used in the experiment. EEG recordings were conducted before training, after training and after testing. The results showed that two participants formed equivalence classes, one participant failed in one of the three test relations, and one participant failed in two of the three test relations. This fourth participant also failed to sort the stimuli in accordance with the experimenter-defined stimulus equivalence classes during post-class formation sorting. The EEG indicated no cognitive decline in the first three participants but possible mild cognitive impairment (MCI) in the fourth participant. The results suggest that equivalence class formation may provide information about cognitive impairments such as those that are likely to occur in the early stages of NCD. The study recommends replications with broader samples. PMID:28377704

A Review of Issues Related to Data Acquisition and Analysis in EEG/MEG Studies.

PubMed

Puce, Aina; Hämäläinen, Matti S

2017-05-31

Electroencephalography (EEG) and magnetoencephalography (MEG) are non-invasive electrophysiological methods, which record electric potentials and magnetic fields due to electric currents in synchronously-active neurons. With MEG being more sensitive to neural activity from tangential currents and EEG being able to detect both radial and tangential sources, the two methods are complementary. Over the years, neurophysiological studies have changed considerably: high-density recordings are becoming de rigueur; there is interest in both spontaneous and evoked activity; and sophisticated artifact detection and removal methods are available. Improved head models for source estimation have also increased the precision of the current estimates, particularly for EEG and combined EEG/MEG. Because of their complementarity, more investigators are beginning to perform simultaneous EEG/MEG studies to gain more complete information about neural activity. Given the increase in methodological complexity in EEG/MEG, it is important to gather data that are of high quality and that are as artifact free as possible. Here, we discuss some issues in data acquisition and analysis of EEG and MEG data. Practical considerations for different types of EEG and MEG studies are also discussed.

Statistical methods to estimate treatment effects from multichannel electroencephalography (EEG) data in clinical trials.

PubMed

Ma, Junshui; Wang, Shubing; Raubertas, Richard; Svetnik, Vladimir

2010-07-15

With the increasing popularity of using electroencephalography (EEG) to reveal the treatment effect in drug development clinical trials, the vast volume and complex nature of EEG data compose an intriguing, but challenging, topic. In this paper the statistical analysis methods recommended by the EEG community, along with methods frequently used in the published literature, are first reviewed. A straightforward adjustment of the existing methods to handle multichannel EEG data is then introduced. In addition, based on the spatial smoothness property of EEG data, a new category of statistical methods is proposed. The new methods use a linear combination of low-degree spherical harmonic (SPHARM) basis functions to represent a spatially smoothed version of the EEG data on the scalp, which is close to a sphere in shape. In total, seven statistical methods, including both the existing and the newly proposed methods, are applied to two clinical datasets to compare their power to detect a drug effect. Contrary to the EEG community's recommendation, our results suggest that (1) the nonparametric method does not outperform its parametric counterpart; and (2) including baseline data in the analysis does not always improve the statistical power. In addition, our results recommend that (3) simple paired statistical tests should be avoided due to their poor power; and (4) the proposed spatially smoothed methods perform better than their unsmoothed versions. Copyright 2010 Elsevier B.V. All rights reserved.

Childhood temporal lobe epilepsy: correlation between electroencephalography and magnetic resonance spectroscopy: a case-control study.

PubMed

Azab, Seham Fa; Sherief, Laila M; Saleh, Safaa H; Elshafeiy, Mona M; Siam, Ahmed G; Elsaeed, Wafaa F; Arafa, Mohamed A; Bendary, Eman A; Sherbiny, Hanan S; Elbehedy, Rabab M; Aziz, Khalid A

2015-04-18

The diagnosis of epilepsy should be made as early as possible to give a child the best chance for treatment success and also to decrease complications such as learning difficulties and social and behavioral problems. In this study, we aimed to assess the ability of magnetic resonance spectroscopy (MRS) in detecting the lateralization side in patients with Temporal lobe epilepsy (TLE) in correlation with EEG and MRI findings. This was a case-control study including 40 patients diagnosed (clinically and by EEG) as having temporal lobe epilepsy aged 8 to 14 years (mean, 10.4 years) and 20 healthy children with comparable age and gender as the control group. All patients were subjected to clinical examination, interictal electroencephalography and magnetic resonance imaging (MRI). Proton magnetic resonance spectroscopic examination (MRS) was performed to the patients and the controls. According to the findings of electroencephalography, our patients were classified to three groups: Group 1 included 20 patients with unitemporal (lateralized) epileptic focus, group 2 included 12 patients with bitemporal (non-lateralized) epileptic focus and group 3 included 8 patients with normal electroencephalography. Magnetic resonance spectroscopy could lateralize the epileptic focus in 19 patients in group 1, nine patients in group2 and five patients in group 3 with overall lateralization of (82.5%), while electroencephalography was able to lateralize the focus in (50%) of patients and magnetic resonance imaging detected lateralization of mesial temporal sclerosis in (57.5%) of patients. Magnetic resonance spectroscopy is a promising tool in evaluating patients with epilepsy and offers increased sensitivity to detect temporal pathology that is not obvious on structural MRI imaging.

Pharmaco-EEG: A Study of Individualized Medicine in Clinical Practice.

PubMed

Swatzyna, Ronald J; Kozlowski, Gerald P; Tarnow, Jay D

2015-07-01

Pharmaco-electroencephalography (Pharmaco-EEG) studies using clinical EEG and quantitative EEG (qEEG) technologies have existed for more than 4 decades. This is a promising area that could improve psychotropic intervention using neurological data. One of the objectives in our clinical practice has been to collect EEG and quantitative EEG (qEEG) data. In the past 5 years, we have identified a subset of refractory cases (n = 386) found to contain commonalities of a small number of electrophysiological features in the following diagnostic categories: mood, anxiety, autistic spectrum, and attention deficit disorders, Four abnormalities were noted in the majority of medication failure cases and these abnormalities did not appear to significantly align with their diagnoses. Those were the following: encephalopathy, focal slowing, beta spindles, and transient discharges. To analyze the relationship noted, they were tested for association with the assigned diagnoses. Fisher's exact test and binary logistics regression found very little (6%) association between particular EEG/qEEG abnormalities and diagnoses. Findings from studies of this type suggest that EEG/qEEG provides individualized understanding of pharmacotherapy failures and has the potential to improve medication selection. © EEG and Clinical Neuroscience Society (ECNS) 2014.

Trajectories of Infants' Biobehavioral Development: Timing and Rate of A-Not-B Performance Gains and EEG Maturation

ERIC Educational Resources Information Center

MacNeill, Leigha A.; Ram, Nilam; Bell, Martha Ann; Fox, Nathan A.; Pérez-Edgar, Koraly

2018-01-01

This study examined how timing (i.e., relative maturity) and rate (i.e., how quickly infants attain proficiency) of A-not-B performance were related to changes in brain activity from age 6 to 12 months. A-not-B performance and resting EEG (electroencephalography) were measured monthly from age 6 to 12 months in 28 infants and were modeled using…

Novel wireless electroencephalography system with a minimal preparation time for use in emergencies and prehospital care

PubMed Central

2014-01-01

Background Although clinical applications such as emergency medicine and prehospital care could benefit from a fast-mounting electroencephalography (EEG) recording system, the lack of specifically designed equipment restricts the use of EEG in these environments. Methods This paper describes the design and testing of a six-channel emergency EEG (emEEG) system with a rapid preparation time intended for use in emergency medicine and prehospital care. The novel system comprises a quick-application cap, a device for recording and transmitting the EEG wirelessly to a computer, and custom software for displaying and streaming the data in real-time to a hospital. Bench testing was conducted, as well as healthy volunteer and patient measurements in three different environments: a hospital EEG laboratory, an intensive care unit, and an ambulance. The EEG data was evaluated by two experienced clinical neurophysiologists and compared with recordings from a commercial system. Results The bench tests demonstrated that the emEEG system's performance is comparable to that of a commercial system while the healthy volunteer and patient measurements confirmed that the system can be applied quickly and that it records quality EEG data in a variety of environments. Furthermore, the recorded data was judged to be of diagnostic quality by two experienced clinical neurophysiologists. Conclusions In the future, the emEEG system may be used to record high-quality EEG data in emergency medicine and during ambulance transportation. Its use could lead to a faster diagnostic, a more accurate treatment, and a shorter recovery time for patients with neurological brain disorders. PMID:24886096

Usefulness of electroencephalography for the management of epilepsy in emergency departments.

PubMed

Viloria Alebesque, A; López Bravo, A; Bellosta Diago, E; Santos Lasaosa, S; Mauri Llerda, J A

2017-11-03

Electroencephalography (EEG) is an essential diagnostic tool in epilepsy. Its use in emergency departments (ED) is usually restricted to the diagnosis and management of non-convulsive status epilepticus (NCSE). However, EDs may also benefit from EEG in the context of other situations in epilepsy. We conducted a retrospective observational study using the clinical histories of patients treated at our hospital's ED for epileptic seizures and suspicion of NCSE and undergoing EEG studies in 2015 and 2016. We collected a series of demographic and clinical variables. Our sample included 87 patients (mean age of 44 years). Epileptic seizures constituted the most common reason for consultation: 59.8% due to the first episode of epileptic seizures (FES), 27.6% due to recurrence, and 12.6% due to suspected NCSE. Interictal epileptiform discharges (IED) were observed in 38.4% of patients reporting FES and in 33.3% of those with a known diagnosis of epilepsy. NCSE was confirmed by EEG in 36.4% of all cases of suspected NCSE. Presence of IED led to administration of or changes in long-term treatment in 59.8% of the patients. EEG is a useful tool for seizure management in EDs, not only for severe, sudden-onset clinical situations such as NCSE but also for diagnosis in cases of non-affiliated epilepsy and in patients experiencing the first episode of epilepsy. Copyright © 2017 Sociedad Española de Neurología. Publicado por Elsevier España, S.L.U. All rights reserved.

A quantitative evaluation of dry-sensor electroencephalography

NASA Astrophysics Data System (ADS)

Uy, E. Timothy

Neurologists, neuroscientists, and experimental psychologists study electrical activity within the brain by recording voltage fluctuations at the scalp. This is electroencephalography (EEG). In conventional or "wet" EEG, scalp abrasion and use of electrolytic paste are required to insure good electrical connection between sensor and skin. Repeated abrasion quickly becomes irritating to subjects, severely limiting the number and frequency of sessions. Several groups have produced "dry" EEG sensors that do not require abrasion or conductive paste. These, in addition to sidestepping the issue of abrasion, promise to reduce setup time from about 30 minutes with a technician to less than 30 seconds without one. The availability of such an instrument would (1) reduce the cost of brain-related medical care, (2) lower the barrier of entry on brain experimentation, and (3) allow individual subjects to contribute substantially more data without fear of abrasion or fatigue. Accuracy of the EEG is paramount in the medical diagnosis of epilepsy, in experimental psychology and in the burgeoning field of brain-computer interface. Without a sufficiently accurate measurement, the advantages of dry sensors remain a moot point. However, even after nearly a decade, demonstrations of dry EEG accuracy with respect to wet have been limited to visual comparison of short snippets of spontaneous EEG, averaged event-related potentials or plots of power spectrum. In this dissertation, I propose a detailed methodology based on single-trial EEG classification for comparing dry EEG sensors to their wet counterparts. Applied to a set of commercially fabricated dry sensors, this work reveals that dry sensors can perform as well their wet counterparts with careful screening and attention to the bandwidth of interest.

Non-convulsive seizures and electroencephalography findings as predictors of clinical outcomes at a tertiary intensive care unit in Saudi Arabia.

PubMed

Al-Said, Youssef A; Baeesa, Saleh S; Shivji, Zaitoon; Kayyali, Husam; Alqadi, Khalid; Kadi, Ghada; Cupler, Edward J; Abuzinadah, Ahmad R

2018-06-05

Electroencephalography (EEG) in the intensive care unit (ICU) is often done to detect non-convulsive seizures (NCS). The outcome of ICU patients with NCS strongly depends on the underlying etiology. The implication of NCS and other EEG findings on clinical outcome independent from their etiology is not well understood and our aim to investigate it. We retrospectively identified all adult patients in the ICU who underwent EEG monitoring between January 2008 and December 2011. The main goals were to define the rate of NCS or non-convulsive status epilepticus (NCSE) occurrence in our center among patients who underwent EEG monitoring and to examine if NCS/NCSE are associated with poor outcome [defined as death or dependence] with and without adjustment for underlying etiology. The rate of poor outcome among different EEG categories were also investigated. During the study period, 177 patients underwent EEG monitoring in our ICU. The overall outcome was poor in 62.7% of those undergoing EEG. The rate of occurrence of NCS/NCSE was 8.5% and was associated with poor outcome in 86.7% with an odds ratio (OR) of 5.1 (95% confidence interval [CI] 1.09-23.8). This association was maintained after adjusting for underlying etiologies with OR 5.6 (95% CI 1.05-29.6). The rate of poor outcome was high in the presence of periodic discharges and sharp and slow waves of 75% and 61.5%, respectively. Our cohort of ICU patients undergoing EEGs had a poor outcome. Those who developed NCS/NCSE experienced an even worse outcome regardless of the underlying etiology. Copyright © 2018 Elsevier B.V. All rights reserved.

Poor outcome prediction by burst suppression ratio in adults with post-anoxic coma without hypothermia.

PubMed

Yang, Qinglin; Su, Yingying; Hussain, Mohammed; Chen, Weibi; Ye, Hong; Gao, Daiquan; Tian, Fei

2014-05-01

Burst suppression ratio (BSR) is a quantitative electroencephalography (qEEG) parameter. The purpose of our study was to compare the accuracy of BSR when compared to other EEG parameters in predicting poor outcomes in adults who sustained post-anoxic coma while not being subjected to therapeutic hypothermia. EEG was registered and recorded at least once within 7 days of post-anoxic coma onset. Electrodes were placed according to the international 10-20 system, using a 16-channel layout. Each EEG expert scored raw EEG using a grading scale adapted from Young and scored amplitude-integrated electroencephalography tracings, in addition to obtaining qEEG parameters defined as BSR with a defined threshold. Glasgow outcome scales of 1 and 2 at 3 months, determined by two blinded neurologists, were defined as poor outcome. Sixty patients with Glasgow coma scale score of 8 or less after anoxic accident were included. The sensitivity (97.1%), specificity (73.3%), positive predictive value (82.5%), and negative prediction value (95.0%) of BSR in predicting poor outcome were higher than other EEG variables. BSR1 and BSR2 were reliable in predicting death (area under the curve > 0.8, P < 0.05), with the respective cutoff points being 39.8% and 61.6%. BSR1 was reliable in predicting poor outcome (area under the curve  =  0.820, P < 0.05) with a cutoff point of 23.9%. BSR1 was also an independent predictor of increased risk of death (odds ratio  =  1.042, 95% confidence intervals: 1.012-1.073, P  =  0.006). BSR may be a better predictor in prognosticating poor outcomes in patients with post-anoxic coma who do not undergo therapeutic hypothermia when compared to other qEEG parameters.

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

PubMed

Walter, U; Noachtar, S; Hinrichs, H

2018-02-01

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

Augmented reality-based electrode guidance system for reliable electroencephalography.

PubMed

Song, Chanho; Jeon, Sangseo; Lee, Seongpung; Ha, Ho-Gun; Kim, Jonghyun; Hong, Jaesung

2018-05-24

In longitudinal electroencephalography (EEG) studies, repeatable electrode positioning is essential for reliable EEG assessment. Conventional methods use anatomical landmarks as fiducial locations for the electrode placement. Since the landmarks are manually identified, the EEG assessment is inevitably unreliable because of individual variations among the subjects and the examiners. To overcome this unreliability, an augmented reality (AR) visualization-based electrode guidance system was proposed. The proposed electrode guidance system is based on AR visualization to replace the manual electrode positioning. After scanning and registration of the facial surface of a subject by an RGB-D camera, the AR of the initial electrode positions as reference positions is overlapped with the current electrode positions in real time. Thus, it can guide the position of the subsequently placed electrodes with high repeatability. The experimental results with the phantom show that the repeatability of the electrode positioning was improved compared to that of the conventional 10-20 positioning system. The proposed AR guidance system improves the electrode positioning performance with a cost-effective system, which uses only RGB-D camera. This system can be used as an alternative to the international 10-20 system.

A novel method for device-related electroencephalography artifact suppression to explore cochlear implant-related cortical changes in single-sided deafness.

PubMed

Kim, Kyungsoo; Punte, Andrea Kleine; Mertens, Griet; Van de Heyning, Paul; Park, Kyung-Joon; Choi, Hongsoo; Choi, Ji-Woong; Song, Jae-Jin

2015-11-30

Quantitative electroencephalography (qEEG) is effective when used to analyze ongoing cortical oscillations in cochlear implant (CI) users. However, localization of cortical activity in such users via qEEG is confounded by the presence of artifacts produced by the device itself. Typically, independent component analysis (ICA) is used to remove CI artifacts in auditory evoked EEG signals collected upon brief stimulation and it is effective for auditory evoked potentials (AEPs). However, AEPs do not reflect the daily environments of patients, and thus, continuous EEG data that are closer to such environments are desirable. In this case, device-related artifacts in EEG data are difficult to remove selectively via ICA due to over-completion of EEG data removal in the absence of preprocessing. EEGs were recorded for a long time under conditions of continuous auditory stimulation. To obviate the over-completion problem, we limited the frequency of CI artifacts to a significant characteristic peak and apply ICA artifact removal. Topographic brain mapping results analyzed via band-limited (BL)-ICA exhibited a better energy distribution, matched to the CI location, than data obtained using conventional ICA. Also, source localization data verified that BL-ICA effectively removed CI artifacts. The proposed method selectively removes CI artifacts from continuous EEG recordings, while ICA removal method shows residual peak and removes important brain activity signals. CI artifacts in EEG data obtained during continuous passive listening can be effectively removed with the aid of BL-ICA, opening up new EEG research possibilities in subjects with CIs. Copyright © 2015 Elsevier B.V. All rights reserved.

Brainstorm: A User-Friendly Application for MEG/EEG Analysis

PubMed Central

Tadel, François; Baillet, Sylvain; Mosher, John C.; Pantazis, Dimitrios; Leahy, Richard M.

2011-01-01

Brainstorm is a collaborative open-source application dedicated to magnetoencephalography (MEG) and electroencephalography (EEG) data visualization and processing, with an emphasis on cortical source estimation techniques and their integration with anatomical magnetic resonance imaging (MRI) data. The primary objective of the software is to connect MEG/EEG neuroscience investigators with both the best-established and cutting-edge methods through a simple and intuitive graphical user interface (GUI). PMID:21584256

Novel bifunctional cap for simultaneous electroencephalography and transcranial electrical stimulation.

PubMed

Wunder, Sophia; Hunold, Alexander; Fiedler, Patrique; Schlegelmilch, Falk; Schellhorn, Klaus; Haueisen, Jens

2018-05-08

Neuromodulation induced by transcranial electric stimulation (TES) exhibited promising potential for clinical practice. However, the underlying mechanisms remain subject of research. The combination of TES and electroencephalography (EEG) offers great potential for investigating these mechanisms and brain function in general, especially when performed simultaneously. In conventional applications, the combination of EEG and TES suffers from limitations on the electrode level (gel for electrode-skin interface) and the usability level (preparation time, reproducibility of positioning). To overcome these limitations, we designed a bifunctional cap for simultaneous TES-EEG applications. We used novel electrode materials, namely textile stimulation electrodes and dry EEG electrodes integrated in a flexible textile cap. We verified the functionality of this cap by analysing the effect of TES on visual evoked potentials (VEPs). In accordance with previous reports using standard TES, the amplitude of the N75 component was significantly decreased post-stimulation, indicating the feasibility of using this novel flexible cap for simultaneous TES and EEG. Further, we found a significant reduction of the P100 component only during TES, indicating a different brain modulation effect during and after TES. In conclusion, the novel bifunctional cap offers a novel tool for simultaneous TES-EEG applications in clinical research, therapy monitoring and closed-loop stimulation.

Effect of Muslim prayer (Salat) on α electroencephalography and its relationship with autonomic nervous system activity.

PubMed

Doufesh, Hazem; Ibrahim, Fatimah; Ismail, Noor Azina; Wan Ahmad, Wan Azman

2014-07-01

This study investigated the effect of Muslim prayer (salat) on the α relative power (RPα) of electroencephalography (EEG) and autonomic nervous activity and the relationship between them by using spectral analysis of EEG and heart rate variability (HRV). Thirty healthy Muslim men participated in the study. Their electrocardiograms and EEGs were continuously recorded before, during, and after salat practice with a computer-based data acquisition system (MP150, BIOPAC Systems Inc., Camino Goleta, California). Power spectral analysis was conducted to extract the RPα and HRV components. During salat, a significant increase (p<.05) was observed in the mean RPα in the occipital and parietal regions and in the normalized unit of high-frequency (nuHF) power of HRV (as a parasympathetic index). Meanwhile, the normalized unit of low-frequency (nuLF) power and LF/HF of HRV (as sympathetic indices) decreased according to HRV analyses. RPα showed a significant positive correlation in the occipital and parietal electrodes with nuHF and significant negative correlations with nuLF and LF/HF. During salat, parasympathetic activity increased and sympathetic activity decreased. Therefore, regular salat practices may help promote relaxation, minimize anxiety, and reduce cardiovascular risk.

The efficacy of routine hyperventilation for seizure activation during prolonged video-electroencephalography monitoring.

PubMed

Abubakr, Abuhuziefa; Ifeayni, Iwuchukwu; Wambacq, Ilse

2010-12-01

Hyperventilation (HV) is considered to be one of the activation procedures that provokes epileptic potentials and clinical seizures. However, the true clinical yield of HV is not well established. We retrospectively reviewed the records of all patients admitted to JFK Hospital, Edison, New Jersey, between October 2001 and December 2004 for long-term video-electroencephalography (EEG). A total of 475 patients (193 males; 282 females; age range 5-89 years) were included in the study. All patients underwent routine 3-minute HV as part of the evaluation of their clinical episodes. During the initial assessment, 165 patients did not experience a seizure event, 92 had non-epileptic events, 16 experienced psychogenic non-epileptic seizures (PNES) and six had a clinical event. During HV, of the 43 patients who had primary generalized epilepsy, nine had an abnormal EEG and two experienced seizures; however, out of the 159 patients who had partial seizures, only one patient demonstrated an abnormal EEG. Our study demonstrates that routine HV generally has a very low yield in our Epilepsy-Monitoring Unit. This finding also lends support to the idea that partial seizures are relatively resistant to HV activation. Copyright © 2010 Elsevier Ltd. All rights reserved.

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

PubMed

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

2017-05-01

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

Acquired auditory agnosia in childhood and normal sleep electroencephalography subsequently diagnosed as Landau-Kleffner syndrome: a report of three cases.

PubMed

van Bogaert, Patrick; King, Mary D; Paquier, Philippe; Wetzburger, Catherine; Labasse, Catherine; Dubru, Jean-Marie; Deonna, Thierry

2013-06-01

  We report three cases of Landau-Kleffner syndrome (LKS) in children (two females, one male) in whom diagnosis was delayed because the sleep electroencephalography (EEG) was initially normal.   Case histories including EEG, positron emission tomography findings, and long-term outcome were reviewed.   Auditory agnosia occurred between the age of 2 years and 3 years 6 months, after a period of normal language development. Initial awake and sleep EEG, recorded weeks to months after the onset of language regression, during a nap period in two cases and during a full night of sleep in the third case, was normal. Repeat EEG between 2 months and 2 years later showed epileptiform discharges during wakefulness and strongly activated by sleep, with a pattern of continuous spike-waves during slow-wave sleep in two patients. Patients were diagnosed with LKS and treated with various antiepileptic regimens, including corticosteroids. One patient in whom EEG became normal on hydrocortisone is making significant recovery. The other two patients did not exhibit a sustained response to treatment and remained severely impaired.   Sleep EEG may be normal in the early phase of acquired auditory agnosia. EEG should be repeated frequently in individuals in whom a firm clinical diagnosis is made to facilitate early treatment. © The Authors. Developmental Medicine & Child Neurology © 2012 Mac Keith Press.

The standardization debate: A conflation trap in critical care electroencephalography

PubMed Central

Ng, Marcus C.; Gaspard, Nicolas; Cole, Andrew J.; Hoch, Daniel B.; Cash, Sydney S.; Bianchi, Matt; O’Rourke, Deirdre A.; Rosenthal, Eric S.; Chu, Catherine J.; Westover, M. Brandon

2015-01-01

Purpose Persistent uncertainty over the clinical significance of various pathological continuous electroencephalography (cEEG) findings in the intensive care unit (ICU) has prompted efforts to standardize ICU cEEG terminology and an ensuing debate. We set out to understand the reasons for, and a satisfactory resolution to, this debate. Method We review the positions for and against standardization, and examine their deeper philosophical basis. Results We find that the positions for and against standardization are not fundamentally irreconcilable. Rather, both positions stem from conflating the three cardinal steps in the classic approach to EEG, which we term “description”, “interpretation”, and “prescription”. Using real-world examples we show how this conflation yields muddled clinical reasoning and unproductive debate among electroencephalographers that is translated into confusion among treating clinicians. We propose a middle way that judiciously uses both standardized terminology and clinical reasoning to disentangle these critical steps and apply them in proper sequence. Conclusion The systematic approach to ICU cEEG findings presented herein not only resolves the standardization debate but also clarifies clinical reasoning by helping electroencephalographers assign appropriate weights to cEEG findings in the face of uncertainty. PMID:25457454

The standardization debate: A conflation trap in critical care electroencephalography.

PubMed

Ng, Marcus C; Gaspard, Nicolas; Cole, Andrew J; Hoch, Daniel B; Cash, Sydney S; Bianchi, Matt; O'Rourke, Deirdre A; Rosenthal, Eric S; Chu, Catherine J; Westover, M Brandon

2015-01-01

Persistent uncertainty over the clinical significance of various pathological continuous electroencephalography (cEEG) findings in the intensive care unit (ICU) has prompted efforts to standardize ICU cEEG terminology and an ensuing debate. We set out to understand the reasons for, and a satisfactory resolution to, this debate. We review the positions for and against standardization, and examine their deeper philosophical basis. We find that the positions for and against standardization are not fundamentally irreconcilable. Rather, both positions stem from conflating the three cardinal steps in the classic approach to EEG, which we term "description", "interpretation", and "prescription". Using real-world examples we show how this conflation yields muddled clinical reasoning and unproductive debate among electroencephalographers that is translated into confusion among treating clinicians. We propose a middle way that judiciously uses both standardized terminology and clinical reasoning to disentangle these critical steps and apply them in proper sequence. The systematic approach to ICU cEEG findings presented herein not only resolves the standardization debate but also clarifies clinical reasoning by helping electroencephalographers assign appropriate weights to cEEG findings in the face of uncertainty. Copyright © 2014 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Automatic Seizure Detection in Rats Using Laplacian EEG and Verification with Human Seizure Signals

PubMed Central

Feltane, Amal; Boudreaux-Bartels, G. Faye; Besio, Walter

2012-01-01

Automated detection of seizures is still a challenging problem. This study presents an approach to detect seizure segments in Laplacian electroencephalography (tEEG) recorded from rats using the tripolar concentric ring electrode (TCRE) configuration. Three features, namely, median absolute deviation, approximate entropy, and maximum singular value were calculated and used as inputs into two different classifiers: support vector machines and adaptive boosting. The relative performance of the extracted features on TCRE tEEG was examined. Results are obtained with an overall accuracy between 84.81 and 96.51%. In addition to using TCRE tEEG data, the seizure detection algorithm was also applied to the recorded EEG signals from Andrzejak et al. database to show the efficiency of the proposed method for seizure detection. PMID:23073989

TMS-EEG: From basic research to clinical applications

NASA Astrophysics Data System (ADS)

Hernandez-Pavon, Julio C.; Sarvas, Jukka; Ilmoniemi, Risto J.

2014-11-01

Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) is a powerful technique for non-invasively studying cortical excitability and connectivity. The combination of TMS and EEG has widely been used to perform basic research and recently has gained importance in different clinical applications. In this paper, we will describe the physical and biological principles of TMS-EEG and different applications in basic research and clinical applications. We will present methods based on independent component analysis (ICA) for studying the TMS-evoked EEG responses. These methods have the capability to remove and suppress large artifacts, making it feasible, for instance, to study language areas with TMS-EEG. We will discuss the different applications and limitations of TMS and TMS-EEG in clinical applications. Potential applications of TMS are presented, for instance in neurosurgical planning, depression and other neurological disorders. Advantages and disadvantages of TMS-EEG and its variants such as repetitive TMS (rTMS) are discussed in comparison to other brain stimulation and neuroimaging techniques. Finally, challenges that researchers face when using this technique will be summarized.

Does power mobility training impact a child's mastery motivation and spectrum of EEG activity? An exploratory project.

PubMed

Kenyon, Lisa K; Farris, John P; Aldrich, Naomi J; Rhodes, Samhita

2017-08-30

The purposes of this exploratory project were: (1) to evaluate the impact of power mobility training with a child who has multiple, severe impairments and (2) to determine if the child's spectrum of electroencephalography (EEG) activity changed during power mobility training. A single-subject A-B-A-B research design was conducted with a four-week duration for each phase. Two target behaviours were explored: (1) mastery motivation assessed via the dimensions of mastery questionnaire (DMQ) and (2) EEG data collected under various conditions. Power mobility skills were also assessed. The participant was a three-year, two-month-old girl with spastic quadriplegic cerebral palsy, gross motor function classification system level V. Each target behaviour was measured weekly. During intervention phases, power mobility training was provided. Improvements were noted in subscale scores of the DMQ. Short-term and long-term EEG changes were also noted. Improvements were noted in power mobility skills. The participant in this exploratory project demonstrated improvements in power mobility skill and function. EEG data collection procedures and variability in an individual's EEG activity make it difficult to determine if the participant's spectrum of EEG activity actually changed in response to power mobility training. Additional studies are needed to investigate the impact of power mobility training on the spectrum of EEG activity in children who have multiple, severe impairments. Implications for Rehabilitation Power mobility training appeared to be beneficial for a child with multiple, severe impairments though the child may never become an independent, community-based power wheelchair user. Electroencephalography may be a valuable addition to the study of power mobility use in children with multiple, severe impairments. Power mobility training appeared to impact mastery motivation (the internal drive to solve complex problems and master new skills) in a child who has multiple, severe impairments.

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

PubMed Central

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

2016-01-01

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

Epilepsy

MedlinePlus

... made great strides in detecting patterns of abnormal electrical activity in the brain that cause epileptic seizures. A technology to measure brain activity, called electroencephalography (EEG), became ...

Orgasm-induced seizures: male studied with ictal electroencephalography.

PubMed

Sengupta, Anshuman; Mahmoud, Ali; Tun, Shwe Z; Goulding, Peter

2010-06-01

Reflex seizures can occur in response to a variety of stimuli, both sensory and emotional. Common triggers include light and music; however, in a growing number of case reports, the phenomenon of sexual activity triggering epileptic seizures is described. The majority of these case reports have been in women so far, and most have been found to localise to the right cerebral hemisphere on interictal electroencephalography (EEG). We report the case of a 34-year-old male with orgasm-induced seizures, recorded on ictal EEG. This gentleman's electrophysiology localised his seizure focus to the left cerebral hemisphere, making his case atypical in comparison with the majority of previous reports. Orgasm-induced seizures are an increasingly well-described phenomenon and we suggest that this should be taken into account when assessing patients with possible reflex seizures. Copyright 2010 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

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

PubMed

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

2015-12-01

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

Electroencephalography for children with autistic spectrum disorder: a sedation protocol.

PubMed

Keidan, Ilan; Ben-Menachem, Erez; Tzadok, Michal; Ben-Zeev, Bruria; Berkenstadt, Haim

2015-02-01

To report the effectiveness and efficiency of a predetermined sedation protocol for providing sedation for electroencephalograph (EEG) studies in children with autism. Sleep EEG has been advocated for the majority of children with autism spectrum disorder. In most cases, sedation is required to allow adequate studies. Most sedation drugs have negative effects on the EEG pattern. The sedation protocol we adopted included chloral hydrate, dexmedetomidine, and ketamine and was evaluated prospectively for 2 years. One hundred and eighty-three children with autistic spectrum disorder were sedated with the described drug protocol that was efficient, provided adequate EEG readings, and was not associated with serious adverse events. Our protocol kept costs to a minimum but provided appropriate escalation in care when required. © 2014 John Wiley & Sons Ltd.

A Review of Issues Related to Data Acquisition and Analysis in EEG/MEG Studies

PubMed Central

Puce, Aina; Hämäläinen, Matti S.

2017-01-01

Electroencephalography (EEG) and magnetoencephalography (MEG) are non-invasive electrophysiological methods, which record electric potentials and magnetic fields due to electric currents in synchronously-active neurons. With MEG being more sensitive to neural activity from tangential currents and EEG being able to detect both radial and tangential sources, the two methods are complementary. Over the years, neurophysiological studies have changed considerably: high-density recordings are becoming de rigueur; there is interest in both spontaneous and evoked activity; and sophisticated artifact detection and removal methods are available. Improved head models for source estimation have also increased the precision of the current estimates, particularly for EEG and combined EEG/MEG. Because of their complementarity, more investigators are beginning to perform simultaneous EEG/MEG studies to gain more complete information about neural activity. Given the increase in methodological complexity in EEG/MEG, it is important to gather data that are of high quality and that are as artifact free as possible. Here, we discuss some issues in data acquisition and analysis of EEG and MEG data. Practical considerations for different types of EEG and MEG studies are also discussed. PMID:28561761

Assessment of Cross-Chemical Predictibility for Changes in Serum Clinical Bioindicators and EEG Produced by Pesticides with Different Modes of Action

EPA Science Inventory

Electroencephalography (EEG) is often used as an apical measure of multiple types of central nervous system (CNS) changes, while biomarkers in blood may serve as predictors for adverse outcomes. Correlation between these two measures would suggest that certain changes in biomarke...

Effects of signal artefacts on electroencephalography spectral power during sleep: quantifying the effectiveness of automated artefact-rejection algorithms.

PubMed

Liu, Jianbo; Ramakrishnan, Sridhar; Laxminarayan, Srinivas; Neal, Maxwell; Cashmere, David J; Germain, Anne; Reifman, Jaques

2018-02-01

Electroencephalography (EEG) recordings during sleep are often contaminated by muscle and ocular artefacts, which can affect the results of spectral power analyses significantly. However, the extent to which these artefacts affect EEG spectral power across different sleep states has not been quantified explicitly. Consequently, the effectiveness of automated artefact-rejection algorithms in minimizing these effects has not been characterized fully. To address these issues, we analysed standard 10-channel EEG recordings from 20 subjects during one night of sleep. We compared their spectral power when the recordings were contaminated by artefacts and after we removed them by visual inspection or by using automated artefact-rejection algorithms. During both rapid eye movement (REM) and non-REM (NREM) sleep, muscle artefacts contaminated no more than 5% of the EEG data across all channels. However, they corrupted delta, beta and gamma power levels substantially by up to 126, 171 and 938%, respectively, relative to the power level computed from artefact-free data. Although ocular artefacts were infrequent during NREM sleep, they affected up to 16% of the frontal and temporal EEG channels during REM sleep, primarily corrupting delta power by up to 33%. For both REM and NREM sleep, the automated artefact-rejection algorithms matched power levels to within ~10% of the artefact-free power level for each EEG channel and frequency band. In summary, although muscle and ocular artefacts affect only a small fraction of EEG data, they affect EEG spectral power significantly. This suggests the importance of using artefact-rejection algorithms before analysing EEG data. © 2017 European Sleep Research Society.

Resting-state qEEG predicts rate of second language learning in adults.

PubMed

Prat, Chantel S; Yamasaki, Brianna L; Kluender, Reina A; Stocco, Andrea

2016-01-01

Understanding the neurobiological basis of individual differences in second language acquisition (SLA) is important for research on bilingualism, learning, and neural plasticity. The current study used quantitative electroencephalography (qEEG) to predict SLA in college-aged individuals. Baseline, eyes-closed resting-state qEEG was used to predict language learning rate during eight weeks of French exposure using an immersive, virtual scenario software. Individual qEEG indices predicted up to 60% of the variability in SLA, whereas behavioral indices of fluid intelligence, executive functioning, and working-memory capacity were not correlated with learning rate. Specifically, power in beta and low-gamma frequency ranges over right temporoparietal regions were strongly positively correlated with SLA. These results highlight the utility of resting-state EEG for studying the neurobiological basis of SLA in a relatively construct-free, paradigm-independent manner. Published by Elsevier Inc.

Prognostic value of electroencephalography (EEG) after out-of-hospital cardiac arrest in successfully resuscitated patients used in daily clinical practice.

PubMed

Søholm, Helle; Kjær, Troels Wesenberg; Kjaergaard, Jesper; Cronberg, Tobias; Bro-Jeppesen, John; Lippert, Freddy K; Køber, Lars; Wanscher, Michael; Hassager, Christian

2014-11-01

Out-of-hospital cardiac arrest (OHCA) is associated with a poor prognosis and predicting outcome is complex with neurophysiological testing and repeated clinical neurological examinations as key components of the assessment. In this study we examine the association between different electroencephalography (EEG) patterns and mortality in a clinical cohort of OHCA-patients. From 2002 to 2011 consecutive patients were admitted to an intensive-care-unit after resuscitation from OHCA. Utstein-criteria for pre-hospital data and review of individual patients' charts for post-resuscitation care were used. EEG reports were analysed according to the 2012 American Clinical Neurophysiology Society's guidelines. A total of 1076 patients were included, and EEG was performed in 20% (n=219) with a median of 3(IQR 2-4) days after OHCA. Rhythmic Delta Activity (RDA) was found in 71 patients (36%) and Periodic Discharges (PD) in 100 patients (45%). Background EEG frequency of Alpha+ or Theta was noted in 107 patients (49%), and change in cerebral EEG activity to stimulation (reactivity) was found in 38 patients (17%). Suppression (all activity <10 μV) was found in 26 (12%) and burst-suppression in 17 (8%) patients. A favourable EEG pattern (reactivity, favourable background frequency and RDA) was independently associated with reduced mortality with hazard ratio (HR) 0.43 (95%CI: 0.24-0.76), p=0.004 (false positive rate: 31%) and a non-favourable EEG pattern (no reactivity, unfavourable background frequency, and PD, suppressed voltage or burst-suppression) was associated with higher mortality (HR=1.62(1.09-2.41), p=0.02) after adjustment for known prognostic factors (false positive rate: 9%). EEG may be useful in work-up in prognostication of patients with OHCA. Findings such as Rhythmic Delta Activity (RDA) seem to be associated with a better prognosis, whereas suppressed voltage and burst-suppression patterns were associated with poor prognosis. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Effects of 20 Hz Repetitive Transcranial Magnetic Stimulation on Disorders of Consciousness: A Resting-State Electroencephalography Study.

PubMed

He, Fangping; Wu, Min; Meng, Fanxia; Hu, Yangfan; Gao, Jian; Chen, Zhongqin; Bao, Wangxiao; Liu, Kehong; Luo, Benyan; Pan, Gang

2018-01-01

Repetitive transcranial magnetic stimulation (rTMS) has been proposed as an experimental approach for the treatment of disorders of consciousness (DOC). To date, there has been little research into the use of rTMS in DOC and the therapeutic effects have been variously documented. This study aimed to examine the effects of 20 Hz rTMS on the electroencephalography (EEG) reactivity and clinical response in patients with DOC and to explore the neuromodulatory effects of high-frequency rTMS. In this randomized, sham-controlled, crossover study, real or sham 20 Hz rTMS was applied to the left primary motor cortex (M1) of patients with DOC for 5 consecutive days. Evaluations were blindly performed at the baseline (T0), immediately after the end of the 5 days of treatment (T1) and 1 week after the treatment (T2) using the JFK coma recovery scale-revised (CRS-R) and resting-state EEG. Only one patient, with a history of 2 months of traumatic brain injury, showed long-lasting (T1, T2) behavioral and neurophysiological modifications after the real rTMS stimulation. The 5 remaining patients presented brain reactivity localized at several electrodes, and the EEG modification was not significant. rTMS stimulation may improve awareness and arousal of DOC. Additionally, EEG represents a potential biomarker for the therapeutic efficacy of rTMS. This trial is registered with (NCT03385278).

The effects of increased fluid viscosity on stationary characteristics of EEG signal in healthy adults

PubMed Central

Jestrović, I.; Coyle, J. L.

2014-01-01

Electroencephalography (EEG) systems can enable us to study cerebral activation patterns during performance of swallowing tasks and possibly infer about the nature of abnormal neurological conditions causing swallowing difficulties. While it is well known that EEG signals are non-stationary, there are still open questions regarding the stationarity of EEG during swallowing activities and how the EEG stationarity is affected by different viscosities of the fluids that are swallowed by subjects during these swallowing activities. In the present study, we investigated the EEG signal collected during swallowing tasks by collecting data from 55 healthy adults (ages 18–65). Each task involved the deliberate swallowing of boluses of fluids of different viscosities. Using time-frequency tests with surrogates, we showed that the EEG during swallowing tasks could be considered non-stationary. Furthermore, the statistical tests and linear regression showed that the parameters of fluid viscosity, sex, and different brain regions significantly influenced the index of non-stationarity values. Therefore, these parameters should be considered in future investigations which use EEG during swallowing activities. PMID:25245522

Human cortical activity related to unilateral movements. A high resolution EEG study.

PubMed

Urbano, A; Babiloni, C; Onorati, P; Babiloni, F

1996-12-20

In the present study a modern high resolution electroencephalography (EEG) technique was used to investigate the dynamic functional topography of human cortical activity related to simple unilateral internally triggered finger movements. The sensorimotor area (M1-S1) contralateral to the movement as well as the supplementary motor area (SMA) and to a lesser extent the ipsilateral M1-S1 were active during the preparation and execution of these movements. These findings suggest that both hemispheres may cooperate in both planning and production of simple unilateral volitional acts.

Inhibition of Lateral Prefrontal Cortex Produces Emotionally Biased First Impressions: A Transcranial Magnetic Stimulation and Electroencephalography Study.

PubMed

Lapate, Regina C; Samaha, Jason; Rokers, Bas; Hamzah, Hamdi; Postle, Bradley R; Davidson, Richard J

2017-07-01

Optimal functioning in everyday life requires the ability to override reflexive emotional responses and prevent affective spillover to situations or people unrelated to the source of emotion. In the current study, we investigated whether the lateral prefrontal cortex (lPFC) causally regulates the influence of emotional information on subsequent judgments. We disrupted left lPFC function using transcranial magnetic stimulation (TMS) and recorded electroencephalography (EEG) before and after. Subjects evaluated the likeability of novel neutral faces after a brief exposure to a happy or fearful face. We found that lPFC inhibition biased evaluations of novel faces according to the previously processed emotional expression. Greater frontal EEG alpha power, reflecting increased inhibition by TMS, predicted increased behavioral bias. TMS-induced affective misattribution was long-lasting: Emotionally biased first impressions formed during lPFC inhibition were still detectable outside of the laboratory 3 days later. These findings indicate that lPFC serves an important emotion-regulation function by preventing incidental emotional encoding from automatically biasing subsequent appraisals.

Working memory training using EEG neurofeedback in normal young adults.

PubMed

Xiong, Shi; Cheng, Chen; Wu, Xia; Guo, Xiaojuan; Yao, Li; Zhang, Jiacai

2014-01-01

Recent studies have shown that working memory (WM) performance can be improved by intensive and adaptive computerized training. Here, we explored the WM training effect using Electroencephalography (EEG) neurofeedback (NF) in normal young adults. In the first study, we identified the EEG features related to WM in normal young adults. The receiver operating characteristic (ROC) curve showed that the power ratio of the theta-to-alpha rhythms in the anterior-parietal region, accurately classified a high percentage of the EEG trials recorded during WM and fixation control (FC) tasks. Based on these results, a second study aimed to assess the training effects of the theta-to-alpha ratio and tested the hypothesis that up-regulating the power ratio can improve working memory behavior. Our results demonstrated that these normal young adults succeeded in improving their WM performance with EEG NF, and the pre- and post-test evaluations also indicated that WM performance increase in experimental group was significantly greater than control groups. In summary, our findings provided preliminarily evidence that WM performance can be improved through learned regulation of the EEG power ratio using EEG NF.

Quantitative EEG analysis of the maturational changes associated with childhood absence epilepsy

NASA Astrophysics Data System (ADS)

Rosso, O. A.; Hyslop, W.; Gerlach, R.; Smith, R. L. L.; Rostas, J. A. P.; Hunter, M.

2005-10-01

This study aimed to examine the background electroencephalography (EEG) in children with childhood absence epilepsy, a condition whose presentation has strong developmental links. EEG hallmarks of absence seizure activity are widely accepted and there is recognition that the bulk of inter-ictal EEG in this group is normal to the naked eye. This multidisciplinary study aimed to use the normalized total wavelet entropy (NTWS) (Signal Processing 83 (2003) 1275) to examine the background EEG of those patients demonstrating absence seizure activity, and compare it with children without absence epilepsy. This calculation can be used to define the degree of order in a system, with higher levels of entropy indicating a more disordered (chaotic) system. Results were subjected to further statistical analyses of significance. Entropy values were calculated for patients versus controls. For all channels combined, patients with absence epilepsy showed (statistically significant) lower entropy values than controls. The size of the difference in entropy values was not uniform, with certain EEG electrodes consistently showing greater differences than others.

Listening to Mozart K.448 decreases electroencephalography oscillatory power associated with an increase in sympathetic tone in adults: a post-intervention study

PubMed Central

Lin, Lung-Chang; Ouyang, Chen-Sen; Chiang, Ching-Tai; Wu, Rong-Ching; Wu, Hui-Chuan

2014-01-01

Summary Objective Listening to Mozart K.448 has been demonstrated to improve spatial task scores, leading to what is known as the Mozart Effect. However, most of these reports only describe the phenomena but lack the scientific evidence needed to properly investigate the mechanism of Mozart Effect. In this study, we used electroencephalography (EEG) and heart rate variability (HRV) to evaluate the effects of Mozart K.448 on healthy volunteers to explore Mozart Effect. Design An EEG-based post-intervention analysis. Setting Kaohsiung Medical University Hospital, Kaohsiung, Taiwan. Participants Twenty-nine college students were enrolled. They received EEG and electrocardiogram examinations simultaneously before, during and after listening to the first movement of Mozart K.448. Main outcome measure EEG alpha, theta and beta power and HRV were compared in each stage. Results The results showed a significant decrease in alpha, theta and beta power when they listened to Mozart K.448. In addition, the average root mean square successive difference, the proportion derived by dividing NN50 by the total number of NN intervals, standard deviations of NN intervals and standard deviations of differences between adjacent NN intervals showed a significant decrease, while the high frequency revealed a significant decrease with a significantly elevated low-frequency/high-frequency ratio. Conclusion Listening to Mozart K.448 significantly decreased EEG alpha, theta and beta power and HRV. This study indicates that there is brain cortical function and sympathetic tone activation in healthy adults when listening to Mozart K.448, which may play an important role in the mechanism of Mozart Effect. PMID:25383198

Serious Game and Virtual World Training: Instrumentation and Assessment

DTIC Science & Technology

2012-12-10

Effectiveness of EEG Neurofeedback Training for ADHD in a Clinical Setting as Measured by Changes in T.O.V.A. Scores, Behavioral Ratings, and WISC-R...Human Physiological Data Collection Methods 24 4.3.1 Electroencephalography ( EEG ) 24 4.3.2 Galvanic Skin Response (GSR) and Heart Rate Variability...Collecting Human Data 24 8 Participant Wearing a 32-Channel EEG Cap 25 9 Future Force Warrior Example Combat Armor 27 10 Screenshot of the Organic

Evaluation of a Prototype Low-Cost, Modular, Wireless Electroencephalography (EEG) Headset Design for Widespread Application

DTIC Science & Technology

2016-06-01

therefore did not implement or test actual sensors or electronic components (analog-to-digital conversion, power , and the wireless transmission ...ARL-TR-7703 ● JUNE 2016 US Army Research Laboratory Evaluation of a Prototype Low-Cost, Modular, Wireless Electroencephalography...originator. ARL-TR-7703 ● JUNE 2016 US Army Research Laboratory Evaluation of a Prototype Low-Cost, Modular, Wireless

A review of classification algorithms for EEG-based brain-computer interfaces.

PubMed

Lotte, F; Congedo, M; Lécuyer, A; Lamarche, F; Arnaldi, B

2007-06-01

In this paper we review classification algorithms used to design brain-computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI.

Long-Term Clinical and Electroencephalography (EEG) Consequences of Idiopathic Partial Epilepsies.

PubMed

Dörtcan, Nimet; Tekin Guveli, Betul; Dervent, Aysin

2016-05-03

BACKGROUND Idiopathic partial epilepsies of childhood (IPE) affect a considerable proportion of children. Three main electroclinical syndromes of IPE are the Benign Childhood Epilepsy with Centro-temporal Spikes (BECTS), Panayiotopoulos Syndrome (PS), and Childhood Epilepsy with Occipital Paroxysms (CEOP). In this study we investigated the long-term prognosis of patients with IPE and discussed the semiological and electroencephalography (EEG) data in terms of syndromic characteristics. MATERIAL AND METHODS This study included a group of consecutive patients with IPE who had been followed since 1990. Demographic and clinical variables were investigated. Patients were divided into 3 groups - A: Cases suitable for a single IPE (BECTS, PS and CEOP); B: cases with intermediate characteristics within IPEs; and C: cases with both IPE and IGE characteristics. Long-term data regarding the individual seizure types and EEG findings were re-evaluated. RESULTS A total of 61 patients were included in the study. Mean follow-up duration was 7.8 ± 4.50 years. The mean age at onset of seizures was 7.7 years. There were 40 patients in group A 40, 14 in group B, and 7 in group C. Seizure and EEG characteristics were also explored independently from the syndromic approach. Incidence of autonomic seizures is considerably high at 2-5 years and incidence of oromotor seizures is high at age 9-11 years. The EEG is most abnormal at 6-8 years. The vast majority (86%) of epileptic activity (EA) with parietooccipital is present at 2-5 years, whereas EA with fronto-temporal or multiple sites become more abundant between ages 6 and 11. CONCLUSIONS Results of the present study provide support for the age-related characteristics of the seizures and EEGs in IPE syndromes. Acknowledgement of those phenomena may improve the management of IPEs and give a better estimate of the future consequences.

The impact of hyperoxia on brain activity: A resting-state and task-evoked electroencephalography (EEG) study.

PubMed

Sheng, Min; Liu, Peiying; Mao, Deng; Ge, Yulin; Lu, Hanzhang

2017-01-01

A better understanding of the effect of oxygen on brain electrophysiological activity may provide a more mechanistic insight into clinical studies that use oxygen treatment in pathological conditions, as well as in studies that use oxygen to calibrate functional magnetic resonance imaging (fMRI) signals. This study applied electroencephalography (EEG) in healthy subjects and investigated how high a concentration of oxygen in inhaled air (i.e., normobaric hyperoxia) alters brain activity under resting-state and task-evoked conditions. Study 1 investigated its impact on resting EEG and revealed that hyperoxia suppressed α (8-13Hz) and β (14-35Hz) band power (by 15.6±2.3% and 14.1±3.1%, respectively), but did not change the δ (1-3Hz), θ (4-7Hz), and γ (36-75Hz) bands. Sham control experiments did not result in such changes. Study 2 reproduced these findings, and, furthermore, examined the effect of hyperoxia on visual stimulation event-related potentials (ERP). It was found that the main peaks of visual ERP, specifically N1 and P2, were both delayed during hyperoxia compared to normoxia (P = 0.04 and 0.02, respectively). In contrast, the amplitude of the peaks did not show a change. Our results suggest that hyperoxia has a pronounced effect on brain neural activity, for both resting-state and task-evoked potentials.

The impact of hyperoxia on brain activity: A resting-state and task-evoked electroencephalography (EEG) study

PubMed Central

Sheng, Min; Liu, Peiying; Mao, Deng; Ge, Yulin

2017-01-01

A better understanding of the effect of oxygen on brain electrophysiological activity may provide a more mechanistic insight into clinical studies that use oxygen treatment in pathological conditions, as well as in studies that use oxygen to calibrate functional magnetic resonance imaging (fMRI) signals. This study applied electroencephalography (EEG) in healthy subjects and investigated how high a concentration of oxygen in inhaled air (i.e., normobaric hyperoxia) alters brain activity under resting-state and task-evoked conditions. Study 1 investigated its impact on resting EEG and revealed that hyperoxia suppressed α (8-13Hz) and β (14-35Hz) band power (by 15.6±2.3% and 14.1±3.1%, respectively), but did not change the δ (1-3Hz), θ (4-7Hz), and γ (36-75Hz) bands. Sham control experiments did not result in such changes. Study 2 reproduced these findings, and, furthermore, examined the effect of hyperoxia on visual stimulation event-related potentials (ERP). It was found that the main peaks of visual ERP, specifically N1 and P2, were both delayed during hyperoxia compared to normoxia (P = 0.04 and 0.02, respectively). In contrast, the amplitude of the peaks did not show a change. Our results suggest that hyperoxia has a pronounced effect on brain neural activity, for both resting-state and task-evoked potentials. PMID:28464001

Design, Fabrication, and Experimental Validation of Novel Flexible Silicon-Based Dry Sensors for Electroencephalography Signal Measurements

PubMed Central

Yu, Yi-Hsin; Lu, Shao-Wei; Liao, Lun-De; Lin, Chin-Teng

2014-01-01

Many commercially available electroencephalography (EEG) sensors, including conventional wet and dry sensors, can cause skin irritation and user discomfort owing to the foreign material. The EEG products, especially sensors, highly prioritize the comfort level during devices wear. To overcome these drawbacks for EEG sensors, this paper designs Societe Generale de Surveillance S \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$\\cdot $ \\end{document} A \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$\\cdot $ \\end{document} (SGS)-certified, silicon-based dry-contact EEG sensors (SBDSs) for EEG signal measurements. According to the SGS testing report, SBDSs extract does not irritate skin or induce noncytotoxic effects on L929 cells according to ISO10993-5. The SBDS is also lightweight, flexible, and nonirritating to the skin, as well as capable of easily fitting to scalps without any skin preparation or use of a conductive gel. For forehead and hairy sites, EEG signals can be measured reliably with the designed SBDSs. In particular, for EEG signal measurements at hairy sites, the acicular and flexible design of SBDS can push the hair aside to achieve satisfactory scalp contact, as well as maintain low skin-electrode interface impedance. Results of this paper demonstrate that the proposed sensors perform well in the EEG measurements and are feasible for practical applications. PMID:27170884

Evaluation of cortical local field potential diffusion in stereotactic electro-encephalography recordings: A glimpse on white matter signal.

PubMed

Mercier, Manuel R; Bickel, Stephan; Megevand, Pierre; Groppe, David M; Schroeder, Charles E; Mehta, Ashesh D; Lado, Fred A

2017-02-15

While there is a strong interest in meso-scale field potential recording using intracranial electroencephalography with penetrating depth electrodes (i.e. stereotactic EEG or S-EEG) in humans, the signal recorded in the white matter remains ignored. White matter is generally considered electrically neutral and often included in the reference montage. Moreover, re-referencing electrophysiological data is a critical preprocessing choice that could drastically impact signal content and consequently the results of any given analysis. In the present stereotactic electroencephalography study, we first illustrate empirically the consequences of commonly used references (subdermal, white matter, global average, local montage) on inter-electrode signal correlation. Since most of these reference montages incorporate white matter signal, we next consider the difference between signals recorded in cortical gray matter and white matter. Our results reveal that electrode contacts located in the white matter record a mixture of activity, with part arising from the volume conduction (zero time delay) of activity from nearby gray matter. Furthermore, our analysis shows that white matter signal may be correlated with distant gray matter signal. While residual passive electrical spread from nearby matter may account for this relationship, our results suggest the possibility that this long distance correlation arises from the white matter fiber tracts themselves (i.e. activity from distant gray matter traveling along axonal fibers with time lag larger than zero); yet definitive conclusions about the origin of the white matter signal would require further experimental substantiation. By characterizing the properties of signals recorded in white matter and in gray matter, this study illustrates the importance of including anatomical prior knowledge when analyzing S-EEG data. Copyright © 2017 Elsevier Inc. All rights reserved.

Electroencephalographic imaging of higher brain function

NASA Technical Reports Server (NTRS)

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

1999-01-01

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

An Intracranial Electroencephalography (iEEG) Brain Function Mapping Tool with an Application to Epilepsy Surgery Evaluation.

PubMed

Wang, Yinghua; Yan, Jiaqing; Wen, Jianbin; Yu, Tao; Li, Xiaoli

2016-01-01

Before epilepsy surgeries, intracranial electroencephalography (iEEG) is often employed in function mapping and epileptogenic foci localization. Although the implanted electrodes provide crucial information for epileptogenic zone resection, a convenient clinical tool for electrode position registration and Brain Function Mapping (BFM) visualization is still lacking. In this study, we developed a BFM Tool, which facilitates electrode position registration and BFM visualization, with an application to epilepsy surgeries. The BFM Tool mainly utilizes electrode location registration and function mapping based on pre-defined brain models from other software. In addition, the electrode node and mapping properties, such as the node size/color, edge color/thickness, mapping method, can be adjusted easily using the setting panel. Moreover, users may manually import/export location and connectivity data to generate figures for further application. The role of this software is demonstrated by a clinical study of language area localization. The BFM Tool helps clinical doctors and researchers visualize implanted electrodes and brain functions in an easy, quick and flexible manner. Our tool provides convenient electrode registration, easy brain function visualization, and has good performance. It is clinical-oriented and is easy to deploy and use. The BFM tool is suitable for epilepsy and other clinical iEEG applications.

An Intracranial Electroencephalography (iEEG) Brain Function Mapping Tool with an Application to Epilepsy Surgery Evaluation

PubMed Central

Wang, Yinghua; Yan, Jiaqing; Wen, Jianbin; Yu, Tao; Li, Xiaoli

2016-01-01

Objects: Before epilepsy surgeries, intracranial electroencephalography (iEEG) is often employed in function mapping and epileptogenic foci localization. Although the implanted electrodes provide crucial information for epileptogenic zone resection, a convenient clinical tool for electrode position registration and Brain Function Mapping (BFM) visualization is still lacking. In this study, we developed a BFM Tool, which facilitates electrode position registration and BFM visualization, with an application to epilepsy surgeries. Methods: The BFM Tool mainly utilizes electrode location registration and function mapping based on pre-defined brain models from other software. In addition, the electrode node and mapping properties, such as the node size/color, edge color/thickness, mapping method, can be adjusted easily using the setting panel. Moreover, users may manually import/export location and connectivity data to generate figures for further application. The role of this software is demonstrated by a clinical study of language area localization. Results: The BFM Tool helps clinical doctors and researchers visualize implanted electrodes and brain functions in an easy, quick and flexible manner. Conclusions: Our tool provides convenient electrode registration, easy brain function visualization, and has good performance. It is clinical-oriented and is easy to deploy and use. The BFM tool is suitable for epilepsy and other clinical iEEG applications. PMID:27199729

Topological properties of flat electroencephalography's state space

NASA Astrophysics Data System (ADS)

Ken, Tan Lit; Ahmad, Tahir bin; Mohd, Mohd Sham bin; Ngien, Su Kong; Suwa, Tohru; Meng, Ong Sie

2016-02-01

Neuroinverse problem are often associated with complex neuronal activity. It involves locating problematic cell which is highly challenging. While epileptic foci localization is possible with the aid of EEG signals, it relies greatly on the ability to extract hidden information or pattern within EEG signals. Flat EEG being an enhancement of EEG is a way of viewing electroencephalograph on the real plane. In the perspective of dynamical systems, Flat EEG is equivalent to epileptic seizure hence, making it a great platform to study epileptic seizure. Throughout the years, various mathematical tools have been applied on Flat EEG to extract hidden information that is hardly noticeable by traditional visual inspection. While these tools have given worthy results, the journey towards understanding seizure process completely is yet to be succeeded. Since the underlying structure of Flat EEG is dynamic and is deemed to contain wealthy information regarding brainstorm, it would certainly be appealing to explore in depth its structures. To better understand the complex seizure process, this paper studies the event of epileptic seizure via Flat EEG in a more general framework by means of topology, particularly, on the state space where the event of Flat EEG lies.

Efficiency test of filtering methods for the removal of transcranial magnetic stimulation artifacts on human electroencephalography with artificially transcranial magnetic stimulation-corrupted signals

NASA Astrophysics Data System (ADS)

Zilber, Nicolas A.; Katayama, Yoshinori; Iramina, Keiji; Erich, Wintermantel

2010-05-01

A new approach is proposed to test the efficiency of methods, such as the Kalman filter and the independent component analysis (ICA), when applied to remove the artifacts induced by transcranial magnetic stimulation (TMS) from electroencephalography (EEG). By using EEG recordings corrupted by TMS induction, the shape of the artifacts is approximately described with a model based on an equivalent circuit simulation. These modeled artifacts are subsequently added to other EEG signals—this time not influenced by TMS. The resulting signals prove of interest since we also know their form without the pseudo-TMS artifacts. Therefore, they enable us to use a fit test to compare the signals we obtain after removing the artifacts with the original signals. This efficiency test turned out very useful in comparing the methods between them, as well as in determining the parameters of the filtering that give satisfactory results with the automatic ICA.

Fuzzy topological digital space and digital fuzzy spline of electroencephalography during epileptic seizures

NASA Astrophysics Data System (ADS)

Shah, Mazlina Muzafar; Wahab, Abdul Fatah

2017-08-01

Epilepsy disease occurs because of there is a temporary electrical disturbance in a group of brain cells (nurons). The recording of electrical signals come from the human brain which can be collected from the scalp of the head is called Electroencephalography (EEG). EEG then considered in digital format and in fuzzy form makes it a fuzzy digital space data form. The purpose of research is to identify the area (curve and surface) in fuzzy digital space affected by inside epilepsy seizure in epileptic patient's brain. The main focus for this research is to generalize fuzzy topological digital space, definition and basic operation also the properties by using digital fuzzy set and the operations. By using fuzzy digital space, the theory of digital fuzzy spline can be introduced to replace grid data that has been use previously to get better result. As a result, the flat of EEG can be fuzzy topological digital space and this type of data can be use to interpolate the digital fuzzy spline.

Syndrome of transient headache and neurological deficits with cerebrospinal fluid lymphocytosis (HaNDL): A case report with serial electroencephalography (EEG) recordings.. Is there an association with human herpes virus type 7 (HHV-7) infection?

PubMed

Stelten, Bianca Ml; Venhovens, Jeroen; van der Velden, Lieven Bj; Meulstee, Jan; Verhagen, Wim Im

2016-11-01

Introduction The syndrome of transient headache and neurological deficits with cerebrospinal fluid lymphocytosis (HaNDL) is a diagnosis made by exclusion. In the literature, different etiological explanations are proposed for HaNDL, including an immune-mediated reaction after a viral infection. Case description We present a case of a 23-year-old woman with several episodes of transient headache, neurological deficits and cerebrospinal fluid lymphocytosis. All diagnostic criteria for the HaNDL syndrome were fulfilled; however, additional cerebrospinal fluid analysis showed a positive polymerase chain reaction (PCR) for human herpes virus type 7 (HHV-7). Discussion The possible role of a (prodromal) viral infection in the etiology of HaNDL is discussed. Also the role of electroencephalography (EEG) recordings is discussed. Serial EEG recordings showed generalized slowing, frontal intermittent rhythmic delta activity (FIRDA) and symmetric triphasic frontal waves with a dilation lag.

Using robust principal component analysis to alleviate day-to-day variability in EEG based emotion classification.

PubMed

Ping-Keng Jao; Yuan-Pin Lin; Yi-Hsuan Yang; Tzyy-Ping Jung

2015-08-01

An emerging challenge for emotion classification using electroencephalography (EEG) is how to effectively alleviate day-to-day variability in raw data. This study employed the robust principal component analysis (RPCA) to address the problem with a posed hypothesis that background or emotion-irrelevant EEG perturbations lead to certain variability across days and somehow submerge emotion-related EEG dynamics. The empirical results of this study evidently validated our hypothesis and demonstrated the RPCA's feasibility through the analysis of a five-day dataset of 12 subjects. The RPCA allowed tackling the sparse emotion-relevant EEG dynamics from the accompanied background perturbations across days. Sequentially, leveraging the RPCA-purified EEG trials from more days appeared to improve the emotion-classification performance steadily, which was not found in the case using the raw EEG features. Therefore, incorporating the RPCA with existing emotion-aware machine-learning frameworks on a longitudinal dataset of each individual may shed light on the development of a robust affective brain-computer interface (ABCI) that can alleviate ecological inter-day variability.

Synchronizing MIDI and wireless EEG measurements during natural piano performance.

PubMed

Zamm, Anna; Palmer, Caroline; Bauer, Anna-Katharina R; Bleichner, Martin G; Demos, Alexander P; Debener, Stefan

2017-07-08

Although music performance has been widely studied in the behavioural sciences, less work has addressed the underlying neural mechanisms, perhaps due to technical difficulties in acquiring high-quality neural data during tasks requiring natural motion. The advent of wireless electroencephalography (EEG) presents a solution to this problem by allowing for neural measurement with minimal motion artefacts. In the current study, we provide the first validation of a mobile wireless EEG system for capturing the neural dynamics associated with piano performance. First, we propose a novel method for synchronously recording music performance and wireless mobile EEG. Second, we provide results of several timing tests that characterize the timing accuracy of our system. Finally, we report EEG time domain and frequency domain results from N=40 pianists demonstrating that wireless EEG data capture the unique temporal signatures of musicians' performances with fine-grained precision and accuracy. Taken together, we demonstrate that mobile wireless EEG can be used to measure the neural dynamics of piano performance with minimal motion constraints. This opens many new possibilities for investigating the brain mechanisms underlying music performance. Copyright © 2017 Elsevier B.V. All rights reserved.

Combined electroencephalography-functional magnetic resonance imaging and electrical source imaging improves localization of pediatric focal epilepsy.

PubMed

Centeno, Maria; Tierney, Tim M; Perani, Suejen; Shamshiri, Elhum A; St Pier, Kelly; Wilkinson, Charlotte; Konn, Daniel; Vulliemoz, Serge; Grouiller, Frédéric; Lemieux, Louis; Pressler, Ronit M; Clark, Christopher A; Cross, J Helen; Carmichael, David W

2017-08-01

Surgical treatment in epilepsy is effective if the epileptogenic zone (EZ) can be correctly localized and characterized. Here we use simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) data to derive EEG-fMRI and electrical source imaging (ESI) maps. Their yield and their individual and combined ability to (1) localize the EZ and (2) predict seizure outcome were then evaluated. Fifty-three children with drug-resistant epilepsy underwent EEG-fMRI. Interictal discharges were mapped using both EEG-fMRI hemodynamic responses and ESI. A single localization was derived from each individual test (EEG-fMRI global maxima [GM]/ESI maximum) and from the combination of both maps (EEG-fMRI/ESI spatial intersection). To determine the localization accuracy and its predictive performance, the individual and combined test localizations were compared to the presumed EZ and to the postsurgical outcome. Fifty-two of 53 patients had significant maps: 47 of 53 for EEG-fMRI, 44 of 53 for ESI, and 34 of 53 for both. The EZ was well characterized in 29 patients; 26 had an EEG-fMRI GM localization that was correct in 11, 22 patients had ESI localization that was correct in 17, and 12 patients had combined EEG-fMRI and ESI that was correct in 11. Seizure outcome following resection was correctly predicted by EEG-fMRI GM in 8 of 20 patients, and by the ESI maximum in 13 of 16. The combined EEG-fMRI/ESI region entirely predicted outcome in 9 of 9 patients, including 3 with no lesion visible on MRI. EEG-fMRI combined with ESI provides a simple unbiased localization that may predict surgery better than each individual test, including in MRI-negative patients. Ann Neurol 2017;82:278-287. © 2017 American Neurological Association.

EEG in the classroom: Synchronised neural recordings during video presentation

PubMed Central

Poulsen, Andreas Trier; Kamronn, Simon; Dmochowski, Jacek; Parra, Lucas C.; Hansen, Lars Kai

2017-01-01

We performed simultaneous recordings of electroencephalography (EEG) from multiple students in a classroom, and measured the inter-subject correlation (ISC) of activity evoked by a common video stimulus. The neural reliability, as quantified by ISC, has been linked to engagement and attentional modulation in earlier studies that used high-grade equipment in laboratory settings. Here we reproduce many of the results from these studies using portable low-cost equipment, focusing on the robustness of using ISC for subjects experiencing naturalistic stimuli. The present data shows that stimulus-evoked neural responses, known to be modulated by attention, can be tracked for groups of students with synchronized EEG acquisition. This is a step towards real-time inference of engagement in the classroom. PMID:28266588

EEG in the classroom: Synchronised neural recordings during video presentation

NASA Astrophysics Data System (ADS)

Poulsen, Andreas Trier; Kamronn, Simon; Dmochowski, Jacek; Parra, Lucas C.; Hansen, Lars Kai

2017-03-01

We performed simultaneous recordings of electroencephalography (EEG) from multiple students in a classroom, and measured the inter-subject correlation (ISC) of activity evoked by a common video stimulus. The neural reliability, as quantified by ISC, has been linked to engagement and attentional modulation in earlier studies that used high-grade equipment in laboratory settings. Here we reproduce many of the results from these studies using portable low-cost equipment, focusing on the robustness of using ISC for subjects experiencing naturalistic stimuli. The present data shows that stimulus-evoked neural responses, known to be modulated by attention, can be tracked for groups of students with synchronized EEG acquisition. This is a step towards real-time inference of engagement in the classroom.

Neural and Behavioral Correlates of Song Prosody

ERIC Educational Resources Information Center

Gordon, Reyna Leigh

2010-01-01

This dissertation studies the neural basis of song, a universal human behavior. The relationship of words and melodies in the perception of song at phonological, semantic, melodic, and rhythmic levels of processing was investigated using the fine temporal resolution of Electroencephalography (EEG). The observations reported here may shed light on…

Working Memory Training: Improving Intelligence--Changing Brain Activity

ERIC Educational Resources Information Center

Jausovec, Norbert; Jausovec, Ksenija

2012-01-01

The main objectives of the study were: to investigate whether training on working memory (WM) could improve fluid intelligence, and to investigate the effects WM training had on neuroelectric (electroencephalography--EEG) and hemodynamic (near-infrared spectroscopy--NIRS) patterns of brain activity. In a parallel group experimental design,…

Interrater agreement in the interpretation of neonatal electroencephalography in hypoxic-ischemic encephalopathy.

PubMed

Wusthoff, Courtney J; Sullivan, Joseph; Glass, Hannah C; Shellhaas, Renée A; Abend, Nicholas S; Chang, Taeun; Tsuchida, Tammy N

2017-03-01

Research using neonatal electroencephalography (EEG) has been limited by a lack of a standardized classification system and interpretation terminology. In 2013, the American Clinical Neurophysiology Society (ACNS) published a guideline for standardized terminology and categorization in the description of continuous EEG in neonates. We sought to assess interrater agreement for this neonatal EEG categorization system as applied by a group of pediatric neurophysiologists. A total of 60 neonatal EEG studies were collected from three institutions. All EEG segments were from term neonates with hypoxic-ischemic encephalopathy. Three pediatric neurophysiologists independently reviewed each record using the ACNS standardized scoring system. Unweighted kappa values were calculated for interrater agreement of categorical data across multiple observers. Interrater agreement was very good for identification of seizures (κ = 0.93, p < 0.001), with perfect agreement in 95% of records (57 of 60). Interrater agreement was moderate for classifying records as normal or having any abnormality (κ = 0.49, p < 0.001), with perfect agreement in 78% of records (47 of 60). Interrater agreement was good in classifying EEG backgrounds on a 5-category scale (normal, excessively discontinuous, burst suppression, status epilepticus, or electrocerebral inactivity) (κ = 0.70, p < 0.001), with perfect agreement in 72% of records (43 of 60). Other specific background features had lower agreement, including voltage (κ = 0.41, p < 0.001), variability (κ = 0.35, p < 0.001), symmetry (κ = 0.18, p = 0.01), presence of abnormal sharp waves (κ < 0.20, p < 0.05), and presence of brief rhythmic discharges (κ < 0.20, p < 0.05). We found good or very good interrater agreement applying the ACNS system for identification of seizures and classification of EEG background. Other specific EEG features showed limited interrater agreement. Of importance to both clinicians and researchers, our findings support using the ACNS system in identifying seizures and classifying backgrounds of neonatal EEG recordings, but also suggest limited reproducibility for certain other EEG features. Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.

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

PubMed

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

2017-09-01

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

The Accuracy, Night-to-Night Variability, and Stability of Frontopolar Sleep Electroencephalography Biomarkers

PubMed Central

Levendowski, Daniel J.; Ferini-Strambi, Luigi; Gamaldo, Charlene; Cetel, Mindy; Rosenberg, Robert; Westbrook, Philip R.

2017-01-01

Study Objectives: To assess the validity of sleep architecture and sleep continuity biomarkers obtained from a portable, multichannel forehead electroencephalography (EEG) recorder. Methods: Forty-seven subjects simultaneously underwent polysomnography (PSG) while wearing a multichannel frontopolar EEG recording device (Sleep Profiler). The PSG recordings independently staged by 5 registered polysomnographic technologists were compared for agreement with the autoscored sleep EEG before and after expert review. To assess the night-to-night variability and first night bias, 2 nights of self-applied, in-home EEG recordings obtained from a clinical cohort of 63 patients were used (41% with a diagnosis of insomnia/depression, 35% with insomnia/obstructive sleep apnea, and 17.5% with all three). The between-night stability of abnormal sleep biomarkers was determined by comparing each night's data to normative reference values. Results: The mean overall interscorer agreements between the 5 technologists were 75.9%, and the mean kappa score was 0.70. After visual review, the mean kappa score between the autostaging and five raters was 0.67, and staging agreed with a majority of scorers in at least 80% of the epochs for all stages except stage N1. Sleep spindles, autonomic activation, and stage N3 exhibited the least between-night variability (P < .0001) and strongest between-night stability. Antihypertensive medications were found to have a significant effect on sleep quality biomarkers (P < .02). Conclusions: A strong agreement was observed between the automated sleep staging and human-scored PSG. One night's recording appeared sufficient to characterize abnormal slow wave sleep, sleep spindle activity, and heart rate variability in patients, but a 2-night average improved the assessment of all other sleep biomarkers. Commentary: Two commentaries on this article appear in this issue on pages 771 and 773. Citation: Levendowski DJ, Ferini-Strambi L, Gamaldo C, Cetel M, Rosenberg R, Westbrook PR. The accuracy, night-to-night variability, and stability of frontopolar sleep electroencephalography biomarkers. J Clin Sleep Med. 2017;13(6):791–803. PMID:28454598

Neural Correlates of Phrase Rhythm: An EEG Study of Bipartite vs. Rondo Sonata Form.

PubMed

Martínez-Rodrigo, Arturo; Fernández-Sotos, Alicia; Latorre, José Miguel; Moncho-Bogani, José; Fernández-Caballero, Antonio

2017-01-01

This paper introduces the neural correlates of phrase rhythm. In short, phrase rhythm is the rhythmic aspect of phrase construction and the relationships between phrases. For the sake of establishing the neural correlates, a musical experiment has been designed to induce music-evoked stimuli related to phrase rhythm. Brain activity is monitored through electroencephalography (EEG) by using a brain-computer interface. The power spectral value of each EEG channel is estimated to obtain how power variance distributes as a function of frequency. Our experiment shows statistical differences in theta and alpha bands in the phrase rhythm variations of two classical sonatas, one in bipartite form and the other in rondo form.

Neural Correlates of Phrase Rhythm: An EEG Study of Bipartite vs. Rondo Sonata Form

PubMed Central

Martínez-Rodrigo, Arturo; Fernández-Sotos, Alicia; Latorre, José Miguel; Moncho-Bogani, José; Fernández-Caballero, Antonio

2017-01-01

This paper introduces the neural correlates of phrase rhythm. In short, phrase rhythm is the rhythmic aspect of phrase construction and the relationships between phrases. For the sake of establishing the neural correlates, a musical experiment has been designed to induce music-evoked stimuli related to phrase rhythm. Brain activity is monitored through electroencephalography (EEG) by using a brain–computer interface. The power spectral value of each EEG channel is estimated to obtain how power variance distributes as a function of frequency. Our experiment shows statistical differences in theta and alpha bands in the phrase rhythm variations of two classical sonatas, one in bipartite form and the other in rondo form. PMID:28496406

The Neu-Prem Trial: Neuromonitoring of Brains of Infants Born Preterm During Resuscitation-A Prospective Observational Cohort Study.

PubMed

Katheria, Anup C; Harbert, M J; Nagaraj, Sunil B; Arnell, Kathy; Poeltler, Debra M; Brown, Melissa K; Rich, Wade; Hassen, Kasim O; Finer, Neil

2018-04-16

To determine whether monitoring cerebral oxygen tissue saturation (StO 2 ) with near-infrared spectroscopy (NIRS) and brain activity with amplitude-integrated electroencephalography (aEEG) can predict infants at risk for intraventricular hemorrhage (IVH) and death in the first 72 hours of life. A NIRS sensor and electroencephalography leads were placed on 127 newborns <32 weeks of gestational age at birth. Ten minutes of continuous NIRS and aEEG along with heart rate, peripheral arterial oxygen saturation, fraction of inspired oxygen, and mean airway pressure measurements were obtained in the delivery room. Once the infant was transferred to the neonatal intensive care unit, NIRS, aEEG, and vital signs were recorded until 72 hours of life. An ultrasound scan of the head was performed within the first 12 hours of life and again at 72 hours of life. Thirteen of the infants developed any IVH or died; of these, 4 developed severe IVH (grade 3-4) within 72 hours. There were no differences in either cerebral StO 2 or aEEG in the infants with low-grade IVH. Infants who developed severe IVH or death had significantly lower cerebral StO 2 from 8 to 10 minutes of life. aEEG was not predictive of IVH or death in the delivery room or in the neonatal intensive care unit. It may be possible to use NIRS in the delivery room to predict severe IVH and early death. ClinicalTrials.gov: NCT02605733. Copyright © 2018 Elsevier Inc. All rights reserved.

Increased beta power in the bereaved families of the Sewol ferry disaster: A paradoxical compensatory phenomenon? A two-channel electroencephalography study.

PubMed

Jang, Kuk-In; Shim, Miseon; Lee, Sang Min; Huh, Hyu Jung; Huh, Seung; Joo, Ji-Young; Lee, Seung-Hwan; Chae, Jeong-Ho

2017-11-01

The Sewol ferry capsizing accident on South Korea's southern coast resulted in the death of 304 people, and serious bereavement problems for their families. Electroencephalography (EEG) beta frequency is associated with psychiatric symptoms, such as insomnia. The aim of this study was to investigate the relation between frontal beta power, psychological symptoms, and insomnia in the bereaved families. Eighty-four family members of the Sewol ferry victims (32 men and 52 women) were recruited and their EEG was compared with that of 25 (13 men and 12 women) healthy controls. A two-channel EEG device was used to measure cortical activity in the frontal lobe. Symptom severity of insomnia, post-traumatic stress disorder, complicated grief, and anxiety were evaluated. The bereaved families showed a higher frontal beta power than healthy controls. Subgroup analysis showed that frontal beta power was lower in the individuals with severe insomnia than in those with normal sleep. There was a significant inverse correlation between frontal beta power and insomnia symptom in the bereaved families. This study suggests that increased beta power, reflecting the psychopathology in the bereaved families of the Sewol ferry disaster, may be a compensatory mechanism that follows complex trauma. Frontal beta power could be a potential marker indicating the severity of sleep disturbances. Our results suggest that sleep disturbance is an important symptom in family members of the Sewol ferry disaster's victims, which may be screened by EEG beta power. © 2017 The Authors. Psychiatry and Clinical Neurosciences © 2017 Japanese Society of Psychiatry and Neurology.

Use of EEG to Diagnose ADHD

PubMed Central

Lenartowicz, Agatha; Loo, Sandra K.

2015-01-01

Electroencephalography (EEG) has, historically, played a focal role in the assessment of neural function in children with attention deficit hyperactivity disorder (ADHD). We review here the most recent developments in the utility of EEG in the diagnosis of ADHD, with emphasis on the most commonly used and emerging EEG metrics and their reliability in diagnostic classification. Considering the clinical heterogeneity of ADHD and the complexity of information available from the EEG signals, we suggest that considerable benefits are to be gained from multivariate analyses and a focus towards understanding of the neural generators of EEG. We conclude that while EEG cannot currently be used as a diagnostic tool, vast developments in analytical and technological tools in its domain anticipate future progress in its utility in the clinical setting. PMID:25234074

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

PubMed Central

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

2018-01-01

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

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

PubMed Central

Bleichner, Martin G.; Debener, Stefan

2017-01-01

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

An accuracy aware low power wireless EEG unit with information content based adaptive data compression.

PubMed

Tolbert, Jeremy R; Kabali, Pratik; Brar, Simeranjit; Mukhopadhyay, Saibal

2009-01-01

We present a digital system for adaptive data compression for low power wireless transmission of Electroencephalography (EEG) data. The proposed system acts as a base-band processor between the EEG analog-to-digital front-end and RF transceiver. It performs a real-time accuracy energy trade-off for multi-channel EEG signal transmission by controlling the volume of transmitted data. We propose a multi-core digital signal processor for on-chip processing of EEG signals, to detect signal information of each channel and perform real-time adaptive compression. Our analysis shows that the proposed approach can provide significant savings in transmitter power with minimal impact on the overall signal accuracy.

Automated electroencephalography system and electroencephalographic correlates of space motion sickness, part 4

NASA Technical Reports Server (NTRS)

Frost, J. D., Jr.

1977-01-01

Comparative data for further assessments of the EEG alterations seen during Skylab are elaborated. The variability of alpha, beta, theta, and delta EEG characteristics was analyzed with quantitative computer techniques in a group of six normal individuals over a period of two months, and the EEG effects of a prolonged period of bed rest were evaluated in two subjects. The results confirm that the inflight EEG changes seen during Skylab are statistically significant, but the absolute values obtained for the various parameters do not exceed the maximal range expected in a normal population. Further, the EEG manifestations of extended bed rest do not appear similar to those of space flight.

Two volume integral equations for the inhomogeneous and anisotropic forward problem in electroencephalography

NASA Astrophysics Data System (ADS)

Rahmouni, Lyes; Mitharwal, Rajendra; Andriulli, Francesco P.

2017-11-01

This work presents two new volume integral equations for the Electroencephalography (EEG) forward problem which, differently from the standard integral approaches in the domain, can handle heterogeneities and anisotropies of the head/brain conductivity profiles. The new formulations translate to the quasi-static regime some volume integral equation strategies that have been successfully applied to high frequency electromagnetic scattering problems. This has been obtained by extending, to the volume case, the two classical surface integral formulations used in EEG imaging and by introducing an extra surface equation, in addition to the volume ones, to properly handle boundary conditions. Numerical results corroborate theoretical treatments, showing the competitiveness of our new schemes over existing techniques and qualifying them as a valid alternative to differential equation based methods.

The effect of methylphenidate on very low frequency electroencephalography oscillations in adult ADHD.

PubMed

Cooper, Ruth E; Skirrow, Caroline; Tye, Charlotte; McLoughlin, Grainne; Rijsdijk, Fruhling; Banaschweski, Tobias; Brandeis, Daniel; Kuntsi, Jonna; Asherson, Philip

2014-04-01

Altered very low-frequency electroencephalographic (VLF-EEG) activity is an endophenotype of ADHD in children and adolescents. We investigated VLF-EEG case-control differences in adult samples and the effects of methylphenidate (MPH). A longitudinal case-control study was conducted examining the effects of MPH on VLF-EEG (.02-0.2Hz) during a cued continuous performance task. 41 untreated adults with ADHD and 47 controls were assessed, and 21 cases followed up after MPH treatment, with a similar follow-up for 38 controls (mean follow-up=9.4months). Cases had enhanced frontal and parietal VLF-EEG and increased omission errors. In the whole sample, increased parietal VLF-EEG correlated with increased omission errors. After controlling for subthreshold comorbid symptoms, VLF-EEG case-control differences and treatment effects remained. Post-treatment, a time by group interaction emerged; VLF-EEG and omission errors reduced to the same level as controls, with decreased inattentive symptoms in cases. Reduced VLF-EEG following MPH treatment provides preliminary evidence that changes in VLF-EEG may relate to MPH treatment effects on ADHD symptoms; and that VLF-EEG may be an intermediate phenotype of ADHD. Further studies of the treatment effect of MPH in larger controlled studies are required to formally evaluate any causal link between MPH, VLF-EEG and ADHD symptoms. Copyright © 2014 Elsevier Inc. All rights reserved.

Change in Mean Frequency of Resting-State Electroencephalography after Transcranial Direct Current Stimulation

PubMed Central

Boonstra, Tjeerd W.; Nikolin, Stevan; Meisener, Ann-Christin; Martin, Donel M.; Loo, Colleen K.

2016-01-01

Transcranial direct current stimulation (tDCS) is proposed as a tool to investigate cognitive functioning in healthy people and as a treatment for various neuropathological disorders. However, the underlying cortical mechanisms remain poorly understood. We aim to investigate whether resting-state electroencephalography (EEG) can be used to monitor the effects of tDCS on cortical activity. To this end we tested whether the spectral content of ongoing EEG activity is significantly different after a single session of active tDCS compared to sham stimulation. Twenty participants were tested in a sham-controlled, randomized, crossover design. Resting-state EEG was acquired before, during and after active tDCS to the left dorsolateral prefrontal cortex (15 min of 2 mA tDCS) and sham stimulation. Electrodes with a diameter of 3.14 cm2 were used for EEG and tDCS. Partial least squares (PLS) analysis was used to examine differences in power spectral density (PSD) and the EEG mean frequency to quantify the slowing of EEG activity after stimulation. PLS revealed a significant increase in spectral power at frequencies below 15 Hz and a decrease at frequencies above 15 Hz after active tDCS (P = 0.001). The EEG mean frequency was significantly reduced after both active tDCS (P < 0.0005) and sham tDCS (P = 0.001), though the decrease in mean frequency was smaller after sham tDCS than after active tDCS (P = 0.073). Anodal tDCS of the left DLPFC using a high current density bi-frontal electrode montage resulted in general slowing of resting-state EEG. The similar findings observed following sham stimulation question whether the standard sham protocol is an appropriate control condition for tDCS. PMID:27375462

Hemimegalencephaly: Clinical, EEG, neuroimaging, and IMP-SPECT correlation

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

Konkol, R.J.; Maister, B.H.; Wells, R.G.

1990-11-01

Iofetamine-single photon emission computed tomography (IMP-SPECT) was performed on 2 girls (5 1/2 and 6 years of age) with histories of intractable seizures, developmental delay, and unilateral hemiparesis secondary to hemimegalencephaly. Electroencephalography (EEG) revealed frequent focal discharges in 1 patient, while a nearly continuous burst suppression pattern over the malformed hemisphere was recorded in the other. IMP-SPECT demonstrated a good correlation with neuroimaging studies. In spite of the different EEG patterns, which had been proposed to predict contrasting clinical outcomes, both IMP-SPECT scans disclosed a similar decrease in tracer uptake in the malformed hemisphere. These results are consistent with themore » pattern of decreased tracer uptake found in other interictal studies of focal seizures without cerebral malformations. In view of recent recommendations for hemispherectomy in these patients, we suggest that the IMP-SPECT scan be used to compliment EEG as a method to define the extent of abnormality which may be more relevant to long-term prognosis than EEG alone.« less

Analysis of spontaneous EEG activity in Alzheimer's disease using cross-sample entropy and graph theory.

PubMed

Gomez, Carlos; Poza, Jesus; Gomez-Pilar, Javier; Bachiller, Alejandro; Juan-Cruz, Celia; Tola-Arribas, Miguel A; Carreres, Alicia; Cano, Monica; Hornero, Roberto

2016-08-01

The aim of this pilot study was to analyze spontaneous electroencephalography (EEG) activity in Alzheimer's disease (AD) by means of Cross-Sample Entropy (Cross-SampEn) and two local measures derived from graph theory: clustering coefficient (CC) and characteristic path length (PL). Five minutes of EEG activity were recorded from 37 patients with dementia due to AD and 29 elderly controls. Our results showed that Cross-SampEn values were lower in the AD group than in the control one for all the interactions among EEG channels. This finding indicates that EEG activity in AD is characterized by a lower statistical dissimilarity among channels. Significant differences were found mainly for fronto-central interactions (p <; 0.01, permutation test). Additionally, the application of graph theory measures revealed diverse neural network changes, i.e. lower CC and higher PL values in AD group, leading to a less efficient brain organization. This study suggests the usefulness of our approach to provide further insights into the underlying brain dynamics associated with AD.

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.

A review on EEG-based methods for screening and diagnosing alcohol use disorder.

PubMed

Mumtaz, Wajid; Vuong, Pham Lam; Malik, Aamir Saeed; Rashid, Rusdi Bin Abd

2018-04-01

The screening test for alcohol use disorder (AUD) patients has been of subjective nature and could be misleading in particular cases such as a misreporting the actual quantity of alcohol intake. Although the neuroimaging modality such as electroencephalography (EEG) has shown promising research results in achieving objectivity during the screening and diagnosis of AUD patients. However, the translation of these findings for clinical applications has been largely understudied and hence less clear. This study advocates the use of EEG as a diagnostic and screening tool for AUD patients that may help the clinicians during clinical decision making. In this context, a comprehensive review on EEG-based methods is provided including related electrophysiological techniques reported in the literature. More specifically, the EEG abnormalities associated with the conditions of AUD patients are summarized. The aim is to explore the potentials of objective techniques involving quantities/features derived from resting EEG, event-related potentials or event-related oscillations data.

Simultaneous electroencephalography-functional MRI at 3 T: an analysis of safety risks imposed by performing anatomical reference scans with the EEG equipment in place.

PubMed

Nöth, Ulrike; Laufs, Helmut; Stoermer, Robert; Deichmann, Ralf

2012-03-01

To describe heating effects to be expected in simultaneous electroencephalography (EEG) and magnetic resonance imaging (MRI) when deviating from the EEG manufacturer's instructions; to test which anatomical MRI sequences have a sufficiently low specific absorption rate (SAR) to be performed with the EEG equipment in place; and to suggest precautions to reduce the risk of heating. Heating was determined in vivo below eight EEG electrodes, using both head and body coil transmission and sequences covering the whole range of SAR values. Head transmit coil: temperature increases were below 2.2°C for low SAR sequences, but reached 4.6°C (one subject, clavicle) for high SAR sequences; the equilibrium temperature T(eq) remained below 39°C. Body transmit coil: temperature increases were higher and more frequent over subjects and electrodes, with values below 2.6°C for low SAR sequences, reaching 6.9°C for high SAR sequences (T8 electrode) with T(eq) exceeding a critical level of 40°C. Anatomical imaging should be based on T1-weighted sequences (FLASH, MPRAGE, MDEFT) with an SAR below values for functional MRI sequences based on gradient echo planar imaging. Anatomical sequences with a high SAR can pose a significant risk, which is reduced by using head coil transmission. Copyright © 2011 Wiley-Liss, Inc.

Characterizing the EEG correlates of exploratory behavior.

PubMed

Bourdaud, Nicolas; Chavarriaga, Ricardo; Galan, Ferran; Millan, José Del R

2008-12-01

This study aims to characterize the electroencephalography (EEG) correlates of exploratory behavior. Decision making in an uncertain environment raises a conflict between two opposing needs: gathering information about the environment and exploiting this knowledge in order to optimize the decision. Exploratory behavior has already been studied using functional magnetic resonance imaging (fMRI). Based on a usual paradigm in reinforcement learning, this study has shown bilateral activation in the frontal and parietal cortex. To our knowledge, no previous study has been done on it using EEG. The study of the exploratory behavior using EEG signals raises two difficulties. First, the labels of trial as exploitation or exploration cannot be directly derived from the subject action. In order to access this information, a model of how the subject makes his decision must be built. The exploration related information can be then derived from it. Second, because of the complexity of the task, its EEG correlates are not necessarily time locked with the action. So the EEG processing methods used should be designed in order to handle signals that shift in time across trials. Using the same experimental protocol as the fMRI study, results show that the bilateral frontal and parietal areas are also the most discriminant. This strongly suggests that the EEG signal also conveys information about the exploratory behavior.

Simultaneous trimodal PET-MR-EEG imaging: Do EEG caps generate artefacts in PET images?

PubMed

Rajkumar, Ravichandran; Rota Kops, Elena; Mauler, Jörg; Tellmann, Lutz; Lerche, Christoph; Herzog, Hans; Shah, N Jon; Neuner, Irene

2017-01-01

Trimodal simultaneous acquisition of positron emission tomography (PET), magnetic resonance imaging (MRI), and electroencephalography (EEG) has become feasible due to the development of hybrid PET-MR scanners. To capture the temporal dynamics of neuronal activation on a millisecond-by-millisecond basis, an EEG system is appended to the quantitative high resolution PET-MR imaging modality already established in our institute. One of the major difficulties associated with the development of simultaneous trimodal acquisition is that the components traditionally used in each modality can cause interferences in its counterpart. The mutual interferences of MRI components and PET components on PET and MR images, and the influence of EEG electrodes on functional MRI images have been studied and reported on. Building on this, this study aims to investigate the influence of the EEG cap on the quality and quantification of PET images acquired during simultaneous PET-MR measurements. A preliminary transmission scan study on the ECAT HR+ scanner, using an Iida phantom, showed visible attenuation effect due to the EEG cap. The BrainPET-MR emission images of the Iida phantom with [18F]Fluordeoxyglucose, as well as of human subjects with the EEG cap, did not show significant effects of the EEG cap, even though the applied attenuation correction did not take into account the attenuation of the EEG cap itself.

Neural Correlates of Phrase Quadrature Perception in Harmonic Rhythm: An EEG Study Using a Brain-Computer Interface.

PubMed

Fernández-Soto, Alicia; Martínez-Rodrigo, Arturo; Moncho-Bogani, José; Latorre, José Miguel; Fernández-Caballero, Antonio

2018-06-01

For the sake of establishing the neural correlates of phrase quadrature perception in harmonic rhythm, a musical experiment has been designed to induce music-evoked stimuli related to one important aspect of harmonic rhythm, namely the phrase quadrature. Brain activity is translated to action through electroencephalography (EEG) by using a brain-computer interface. The power spectral value of each EEG channel is estimated to obtain how power variance distributes as a function of frequency. The results of processing the acquired signals are in line with previous studies that use different musical parameters to induce emotions. Indeed, our experiment shows statistical differences in theta and alpha bands between the fulfillment and break of phrase quadrature, an important cue of harmonic rhythm, in two classical sonatas.

Resting state EEG abnormalities in autism spectrum disorders

PubMed Central

2013-01-01

Autism spectrum disorders (ASD) are a group of complex and heterogeneous developmental disorders involving multiple neural system dysfunctions. In an effort to understand neurophysiological substrates, identify etiopathophysiologically distinct subgroups of patients, and track outcomes of novel treatments with translational biomarkers, EEG (electroencephalography) studies offer a promising research strategy in ASD. Resting-state EEG studies of ASD suggest a U-shaped profile of electrophysiological power alterations, with excessive power in low-frequency and high-frequency bands, abnormal functional connectivity, and enhanced power in the left hemisphere of the brain. In this review, we provide a summary of recent findings, discuss limitations in available research that may contribute to inconsistencies in the literature, and offer suggestions for future research in this area for advancing the understanding of ASD. PMID:24040879

Combining near-infrared spectroscopy with electroencephalography and repetitive transcranial magnetic stimulation

NASA Astrophysics Data System (ADS)

Näsi, Tiina; Kotilahti, Kalle; Mäki, Hanna; Nissilä, Ilkka; Meriläinen, Pekka

2009-07-01

The objective of the study was to assess the usability of a near-infrared spectroscopy (NIRS) device in multimodal measurements. We combined NIRS with electroencephalography (EEG) to record hemodynamic responses and evoked potentials simultaneously, and with transcranial magnetic stimulation (TMS) to investigate hemodynamic responses to repetitive TMS (rTMS). Hemodynamic responses and visual evoked potentials (VEPs) to 3, 6, and 12 s stimuli consisting of pattern-reversing checkerboards were successfully recorded in the NIRS/EEG measurement, and ipsi- and contralateral hemodynamic responses to 0.5, 1, and 2 Hz rTMS in the NIRS/TMS measurement. In the NIRS/EEG measurements, the amplitudes of the hemodynamic responses increased from 3- to 6-s stimulus, but not from 6- to 12-s stimulus, and the VEPs showed peaks N75, P100, and N135. In the NIRS/TMS measurements, the 2-Hz stimulus produced the strongest hemodynamic responses compared to the 0.5- and 1-Hz stimuli. In two subjects oxyhemoglobin concentration decreased and in one increased as a consequence of the 2-Hz rTMS. To locate the origin of the measured NIRS responses, methods have to be developed to investigate TMS-induced scalp muscle contractions. In the future, multimodal measurements may prove useful in monitoring or treating diseases such as stroke or Alzheimer's disease.

Relation of insulin resistance to neurocognitive function and electroencephalography in obese children.

PubMed

Akın, Onur; Eker, İbrahim; Arslan, Mutluay; Yavuz, Süleyman Tolga; Akman, Sevil; Taşçılar, Mehmet Emre; Ünay, Bülent

2017-10-26

Childhood obesity may lead to neuronal impairment in both the peripheral and the central nervous system. This study aimed to investigate the impact of obesity and insulin resistance (IR) on the central nervous system and neurocognitive functions in children. Seventy-three obese children (38 male and 35 female) and 42 healthy children (21 male and 21 female) were recruited. Standard biochemical indices and IR were evaluated. The Wechsler Intelligence Scale for Children-Revised (WISC-R) and electroencephalography (EEG) were administered to all participants. The obese participants were divided into two groups based on the presence or absence of IR, and the data were compared between the subgroups. Only verbal scores on the WISC-R in the IR+ group were significantly lower than those of the control and IR- groups. There were no differences between the groups with respect to other parameters of the WISC-R or the EEG. Verbal scores of the WISC-R were negatively correlated with obesity duration and homeostatic model assessment-insulin resistance (HOMA-IR) values. EEGs showed significantly more frequent 'slowing during hyperventilation' (SDHs) in obese children than non-obese children. Neurocognitive functions, particularly verbal abilities, were impaired in obese children with IR. An early examination of cognitive functions may help identify and correct such abnormalities in obese children.

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

PubMed

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

2012-08-01

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

EEG in Sarcoidosis Patients Without Neurological Findings.

PubMed

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

2017-01-01

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

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

PubMed Central

Gupta, Rishabh; Falk, Tiago H.

2017-01-01

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

Prevalence and etiology of false normal aEEG recordings in neonatal hypoxic-ischaemic encephalopathy

PubMed Central

2013-01-01

Background Amplitude-integrated electroencephalography (aEEG) is a useful tool to determine the severity of neonatal hypoxic-ischemic encephalopathy (HIE). Our aim was to assess the prevalence and study the origin of false normal aEEG recordings based on 85 aEEG recordings registered before six hours of age. Methods Raw EEG recordings were reevaluated retrospectively with Fourier analysis to identify and describe the frequency patterns of the raw EEG signal, in cases with inconsistent aEEG recordings and clinical symptoms. Power spectral density curves, power (P) and median frequency (MF) were determined using the raw EEG. In 7 patients non-depolarizing muscle relaxant (NDMR) exposure was found. The EEG sections were analyzed and compared before and after NDMR administration. Results The reevaluation found that the aEEG was truly normal in 4 neonates. In 3 neonates, high voltage electrocardiographic (ECG) artifacts were found with flat trace on raw EEG. High frequency component (HFC) was found as a cause of normal appearing aEEG in 10 neonates. HFC disappeared while P and MF decreased significantly upon NDMR administration in each observed case. Conclusion Occurrence of false normal aEEG background pattern is relatively high in neonates with HIE and hypothermia. High frequency EEG artifacts suggestive of shivering were found to be the most common cause of false normal aEEG in hypothermic neonates while high voltage ECG artifacts are less common. PMID:24268061

Wireless multichannel electroencephalography in the newborn.

PubMed

Ibrahim, Z H; Chari, G; Abdel Baki, S; Bronshtein, V; Kim, M R; Weedon, J; Cracco, J; Aranda, J V

2016-01-01

First, to determine the feasibility of an ultra-compact wireless device (microEEG) to obtain multichannel electroencephalographic (EEG) recording in the Neonatal Intensive Care Unit (NICU). Second, to identify problem areas in order to improve wireless EEG performance. 28 subjects (gestational age 24-30 weeks, postnatal age <30 days) were recruited at 2 sites as part of an ongoing study of neonatal apnea and wireless EEG. Infants underwent 8-9 hour EEG recordings every 2-4 weeks using an electrode cap (ANT-Neuro) connected to the wireless EEG device (Bio-Signal Group). A 23 electrode configuration was used incorporating the International 10-20 System. The device transmitted recordings wirelessly to a laptop computer for bedside assessment. The recordings were assessed by a pediatric neurophysiologist for interpretability. A total of 84 EEGs were recorded from 28 neonates. 61 EEG studies were obtained in infants prior to 35 weeks corrected gestational age (CGA). NICU staff placed all electrode caps and initiated all recordings. Of these recordings 6 (10%) were uninterpretable due to artifacts and one study could not be accessed. The remaining 54 (89%) EEG recordings were acceptable for clinical review and interpretation by a pediatric neurophysiologist. Of the recordings obtained at 35 weeks corrected gestational age or later only 11 out of 23 (48%) were interpretable. Wireless EEG devices can provide practical, continuous, multichannel EEG monitoring in preterm neonates. Their small size and ease of use could overcome obstacles associated with EEG recording and interpretation in the NICU.

Seizure semiology and electroencephalography in young children with lesional temporal lobe epilepsy.

PubMed

Lv, Rui-Juan; Sun, Zhen-Rong; Cui, Tao; Shao, Xiao-Qiu

2014-02-01

This study aimed to discuss the clinical features of seizure semiology and electroencephalography (EEG) in young children with lesional temporal lobe epilepsy (TLE). Children with lesional TLE received presurgical evaluation for intractable epilepsy. They were followed up for more than one year after temporal lobectomy. We reviewed the medical history and video-EEG monitoring of children with TLE to analyze the semiology of seizures and EEG findings and compared the semiology of seizures and EEG findings of childhood TLE and adult TLE. A total of 84 seizures were analyzed in 11 children (aged 23-108 months). The age of seizure onset was from 1 month to 26 months (a mean of 17.6 months). All of the patients exhibited prominent motor manifestations including epileptic spasm, tonic seizure, and unilateral clonic seizure. Seven children manifested behavioral arrest similar to an automotor seizure in adult TLE but with a shorter duration and higher frequency. The automatisms were typically orofacial, whereas manual automatisms were rarely observed. The EEG recordings revealed that diffuse discharge patterns were more common in younger children, whereas focal or unilateral patterns were more typical in older children. All of the patients were seizure-free after temporal lobectomy with more than one-year follow-up. All of the children had a mental development delay or regression; however, there was improvement after surgery, especially in those with surgery performed early. In contrast to TLE in adults, young children with lesional TLE probably represent a distinct nosological and probably less homogeneous syndrome. Although they had generalized clinical and electrographic features, resective epilepsy surgery should be considered as early as possible to obtain seizure control and improvement in mental development. Copyright © 2013 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

High-resolution imaging-guided electroencephalography source localization: temporal effect regularization incorporation in LORETA inverse solution

NASA Astrophysics Data System (ADS)

Boughariou, Jihene; Zouch, Wassim; Slima, Mohamed Ben; Kammoun, Ines; Hamida, Ahmed Ben

2015-11-01

Electroencephalography (EEG) and magnetic resonance imaging (MRI) are noninvasive neuroimaging modalities. They are widely used and could be complementary. The fusion of these modalities may enhance some emerging research fields targeting the exploration better brain activities. Such research attracted various scientific investigators especially to provide a convivial and helpful advanced clinical-aid tool enabling better neurological explorations. Our present research was, in fact, in the context of EEG inverse problem resolution and investigated an advanced estimation methodology for the localization of the cerebral activity. Our focus was, therefore, on the integration of temporal priors to low-resolution brain electromagnetic tomography (LORETA) formalism and to solve the inverse problem in the EEG. The main idea behind our proposed method was in the integration of a temporal projection matrix within the LORETA weighting matrix. A hyperparameter is the principal fact for such a temporal integration, and its importance would be obvious when obtaining a regularized smoothness solution. Our experimental results clearly confirmed the impact of such an optimization procedure adopted for the temporal regularization parameter comparatively to the LORETA method.

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.

Organization and Execution of Current Practices of Deployment-related Mental Health Support

DTIC Science & Technology

2011-04-01

of feedback research with electro-electroencephalography ( EEG ) signals have shown that participants can be trained to influence the characteristics...Birbaumer et al., 2006). This type of training, using brain signals, is referred to as Neurofeedback , while the use of peripheral signals is often...responses in reaction to stress like increased skin conductance, hart rate or blood pressure or certain neurological characteristics captured by EEG

Auto-correlation in the motor/imaginary human EEG signals: A vision about the FDFA fluctuations.

PubMed

Zebende, Gilney Figueira; Oliveira Filho, Florêncio Mendes; Leyva Cruz, Juan Alberto

2017-01-01

In this paper we analyzed, by the FDFA root mean square fluctuation (rms) function, the motor/imaginary human activity produced by a 64-channel electroencephalography (EEG). We utilized the Physionet on-line databank, a publicly available database of human EEG signals, as a standardized reference database for this study. Herein, we report the use of detrended fluctuation analysis (DFA) method for EEG analysis. We show that the complex time series of the EEG exhibits characteristic fluctuations depending on the analyzed channel in the scalp-recorded EEG. In order to demonstrate the effectiveness of the proposed technique, we analyzed four distinct channels represented here by F332, F637 (frontal region of the head) and P349, P654 (parietal region of the head). We verified that the amplitude of the FDFA rms function is greater for the frontal channels than for the parietal. To tabulate this information in a better way, we define and calculate the difference between FDFA (in log scale) for the channels, thus defining a new path for analysis of EEG signals. Finally, related to the studied EEG signals, we obtain the auto-correlation exponent, αDFA by DFA method, that reveals self-affinity at specific time scale. Our results shows that this strategy can be applied to study the human brain activity in EEG processing.

Mental stress assessment using simultaneous measurement of EEG and fNIRS

PubMed Central

Al-Shargie, Fares; Kiguchi, Masashi; Badruddin, Nasreen; Dass, Sarat C.; Hani, Ahmad Fadzil Mohammad; Tang, Tong Boon

2016-01-01

Previous studies reported mental stress as one of the major contributing factors leading to various diseases such as heart attack, depression and stroke. An accurate stress assessment method may thus be of importance to clinical intervention and disease prevention. We propose a joint independent component analysis (jICA) based approach to fuse simultaneous measurement of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) on the prefrontal cortex (PFC) as a means of stress assessment. For the purpose of this study, stress was induced by using an established mental arithmetic task under time pressure with negative feedback. The induction of mental stress was confirmed by salivary alpha amylase test. Experiment results showed that the proposed fusion of EEG and fNIRS measurements improves the classification accuracy of mental stress by +3.4% compared to EEG alone and +11% compared to fNIRS alone. Similar improvements were also observed in sensitivity and specificity of proposed approach over unimodal EEG/fNIRS. Our study suggests that combination of EEG (frontal alpha rhythm) and fNIRS (concentration change of oxygenated hemoglobin) could be a potential means to assess mental stress objectively. PMID:27867700

Decoding the attended speech stream with multi-channel EEG: implications for online, daily-life applications

NASA Astrophysics Data System (ADS)

Mirkovic, Bojana; Debener, Stefan; Jaeger, Manuela; De Vos, Maarten

2015-08-01

Objective. Recent studies have provided evidence that temporal envelope driven speech decoding from high-density electroencephalography (EEG) and magnetoencephalography recordings can identify the attended speech stream in a multi-speaker scenario. The present work replicated the previous high density EEG study and investigated the necessary technical requirements for practical attended speech decoding with EEG. Approach. Twelve normal hearing participants attended to one out of two simultaneously presented audiobook stories, while high density EEG was recorded. An offline iterative procedure eliminating those channels contributing the least to decoding provided insight into the necessary channel number and optimal cross-subject channel configuration. Aiming towards the future goal of near real-time classification with an individually trained decoder, the minimum duration of training data necessary for successful classification was determined by using a chronological cross-validation approach. Main results. Close replication of the previously reported results confirmed the method robustness. Decoder performance remained stable from 96 channels down to 25. Furthermore, for less than 15 min of training data, the subject-independent (pre-trained) decoder performed better than an individually trained decoder did. Significance. Our study complements previous research and provides information suggesting that efficient low-density EEG online decoding is within reach.

Wireless multichannel electroencephalography in the newborn

PubMed Central

Ibrahim, Z.H.; Chari, G.; Abdel Baki, S.; Bronshtein, V.; Kim, M.R.; Weedon, J.; Cracco, J.; Aranda, J.V.

2016-01-01

OBJECTIVES: First, to determine the feasibility of an ultra-compact wireless device (microEEG) to obtain multichannel electroencephalographic (EEG) recording in the Neonatal Intensive Care Unit (NICU). Second, to identify problem areas in order to improve wireless EEG performance. STUDY DESIGN: 28 subjects (gestational age 24–30 weeks, postnatal age <30 days) were recruited at 2 sites as part of an ongoing study of neonatal apnea and wireless EEG. Infants underwent 8-9 hour EEG recordings every 2–4 weeks using an electrode cap (ANT-Neuro) connected to the wireless EEG device (Bio-Signal Group). A 23 electrode configuration was used incorporating the International 10–20 System. The device transmitted recordings wirelessly to a laptop computer for bedside assessment. The recordings were assessed by a pediatric neurophysiologist for interpretability. RESULTS: A total of 84 EEGs were recorded from 28 neonates. 61 EEG studies were obtained in infants prior to 35 weeks corrected gestational age (CGA). NICU staff placed all electrode caps and initiated all recordings. Of these recordings 6 (10%) were uninterpretable due to artifacts and one study could not be accessed. The remaining 54 (89%) EEG recordings were acceptable for clinical review and interpretation by a pediatric neurophysiologist. Of the recordings obtained at 35 weeks corrected gestational age or later only 11 out of 23 (48%) were interpretable. CONCLUSIONS: Wireless EEG devices can provide practical, continuous, multichannel EEG monitoring in preterm neonates. Their small size and ease of use could overcome obstacles associated with EEG recording and interpretation in the NICU. PMID:28009337

Ear-EEG detects ictal and interictal abnormalities in focal and generalized epilepsy - A comparison with scalp EEG monitoring.

PubMed

Zibrandtsen, I C; Kidmose, P; Christensen, C B; Kjaer, T W

2017-12-01

Ear-EEG is recording of electroencephalography from a small device in the ear. This is the first study to compare ictal and interictal abnormalities recorded with ear-EEG and simultaneous scalp-EEG in an epilepsy monitoring unit. We recorded and compared simultaneous ear-EEG and scalp-EEG from 15 patients with suspected temporal lobe epilepsy. EEGs were compared visually by independent neurophysiologists. Correlation and time-frequency analysis was used to quantify the similarity between ear and scalp electrodes. Spike-averages were used to assess similarity of interictal spikes. There were no differences in sensitivity or specificity for seizure detection. Mean correlation coefficient between ear-EEG and nearest scalp electrode was above 0.6 with a statistically significant decreasing trend with increasing distance away from the ear. Ictal morphology and frequency dynamics can be observed from visual inspection and time-frequency analysis. Spike averages derived from ear-EEG electrodes yield a recognizable spike appearance. Our results suggest that ear-EEG can reliably detect electroencephalographic patterns associated with focal temporal lobe seizures. Interictal spike morphology from sufficiently large temporal spike sources can be sampled using ear-EEG. Ear-EEG is likely to become an important tool in clinical epilepsy monitoring and diagnosis. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

EEG potentials associated with artificial grammar learning in the primate brain.

PubMed

Attaheri, Adam; Kikuchi, Yukiko; Milne, Alice E; Wilson, Benjamin; Alter, Kai; Petkov, Christopher I

2015-09-01

Electroencephalography (EEG) has identified human brain potentials elicited by Artificial Grammar (AG) learning paradigms, which present participants with rule-based sequences of stimuli. Nonhuman animals are sensitive to certain AGs; therefore, evaluating which EEG Event Related Potentials (ERPs) are associated with AG learning in nonhuman animals could identify evolutionarily conserved processes. We recorded EEG potentials during an auditory AG learning experiment in two Rhesus macaques. The animals were first exposed to sequences of nonsense words generated by the AG. Then surface-based ERPs were recorded in response to sequences that were 'consistent' with the AG and 'violation' sequences containing illegal transitions. The AG violations strongly modulated an early component, potentially homologous to the Mismatch Negativity (mMMN), a P200 and a late frontal positivity (P500). The macaque P500 is similar in polarity and time of occurrence to a late EEG positivity reported in human AG learning studies but might differ in functional role. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

EEG - A Valuable Biomarker of Brain Injury in Preterm Infants.

PubMed

Pavlidis, Elena; Lloyd, Rhodri O; Boylan, Geraldine B

2017-01-01

This review focuses on the role of electroencephalography (EEG) in monitoring abnormalities of preterm brain function. EEG features of the most common developmental brain injuries in preterm infants, including intraventricular haemorrhage, periventricular leukomalacia, and perinatal asphyxia, are described. We outline the most common EEG biomarkers associated with these injuries, namely seizures, positive rolandic sharp waves, EEG suppression/increased interburst intervals, mechanical delta brush activity, and other deformed EEG waveforms, asymmetries, and asynchronies. The increasing survival rate of preterm infants, in particular those that are very and extremely preterm, has led to a growing demand for a specific and shared characterization of the patterns related to adverse outcome in this unique population. This review includes abundant high-quality images of the EEG patterns seen in premature infants and will provide a valuable resource for everyone working in developmental neuroscience. © 2017 S. Karger AG, Basel.

MRI with and without a high-density EEG cap--what makes the difference?

PubMed

Klein, Carina; Hänggi, Jürgen; Luechinger, Roger; Jäncke, Lutz

2015-02-01

Besides the benefit of combining electroencephalography (EEG) and magnetic resonance imaging (MRI), much effort has been spent to develop algorithms aimed at successfully cleaning the EEG data from MRI-related gradient and ballistocardiological artifacts. However, there are also studies showing a negative influence of the EEG on MRI data quality. Therefore, in the present study, we focused for the first time on the influence of the EEG on morphometric measurements of T1-weighted MRI data (voxel- and surfaced-based morphometry). Here, we demonstrate a strong influence of the EEG on cortical thickness, surface area, and volume as well as subcortical volumes due to local EEG-related inhomogeneities of the static magnetic (B0) and the gradient field (B1). In a second step, we analyzed the signal-to-noise ratios for both the anatomical and the functional data when recorded simultaneously with EEG and MRI and compared them to the ratios of the MRI data without simultaneous EEG measurements. These analyses revealed consistently lower signal-to-noise ratios for anatomical as well as functional MRI data during simultaneous EEG registration. In contrast, further analyses of T2*-weighted images provided reliable results independent of whether including the individuals' T1-weighted image with or without the EEG cap in the fMRI preprocessing stream. Based on our findings, we strongly recommend against using the structural images obtained during simultaneous EEG-MRI recordings for further anatomical data analysis. Copyright © 2014 Elsevier Inc. All rights reserved.

Comparison of a single-channel EEG sleep study to polysomnography

PubMed Central

Lucey, Brendan P.; McLeland, Jennifer S.; Toedebusch, Cristina D.; Boyd, Jill; Morris, John C.; Landsness, Eric C.; Yamada, Kelvin; Holtzman, David M.

2016-01-01

Summary An accurate home sleep study to assess electroencephalography (EEG)-based sleep stages and EEG power would be advantageous for both clinical and research purposes, such as for longitudinal studies measuring changes in sleep stages over time. The purpose of this study was to compare sleep scoring of a single-channel EEG recorded simultaneously on the forehead against attended polysomnography. Participants were recruited from both a clinical sleep center and a longitudinal research study investigating cognitively-normal aging and Alzheimer's disease. Analysis for overall epoch-by-epoch agreement found strong and substantial agreement between the single-channel EEG compared to polysomnography (kappa=0.67). Slow wave activity in the frontal regions was also similar when comparing the single-channel EEG device to polysomnography. As expected, stage N1 showed poor agreement (sensitivity 0.2) due to lack of occipital electrodes. Other sleep parameters such as sleep latency and REM onset latency had decreased agreement. Participants with disrupted sleep consolidation, such as from obstructive sleep apnea, also had poor agreement. We suspect that disagreement in sleep parameters between the single-channel EEG and polysomnography is partially due to altered waveform morphology and/or poorer signal quality in the single-channel derivation. Our results show that single-channel EEG provides comparable results to polysomnography in assessing REM, combined stages N2 and N3 sleep, and several other parameters including frontal slow wave activity. The data establish that single-channel EEG can be a useful research tool. PMID:27252090

Diagnostic yield of five minutes compared to three minutes hyperventilation during electroencephalography.

PubMed

Craciun, Laura; Varga, Edina Timea; Mindruta, Ioana; Meritam, Pirgit; Horváth, Zoltán; Terney, Daniella; Gardella, Elena; Alving, Jørgen; Vécsei, László; Beniczky, Sándor

2015-08-01

To investigate whether hyperventilation (HV) for 5min increases the diagnostic yield of electroencephalography (EEG) compared to 3min HV. data were evaluated from 1084 consecutive patients, from three European centres, referred to EEG on suspicion of epilepsy. Seizures and interictal EEG abnormalities precipitated during the first 3min and during the last 2min of the HV period (totally 5min) were determined. Eight hundred seventy-seven patients (81%) completed 5min HV. Seizures were precipitated during the first 3min of HV in 21 patients, and during the last 2min in four more patients. Interictal EEG abnormalities were precipitated in the first 3min of HV in 16 patients, and during the last 2min in 7 more patients. Psychogenic nonepileptic seizures occurred in eight patients during the first 3min of HV and in two more patients during the last 2min. No adverse events occurred during the last 2min of HV, but eight patients (1%) stopped HV during the last 2min because they were not able to hyperventilate further. 16% of seizures and 30% of interictal EEG abnormalities triggered by HV occurred during the last 2min of HV, suggesting the clinical usefulness of prolonged hyperventilation for 5min. The vast majority of patients (99%) who are able to hyperventilate for 3min can complete 5min HV, without additional adverse events. Copyright © 2015 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

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

PubMed

Lopez Rincon, Alejandro; Shimoda, Shingo

2016-12-01

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

On the identification of sleep stages in mouse electroencephalography time-series.

PubMed

Lampert, Thomas; Plano, Andrea; Austin, Jim; Platt, Bettina

2015-05-15

The automatic identification of sleep stages in electroencephalography (EEG) time-series is a long desired goal for researchers concerned with the study of sleep disorders. This paper presents advances towards achieving this goal, with particular application to EEG time-series recorded from mice. Approaches in the literature apply supervised learning classifiers, however, these do not reach the performance levels required for use within a laboratory. In this paper, detection reliability is increased, most notably in the case of REM stage identification, by naturally decomposing the problem and applying a support vector machine (SVM) based classifier to each of the EEG channels. Their outputs are integrated within a multiple classifier system. Furthermore, there exists no general consensus on the ideal choice of parameter values in such systems. Therefore, an investigation into the effects upon the classification performance is presented by varying parameters such as the epoch length; features size; number of training samples; and the method for calculating the power spectral density estimate. Finally, the results of these investigations are brought together to demonstrate the performance of the proposed classification algorithm in two cases: intra-animal classification and inter-animal classification. It is shown that, within a dataset of 10 EEG recordings, and using less than 1% of an EEG as training data, a mean classification errors of Awake 6.45%, NREM 5.82%, and REM 6.65% (with standard deviations less than 0.6%) are achieved in intra-animal analysis and, when using the equivalent of 7% of one EEG as training data, Awake 10.19%, NREM 7.75%, and REM 17.43% are achieved in inter-animal analysis (with mean standard deviations of 6.42%, 2.89%, and 9.69% respectively). A software package implementing the proposed approach will be made available through Cybula Ltd. Copyright © 2015 Elsevier B.V. All rights reserved.

New Flexible Silicone-Based EEG Dry Sensor Material Compositions Exhibiting Improvements in Lifespan, Conductivity, and Reliability

PubMed Central

Yu, Yi-Hsin; Chen, Shih-Hsun; Chang, Che-Lun; Lin, Chin-Teng; Hairston, W. David; Mrozek, Randy A.

2016-01-01

This study investigates alternative material compositions for flexible silicone-based dry electroencephalography (EEG) electrodes to improve the performance lifespan while maintaining high-fidelity transmission of EEG signals. Electrode materials were fabricated with varying concentrations of silver-coated silica and silver flakes to evaluate their electrical, mechanical, and EEG transmission performance. Scanning electron microscope (SEM) analysis of the initial electrode development identified some weak points in the sensors’ construction, including particle pull-out and ablation of the silver coating on the silica filler. The newly-developed sensor materials achieved significant improvement in EEG measurements while maintaining the advantages of previous silicone-based electrodes, including flexibility and non-toxicity. The experimental results indicated that the proposed electrodes maintained suitable performance even after exposure to temperature fluctuations, 85% relative humidity, and enhanced corrosion conditions demonstrating improvements in the environmental stability. Fabricated flat (forehead) and acicular (hairy sites) electrodes composed of the optimum identified formulation exhibited low impedance and reliable EEG measurement; some initial human experiments demonstrate the feasibility of using these silicone-based electrodes for typical lab data collection applications. PMID:27809260

Headache Following Occipital Brain Lesion: A Case of Migraine Triggered by Occipital Spikes?

PubMed

Vollono, Catello; Mariotti, Paolo; Losurdo, Anna; Giannantoni, Nadia Mariagrazia; Mazzucchi, Edoardo; Valentini, Piero; De Rose, Paola; Della Marca, Giacomo

2015-10-01

This study describes the case of an 8-year-old boy who developed a genuine migraine after the surgical excision, from the right occipital lobe, of brain abscesses due to selective infestation of the cerebrum by Entamoeba histolytica. After the surgical treatment, the boy presented daily headaches with typical migraine features, including right-side parieto-temporal pain, nausea, vomiting, and photophobia. Electroencephalography (EEG) showed epileptiform discharges in the right occipital lobe, although he never presented seizures. Clinical and neurophysiological observations were performed, including video-EEG and polygraphic recordings. EEG showed "interictal" epileptiform discharges in the right occipital lobe. A prolonged video-EEG recording performed before, during, and after an acute attack ruled out ictal or postictal migraine. In this boy, an occipital lesion caused occipital epileptiform EEG discharges without seizures, probably prevented by the treatment. We speculate that occipital spikes, in turn, could have caused a chronic headache with features of migraine without aura. Occipital epileptiform discharges, even in absence of seizures, may trigger a genuine migraine, probably by means of either the trigeminovascular or brainstem system. © EEG and Clinical Neuroscience Society (ECNS) 2014.

Long-term EEG in patients with the ring chromosome 20 epilepsy syndrome.

PubMed

Freire de Moura, Maria; Flores-Guevara, Roberto; Gueguen, Bernard; Biraben, Arnaud; Renault, Francis

2016-05-01

The recognizable electroencephalography (EEG) pattern of ring chromosome 20 epilepsy syndrome can be missing in patients with r(20) chromosomal anomaly, and may be found in patients with frontal lobe epilepsy of other origin. This study aims to search for more specific EEG signs by using long-term recordings and measuring the duration of paroxysmal anomalies. The series included 12 adult patients with r(20) anomaly, and 12 controls without any chromosomal aberration. We measured the duration of every paroxysmal burst and calculated the sum of their durations for each long-term EEG recording. We compared patients to controls using the Mann-Whitney U-test. Every patient showed long-lasting paroxysmal EEG bursts, up to 60 min; controls did not show any bursts longer than 60 s (p < 0.0001). The total duration of paroxysmal anomalies was significantly longer in patients (31-692 min) compared to controls (0-48 min) (p < 0.0001). Thus, long-term recordings enhance the contribution of EEG methods for characterizing the ring 20 chromosome epilepsy syndrome. Wiley Periodicals, Inc. © 2016 International League Against Epilepsy.

New Flexible Silicone-Based EEG Dry Sensor Material Compositions Exhibiting Improvements in Lifespan, Conductivity, and Reliability.

PubMed

Yu, Yi-Hsin; Chen, Shih-Hsun; Chang, Che-Lun; Lin, Chin-Teng; Hairston, W David; Mrozek, Randy A

2016-10-31

This study investigates alternative material compositions for flexible silicone-based dry electroencephalography (EEG) electrodes to improve the performance lifespan while maintaining high-fidelity transmission of EEG signals. Electrode materials were fabricated with varying concentrations of silver-coated silica and silver flakes to evaluate their electrical, mechanical, and EEG transmission performance. Scanning electron microscope (SEM) analysis of the initial electrode development identified some weak points in the sensors' construction, including particle pull-out and ablation of the silver coating on the silica filler. The newly-developed sensor materials achieved significant improvement in EEG measurements while maintaining the advantages of previous silicone-based electrodes, including flexibility and non-toxicity. The experimental results indicated that the proposed electrodes maintained suitable performance even after exposure to temperature fluctuations, 85% relative humidity, and enhanced corrosion conditions demonstrating improvements in the environmental stability. Fabricated flat (forehead) and acicular (hairy sites) electrodes composed of the optimum identified formulation exhibited low impedance and reliable EEG measurement; some initial human experiments demonstrate the feasibility of using these silicone-based electrodes for typical lab data collection applications.

Multivariate pattern analysis of MEG and EEG: A comparison of representational structure in time and space.

PubMed

Cichy, Radoslaw Martin; Pantazis, Dimitrios

2017-09-01

Multivariate pattern analysis of magnetoencephalography (MEG) and electroencephalography (EEG) data can reveal the rapid neural dynamics underlying cognition. However, MEG and EEG have systematic differences in sampling neural activity. This poses the question to which degree such measurement differences consistently bias the results of multivariate analysis applied to MEG and EEG activation patterns. To investigate, we conducted a concurrent MEG/EEG study while participants viewed images of everyday objects. We applied multivariate classification analyses to MEG and EEG data, and compared the resulting time courses to each other, and to fMRI data for an independent evaluation in space. We found that both MEG and EEG revealed the millisecond spatio-temporal dynamics of visual processing with largely equivalent results. Beyond yielding convergent results, we found that MEG and EEG also captured partly unique aspects of visual representations. Those unique components emerged earlier in time for MEG than for EEG. Identifying the sources of those unique components with fMRI, we found the locus for both MEG and EEG in high-level visual cortex, and in addition for MEG in low-level visual cortex. Together, our results show that multivariate analyses of MEG and EEG data offer a convergent and complimentary view on neural processing, and motivate the wider adoption of these methods in both MEG and EEG research. Copyright © 2017 Elsevier Inc. All rights reserved.

Revealing spatio-spectral electroencephalographic dynamics of musical mode and tempo perception by independent component analysis.

PubMed

Lin, Yuan-Pin; Duann, Jeng-Ren; Feng, Wenfeng; Chen, Jyh-Horng; Jung, Tzyy-Ping

2014-02-28

Music conveys emotion by manipulating musical structures, particularly musical mode- and tempo-impact. The neural correlates of musical mode and tempo perception revealed by electroencephalography (EEG) have not been adequately addressed in the literature. This study used independent component analysis (ICA) to systematically assess spatio-spectral EEG dynamics associated with the changes of musical mode and tempo. Empirical results showed that music with major mode augmented delta-band activity over the right sensorimotor cortex, suppressed theta activity over the superior parietal cortex, and moderately suppressed beta activity over the medial frontal cortex, compared to minor-mode music, whereas fast-tempo music engaged significant alpha suppression over the right sensorimotor cortex. The resultant EEG brain sources were comparable with previous studies obtained by other neuroimaging modalities, such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET). In conjunction with advanced dry and mobile EEG technology, the EEG results might facilitate the translation from laboratory-oriented research to real-life applications for music therapy, training and entertainment in naturalistic environments.

Investigation of the Correlation Between Neurocognitive Function with Advanced Magnetic Resonance Imaging (MRI), Electroencephalography (EEG) in Patients with Traumatic Brain Injury Exposure: Neurocognitive function and advanced MRI and EEG

DTIC Science & Technology

2011-01-01

rotation soudaine , à la tête engendré par des forces externes. Des symptômes persistants tels que maux de tête, troubles du sommeil, problèmes...neuropsychological findings in veterans with traumatic brain injury and/or post traumatic stress disorder. Military Medicine. Brenner, L.A. et al . (2010

Initial experience with SPECT imaging of the brain using I-123 p-iodoamphetamine in focal epilepsy

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

LaManna, M.M.; Sussman, N.M.; Harner, R.N.

1989-06-01

Nineteen patients with complex partial seizures refractory to medical treatment were examined with routine electroencephalography (EEG), video EEG monitoring, computed tomography or magnetic resonance imaging, neuropsychological tests and interictal single photon emission computed tomography (SPECT) with I-123 iodoamphetamine (INT). In 18 patients, SPECT identified areas of focal reduction in tracer uptake that correlated with the epileptogenic focus identified on the EEG. In addition, SPECT disclosed other areas of neurologic dysfunction as elicited on neuropsychological tests. Thus, IMP SPECT is a useful tool for localizing epileptogenic foci and their associated dynamic deficits.

Requests for electroencephalography in a district general hospital: retrospective and prospective audit

PubMed Central

Smith, D; Bartolo, R; Pickles, R M; Tedman, B M

2001-01-01

Objectives To determine the number of inappropriate requests for electroencephalography (EEG) and whether guidelines on use could reduce this number. Design Audit with retrospective and prospective components. Setting EEG department in district general hospital and centre for neurology and neurosurgery. Participants Retrospective: 368 at the general hospital and 143 patients at the neurology centre. Prospective: 241 patients undergoing EEG at the general hospital. Interventions Guidelines for EEG issued to users of service at the general hospital. Outcomes Retrospective: differences in requesting practice, result in different clinical scenarios, relative roles of procedure, clinical acumen in establishing diagnosis, usefulness of procedure. Prospective: change of requesting practice, impact on use. Results There were considerable differences in requesting practice. Non-specialists seem to use EEG as a diagnostic tool, especially in patients with “funny turns,” when it is much more likely to yield potentially misleading than clinically useful information. The overall proportion of procedures considered to influence management, to be justifiable, and to be inappropriate were 16% (59), 28.3% (104), and 55.7% (205), respectively. In the prospective study the total number of requests was significantly reduced (χ2=33.85, df=5, P<0.0001), mainly because of fewer requests in patients with non-specific “funny turns” (χ2=21.90, df=6, P=0.0013). There was a concomitant change in the usefulness of EEG (χ2 =26.99, df=2, P<0.0001). Conclusions This original audit informed clinical practice and had potential benefits for patients, clinicians, and provision of service. Systematic replication of this project, possibly on a regional basis, could result in financial savings, which would allow development of accessible local neurophysiology services. What is already known on this topicThere is unrestricted access to EEG in most district general hospitals throughout the United KingdomThe combination of equivocal symptoms and non-specific abnormalities carries a risk of misdiagnosis of epilepsyWhat this study addsAn audit of requests for EEG showed that a large proportion were inappropriate, mainly because of the prevalent misconception that the procedure could prove or exclude a diagnosis of epilepsy in patients with “funny turns”After intervention with clinicians, which used an educative approach, there was a considerable and sustained change in requesting practice PMID:11312226

Decoding human mental states by whole-head EEG+fNIRS during category fluency task performance

NASA Astrophysics Data System (ADS)

Omurtag, Ahmet; Aghajani, Haleh; Onur Keles, Hasan

2017-12-01

Objective. Concurrent scalp electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), which we refer to as EEG+fNIRS, promises greater accuracy than the individual modalities while remaining nearly as convenient as EEG. We sought to quantify the hybrid system’s ability to decode mental states and compare it with its unimodal components. Approach. We recorded from healthy volunteers taking the category fluency test and applied machine learning techniques to the data. Main results. EEG+fNIRS’s decoding accuracy was greater than that of its subsystems, partly due to the new type of neurovascular features made available by hybrid data. Significance. Availability of an accurate and practical decoding method has potential implications for medical diagnosis, brain-computer interface design, and neuroergonomics.

Effect of Sertraline on Current-Source Distribution of the High Beta Frequency Band: Analysis of Electroencephalography under Audiovisual Erotic Stimuli in Healthy, Right-Handed Males.

PubMed

Lee, Seung Hyun; Hyun, Jae Seog; Kwon, Oh-Young

2010-08-01

The purpose of this study was to examine the cerebral changes in high beta frequency oscillations (22-30 Hz) induced by sertraline and by audiovisual erotic stimuli in healthy adult males. Scalp electroencephalographies (EEGs) were conducted twice in 11 healthy, right-handed males, once before sertraline intake and again 4 hours thereafter. The EEGs included four sessions recorded sequentially while the subjects were resting, watching a music video, resting, and watching an erotic video for 3 minutes, 5 minutes, 3 minutes, and 5 minutes, respectively. We performed frequency-domain analysis using the EEGs with a distributed model of current-source analysis. The statistical nonparametric maps were obtained from the sessions of watching erotic and music videos (p<0.05). The erotic stimuli decreased the current-source density of the high beta frequency band in the middle frontal gyrus, the precentral gyrus, the postcentral gyrus, and the supramarginal gyrus of the left cerebral hemisphere in the baseline EEGs taken before sertraline intake (p<0.05). The erotic stimuli did not induce any changes in current-source distribution of the brain 4 hours after sertraline intake. It is speculated that erotic stimuli may decrease the function of the middle frontal gyrus, the precentral gyrus, the postcentral gyrus, and the supramarginal gyrus of the left cerebral hemisphere in healthy adult males. This change may debase the inhibitory control of the brain against erotic stimuli. Sertraline may reduce the decrement in inhibitory control.

Effect of Sertraline on Current-Source Distribution of the High Beta Frequency Band: Analysis of Electroencephalography under Audiovisual Erotic Stimuli in Healthy, Right-Handed Males

PubMed Central

Lee, Seung Hyun; Hyun, Jae Seog

2010-01-01

Purpose The purpose of this study was to examine the cerebral changes in high beta frequency oscillations (22-30 Hz) induced by sertraline and by audiovisual erotic stimuli in healthy adult males. Materials and Methods Scalp electroencephalographies (EEGs) were conducted twice in 11 healthy, right-handed males, once before sertraline intake and again 4 hours thereafter. The EEGs included four sessions recorded sequentially while the subjects were resting, watching a music video, resting, and watching an erotic video for 3 minutes, 5 minutes, 3 minutes, and 5 minutes, respectively. We performed frequency-domain analysis using the EEGs with a distributed model of current-source analysis. The statistical nonparametric maps were obtained from the sessions of watching erotic and music videos (p<0.05). Results The erotic stimuli decreased the current-source density of the high beta frequency band in the middle frontal gyrus, the precentral gyrus, the postcentral gyrus, and the supramarginal gyrus of the left cerebral hemisphere in the baseline EEGs taken before sertraline intake (p<0.05). The erotic stimuli did not induce any changes in current-source distribution of the brain 4 hours after sertraline intake. Conclusions It is speculated that erotic stimuli may decrease the function of the middle frontal gyrus, the precentral gyrus, the postcentral gyrus, and the supramarginal gyrus of the left cerebral hemisphere in healthy adult males. This change may debase the inhibitory control of the brain against erotic stimuli. Sertraline may reduce the decrement in inhibitory control. PMID:20733961

[Electrical activity and circulatory effects of nitrite in the rat cerebrum].

PubMed

Shumilova, T E; Smirnov, A G; Shereshkov, V I; Fedorova, M A; Nozdrachev, A D

2015-01-01

An association between the cerebrum electrical activity (CEA) in rats, blood supply of its cortex microregions (linear blood flow), and general cerebrum blood flow under acute nitrite hypoxia was studied. The phase character of the change in hemodynamic indices and the total capacity of electroencephalography (EEG) spectrum for 75 min after sodium nitrite introduction (30 mg/kg of body weight) was detected. The first phase (30 min) was associated with cerebrum adaptation to hypotension caused by nitrite and was completed by EEG normalization. The second phase was characterized by pathological EEG changes (in spite of restoration of hemodynamics in the cerebrum) caused by the growth of oxygen debt in the nervous tissue as a result of a decrease in the blood oxygen capacity by 60-75 min of the effect of nitrite.

Abnormal EEG Power Spectra in Acute Transient Global Amnesia: A Quantitative EEG Study.

PubMed

Imperatori, Claudio; Farina, Benedetto; Todini, Federico; Di Blasi, Chiara; Mazzucchi, Edoardo; Brunetti, Valerio; Della Marca, Giacomo

2018-06-01

Transient global amnesia (TGA) is a clinical syndrome characterized by retrograde and anterograde amnesia without other neurological deficits. Although electroencephalography (EEG) methods are commonly used in both clinical and research setting with TGA patients, few studies have investigated neurophysiological pattern in TGA using quantitative EEG (qEEG). The main aim of the present study was to extend these previous findings by exploring EEG power spectra differences between patients with acute TGA and healthy controls using the exact low-resolution brain electromagnetic tomography software (eLORETA). EEG was recorded during 5 minutes of resting state. Sixteen patients (mean age: 66.81 ± 7.94 years) during acute TGA and 16 healthy subjects were enrolled. All patients showed hippocampal or parahippocampal signal abnormalities in diffusion-weighted magnetic resonance imaging performed from 2 to 5 days after the onset of TGA. Compared with healthy controls, TGA patients showed a decrease of theta power localized in the temporal lobe (Brodmann areas, BAs 21-22-38) and frontal lobe (BAs 8-9-44-45). A decrease of EEG beta power in the bilateral precuneus (BA 7) and in the bilateral postcentral gyrus (BAs 3-4-5) was also observed in TGA individuals. Taken together, our results could reflect the neurophysiological substrate of the severe impairment of both episodic memory and autobiographical memory which affect TGA patients during the acute phase.

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.

Classification of driver fatigue in an electroencephalography-based countermeasure system with source separation module.

PubMed

Rifai Chai; Naik, Ganesh R; Tran, Yvonne; Sai Ho Ling; Craig, Ashley; Nguyen, Hung T

2015-08-01

An electroencephalography (EEG)-based counter measure device could be used for fatigue detection during driving. This paper explores the classification of fatigue and alert states using power spectral density (PSD) as a feature extractor and fuzzy swarm based-artificial neural network (ANN) as a classifier. An independent component analysis of entropy rate bound minimization (ICA-ERBM) is investigated as a novel source separation technique for fatigue classification using EEG analysis. A comparison of the classification accuracy of source separator versus no source separator is presented. Classification performance based on 43 participants without the inclusion of the source separator resulted in an overall sensitivity of 71.67%, a specificity of 75.63% and an accuracy of 73.65%. However, these results were improved after the inclusion of a source separator module, resulting in an overall sensitivity of 78.16%, a specificity of 79.60% and an accuracy of 78.88% (p <; 0.05).

Protocol for the Electroencephalography Guidance of Anesthesia to Alleviate Geriatric Syndromes (ENGAGES) study: a pragmatic, randomised clinical trial

PubMed Central

Wildes, T S; Winter, A C; Maybrier, H R; Mickle, A M; Lenze, E J; Stark, S; Lin, N; Inouye, S K; Schmitt, E M; McKinnon, S L; Muench, M R; Murphy, M R; Upadhyayula, R T; Fritz, B A; Escallier, K E; Apakama, G P; Emmert, D A; Graetz, T J; Stevens, T W; Palanca, B J; Hueneke, R L; Melby, S; Torres, B; Leung, J; Jacobsohn, E; Avidan, M S

2016-01-01

Introduction Postoperative delirium, arbitrarily defined as occurring within 5 days of surgery, affects up to 50% of patients older than 60 after a major operation. This geriatric syndrome is associated with longer intensive care unit and hospital stay, readmission, persistent cognitive deterioration and mortality. No effective preventive methods have been identified, but preliminary evidence suggests that EEG monitoring during general anaesthesia, by facilitating reduced anaesthetic exposure and EEG suppression, might decrease incident postoperative delirium. This study hypothesises that EEG-guidance of anaesthetic administration prevents postoperative delirium and downstream sequelae, including falls and decreased quality of life. Methods and analysis This is a 1232 patient, block-randomised, double-blinded, comparative effectiveness trial. Patients older than 60, undergoing volatile agent-based general anaesthesia for major surgery, are eligible. Patients are randomised to 1 of 2 anaesthetic approaches. One group receives general anaesthesia with clinicians blinded to EEG monitoring. The other group receives EEG-guidance of anaesthetic agent administration. The outcomes of postoperative delirium (≤5 days), falls at 1 and 12 months and health-related quality of life at 1 and 12 months will be compared between groups. Postoperative delirium is assessed with the confusion assessment method, falls with ProFaNE consensus questions and quality of life with the Veteran's RAND 12-item Health Survey. The intention-to-treat principle will be followed for all analyses. Differences between groups will be presented with 95% CIs and will be considered statistically significant at a two-sided p<0.05. Ethics and dissemination Electroencephalography Guidance of Anesthesia to Alleviate Geriatric Syndromes (ENGAGES) is approved by the ethics board at Washington University. Recruitment began in January 2015. Dissemination plans include presentations at scientific conferences, scientific publications, internet-based educational materials and mass media. Trial registration number NCT02241655; Pre-results. PMID:27311914

Effect of mental fatigue on the central nervous system: an electroencephalography study

PubMed Central

2012-01-01

Background Fatigue can be classified as mental and physical depending on its cause, and each type of fatigue has a multi-factorial nature. We examined the effect of mental fatigue on the central nervous system using electroencephalography (EEG) in eighteen healthy male volunteers. Methods After enrollment, subjects were randomly assigned to two groups in a single-blinded, crossover fashion to perform two types of mental fatigue-inducing experiments. Each experiment consisted of four 30-min fatigue-inducing 0- or 2-back test sessions and two evaluation sessions performed just before and after the fatigue-inducing sessions. During the evaluation session, the participants were assessed using EEG. Eleven electrodes were attached to the head skin, from positions F3, Fz, F4, C3, Cz, C4, P3, Pz, P4, O1, and O2. Results In the 2-back test, the beta power density on the Pz electrode and the alpha power densities on the P3 and O2 electrodes were decreased, and the theta power density on the Cz electrode was increased after the fatigue-inducing mental task sessions. In the 0-back test, no electrodes were altered after the fatigue-inducing sessions. Conclusions Different types of mental fatigue produced different kinds of alterations of the spontaneous EEG variables. Our findings provide new perspectives on the neural mechanisms underlying mental fatigue. PMID:22954020

Older People's Experiences of Mobility and Mood in an Urban Environment: A Mixed Methods Approach Using Electroencephalography (EEG) and Interviews.

PubMed

Tilley, Sara; Neale, Chris; Patuano, Agnès; Cinderby, Steve

2017-02-04

There are concerns about mental wellbeing in later life in older people as the global population becomes older and more urbanised. Mobility in the built environment has a role to play in improving quality of life and wellbeing, as it facilitates independence and social interaction. Recent studies using neuroimaging methods in environmental psychology research have shown that different types of urban environments may be associated with distinctive patterns of brain activity, suggesting that we interact differently with varying environments. This paper reports on research that explores older people's responses to urban places and their mobility in and around the built environment. The project aim was to understand how older people experience different urban environments using a mixed methods approach including electroencephalography (EEG), self-reported measures, and interview results. We found that older participants experience changing levels of "excitement", "engagement" and "frustration" (as interpreted by proprietary EEG software) whilst walking between a busy built urban environment and an urban green space environment. These changes were further reflected in the qualitative themes that emerged from transcribed interviews undertaken one week post-walk. There has been no research to date that has directly assessed neural responses to an urban environment combined with qualitative interview analysis. A synergy of methods offers a deeper understanding of the changing moods of older people across time whilst walking in city settings.

EEG-Informed fMRI: A Review of Data Analysis Methods

PubMed Central

Abreu, Rodolfo; Leal, Alberto; Figueiredo, Patrícia

2018-01-01

The simultaneous acquisition of electroencephalography (EEG) with functional magnetic resonance imaging (fMRI) is a very promising non-invasive technique for the study of human brain function. Despite continuous improvements, it remains a challenging technique, and a standard methodology for data analysis is yet to be established. Here we review the methodologies that are currently available to address the challenges at each step of the data analysis pipeline. We start by surveying methods for pre-processing both EEG and fMRI data. On the EEG side, we focus on the correction for several MR-induced artifacts, particularly the gradient and pulse artifacts, as well as other sources of EEG artifacts. On the fMRI side, we consider image artifacts induced by the presence of EEG hardware inside the MR scanner, and the contamination of the fMRI signal by physiological noise of non-neuronal origin, including a review of several approaches to model and remove it. We then provide an overview of the approaches specifically employed for the integration of EEG and fMRI when using EEG to predict the blood oxygenation level dependent (BOLD) fMRI signal, the so-called EEG-informed fMRI integration strategy, the most commonly used strategy in EEG-fMRI research. Finally, we systematically review methods used for the extraction of EEG features reflecting neuronal phenomena of interest. PMID:29467634

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.

EEG-fMRI Bayesian framework for neural activity estimation: a simulation study

NASA Astrophysics Data System (ADS)

Croce, Pierpaolo; Basti, Alessio; Marzetti, Laura; Zappasodi, Filippo; Del Gratta, Cosimo

2016-12-01

Objective. Due to the complementary nature of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), and given the possibility of simultaneous acquisition, the joint data analysis can afford a better understanding of the underlying neural activity estimation. In this simulation study we want to show the benefit of the joint EEG-fMRI neural activity estimation in a Bayesian framework. Approach. We built a dynamic Bayesian framework in order to perform joint EEG-fMRI neural activity time course estimation. The neural activity is originated by a given brain area and detected by means of both measurement techniques. We have chosen a resting state neural activity situation to address the worst case in terms of the signal-to-noise ratio. To infer information by EEG and fMRI concurrently we used a tool belonging to the sequential Monte Carlo (SMC) methods: the particle filter (PF). Main results. First, despite a high computational cost, we showed the feasibility of such an approach. Second, we obtained an improvement in neural activity reconstruction when using both EEG and fMRI measurements. Significance. The proposed simulation shows the improvements in neural activity reconstruction with EEG-fMRI simultaneous data. The application of such an approach to real data allows a better comprehension of the neural dynamics.

EEG-fMRI Bayesian framework for neural activity estimation: a simulation study.

PubMed

Croce, Pierpaolo; Basti, Alessio; Marzetti, Laura; Zappasodi, Filippo; Gratta, Cosimo Del

2016-12-01

Due to the complementary nature of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), and given the possibility of simultaneous acquisition, the joint data analysis can afford a better understanding of the underlying neural activity estimation. In this simulation study we want to show the benefit of the joint EEG-fMRI neural activity estimation in a Bayesian framework. We built a dynamic Bayesian framework in order to perform joint EEG-fMRI neural activity time course estimation. The neural activity is originated by a given brain area and detected by means of both measurement techniques. We have chosen a resting state neural activity situation to address the worst case in terms of the signal-to-noise ratio. To infer information by EEG and fMRI concurrently we used a tool belonging to the sequential Monte Carlo (SMC) methods: the particle filter (PF). First, despite a high computational cost, we showed the feasibility of such an approach. Second, we obtained an improvement in neural activity reconstruction when using both EEG and fMRI measurements. The proposed simulation shows the improvements in neural activity reconstruction with EEG-fMRI simultaneous data. The application of such an approach to real data allows a better comprehension of the neural dynamics.

Longitudinal Associations between the Quality of Mother-Infant Interactions and Brain Development across Infancy

ERIC Educational Resources Information Center

Bernier, Annie; Calkins, Susan D.; Bell, Martha Ann

2016-01-01

The aim of this study was to investigate if normative variations in parenting relate to brain development among typically developing children. A sample of 352 mother-infant dyads came to the laboratory when infants were 5, 10, and 24 months of age (final N = 215). At each visit, child resting electroencephalography (EEG) was recorded.…

Cerebral hemodynamic responses to seizure in the mouse brain: simultaneous near-infrared spectroscopy-electroencephalography study

NASA Astrophysics Data System (ADS)

Lee, Seungduk; Lee, Mina; Koh, Dalkwon; Kim, Beop-Min; Choi, Jee Hyun

2010-05-01

We applied near-infrared spectroscopy (NIRS) and electroencephalography (EEG) simultaneously on the mouse brain and investigated the hemodynamic response to epileptic episodes under pharmacologically driven seizure. γ-butyrolactone (GBL) and 4-aminopyridine (4-AP) were applied to induce absence and tonic-clonic seizures, respectively. The epileptic episodes were identified from the single-channel EEG, and the corresponding hemodynamic changes in different regions of the brain were characterized by multichannel frequency-domain NIRS. Our results are the following: (i) the oxyhemoglobin level increases in the case of GBL-treated mice but not 4-AP-treated mice compared to the predrug state; (ii) the dominant response to each absence seizure is a decrease in deoxyhemolobin; (iii) the phase shift between oxy- and deoxyhemoglobin reduces in GBL-treated mice but no 4-AP-treated mice; and (iv) the spatial correlation of hemodynamics increased significantly in 4-AP-treated mice but not in GBL-treated mice. Our results shows that spatiotemporal tracking of cerebral hemodynamics using NIRS can be successfully applied to the mouse brain in conjunction with electrophysiological recording, which will support the study of molecular, cellular, and network origin of neurovascular coupling in vivo.

Acute CT perfusion changes in seizure patients presenting to the emergency department with stroke-like symptoms: correlation with clinical and electroencephalography findings.

PubMed

Payabvash, S; Oswood, M C; Truwit, C L; McKinney, A M

2015-10-01

To determine acute computed tomography perfusion (CTP) changes in seizure patients presenting with stroke-like symptoms and to correlate those changes with clinical presentation and electroencephalography (EEG). The medical records of all patients who presented to the emergency department with acute stroke-like symptoms and underwent CTP (n=1085) over a 5.5-year period were reviewed. Patients were included who had primary seizure as the final diagnosis, and underwent CTP within 3 hours of symptom onset. A subset of patients had a follow-up EEG within 7 days. The perfusion changes and EEG findings were compared between different clinical presentations. Eighteen of 1085 patients (1.7%) who underwent CTP following an acute stroke-like presentation were included. The abnormality on CTP was usually focal, unilateral hyperperfusion - increased relative cerebral blood flow (rCBF) and volume (rCBV) (n=14/18), which most often affected the temporal lobe. Those patients who presented with a motor or speech deficit (n=12) had a higher temporal lobe rCBV, and rCBF, and lower relative mean transit time (rMTT) compared to those with non-focal neurological deficit at presentation. Early EEG was available in 13 patients; a sharp-spike epileptiform EEG discharge pattern (n=5) was associated with higher temporal lobe ipsilateral rCBF and rCBV, and lower rMTT on admission CTP examination. Seizure patients who present with a unilateral motor or speech deficit most commonly have contralateral hyperperfusion in the corresponding eloquent brain regions on the acute-stage CTP examination. In such patients, epileptiform discharges on the early follow-up EEG are associated with ipsilateral hyperperfusion on the admission CTP. Copyright © 2015 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

The effects of automated artifact removal algorithms on electroencephalography-based Alzheimer's disease diagnosis

PubMed Central

Cassani, Raymundo; Falk, Tiago H.; Fraga, Francisco J.; Kanda, Paulo A. M.; Anghinah, Renato

2014-01-01

Over the last decade, electroencephalography (EEG) has emerged as a reliable tool for the diagnosis of cortical disorders such as Alzheimer's disease (AD). EEG signals, however, are susceptible to several artifacts, such as ocular, muscular, movement, and environmental. To overcome this limitation, existing diagnostic systems commonly depend on experienced clinicians to manually select artifact-free epochs from the collected multi-channel EEG data. Manual selection, however, is a tedious and time-consuming process, rendering the diagnostic system “semi-automated.” Notwithstanding, a number of EEG artifact removal algorithms have been proposed in the literature. The (dis)advantages of using such algorithms in automated AD diagnostic systems, however, have not been documented; this paper aims to fill this gap. Here, we investigate the effects of three state-of-the-art automated artifact removal (AAR) algorithms (both alone and in combination with each other) on AD diagnostic systems based on four different classes of EEG features, namely, spectral, amplitude modulation rate of change, coherence, and phase. The three AAR algorithms tested are statistical artifact rejection (SAR), blind source separation based on second order blind identification and canonical correlation analysis (BSS-SOBI-CCA), and wavelet enhanced independent component analysis (wICA). Experimental results based on 20-channel resting-awake EEG data collected from 59 participants (20 patients with mild AD, 15 with moderate-to-severe AD, and 24 age-matched healthy controls) showed the wICA algorithm alone outperforming other enhancement algorithm combinations across three tasks: diagnosis (control vs. mild vs. moderate), early detection (control vs. mild), and disease progression (mild vs. moderate), thus opening the doors for fully-automated systems that can assist clinicians with early detection of AD, as well as disease severity progression assessment. PMID:24723886

Electroencephalography in the Diagnosis of Genetic Generalized Epilepsy Syndromes

PubMed Central

Seneviratne, Udaya; Cook, Mark J.; D’Souza, Wendyl Jude

2017-01-01

Genetic generalized epilepsy (GGE) consists of several syndromes diagnosed and classified on the basis of clinical features and electroencephalographic (EEG) abnormalities. The main EEG feature of GGE is bilateral, synchronous, symmetric, and generalized spike-wave complex. Other classic EEG abnormalities are polyspikes, epileptiform K-complexes and sleep spindles, polyspike-wave discharges, occipital intermittent rhythmic delta activity, eye-closure sensitivity, fixation-off sensitivity, and photoparoxysmal response. However, admixed with typical changes, atypical epileptiform discharges are also commonly seen in GGE. There are circadian variations of generalized epileptiform discharges. Sleep, sleep deprivation, hyperventilation, intermittent photic stimulation, eye closure, and fixation-off are often used as activation techniques to increase the diagnostic yield of EEG recordings. Reflex seizure-related EEG abnormalities can be elicited by the use of triggers such as cognitive tasks and pattern stimulation during the EEG recording in selected patients. Distinct electrographic abnormalities to help classification can be identified among different electroclinical syndromes. PMID:28993753

Video Game Adapts To Brain Waves

NASA Technical Reports Server (NTRS)

Pope, Alan T.; Bogart, Edward H.

1994-01-01

Electronic training system based on video game developed to help children afflicted with attention-deficit disorder (ADD) learn to prolong their attention spans. Uses combination of electroencephalography (EEG) and adaptive control to encourage attentiveness. Monitors trainee's brain-wave activity: if EEG signal indicates attention is waning, system increases difficulty of game, forcing trainee to devote more attention to it. Game designed to make trainees want to win and, in so doing, learn to pay attention for longer times.

Brain-computer interface with language model-electroencephalography fusion for locked-in syndrome.

PubMed

Oken, Barry S; Orhan, Umut; Roark, Brian; Erdogmus, Deniz; Fowler, Andrew; Mooney, Aimee; Peters, Betts; Miller, Meghan; Fried-Oken, Melanie B

2014-05-01

Some noninvasive brain-computer interface (BCI) systems are currently available for locked-in syndrome (LIS) but none have incorporated a statistical language model during text generation. To begin to address the communication needs of individuals with LIS using a noninvasive BCI that involves rapid serial visual presentation (RSVP) of symbols and a unique classifier with electroencephalography (EEG) and language model fusion. The RSVP Keyboard was developed with several unique features. Individual letters are presented at 2.5 per second. Computer classification of letters as targets or nontargets based on EEG is performed using machine learning that incorporates a language model for letter prediction via Bayesian fusion enabling targets to be presented only 1 to 4 times. Nine participants with LIS and 9 healthy controls were enrolled. After screening, subjects first calibrated the system, and then completed a series of balanced word generation mastery tasks that were designed with 5 incremental levels of difficulty, which increased by selecting phrases for which the utility of the language model decreased naturally. Six participants with LIS and 9 controls completed the experiment. All LIS participants successfully mastered spelling at level 1 and one subject achieved level 5. Six of 9 control participants achieved level 5. Individuals who have incomplete LIS may benefit from an EEG-based BCI system, which relies on EEG classification and a statistical language model. Steps to further improve the system are discussed.

Outcome of no resection after long-term subdural electroencephalography evaluation in children with epilepsy.

PubMed

Pestana Knight, Elia M; Loddenkemper, Tobias; Lachhwani, Deepak; Kotagal, Prakash; Wyllie, Elaine; Bingaman, William; Gupta, Ajay

2011-09-01

The aim of this study was to identify the reasons for and predictors of no resection of the epileptogenic zone in children with epilepsy who had undergone long-term invasive subdural grid electroencephalography (SDG-EEG) evaluation. The authors retrospectively reviewed the consecutive medical records of children (< 19 years of age) who had undergone SDG-EEG evaluation over a 7-year period (1997-2004). To determine the predictors of no resection, the authors obtained the clinical characteristics and imaging and EEG findings of children who had no resection after long-term invasive SDG-EEG evaluation and compared these data with those in a group of children who did undergo resection. They describe the indications for SDG-EEG evaluation and the reasons for no resection in these patients. Of 66 children who underwent SDG-EEG evaluation, 9 (13.6%) did not undergo subsequent resection (no-resection group; 6 males). Of these 9 patients, 6 (66.7%) had normal neurological examinations and 5 (55.6%) had normal findings on brain MR imaging. Scalp video EEG localized epilepsy to the left hemisphere in 6 of the 9 patients and to the right hemisphere in 2; it was nonlocalizable in 1 of the 9 patients. Indications for SDG-EEG in the no-resection group were ictal onset zone (IOZ) localization (9 of 9 patients), motor cortex localization (5 of 9 patients), and language area localization (4 of 9 patients). Reasons for no resection after SDG-EEG evaluation were the lack of a well-defined IOZ in 5 of 9 patients (4 multifocal IOZs and 1 nonlocalizable IOZ) and anticipated new permanent postoperative neurological deficits in 7 of 9 patients (3 motor, 2 language, and 2 motor and language deficits). Comparison with the resection group (57 patients) demonstrated that postictal Todd paralysis in the dominant hand was the only variable seen more commonly (χ(2) = 4.781, p = 0.029) in the no-resection group (2 [22.2%] of 9 vs 2 [3.5%] of 57 patients). The no-resection group had a larger number of SDG electrode contacts (mean 126. 5 ± 26.98) as compared with the resection group (100.56 ± 25.52; p = 0.010). There were no significant differences in the demographic data, seizure characteristics, scalp and invasive EEG findings, and imaging variables between the resection and no-resection groups. Children who did not undergo resection of the epileptogenic zone after SDG-EEG evaluation were likely to have normal neurological examinations without preexisting neurological deficits, a high probability of a new unacceptable permanent neurological deficit following resection, or multifocal or nonlocalizable IOZs. In comparison with the group that underwent resection after SDG-EEG, a history of Todd paralysis in the dominant hand and arm was the only predictor of no resection following SDG-EEG evaluation. Data in this study will help to better select pediatric patients for SDG-EEG and to counsel families prior to epilepsy surgery.

Entropy changes in brain function.

PubMed

Rosso, Osvaldo A

2007-04-01

The traditional way of analyzing brain electrical activity, on the basis of electroencephalography (EEG) records, relies mainly on visual inspection and years of training. Although it is quite useful, of course, one has to acknowledge its subjective nature that hardly allows for a systematic protocol. In the present work quantifiers based on information theory and wavelet transform are reviewed. The "relative wavelet energy" provides information about the relative energy associated with different frequency bands present in the EEG and their corresponding degree of importance. The "normalized total wavelet entropy" carries information about the degree of order-disorder associated with a multi-frequency signal response. Their application in the analysis and quantification of short duration EEG signals (event-related potentials) and epileptic EEG records are summarized.

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

[Neurofeedback for the treatment of chronic tinnitus : Review and future perspectives].

PubMed

Kleinjung, T; Thüring, C; Güntensperger, D; Neff, P; Meyer, M

2018-03-01

Neurofeedback is a noninvasive neuromodulation technique employing real-time display of brain activity in terms of electroencephalography (EEG) signals to teach self-regulation of distinct patterns of brain activity or influence brain activity in a targeted manner. The benefit of this approach for control of symptoms in attention deficit disorders, hyperactivity, depression, and migraine has been proven. Studies in recent years have also repeatedly shown this treatment to improve tinnitus symptoms, although it has not become established as routine therapy. The primary focus of this review is the rational of EEG neurofeedback for tinnitus treatment and the currently available data from published studies. Furthermore, alternative neurofeedback protocols using real-time functional magnetic resonance imaging (fMRI) measurements for tinnitus control are considered. Finally, this article highlights how modern EEG analysis (source localization, connectivity) and the improving understanding of tinnitus pathology can contribute to development of more focused neurofeedback protocols for more sustainable control of tinnitus.

Trajectories of Infants' Biobehavioral Development: Timing and Rate of A-Not-B Performance Gains and EEG Maturation.

PubMed

MacNeill, Leigha A; Ram, Nilam; Bell, Martha Ann; Fox, Nathan A; Pérez-Edgar, Koraly

2018-05-01

This study examined how timing (i.e., relative maturity) and rate (i.e., how quickly infants attain proficiency) of A-not-B performance were related to changes in brain activity from age 6 to 12 months. A-not-B performance and resting EEG (electroencephalography) were measured monthly from age 6 to 12 months in 28 infants and were modeled using logistic and linear growth curve models. Infants with faster performance rates reached performance milestones earlier. Infants with faster rates of increase in A-not-B performance had lower occipital power at 6 months and greater linear increases in occipital power. The results underscore the importance of considering nonlinear change processes for studying infants' cognitive development as well as how these changes are related to trajectories of EEG power. © 2018 The Authors. Child Development © 2018 Society for Research in Child Development, Inc.

Evidence of emotion-antecedent appraisal checks in electroencephalography and facial electromyography

PubMed Central

Scherer, Klaus R.; Schuller, Björn W.

2018-01-01

In the present study, we applied Machine Learning (ML) methods to identify psychobiological markers of cognitive processes involved in the process of emotion elicitation as postulated by the Component Process Model (CPM). In particular, we focused on the automatic detection of five appraisal checks—novelty, intrinsic pleasantness, goal conduciveness, control, and power—in electroencephalography (EEG) and facial electromyography (EMG) signals. We also evaluated the effects on classification accuracy of averaging the raw physiological signals over different numbers of trials, and whether the use of minimal sets of EEG channels localized over specific scalp regions of interest are sufficient to discriminate between appraisal checks. We demonstrated the effectiveness of our approach on two data sets obtained from previous studies. Our results show that novelty and power appraisal checks can be consistently detected in EEG signals above chance level (binary tasks). For novelty, the best classification performance in terms of accuracy was achieved using features extracted from the whole scalp, and by averaging across 20 individual trials in the same experimental condition (UAR = 83.5 ± 4.2; N = 25). For power, the best performance was obtained by using the signals from four pre-selected EEG channels averaged across all trials available for each participant (UAR = 70.6 ± 5.3; N = 24). Together, our results indicate that accurate classification can be achieved with a relatively small number of trials and channels, but that averaging across a larger number of individual trials is beneficial for the classification for both appraisal checks. We were not able to detect any evidence of the appraisal checks under study in the EMG data. The proposed methodology is a promising tool for the study of the psychophysiological mechanisms underlying emotional episodes, and their application to the development of computerized tools (e.g., Brain-Computer Interface) for the study of cognitive processes involved in emotions. PMID:29293572

Evidence of emotion-antecedent appraisal checks in electroencephalography and facial electromyography.

PubMed

Coutinho, Eduardo; Gentsch, Kornelia; van Peer, Jacobien; Scherer, Klaus R; Schuller, Björn W

2018-01-01

In the present study, we applied Machine Learning (ML) methods to identify psychobiological markers of cognitive processes involved in the process of emotion elicitation as postulated by the Component Process Model (CPM). In particular, we focused on the automatic detection of five appraisal checks-novelty, intrinsic pleasantness, goal conduciveness, control, and power-in electroencephalography (EEG) and facial electromyography (EMG) signals. We also evaluated the effects on classification accuracy of averaging the raw physiological signals over different numbers of trials, and whether the use of minimal sets of EEG channels localized over specific scalp regions of interest are sufficient to discriminate between appraisal checks. We demonstrated the effectiveness of our approach on two data sets obtained from previous studies. Our results show that novelty and power appraisal checks can be consistently detected in EEG signals above chance level (binary tasks). For novelty, the best classification performance in terms of accuracy was achieved using features extracted from the whole scalp, and by averaging across 20 individual trials in the same experimental condition (UAR = 83.5 ± 4.2; N = 25). For power, the best performance was obtained by using the signals from four pre-selected EEG channels averaged across all trials available for each participant (UAR = 70.6 ± 5.3; N = 24). Together, our results indicate that accurate classification can be achieved with a relatively small number of trials and channels, but that averaging across a larger number of individual trials is beneficial for the classification for both appraisal checks. We were not able to detect any evidence of the appraisal checks under study in the EMG data. The proposed methodology is a promising tool for the study of the psychophysiological mechanisms underlying emotional episodes, and their application to the development of computerized tools (e.g., Brain-Computer Interface) for the study of cognitive processes involved in emotions.

Remembered or Forgotten?—An EEG-Based Computational Prediction Approach

PubMed Central

Sun, Xuyun; Qian, Cunle; Chen, Zhongqin; Wu, Zhaohui; Luo, Benyan; Pan, Gang

2016-01-01

Prediction of memory performance (remembered or forgotten) has various potential applications not only for knowledge learning but also for disease diagnosis. Recently, subsequent memory effects (SMEs)—the statistical differences in electroencephalography (EEG) signals before or during learning between subsequently remembered and forgotten events—have been found. This finding indicates that EEG signals convey the information relevant to memory performance. In this paper, based on SMEs we propose a computational approach to predict memory performance of an event from EEG signals. We devise a convolutional neural network for EEG, called ConvEEGNN, to predict subsequently remembered and forgotten events from EEG recorded during memory process. With the ConvEEGNN, prediction of memory performance can be achieved by integrating two main stages: feature extraction and classification. To verify the proposed approach, we employ an auditory memory task to collect EEG signals from scalp electrodes. For ConvEEGNN, the average prediction accuracy was 72.07% by using EEG data from pre-stimulus and during-stimulus periods, outperforming other approaches. It was observed that signals from pre-stimulus period and those from during-stimulus period had comparable contributions to memory performance. Furthermore, the connection weights of ConvEEGNN network can reveal prominent channels, which are consistent with the distribution of SME studied previously. PMID:27973531

Patient prognosis based on feature extraction, selection and classification of EEG periodic activity.

PubMed

Sánchez-González, Alain; García-Zapirain, Begoña; Maestro Saiz, Iratxe; Yurrebaso Santamaría, Izaskun

2015-01-01

Periodic activity in electroencephalography (PA-EEG) is shown as comprising a series of repetitive wave patterns that may appear in different cerebral regions and are due to many different pathologies. The diagnosis based on PA-EEG is an arduous task for experts in Clinical Neurophysiology, being mainly based on other clinical features of patients. Considering this difficulty in the diagnosis it is also very complicated to establish the prognosis of patients who present PA-EEG. The goal of this paper is to propose a method capable of determining patient prognosis based on characteristics of the PA-EEG activity. The approach, based on a parallel classification architecture and a majority vote system has proven successful by obtaining a success rate of 81.94% in the classification of patient prognosis of our database.

Portable Amplifier Design for a Novel EEG Monitor in Point-of-Care Applications.

PubMed

Luan, Bo; Sun, Mingui; Jia, Wenyan

2012-01-01

The Electroencephalography (EEG) is a common diagnostic tool for neurological diseases and dysfunctions, such as epilepsy and insomnia. However, the current EEG technology cannot be utilized quickly and conveniently at the point of care due to the complex skin preparation procedures required and the inconvenient EEG data acquisition systems. This work presents a portable amplifier design that integrates a set of skin screw electrodes and a wireless data link. The battery-operated amplifier contains an instrumentation amplifier, two noninverting amplifiers, two high-pass filters, and a low-pass filter. It is able to magnify the EEG signals over 10,000 times and has a high impedance, low noise, small size and low weight. Our electrode and amplifier are ideal for point-of-care applications, especially during transportation of patients suffering from traumatic brain injury or stroke.

Multimodal recording of brain activity in term newborns during photic stimulation by near-infrared spectroscopy and electroencephalography

NASA Astrophysics Data System (ADS)

Biallas, Martin; Trajkovic, Ivo; Hagmann, Cornelia; Scholkmann, Felix; Jenny, Carmen; Holper, Lisa; Beck, Andreas; Wolf, Martin

2012-08-01

In this study 14 healthy term newborns (postnatal mean age 2.1 days) underwent photic stimulation during sleep on two different days. Near-infrared spectroscopy (NIRS) and electroencephalography (EEG) was acquired simultaneously. The aims of the study were: to determine (i) the sensitivity and (ii) the repeatability of NIRS to detect the hemodynamic response, (iii) the sensitivity and (iv) the repeatability of EEG to detect a visual evoked potential (VEP), (v) to analyze optical data for the optical neuronal signal, and (vi) to test whether inadequate stimulation could be reason for absent hemodynamic responses. The results of the study were as follows. (i) Sensitivity of NIRS was 61.5% to detect hemodynamic responses; (ii) their reproducibility was 41.7%. A VEP was detected (iii) in 96.3% of all subjects with (iv) a reproducibility of 92.3%. (v) In two measurements data met the criteria for an optical neuronal signal. The noise level was 9.6.10-5% change in optical density. (vi) Insufficient stimulation was excluded as reason for absent hemodynamic responses. We conclude that NIRS is an promising tool to study cognitive activation and development of the brain. For clinical application, however, the sensitivity and reproducibility on an individual level needs to be improved.

Investigating social cognition in infants and adults using dense array electroencephalography ((d)EEG).

PubMed

Akano, Adekemi J; Haley, David W; Dudek, Joanna

2011-06-27

Dense array electroencephalography ((d)EEG), which provides a non-invasive window for measuring brain activity and a temporal resolution unsurpassed by any other current brain imaging technology¹, ² is being used increasingly in the study of social cognitive functioning in infants and adults. While (d)EEG is enabling researchers to examine brain activity patterns with unprecedented levels of sensitivity, conventional EEG recording systems continue to face certain limitations, including 1) poor spatial resolution and source localization³,⁴2) the physical discomfort for test subjects of enduring the individual application of numerous electrodes to the surface of the scalp, and 3) the complexity for researchers of learning to use multiple software packages to collect and process data. Here we present an overview of an established methodology that represents a significant improvement on conventional methodologies for studying EEG in infants and adults. Although several analytical software techniques can be used to establish indirect indices of source localization to improve the spatial resolution of (d)EEG, the HydroCel Geodesic Sensor Net (HCGSN) by Electrical Geodesics, Inc. (EGI), a dense sensory array that maintains equal distances among adjacent recording electrodes on all surfaces of the scalp, further enhances spatial resolution⁴,⁵(,)⁶ compared to standard (d)EEG systems. The sponge-based HCGSN can be applied rapidly and without scalp abrasion, making it ideal for use with adults⁷,⁸ children⁹,¹⁰, ¹¹,¹² and infants¹², in both research and clinical ⁴,⁵,⁶,¹³,¹⁴,¹⁵settings. This feature allows for considerable cost and time savings by decreasing the average net application time compared to other (d)EEG systems. Moreover, the HCGSN includes unified, seamless software applications for all phases of data, greatly simplifying the collection, processing, and analysis of (d)EEG data. The HCGSN features a low-profile electrode pedestal, which, when filled with electrolyte solution, creates a sealed microenvironment and an electrode-scalp interface. In all Geodesic (d;)EEG systems, EEG sensors detect changes in voltage originating from the participant's scalp, along with a small amount of electrical noise originating from the room environment. Electrical signals from all sensors of the Geodesic sensor net are received simultaneously by the amplifier, where they are automatically processed, packaged, and sent to the data-acquisition computer (DAC). Once received by the DAC, scalp electrical activity can be isolated from artifacts for analysis using the filtering and artifact detection tools included in the EGI software. Typically, the HCGSN can be used continuously for only up to two hours because the electrolyte solution dries out over time, gradually decreasing the quality of the scalp-electrode interface. In the Parent-Infant Research Lab at the University of Toronto, we are using (d)EEG to study social cognitive processes including memory, emotion, goals, intentionality, anticipation, and executive functioning in both adult and infant participants.

A Study on Analysis of EEG Caused by Grating Stimulation Imaging

NASA Astrophysics Data System (ADS)

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

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

The Removal of EOG Artifacts From EEG Signals Using Independent Component Analysis and Multivariate Empirical Mode Decomposition.

PubMed

Wang, Gang; Teng, Chaolin; Li, Kuo; Zhang, Zhonglin; Yan, Xiangguo

2016-09-01

The recorded electroencephalography (EEG) signals are usually contaminated by electrooculography (EOG) artifacts. In this paper, by using independent component analysis (ICA) and multivariate empirical mode decomposition (MEMD), the ICA-based MEMD method was proposed to remove EOG artifacts (EOAs) from multichannel EEG signals. First, the EEG signals were decomposed by the MEMD into multiple multivariate intrinsic mode functions (MIMFs). The EOG-related components were then extracted by reconstructing the MIMFs corresponding to EOAs. After performing the ICA of EOG-related signals, the EOG-linked independent components were distinguished and rejected. Finally, the clean EEG signals were reconstructed by implementing the inverse transform of ICA and MEMD. The results of simulated and real data suggested that the proposed method could successfully eliminate EOAs from EEG signals and preserve useful EEG information with little loss. By comparing with other existing techniques, the proposed method achieved much improvement in terms of the increase of signal-to-noise and the decrease of mean square error after removing EOAs.

Embedding Dimension Selection for Adaptive Singular Spectrum Analysis of EEG Signal.

PubMed

Xu, Shanzhi; Hu, Hai; Ji, Linhong; Wang, Peng

2018-02-26

The recorded electroencephalography (EEG) signal is often contaminated with different kinds of artifacts and noise. Singular spectrum analysis (SSA) is a powerful tool for extracting the brain rhythm from a noisy EEG signal. By analyzing the frequency characteristics of the reconstructed component (RC) and the change rate in the trace of the Toeplitz matrix, it is demonstrated that the embedding dimension is related to the frequency bandwidth of each reconstructed component, in consistence with the component mixing in the singular value decomposition step. A method for selecting the embedding dimension is thereby proposed and verified by simulated EEG signal based on the Markov Process Amplitude (MPA) EEG Model. Real EEG signal is also collected from the experimental subjects under both eyes-open and eyes-closed conditions. The experimental results show that based on the embedding dimension selection method, the alpha rhythm can be extracted from the real EEG signal by the adaptive SSA, which can be effectively utilized to distinguish between the eyes-open and eyes-closed states.

fMRI activation patterns in an analytic reasoning task: consistency with EEG source localization

NASA Astrophysics Data System (ADS)

Li, Bian; Vasanta, Kalyana C.; O'Boyle, Michael; Baker, Mary C.; Nutter, Brian; Mitra, Sunanda

2010-03-01

Functional magnetic resonance imaging (fMRI) is used to model brain activation patterns associated with various perceptual and cognitive processes as reflected by the hemodynamic (BOLD) response. While many sensory and motor tasks are associated with relatively simple activation patterns in localized regions, higher-order cognitive tasks may produce activity in many different brain areas involving complex neural circuitry. We applied a recently proposed probabilistic independent component analysis technique (PICA) to determine the true dimensionality of the fMRI data and used EEG localization to identify the common activated patterns (mapped as Brodmann areas) associated with a complex cognitive task like analytic reasoning. Our preliminary study suggests that a hybrid GLM/PICA analysis may reveal additional regions of activation (beyond simple GLM) that are consistent with electroencephalography (EEG) source localization patterns.

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.

Electroencephalography Spectral Power Density in First-Episode Mania: A Comparative Study with Subsequent Remission Period.

PubMed

Güven, Sertaç; Kesebir, Sermin; Demirer, R Murat; Bilici, Mustafa

2015-06-01

Our aim in this study was to investigate spectral power density (PSD) in first-episode mania and subsequent remission period and to evaluate their difference. Sixty-nine consecutive cases referring to our hospital within the previous 1 year, who were evaluated as bipolar disorder manic episode according to The Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) at the first episode and had the informed consent form signed by first degree relatives, were included in this study. Exclusion criteria included having previous depressive episode, using drugs which could influence electroencephalographic activity before electroencephalography (EEG), and having previous neurological disease, particularly epilepsy, head trauma, and/or loss of consciousness. EEG records were obtained using a digital device in 16 channels; 23 surface electrodes were placed according to the International 10-20 system. Spectral power density (dbμV/Hz) of EEG signal provided information on the power carried out by EEG waves in defined frequancy range per unit frequency in the present study. A peak power value detected on the right with FP2P4 and on the left with F7T3 electrodes were found to be higher in the manic episode than in the remission period (p=0.018 and 0.025). In the remission period, in cases with psychotic symptoms during the manic period, F4C4 peak power value was found to be lower than that in cases with no psychotic findings during the manic period (p=0.027). There was no relation was found between YMRS scores and peak power scores. Electrophysiological corollary of mood episode is present from the onset of the disease, and it differs between the manic and remission periods of bipolar disorder. In the remission period, peak power values of PSD distinguish cases with psychotic findings from cases without psychotic findings when they were manic.

Evaluating interhemispheric cortical responses to transcranial magnetic stimulation in chronic stroke: A TMS-EEG investigation.

PubMed

Borich, Michael R; Wheaton, Lewis A; Brodie, Sonia M; Lakhani, Bimal; Boyd, Lara A

2016-04-08

TMS-evoked cortical responses can be measured using simultaneous electroencephalography (TMS-EEG) to directly quantify cortical connectivity in the human brain. The purpose of this study was to evaluate interhemispheric cortical connectivity between the primary motor cortices (M1s) in participants with chronic stroke and controls using TMS-EEG. Ten participants with chronic stroke and four controls were tested. TMS-evoked responses were recorded at rest and during a typical TMS assessment of transcallosal inhibition (TCI). EEG recordings from peri-central gyral electrodes (C3 and C4) were evaluated using imaginary phase coherence (IPC) analyses to quantify levels of effective interhemispheric connectivity. Significantly increased TMS-evoked beta (15-30Hz frequency range) IPC was observed in the stroke group during ipsilesional M1 stimulation compared to controls during TCI assessment but not at rest. TMS-evoked beta IPC values were associated with TMS measures of transcallosal inhibition across groups. These results suggest TMS-evoked EEG responses can index abnormal effective interhemispheric connectivity in chronic stroke. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Mushu, a free- and open source BCI signal acquisition, written in Python.

PubMed

Venthur, Bastian; Blankertz, Benjamin

2012-01-01

The following paper describes Mushu, a signal acquisition software for retrieval and online streaming of Electroencephalography (EEG) data. It is written, but not limited, to the needs of Brain Computer Interfacing (BCI). It's main goal is to provide a unified interface to EEG data regardless of the amplifiers used. It runs under all major operating systems, like Windows, Mac OS and Linux, is written in Python and is free- and open source software licensed under the terms of the GNU General Public License.

Amplitude-integrated EEG in newborns with critical congenital heart disease predicts preoperative brain magnetic resonance imaging findings.

PubMed

Mulkey, Sarah B; Yap, Vivien L; Bai, Shasha; Ramakrishnaiah, Raghu H; Glasier, Charles M; Bornemeier, Renee A; Schmitz, Michael L; Bhutta, Adnan T

2015-06-01

The study aims are to evaluate cerebral background patterns using amplitude-integrated electroencephalography in newborns with critical congenital heart disease, determine if amplitude-integrated electroencephalography is predictive of preoperative brain injury, and assess the incidence of preoperative seizures. We hypothesize that amplitude-integrated electroencephalography will show abnormal background patterns in the early preoperative period in infants with congenital heart disease that have preoperative brain injury on magnetic resonance imaging. Twenty-four newborns with congenital heart disease requiring surgery at younger than 30 days of age were prospectively enrolled within the first 3 days of age at a tertiary care pediatric hospital. Infants had amplitude-integrated electroencephalography for 24 hours beginning close to birth and preoperative brain magnetic resonance imaging. The amplitude-integrated electroencephalographies were read to determine if the background pattern was normal, mildly abnormal, or severely abnormal. The presence of seizures and sleep-wake cycling were noted. The preoperative brain magnetic resonance imaging scans were used for brain injury and brain atrophy assessment. Fifteen of 24 infants had abnormal amplitude-integrated electroencephalography at 0.71 (0-2) (mean [range]) days of age. In five infants, the background pattern was severely abnormal. (burst suppression and/or continuous low voltage). Of the 15 infants with abnormal amplitude-integrated electroencephalography, 9 (60%) had brain injury. One infant with brain injury had a seizure on amplitude-integrated electroencephalography. A severely abnormal background pattern on amplitude-integrated electroencephalography was associated with brain atrophy (P = 0.03) and absent sleep-wake cycling (P = 0.022). Background cerebral activity is abnormal on amplitude-integrated electroencephalography following birth in newborns with congenital heart disease who have findings of brain injury and/or brain atrophy on preoperative brain magnetic resonance imaging. Copyright © 2015 Elsevier Inc. All rights reserved.

Standards for data acquisition and software-based analysis of in vivo electroencephalography recordings from animals. A TASK1-WG5 report of the AES/ILAE Translational Task Force of the ILAE.

PubMed

Moyer, Jason T; Gnatkovsky, Vadym; Ono, Tomonori; Otáhal, Jakub; Wagenaar, Joost; Stacey, William C; Noebels, Jeffrey; Ikeda, Akio; Staley, Kevin; de Curtis, Marco; Litt, Brian; Galanopoulou, Aristea S

2017-11-01

Electroencephalography (EEG)-the direct recording of the electrical activity of populations of neurons-is a tremendously important tool for diagnosing, treating, and researching epilepsy. Although standard procedures for recording and analyzing human EEG exist and are broadly accepted, there are no such standards for research in animal models of seizures and epilepsy-recording montages, acquisition systems, and processing algorithms may differ substantially among investigators and laboratories. The lack of standard procedures for acquiring and analyzing EEG from animal models of epilepsy hinders the interpretation of experimental results and reduces the ability of the scientific community to efficiently translate new experimental findings into clinical practice. Accordingly, the intention of this report is twofold: (1) to review current techniques for the collection and software-based analysis of neural field recordings in animal models of epilepsy, and (2) to offer pertinent standards and reporting guidelines for this research. Specifically, we review current techniques for signal acquisition, signal conditioning, signal processing, data storage, and data sharing, and include applicable recommendations to standardize collection and reporting. We close with a discussion of challenges and future opportunities, and include a supplemental report of currently available acquisition systems and analysis tools. This work represents a collaboration on behalf of the American Epilepsy Society/International League Against Epilepsy (AES/ILAE) Translational Task Force (TASK1-Workgroup 5), and is part of a larger effort to harmonize video-EEG interpretation and analysis methods across studies using in vivo and in vitro seizure and epilepsy models. Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.

EEG Differences in Two Clinically Similar Rapid Dementias: Voltage-Gated Potassium Channel Complex-Associated Autoimmune Encephalitis and Creutzfeldt-Jakob Disease.

PubMed

Freund, Brin; Probasco, John C; Cervenka, Mackenzie C; Sutter, Raoul; Kaplan, Peter W

2018-05-01

Distinguishing treatable causes for rapidly progressive dementia from those that are incurable is vital. Creutzfeldt-Jakob disease (CJD) and voltage-gated potassium channel complex-associated autoimmune encephalitis (VGKC AE) are 2 such conditions with disparate outcomes and response to treatment. To determine the differences in electroencephalography between CJD and VGKC AE, we performed a retrospective review of medical records and examined clinical data, neuroimaging, and electroencephalographs performed in patients admitted for evaluation for rapidly progressive dementia diagnosed with CJD and VGKC AE at the Johns Hopkins Hospital and Bayview Medical Center between January 1, 2007 and December 31, 2015. More patients in the VGKC AE group had seizures (12/17) than those with CJD (3/14; P = .008). Serum sodium levels were lower in those with VGKC AE ( P = .001). Cerebrospinal fluid (CSF) white blood cell count was higher in VGKC AE ( P = .008). CSF protein 14-3-3 ( P = .018) was more commonly detected in CJD, and tau levels were higher in those with CJD ( P < .006). On neuroimaging, diffusion restriction in the cortex ( P = .001), caudate ( P < .001), and putamen ( P = .001) was more frequent in CJD. Periodic sharp wave complexes ( P = .001) and generalized suppressed activity ( P = .008) were more common on initial EEG in CJD. On serial EEGs, generalized periodic discharges ( P = .004), generalized suppressed activity (P=0.008), and periodic sharp wave complexes ( P < .001) were detected more in CJD. This study shows that there are a number of differentiating features between CJD and VGKC AE, and electroencephalography can aid in their diagnoses. Performing serial EEGs better delineates these conditions.

Mouse EEG spike detection based on the adapted continuous wavelet transform

NASA Astrophysics Data System (ADS)

Tieng, Quang M.; Kharatishvili, Irina; Chen, Min; Reutens, David C.

2016-04-01

Objective. Electroencephalography (EEG) is an important tool in the diagnosis of epilepsy. Interictal spikes on EEG are used to monitor the development of epilepsy and the effects of drug therapy. EEG recordings are generally long and the data voluminous. Thus developing a sensitive and reliable automated algorithm for analyzing EEG data is necessary. Approach. A new algorithm for detecting and classifying interictal spikes in mouse EEG recordings is proposed, based on the adapted continuous wavelet transform (CWT). The construction of the adapted mother wavelet is founded on a template obtained from a sample comprising the first few minutes of an EEG data set. Main Result. The algorithm was tested with EEG data from a mouse model of epilepsy and experimental results showed that the algorithm could distinguish EEG spikes from other transient waveforms with a high degree of sensitivity and specificity. Significance. Differing from existing approaches, the proposed approach combines wavelet denoising, to isolate transient signals, with adapted CWT-based template matching, to detect true interictal spikes. Using the adapted wavelet constructed from a predefined template, the adapted CWT is calculated on small EEG segments to fit dynamical changes in the EEG recording.

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

PubMed Central

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

2014-01-01

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

Investigation of phase synchronization of interictal EEG in right temporal lobe epilepsy

NASA Astrophysics Data System (ADS)

Yu, Haitao; Cai, Lihui; Wu, Xinyu; Song, Zhenxi; Wang, Jiang; Xia, Zijie; Liu, Jing; Cao, Yibin

2018-02-01

Epilepsy is commonly associated with abnormally synchronous activity of neurons located in epileptogenic zones. In this study, we investigated the synchronization characteristic of right temporal lobe epilepsy (RTLE). Multichannel electroencephalography (EEG) data were recorded from the RTLE patients during interictal period and normal controls. Power spectral density was first used to analyze the EEG power for two groups of subjects. It was found that the power of epileptics is increased in the whole brain compared with that of the control. We calculated phase lag index (PLI) to measure the phase synchronization between each pair of EEG signals. A higher degree of synchronization was observed in the epileptics especially between distant channels. In particular, the regional synchronization degree was negatively correlated with power spectral density and the correlation was weaker for epileptics. Moreover, the synchronization degree decayed with the increase of relative distance of channels for both the epilepsy and control, but the dependence was weakened in the former. The obtained results may provide new insights into the generation mechanism of epilepsy.

Temporal Comparison Between NIRS and EEG Signals During a Mental Arithmetic Task Evaluated with Self-Organizing Maps.

PubMed

Oyama, Katsunori; Sakatani, Kaoru

2016-01-01

Simultaneous monitoring of brain activity with near-infrared spectroscopy and electroencephalography allows spatiotemporal reconstruction of the hemodynamic response regarding the concentration changes in oxyhemoglobin and deoxyhemoglobin that are associated with recorded brain activity such as cognitive functions. However, the accuracy of state estimation during mental arithmetic tasks is often different depending on the length of the segment for sampling of NIRS and EEG signals. This study compared the results of a self-organizing map and ANOVA, which were both used to assess the accuracy of state estimation. We conducted an experiment with a mental arithmetic task performed by 10 participants. The lengths of the segment in each time frame for observation of NIRS and EEG signals were compared with the 30-s, 1-min, and 2-min segment lengths. The optimal segment lengths were different for NIRS and EEG signals in the case of classification of feature vectors into the states of performing a mental arithmetic task and being at rest.

Neural correlates of mathematical problem solving.

PubMed

Lin, Chun-Ling; Jung, Melody; Wu, Ying Choon; She, Hsiao-Ching; Jung, Tzyy-Ping

2015-03-01

This study explores electroencephalography (EEG) brain dynamics associated with mathematical problem solving. EEG and solution latencies (SLs) were recorded as 11 neurologically healthy volunteers worked on intellectually challenging math puzzles that involved combining four single-digit numbers through basic arithmetic operators (addition, subtraction, division, multiplication) to create an arithmetic expression equaling 24. Estimates of EEG spectral power were computed in three frequency bands - θ (4-7 Hz), α (8-13 Hz) and β (14-30 Hz) - over a widely distributed montage of scalp electrode sites. The magnitude of power estimates was found to change in a linear fashion with SLs - that is, relative to a base of power spectrum, theta power increased with longer SLs, while alpha and beta power tended to decrease. Further, the topographic distribution of spectral fluctuations was characterized by more pronounced asymmetries along the left-right and anterior-posterior axes for solutions that involved a longer search phase. These findings reveal for the first time the topography and dynamics of EEG spectral activities important for sustained solution search during arithmetical problem solving.

Multimodal neuroimaging evidence linking memory and attention systems during visual search cued by context.

PubMed

Kasper, Ryan W; Grafton, Scott T; Eckstein, Miguel P; Giesbrecht, Barry

2015-03-01

Visual search can be facilitated by the learning of spatial configurations that predict the location of a target among distractors. Neuropsychological and functional magnetic resonance imaging (fMRI) evidence implicates the medial temporal lobe (MTL) memory system in this contextual cueing effect, and electroencephalography (EEG) studies have identified the involvement of visual cortical regions related to attention. This work investigated two questions: (1) how memory and attention systems are related in contextual cueing; and (2) how these systems are involved in both short- and long-term contextual learning. In one session, EEG and fMRI data were acquired simultaneously in a contextual cueing task. In a second session conducted 1 week later, EEG data were recorded in isolation. The fMRI results revealed MTL contextual modulations that were correlated with short- and long-term behavioral context enhancements and attention-related effects measured with EEG. An fMRI-seeded EEG source analysis revealed that the MTL contributed the most variance to the variability in the attention enhancements measured with EEG. These results support the notion that memory and attention systems interact to facilitate search when spatial context is implicitly learned. © 2015 New York Academy of Sciences.

The Influence of Age and Skull Conductivity on Surface and Subdermal Bipolar EEG Leads

PubMed Central

Wendel, Katrina; Väisänen, Juho; Seemann, Gunnar; Hyttinen, Jari; Malmivuo, Jaakko

2010-01-01

Bioelectric source measurements are influenced by the measurement location as well as the conductive properties of the tissues. Volume conductor effects such as the poorly conducting bones or the moderately conducting skin are known to affect the measurement precision and accuracy of the surface electroencephalography (EEG) measurements. This paper investigates the influence of age via skull conductivity upon surface and subdermal bipolar EEG measurement sensitivity conducted on two realistic head models from the Visible Human Project. Subdermal electrodes (a.k.a. subcutaneous electrodes) are implanted on the skull beneath the skin, fat, and muscles. We studied the effect of age upon these two electrode types according to the scalp-to-skull conductivity ratios of 5, 8, 15, and 30 : 1. The effects on the measurement sensitivity were studied by means of the half-sensitivity volume (HSV) and the region of interest sensitivity ratio (ROISR). The results indicate that the subdermal implantation notably enhances the precision and accuracy of EEG measurements by a factor of eight compared to the scalp surface measurements. In summary, the evidence indicates that both surface and subdermal EEG measurements benefit better recordings in terms of precision and accuracy on younger patients. PMID:20130812

Selection of Mother Wavelet Functions for Multi-Channel EEG Signal Analysis during a Working Memory Task

PubMed Central

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

2015-01-01

We performed a comparative study to select the efficient mother wavelet (MWT) basis functions that optimally represent the signal characteristics of the electrical activity of the human brain during a working memory (WM) task recorded through electro-encephalography (EEG). Nineteen EEG electrodes were placed on the scalp following the 10–20 system. These electrodes were then grouped into five recording regions corresponding to the scalp area of the cerebral cortex. Sixty-second WM task data were recorded from ten control subjects. Forty-five MWT basis functions from orthogonal families were investigated. These functions included Daubechies (db1–db20), Symlets (sym1–sym20), and Coiflets (coif1–coif5). Using ANOVA, we determined the MWT basis functions with the most significant differences in the ability of the five scalp regions to maximize their cross-correlation with the EEG signals. The best results were obtained using “sym9” across the five scalp regions. Therefore, the most compatible MWT with the EEG signals should be selected to achieve wavelet denoising, decomposition, reconstruction, and sub-band feature extraction. This study provides a reference of the selection of efficient MWT basis functions. PMID:26593918

Successful Object Encoding Induces Increased Directed Connectivity in Presymptomatic Early-Onset Alzheimer’s Disease

PubMed Central

Ochoa, John Fredy; Alonso, Joan Francesc; Duque, Jon Edinson; Tobón, Carlos Andrés; Mañanas, Miguel Angel; Lopera, Francisco; Hernández, Alher Mauricio

2016-01-01

Background: Recent studies report increases in neural activity in brain regions critical to episodic memory at preclinical stages of Alzheimer’s disease (AD). Although electroencephalography (EEG) is widely used in AD studies, given its non-invasiveness and low cost, there is a need to translate the findings in other neuroimaging methods to EEG. Objective: To examine how the previous findings using functional magnetic resonance imaging (fMRI) at preclinical stage in presenilin-1 E280A mutation carriers could be assessed and extended, using EEG and a connectivity approach. Methods: EEG signals were acquired during resting and encoding in 30 normal cognitive young subjects, from an autosomal dominant early-onset AD kindred from Antioquia, Colombia. Regions of the brain previously reported as hyperactive were used for connectivity analysis. Results: Mutation carriers exhibited increasing connectivity at analyzed regions. Among them, the right precuneus exhibited the highest changes in connectivity. Conclusion: Increased connectivity in hyperactive cerebral regions is seen in individuals, genetically-determined to develop AD, at preclinical stage. The use of a connectivity approach and a widely available neuroimaging technique opens the possibility to increase the use of EEG in early detection of preclinical AD. PMID:27792014

Selection of Mother Wavelet Functions for Multi-Channel EEG Signal Analysis during a Working Memory Task.

PubMed

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

2015-11-17

We performed a comparative study to select the efficient mother wavelet (MWT) basis functions that optimally represent the signal characteristics of the electrical activity of the human brain during a working memory (WM) task recorded through electro-encephalography (EEG). Nineteen EEG electrodes were placed on the scalp following the 10-20 system. These electrodes were then grouped into five recording regions corresponding to the scalp area of the cerebral cortex. Sixty-second WM task data were recorded from ten control subjects. Forty-five MWT basis functions from orthogonal families were investigated. These functions included Daubechies (db1-db20), Symlets (sym1-sym20), and Coiflets (coif1-coif5). Using ANOVA, we determined the MWT basis functions with the most significant differences in the ability of the five scalp regions to maximize their cross-correlation with the EEG signals. The best results were obtained using "sym9" across the five scalp regions. Therefore, the most compatible MWT with the EEG signals should be selected to achieve wavelet denoising, decomposition, reconstruction, and sub-band feature extraction. This study provides a reference of the selection of efficient MWT basis functions.

Online Reduction of Artifacts in EEG of Simultaneous EEG-fMRI Using Reference Layer Adaptive Filtering (RLAF).

PubMed

Steyrl, David; Krausz, Gunther; Koschutnig, Karl; Edlinger, Günter; Müller-Putz, Gernot R

2018-01-01

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow us to study the active human brain from two perspectives concurrently. Signal processing based artifact reduction techniques are mandatory for this, however, to obtain reasonable EEG quality in simultaneous EEG-fMRI. Current artifact reduction techniques like average artifact subtraction (AAS), typically become less effective when artifact reduction has to be performed on-the-fly. We thus present and evaluate a new technique to improve EEG quality online. This technique adds up with online AAS and combines a prototype EEG-cap for reference recordings of artifacts, with online adaptive filtering and is named reference layer adaptive filtering (RLAF). We found online AAS + RLAF to be highly effective in improving EEG quality. Online AAS + RLAF outperformed online AAS and did so in particular online in terms of the chosen performance metrics, these being specifically alpha rhythm amplitude ratio between closed and opened eyes (3-45% improvement), signal-to-noise-ratio of visual evoked potentials (VEP) (25-63% improvement), and VEPs variability (16-44% improvement). Further, we found that EEG quality after online AAS + RLAF is occasionally even comparable with the offline variant of AAS at a 3T MRI scanner. In conclusion RLAF is a very effective add-on tool to enable high quality EEG in simultaneous EEG-fMRI experiments, even when online artifact reduction is necessary.

MATLAB Toolboxes for Reference Electrode Standardization Technique (REST) of Scalp EEG

PubMed Central

Dong, Li; Li, Fali; Liu, Qiang; Wen, Xin; Lai, Yongxiu; Xu, Peng; Yao, Dezhong

2017-01-01

Reference electrode standardization technique (REST) has been increasingly acknowledged and applied as a re-reference technique to transform an actual multi-channels recordings to approximately zero reference ones in electroencephalography/event-related potentials (EEG/ERPs) community around the world in recent years. However, a more easy-to-use toolbox for re-referencing scalp EEG data to zero reference is still lacking. Here, we have therefore developed two open-source MATLAB toolboxes for REST of scalp EEG. One version of REST is closely integrated into EEGLAB, which is a popular MATLAB toolbox for processing the EEG data; and another is a batch version to make it more convenient and efficient for experienced users. Both of them are designed to provide an easy-to-use for novice researchers and flexibility for experienced researchers. All versions of the REST toolboxes can be freely downloaded at http://www.neuro.uestc.edu.cn/rest/Down.html, and the detailed information including publications, comments and documents on REST can also be found from this website. An example of usage is given with comparative results of REST and average reference. We hope these user-friendly REST toolboxes could make the relatively novel technique of REST easier to study, especially for applications in various EEG studies. PMID:29163006

MATLAB Toolboxes for Reference Electrode Standardization Technique (REST) of Scalp EEG.

PubMed

Dong, Li; Li, Fali; Liu, Qiang; Wen, Xin; Lai, Yongxiu; Xu, Peng; Yao, Dezhong

2017-01-01

Reference electrode standardization technique (REST) has been increasingly acknowledged and applied as a re-reference technique to transform an actual multi-channels recordings to approximately zero reference ones in electroencephalography/event-related potentials (EEG/ERPs) community around the world in recent years. However, a more easy-to-use toolbox for re-referencing scalp EEG data to zero reference is still lacking. Here, we have therefore developed two open-source MATLAB toolboxes for REST of scalp EEG. One version of REST is closely integrated into EEGLAB, which is a popular MATLAB toolbox for processing the EEG data; and another is a batch version to make it more convenient and efficient for experienced users. Both of them are designed to provide an easy-to-use for novice researchers and flexibility for experienced researchers. All versions of the REST toolboxes can be freely downloaded at http://www.neuro.uestc.edu.cn/rest/Down.html, and the detailed information including publications, comments and documents on REST can also be found from this website. An example of usage is given with comparative results of REST and average reference. We hope these user-friendly REST toolboxes could make the relatively novel technique of REST easier to study, especially for applications in various EEG studies.

Isolating gait-related movement artifacts in electroencephalography during human walking

PubMed Central

Kline, Julia E.; Huang, Helen J.; Snyder, Kristine L.; Ferris, Daniel P.

2016-01-01

Objective High-density electroencephelography (EEG) can provide insight into human brain function during real-world activities with walking. Some recent studies have used EEG to characterize brain activity during walking, but the relative contributions of movement artifact and electrocortical activity have been difficult to quantify. We aimed to characterize movement artifact recorded by EEG electrodes at a range of walking speeds and to test the efficacy of artifact removal methods. We also quantified the similarity between movement artifact recorded by EEG electrodes and a head-mounted accelerometer. Approach We used a novel experimental method to isolate and record movement artifact with EEG electrodes during walking. We blocked electrophysiological signals using a nonconductive layer (silicone swim cap) and simulated an electrically conductive scalp on top of the swim cap using a wig coated with conductive gel. We recorded motion artifact EEG data from nine young human subjects walking on a treadmill at speeds from 0.4–1.6 m/s. We then tested artifact removal methods including moving average and wavelet-based techniques. Main Results Movement artifact recorded with EEG electrodes varied considerably, across speed, subject, and electrode location. The movement artifact measured with EEG electrodes did not correlate well with head acceleration. All of the tested artifact removal methods attenuated low-frequency noise but did not completely remove movement artifact. The spectral power fluctuations in the movement artifact data resembled data from some previously published studies of EEG during walking. Significance Our results suggest that EEG data recorded during walking likely contains substantial movement artifact that: cannot be explained by head accelerations; varies across speed, subject, and channel; and cannot be removed using traditional signal processing methods. Future studies should focus on more sophisticated methods for removing of EEG movement artifact to advance the field. PMID:26083595

Isolating gait-related movement artifacts in electroencephalography during human walking.

PubMed

Kline, Julia E; Huang, Helen J; Snyder, Kristine L; Ferris, Daniel P

2015-08-01

High-density electroencephelography (EEG) can provide an insight into human brain function during real-world activities with walking. Some recent studies have used EEG to characterize brain activity during walking, but the relative contributions of movement artifact and electrocortical activity have been difficult to quantify. We aimed to characterize movement artifact recorded by EEG electrodes at a range of walking speeds and to test the efficacy of artifact removal methods. We also quantified the similarity between movement artifact recorded by EEG electrodes and a head-mounted accelerometer. We used a novel experimental method to isolate and record movement artifact with EEG electrodes during walking. We blocked electrophysiological signals using a nonconductive layer (silicone swim cap) and simulated an electrically conductive scalp on top of the swim cap using a wig coated with conductive gel. We recorded motion artifact EEG data from nine young human subjects walking on a treadmill at speeds from 0.4 to 1.6 m s(-1). We then tested artifact removal methods including moving average and wavelet-based techniques. Movement artifact recorded with EEG electrodes varied considerably, across speed, subject, and electrode location. The movement artifact measured with EEG electrodes did not correlate well with head acceleration. All of the tested artifact removal methods attenuated low-frequency noise but did not completely remove movement artifact. The spectral power fluctuations in the movement artifact data resembled data from some previously published studies of EEG during walking. Our results suggest that EEG data recorded during walking likely contains substantial movement artifact that: cannot be explained by head accelerations; varies across speed, subject, and channel; and cannot be removed using traditional signal processing methods. Future studies should focus on more sophisticated methods for removal of EEG movement artifact to advance the field.

Electrophysiological correlates of the BOLD signal for EEG-informed fMRI

PubMed Central

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

2015-01-01

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

Continuous electroencephalography predicts delayed cerebral ischemia after subarachnoid hemorrhage: A prospective study of diagnostic accuracy.

PubMed

Rosenthal, Eric S; Biswal, Siddharth; Zafar, Sahar F; O'Connor, Kathryn L; Bechek, Sophia; Shenoy, Apeksha V; Boyle, Emily J; Shafi, Mouhsin M; Gilmore, Emily J; Foreman, Brandon P; Gaspard, Nicolas; Leslie-Mazwi, Thabele M; Rosand, Jonathan; Hoch, Daniel B; Ayata, Cenk; Cash, Sydney S; Cole, Andrew J; Patel, Aman B; Westover, M Brandon

2018-04-16

Delayed cerebral ischemia (DCI) is a common, disabling complication of subarachnoid hemorrhage (SAH). Preventing DCI is a key focus of neurocritical care, but interventions carry risk and cannot be applied indiscriminately. Although retrospective studies have identified continuous electroencephalographic (cEEG) measures associated with DCI, no study has characterized the accuracy of cEEG with sufficient rigor to justify using it to triage patients to interventions or clinical trials. We therefore prospectively assessed the accuracy of cEEG for predicting DCI, following the Standards for Reporting Diagnostic Accuracy Studies. We prospectively performed cEEG in nontraumatic, high-grade SAH patients at a single institution. The index test consisted of clinical neurophysiologists prospectively reporting prespecified EEG alarms: (1) decreasing relative alpha variability, (2) decreasing alpha-delta ratio, (3) worsening focal slowing, or (4) late appearing epileptiform abnormalities. The diagnostic reference standard was DCI determined by blinded, adjudicated review. Primary outcome measures were sensitivity and specificity of cEEG for subsequent DCI, determined by multistate survival analysis, adjusted for baseline risk. One hundred three of 227 consecutive patients were eligible and underwent cEEG monitoring (7.7-day mean duration). EEG alarms occurred in 96.2% of patients with and 19.6% without subsequent DCI (1.9-day median latency, interquartile range = 0.9-4.1). Among alarm subtypes, late onset epileptiform abnormalities had the highest predictive value. Prespecified EEG findings predicted DCI among patients with low (91% sensitivity, 83% specificity) and high (95% sensitivity, 77% specificity) baseline risk. cEEG accurately predicts DCI following SAH and may help target therapies to patients at highest risk of secondary brain injury. Ann Neurol 2018. © 2018 American Neurological Association.

Chloral hydrate, chloral hydrate--promethazine and chloral hydrate -hydroxyzine efficacy in electroencephalography sedation.

PubMed

Fallah, Razieh; Alaei, Ali; Akhavan Karbasi, Sedighah; Shajari, Ahmad

2014-06-01

To compare efficacy and safety of chloral hydrate (CH), chloral hydrate and promethazine (CH + P) and chloral hydrate and hydroxyzine (CH + H) in electroencephalography (EEG) sedation. In a parallel single-blinded randomized clinical trial, ninety 1-7 y-old uncooperative kids who were referred to Pediatric Neurology Clinic of Shahid Sadoughi University, Yazd, Iran from April through August 2012, were randomly assigned to receive 40 mg/kg of chloral hydrate or 40 mg/kg of chloral hydrate and 1 mg/kg of promethazine or 40 mg/kg of chloral hydrate and 2 mg/kg of hydroxyzine. The primary endpoint was efficacy in sufficient sedation (obtaining four Ramsay sedation score) and successful completion of EEG. Secondary endpoint was clinical adverse events. Thirty nine girls (43.3 %) and 51 boys (56.7 %) with mean age of 3.34 ± 1.47 y were assessed. Sufficient sedation and completion of EEG were achieved in 70 % (N = 21) of chloral hydrate group, in 83.3 % (N = 25) of CH + H group and in 96.7 % (N = 29) of CH + P group (p = 0.02). Mild clinical adverse events including vomiting [16.7 % (N = 5) in CH, 6.7 % (N = 2) in CH + P, 6.7 % (N = 2) in CH + H], agitation in 3.3 % of CH + P (N = 1) group and mild transient hypotension in 3.3 % of CH + H (N = 1) group occurred. Safety of these three sedation regimens was not statistically significant different (p = 0.14). Combination of chloral hydrate-antihistamines can be used as the most effective and safe sedation regimen in drug induced sleep electroencephalography of kids.

Hardware enhance of brain computer interfaces

NASA Astrophysics Data System (ADS)

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

2015-05-01

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

Forward and inverse effects of the complete electrode model in neonatal EEG

PubMed Central

Lew, S.; Wolters, C. H.

2016-01-01

This paper investigates finite element method-based modeling in the context of neonatal electroencephalography (EEG). In particular, the focus lies on electrode boundary conditions. We compare the complete electrode model (CEM) with the point electrode model (PEM), which is the current standard in EEG. In the CEM, the voltage experienced by an electrode is modeled more realistically as the integral average of the potential distribution over its contact surface, whereas the PEM relies on a point value. Consequently, the CEM takes into account the subelectrode shunting currents, which are absent in the PEM. In this study, we aim to find out how the electrode voltage predicted by these two models differ, if standard size electrodes are attached to a head of a neonate. Additionally, we study voltages and voltage variation on electrode surfaces with two source locations: 1) next to the C6 electrode and 2) directly under the Fz electrode and the frontal fontanel. A realistic model of a neonatal head, including a skull with fontanels and sutures, is used. Based on the results, the forward simulation differences between CEM and PEM are in general small, but significant outliers can occur in the vicinity of the electrodes. The CEM can be considered as an integral part of the outer head model. The outcome of this study helps understanding volume conduction of neonatal EEG, since it enlightens the role of advanced skull and electrode modeling in forward and inverse computations. NEW & NOTEWORTHY The effect of the complete electrode model on electroencephalography forward and inverse computations is explored. A realistic neonatal head model, including a skull structure with fontanels and sutures, is used. The electrode and skull modeling differences are analyzed and compared with each other. The results suggest that the complete electrode model can be considered as an integral part of the outer head model. To achieve optimal source localization results, accurate electrode modeling might be necessary. PMID:27852731

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.

The use of routine EEG in acute ischemic stroke patients without seizures: generalized but not focal EEG pathology is associated with clinical deterioration.

PubMed

Wolf, Marc E; Ebert, Anne D; Chatzikonstantinou, Anastasios

2017-05-01

Specialized electroencephalography (EEG) methods have been used to provide clues about stroke features and prognosis. However, the value of routine EEG in stroke patients without (suspected) seizures has been somewhat neglected. We aimed to assess this in a group of acute ischemic stroke patients in regard to short-term prognosis and basic stroke features. We assessed routine (10-20) EEG findings in 69 consecutive acute ischemic stroke patients without seizures. Associations between EEG abnormalities and NIHSS scores, clinical improvement or deterioration as well as MRI stroke characteristics were evaluated. Mean age was 69 ± 18 years, 43 of the patients (62.3%) were men. Abnormal EEG was found in 40 patients (58%) and was associated with higher age (p = 0.021). The most common EEG pathology was focal slowing (30; 43.5%). No epileptiform potentials were found. Abnormal EEG in general and generalized or focal slowing in particular was significantly associated with higher NIHSS score on admission and discharge as well as with hemorrhagic transformation of the ischemic lesion. Abnormal EEG and generalized (but not focal) slowing were associated with clinical deterioration ( p = 0.036, p = 0.003). Patients with lacunar strokes had no EEG abnormalities. Abnormal EEG in general and generalized slowing in particular are associated with clinical deterioration after acute ischemic stroke. The study demonstrates the value of routine EEG as a simple diagnostic tool in the evaluation of stroke patients especially with regard to short-term prognosis.

Epileptic seizure classification of EEG time-series using rational discrete short-time fourier transform.

PubMed

Samiee, Kaveh; Kovács, Petér; Gabbouj, Moncef

2015-02-01

A system for epileptic seizure detection in electroencephalography (EEG) is described in this paper. One of the challenges is to distinguish rhythmic discharges from nonstationary patterns occurring during seizures. The proposed approach is based on an adaptive and localized time-frequency representation of EEG signals by means of rational functions. The corresponding rational discrete short-time Fourier transform (DSTFT) is a novel feature extraction technique for epileptic EEG data. A multilayer perceptron classifier is fed by the coefficients of the rational DSTFT in order to separate seizure epochs from seizure-free epochs. The effectiveness of the proposed method is compared with several state-of-art feature extraction algorithms used in offline epileptic seizure detection. The results of the comparative evaluations show that the proposed method outperforms competing techniques in terms of classification accuracy. In addition, it provides a compact representation of EEG time-series.

Recognizing the degree of human attention using EEG signals from mobile sensors.

PubMed

Liu, Ning-Han; Chiang, Cheng-Yu; Chu, Hsuan-Chin

2013-08-09

During the learning process, whether students remain attentive throughout instruction generally influences their learning efficacy. If teachers can instantly identify whether students are attentive they can be suitably reminded to remain focused, thereby improving their learning effects. Traditional teaching methods generally require that teachers observe students' expressions to determine whether they are attentively learning. However, this method is often inaccurate and increases the burden on teachers. With the development of electroencephalography (EEG) detection tools, mobile brainwave sensors have become mature and affordable equipment. Therefore, in this study, whether students are attentive or inattentive during instruction is determined by observing their EEG signals. Because distinguishing between attentiveness and inattentiveness is challenging, two scenarios were developed for this study to measure the subjects' EEG signals when attentive and inattentive. After collecting EEG data using mobile sensors, various common features were extracted from the raw data. A support vector machine (SVM) classifier was used to calculate and analyze these features to identify the combination of features that best indicates whether students are attentive. Based on the experiment results, the method proposed in this study provides a classification accuracy of up to 76.82%. The study results can be used as a reference for learning system designs in the future.

Quantifying and Characterizing Tonic Thermal Pain Across Subjects From EEG Data Using Random Forest Models.

PubMed

Vijayakumar, Vishal; Case, Michelle; Shirinpour, Sina; He, Bin

2017-12-01

Effective pain assessment and management strategies are needed to better manage pain. In addition to self-report, an objective pain assessment system can provide a more complete picture of the neurophysiological basis for pain. In this study, a robust and accurate machine learning approach is developed to quantify tonic thermal pain across healthy subjects into a maximum of ten distinct classes. A random forest model was trained to predict pain scores using time-frequency wavelet representations of independent components obtained from electroencephalography (EEG) data, and the relative importance of each frequency band to pain quantification is assessed. The mean classification accuracy for predicting pain on an independent test subject for a range of 1-10 is 89.45%, highest among existing state of the art quantification algorithms for EEG. The gamma band is the most important to both intersubject and intrasubject classification accuracy. The robustness and generalizability of the classifier are demonstrated. Our results demonstrate the potential of this tool to be used clinically to help us to improve chronic pain treatment and establish spectral biomarkers for future pain-related studies using EEG.

Study of interhemispheric asymmetries in electroencephalographic signals by frequency analysis

NASA Astrophysics Data System (ADS)

Zapata, J. F.; Garzón, J.

2011-01-01

This study provides a new method for the detection of interhemispheric asymmetries in patients with continuous video-electroencephalography (EEG) monitoring at Intensive Care Unit (ICU), using wavelet energy. We obtained the registration of EEG signals in 42 patients with different pathologies, and then we proceeded to perform signal processing using the Matlab program, we compared the abnormalities recorded in the report by the neurophysiologist, the images of each patient and the result of signals analysis with the Discrete Wavelet Transform (DWT). Conclusions: there exists correspondence between the abnormalities found in the processing of the signal with the clinical reports of findings in patients; according to previous conclusion, the methodology used can be a useful tool for diagnosis and early quantitative detection of interhemispheric asymmetries.

Older People’s Experiences of Mobility and Mood in an Urban Environment: A Mixed Methods Approach Using Electroencephalography (EEG) and Interviews

PubMed Central

Tilley, Sara; Neale, Chris; Patuano, Agnès; Cinderby, Steve

2017-01-01

There are concerns about mental wellbeing in later life in older people as the global population becomes older and more urbanised. Mobility in the built environment has a role to play in improving quality of life and wellbeing, as it facilitates independence and social interaction. Recent studies using neuroimaging methods in environmental psychology research have shown that different types of urban environments may be associated with distinctive patterns of brain activity, suggesting that we interact differently with varying environments. This paper reports on research that explores older people’s responses to urban places and their mobility in and around the built environment. The project aim was to understand how older people experience different urban environments using a mixed methods approach including electroencephalography (EEG), self-reported measures, and interview results. We found that older participants experience changing levels of “excitement”, “engagement” and “frustration” (as interpreted by proprietary EEG software) whilst walking between a busy built urban environment and an urban green space environment. These changes were further reflected in the qualitative themes that emerged from transcribed interviews undertaken one week post-walk. There has been no research to date that has directly assessed neural responses to an urban environment combined with qualitative interview analysis. A synergy of methods offers a deeper understanding of the changing moods of older people across time whilst walking in city settings. PMID:28165409

Preterm EEG: a multimodal neurophysiological protocol.

PubMed

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

2012-02-18

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

Epileptic encephalopathy with continuous spike-waves during sleep: the need for transition from childhood to adulthood medical care appears to be related to etiology.

PubMed

de Saint-Martin, Anne; Rudolf, Gabrielle; Seegmuller, Caroline; Valenti-Hirsch, Maria Paola; Hirsch, Edouard

2014-08-01

Epileptic encephalopathy with continuous diffuse spike-waves during slow-wave sleep (ECSWS) presents clinically with infrequent nocturnal focal seizures, atypical absences related to secondary bilateral synchrony, negative myoclonia, and atonic and rare generalized tonic-clonic seizures. The unique electroencephalography (EEG) pattern found in ECSWS consists of continuous, diffuse, bilateral spike-waves during slow-wave sleep. Despite the eventual disappearance of clinical seizures and EEG abnormalities by adolescence, the prognosis is guarded in most cases because of neuropsychological and behavioral deficits. ECSWS has a heterogeneous etiology (genetic, structural, and unknown). Because epilepsy and electroencephalography (EEG) abnormalities in epileptic encephalopathy with continuous diffuse spike-waves during slow-wave sleep (ECSWS) are self-limited and age related, the need for ongoing medical care and transition to adult care might be questioned. For adolescents in whom etiology remains unknown (possibly genetic) and who experience the disappearance of seizures and EEG abnormalities, there is rarely need for long-term neurologic follow-up, because often a relatively normal cognitive and social evolution follows. However, the majority of patients with structural and possibly "genetic syndromic" etiologies will have persistent cognitive deficits and will need suitable socioeducative care. Therefore, the transition process in ECSWS will depend mainly on etiology and its related features (epileptic active phase duration, and cognitive and behavioral evolution) and revolve around neuropsychological and social support rather than medical and pharmacologic follow-up. Wiley Periodicals, Inc. © 2014 International League Against Epilepsy.

Epilepsy, Anticonvulsants and Cognitive Functions in School Students.

ERIC Educational Resources Information Center

Keister, Douglas Charles

Research is reviewed on epilepsy and findings summarized in terms of intelligence, relationship between etiology and intelligence, seizure frequency, age of onset, duration, premorbid intelligence, and specific psychological defects, electroencephalography (EEG) and IQ, and learning. Among findings noted are that the widespread belief among…

Brain Oscillations during Semantic Evaluation of Speech

ERIC Educational Resources Information Center

Shahin, Antoine J.; Picton, Terence W.; Miller, Lee M.

2009-01-01

Changes in oscillatory brain activity have been related to perceptual and cognitive processes such as selective attention and memory matching. Here we examined brain oscillations, measured with electroencephalography (EEG), during a semantic speech processing task that required both lexically mediated memory matching and selective attention.…

Multiclass Posterior Probability Twin SVM for Motor Imagery EEG Classification.

PubMed

She, Qingshan; Ma, Yuliang; Meng, Ming; Luo, Zhizeng

2015-01-01

Motor imagery electroencephalography is widely used in the brain-computer interface systems. Due to inherent characteristics of electroencephalography signals, accurate and real-time multiclass classification is always challenging. In order to solve this problem, a multiclass posterior probability solution for twin SVM is proposed by the ranking continuous output and pairwise coupling in this paper. First, two-class posterior probability model is constructed to approximate the posterior probability by the ranking continuous output techniques and Platt's estimating method. Secondly, a solution of multiclass probabilistic outputs for twin SVM is provided by combining every pair of class probabilities according to the method of pairwise coupling. Finally, the proposed method is compared with multiclass SVM and twin SVM via voting, and multiclass posterior probability SVM using different coupling approaches. The efficacy on the classification accuracy and time complexity of the proposed method has been demonstrated by both the UCI benchmark datasets and real world EEG data from BCI Competition IV Dataset 2a, respectively.

FFT transformed quantitative EEG analysis of short term memory load.

PubMed

Singh, Yogesh; Singh, Jayvardhan; Sharma, Ratna; Talwar, Anjana

2015-07-01

The EEG is considered as building block of functional signaling in the brain. The role of EEG oscillations in human information processing has been intensively investigated. To study the quantitative EEG correlates of short term memory load as assessed through Sternberg memory test. The study was conducted on 34 healthy male student volunteers. The intervention consisted of Sternberg memory test, which runs on a version of the Sternberg memory scanning paradigm software on a computer. Electroencephalography (EEG) was recorded from 19 scalp locations according to 10-20 international system of electrode placement. EEG signals were analyzed offline. To overcome the problems of fixed band system, individual alpha frequency (IAF) based frequency band selection method was adopted. The outcome measures were FFT transformed absolute powers in the six bands at 19 electrode positions. Sternberg memory test served as model of short term memory load. Correlation analysis of EEG during memory task was reflected as decreased absolute power in Upper alpha band in nearly all the electrode positions; increased power in Theta band at Fronto-Temporal region and Lower 1 alpha band at Fronto-Central region. Lower 2 alpha, Beta and Gamma band power remained unchanged. Short term memory load has distinct electroencephalographic correlates resembling the mentally stressed state. This is evident from decreased power in Upper alpha band (corresponding to Alpha band of traditional EEG system) which is representative band of relaxed mental state. Fronto-temporal Theta power changes may reflect the encoding and execution of memory task.

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

PubMed

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

2007-01-01

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

Electroencephalographic characteristics of Iranian schizophrenia patients.

PubMed

Chaychi, Irman; Foroughipour, Mohsen; Haghir, Hossein; Talaei, Ali; Chaichi, Ashkan

2015-12-01

Schizophrenia is a prevalent psychiatric disease with heterogeneous causes that is diagnosed based on history and mental status examination. Applied electrophysiology is a non-invasive method to investigate the function of the involved brain areas. In a previously understudied population, we examined acute phase electroencephalography (EEG) records along with pertinent Positive and Negative Syndrome Scale (PANSS) and Mini Mental State Examination (MMSE) scores for each patient. Sixty-four hospitalized patients diagnosed to have schizophrenia in Ebn-e-Sina Hospital were included in this study. PANSS and MMSE were completed and EEG tracings for every patient were recorded. Also, EEG tracings were recorded for 64 matched individuals of the control group. Although the predominant wave pattern in both patients and controls was alpha, theta waves were almost exclusively found in eight (12.5 %) patients with schizophrenia. Pathological waves in schizophrenia patients were exclusively found in the frontal brain region, while identified pathological waves in controls were limited to the temporal region. No specific EEG finding supported laterality in schizophrenia patients. PANSS and MMSE scores were significantly correlated with specific EEG parameters (all P values <0.04). Patients with schizophrenia demonstrate specific EEG patterns and show a clear correlation between EEG parameters and PANSS and MMSE scores. These characteristics are not observed in all patients, which imply that despite an acceptable specificity, they are not applicable for the majority of schizophrenia patients. Any deduction drawn based on EEG and scoring systems is in need of larger studies incorporating more patients and using better functional imaging techniques for the brain.

EEG-Based Computer Aided Diagnosis of Autism Spectrum Disorder Using Wavelet, Entropy, and ANN

PubMed Central

AlSharabi, Khalil; Ibrahim, Sutrisno; Alsuwailem, Abdullah

2017-01-01

Autism spectrum disorder (ASD) is a type of neurodevelopmental disorder with core impairments in the social relationships, communication, imagination, or flexibility of thought and restricted repertoire of activity and interest. In this work, a new computer aided diagnosis (CAD) of autism ‎based on electroencephalography (EEG) signal analysis is investigated. The proposed method is based on discrete wavelet transform (DWT), entropy (En), and artificial neural network (ANN). DWT is used to decompose EEG signals into approximation and details coefficients to obtain EEG subbands. The feature vector is constructed by computing Shannon entropy values from each EEG subband. ANN classifies the corresponding EEG signal into normal or autistic based on the extracted features. The experimental results show the effectiveness of the proposed method for assisting autism diagnosis. A receiver operating characteristic (ROC) curve metric is used to quantify the performance of the proposed method. The proposed method obtained promising results tested using real dataset provided by King Abdulaziz Hospital, Jeddah, Saudi Arabia. PMID:28484720

Fusion of electroencephalographic dynamics and musical contents for estimating emotional responses in music listening.

PubMed

Lin, Yuan-Pin; Yang, Yi-Hsuan; Jung, Tzyy-Ping

2014-01-01

Electroencephalography (EEG)-based emotion classification during music listening has gained increasing attention nowadays due to its promise of potential applications such as musical affective brain-computer interface (ABCI), neuromarketing, music therapy, and implicit multimedia tagging and triggering. However, music is an ecologically valid and complex stimulus that conveys certain emotions to listeners through compositions of musical elements. Using solely EEG signals to distinguish emotions remained challenging. This study aimed to assess the applicability of a multimodal approach by leveraging the EEG dynamics and acoustic characteristics of musical contents for the classification of emotional valence and arousal. To this end, this study adopted machine-learning methods to systematically elucidate the roles of the EEG and music modalities in the emotion modeling. The empirical results suggested that when whole-head EEG signals were available, the inclusion of musical contents did not improve the classification performance. The obtained performance of 74~76% using solely EEG modality was statistically comparable to that using the multimodality approach. However, if EEG dynamics were only available from a small set of electrodes (likely the case in real-life applications), the music modality would play a complementary role and augment the EEG results from around 61-67% in valence classification and from around 58-67% in arousal classification. The musical timber appeared to replace less-discriminative EEG features and led to improvements in both valence and arousal classification, whereas musical loudness was contributed specifically to the arousal classification. The present study not only provided principles for constructing an EEG-based multimodal approach, but also revealed the fundamental insights into the interplay of the brain activity and musical contents in emotion modeling.

Fusion of electroencephalographic dynamics and musical contents for estimating emotional responses in music listening

PubMed Central

Lin, Yuan-Pin; Yang, Yi-Hsuan; Jung, Tzyy-Ping

2014-01-01

Electroencephalography (EEG)-based emotion classification during music listening has gained increasing attention nowadays due to its promise of potential applications such as musical affective brain-computer interface (ABCI), neuromarketing, music therapy, and implicit multimedia tagging and triggering. However, music is an ecologically valid and complex stimulus that conveys certain emotions to listeners through compositions of musical elements. Using solely EEG signals to distinguish emotions remained challenging. This study aimed to assess the applicability of a multimodal approach by leveraging the EEG dynamics and acoustic characteristics of musical contents for the classification of emotional valence and arousal. To this end, this study adopted machine-learning methods to systematically elucidate the roles of the EEG and music modalities in the emotion modeling. The empirical results suggested that when whole-head EEG signals were available, the inclusion of musical contents did not improve the classification performance. The obtained performance of 74~76% using solely EEG modality was statistically comparable to that using the multimodality approach. However, if EEG dynamics were only available from a small set of electrodes (likely the case in real-life applications), the music modality would play a complementary role and augment the EEG results from around 61–67% in valence classification and from around 58–67% in arousal classification. The musical timber appeared to replace less-discriminative EEG features and led to improvements in both valence and arousal classification, whereas musical loudness was contributed specifically to the arousal classification. The present study not only provided principles for constructing an EEG-based multimodal approach, but also revealed the fundamental insights into the interplay of the brain activity and musical contents in emotion modeling. PMID:24822035

Mobile Device Applications for the Visualization of Functional Connectivity Networks and EEG Electrodes: iBraiN and iBraiNEEG.

PubMed

Rojas, Gonzalo M; Fuentes, Jorge A; Gálvez, Marcelo

2016-01-01

Multiple functional MRI (fMRI)-based functional connectivity networks were obtained by Yeo et al. (2011), and the visualization of these complex networks is a difficult task. Also, the combination of functional connectivity networks determined by fMRI with electroencephalography (EEG) data could be a very useful tool. Mobile devices are becoming increasingly common among users, and for this reason, we describe here two applications for Android and iOS mobile devices: one that shows in an interactive way the seven Yeo functional connectivity networks, and another application that shows the relative position of 10-20 EEG electrodes with Yeo's seven functional connectivity networks.

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

PubMed

Sviatogor, I A; Mokhovikova, I A

2005-01-01

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

Multi-modal Patient Cohort Identification from EEG Report and Signal Data

PubMed Central

Goodwin, Travis R.; Harabagiu, Sanda M.

2016-01-01

Clinical electroencephalography (EEG) is the most important investigation in the diagnosis and management of epilepsies. An EEG records the electrical activity along the scalp and measures spontaneous electrical activity of the brain. Because the EEG signal is complex, its interpretation is known to produce moderate inter-observer agreement among neurologists. This problem can be addressed by providing clinical experts with the ability to automatically retrieve similar EEG signals and EEG reports through a patient cohort retrieval system operating on a vast archive of EEG data. In this paper, we present a multi-modal EEG patient cohort retrieval system called MERCuRY which leverages the heterogeneous nature of EEG data by processing both the clinical narratives from EEG reports as well as the raw electrode potentials derived from the recorded EEG signal data. At the core of MERCuRY is a novel multimodal clinical indexing scheme which relies on EEG data representations obtained through deep learning. The index is used by two clinical relevance models that we have generated for identifying patient cohorts satisfying the inclusion and exclusion criteria expressed in natural language queries. Evaluations of the MERCuRY system measured the relevance of the patient cohorts, obtaining MAP scores of 69.87% and a NDCG of 83.21%. PMID:28269938

A new method for quantifying the performance of EEG blind source separation algorithms by referencing a simultaneously recorded ECoG signal.

PubMed

Oosugi, Naoya; Kitajo, Keiichi; Hasegawa, Naomi; Nagasaka, Yasuo; Okanoya, Kazuo; Fujii, Naotaka

2017-09-01

Blind source separation (BSS) algorithms extract neural signals from electroencephalography (EEG) data. However, it is difficult to quantify source separation performance because there is no criterion to dissociate neural signals and noise in EEG signals. This study develops a method for evaluating BSS performance. The idea is neural signals in EEG can be estimated by comparison with simultaneously measured electrocorticography (ECoG). Because the ECoG electrodes cover the majority of the lateral cortical surface and should capture most of the original neural sources in the EEG signals. We measured real EEG and ECoG data and developed an algorithm for evaluating BSS performance. First, EEG signals are separated into EEG components using the BSS algorithm. Second, the EEG components are ranked using the correlation coefficients of the ECoG regression and the components are grouped into subsets based on their ranks. Third, canonical correlation analysis estimates how much information is shared between the subsets of the EEG components and the ECoG signals. We used our algorithm to compare the performance of BSS algorithms (PCA, AMUSE, SOBI, JADE, fastICA) via the EEG and ECoG data of anesthetized nonhuman primates. The results (Best case >JADE = fastICA >AMUSE = SOBI ≥ PCA >random separation) were common to the two subjects. To encourage the further development of better BSS algorithms, our EEG and ECoG data are available on our Web site (http://neurotycho.org/) as a common testing platform. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

'Is Going through Clinical Test a Headache?' An HRV Study and Descriptive Report of Subjective Experience of Undergoing EEG Testing.

PubMed

Kathrotia, Rajesh; Singh, Yogesh; Goel, Arun; Patil, Prashant

2016-03-01

To explore the heart rate variability (HRV) changes and subjective perception of undergoing electroencephalography (EEG). We conducted a study on 35 healthy male volunteers. The intervention consisted of placing 23 disc-type EEG electrodes of 5-7 mm diameter with long flexible lead according to international 10-20 system for the duration of 30 min, in a sitting position, on the scalp. The outcome measures were time and frequency domain parameters of HRV analysis and descriptive report of subjective experiences on a 3-point Likert scale. The perception of undergoing EEG ranged from pleasant to uneasy. For 13 (37%) participants it was soothing and relaxing, for 11 (31.5%) it was neutral and for the rest 11 (31.5%) it was uneasy and restrictive in nature. However, HRV analysis of the pre and post EEG, showed no statistically significant difference. In our study, the mixed subjective experience of undergoing EEG may be due to individual variation in the perception of the intervention. No difference in HRV parameters may be because of 2 possibilities. The first possibility is varied experiences of procedure with temporal progression. Same participants may have experienced 2 opposite extremes of experiences over and over again, which may have cancelled out sympathetic and parasympathetic responses. The second possibility may be that no stress is generated during clinical test.

Electroencephalogram of Healthy Horses During Inhaled Anesthesia.

PubMed

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

2016-01-01

Previous study of the diagnostic validity of electroencephalography (EEG) to detect abnormalities in equine cerebral cortical function relied on the administration of various drugs for sedation, induction, and maintenance of general anesthesia but used identical criteria to interpret recordings. To determine the effects of 2 inhalation anesthetics on the EEG of healthy horses. Six healthy horses. Prospective study. After the sole administration of one of either isoflurane or halothane at 1.2, 1.4, and 1.6 times the minimum alveolar concentration, EEG was recorded during controlled ventilation, spontaneous ventilation, and nerve stimulation. Burst suppression was observed with isoflurane, along with EEG events that resembled epileptiform discharges. Halothane results were variable between horses, with epileptiform-like discharges and bursts of theta, alpha, and beta recorded intermittently. One horse died and 2 were euthanized as the result of anesthesia-related complications. The results of this study indicate that the effects of halothane and isoflurane on EEG activity in the normal horse can be quite variable, even when used in the absence of other drugs. It is recommended that equine EEG be performed without the use of these inhalation anesthetics and that general anesthesia be induced and maintained by other contemporary means. Copyright © 2015 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine.

The psychology of neurofeedback: Clinical intervention even if applied placebo.

PubMed

Thibault, Robert T; Raz, Amir

2017-10-01

Advocates of neurofeedback make bold claims concerning brain regulation, treatment of disorders, and mental health. Decades of research and thousands of peer-reviewed publications support neurofeedback using electroencephalography (EEG-nf); yet, few experiments isolate the act of receiving feedback from a specific brain signal as a necessary precursor to obtain the purported benefits. Moreover, while psychosocial parameters including participant motivation and expectation, rather than neurobiological substrates, seem to fuel clinical improvement across a wide range of disorders, for-profit clinics continue to sprout across North America and Europe. Here, we highlight the tenuous evidence supporting EEG-nf and sketch out the weaknesses of this approach. We challenge classic arguments often articulated by proponents of EEG-nf and underscore how psychologists and mental health professionals stand to benefit from studying the ubiquitous placebo influences that likely drive these treatment outcomes. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Classifying Drivers' Cognitive Load Using EEG Signals.

PubMed

Barua, Shaibal; Ahmed, Mobyen Uddin; Begum, Shahina

2017-01-01

A growing traffic safety issue is the effect of cognitive loading activities on traffic safety and driving performance. To monitor drivers' mental state, understanding cognitive load is important since while driving, performing cognitively loading secondary tasks, for example talking on the phone, can affect the performance in the primary task, i.e. driving. Electroencephalography (EEG) is one of the reliable measures of cognitive load that can detect the changes in instantaneous load and effect of cognitively loading secondary task. In this driving simulator study, 1-back task is carried out while the driver performs three different simulated driving scenarios. This paper presents an EEG based approach to classify a drivers' level of cognitive load using Case-Based Reasoning (CBR). The results show that for each individual scenario as well as using data combined from the different scenarios, CBR based system achieved approximately over 70% of classification accuracy.

Stable Scalp EEG Spatiospectral Patterns Across Paradigms Estimated by Group ICA.

PubMed

Labounek, René; Bridwell, David A; Mareček, Radek; Lamoš, Martin; Mikl, Michal; Slavíček, Tomáš; Bednařík, Petr; Baštinec, Jaromír; Hluštík, Petr; Brázdil, Milan; Jan, Jiří

2018-01-01

Electroencephalography (EEG) oscillations reflect the superposition of different cortical sources with potentially different frequencies. Various blind source separation (BSS) approaches have been developed and implemented in order to decompose these oscillations, and a subset of approaches have been developed for decomposition of multi-subject data. Group independent component analysis (Group ICA) is one such approach, revealing spatiospectral maps at the group level with distinct frequency and spatial characteristics. The reproducibility of these distinct maps across subjects and paradigms is relatively unexplored domain, and the topic of the present study. To address this, we conducted separate group ICA decompositions of EEG spatiospectral patterns on data collected during three different paradigms or tasks (resting-state, semantic decision task and visual oddball task). K-means clustering analysis of back-reconstructed individual subject maps demonstrates that fourteen different independent spatiospectral maps are present across the different paradigms/tasks, i.e. they are generally stable.

Cot-side electro-encephalography and interstitial glucose monitoring during insulin-induced hypoglycaemia in newborn lambs.

PubMed

Harris, Deborah L; Battin, Malcolm R; Williams, Chris E; Weston, Philip J; Harding, Jane E

2009-01-01

The optimal approach to detection and management of neonatal hypoglycaemia remains unclear. We sought to demonstrate whether electro-encephalography (EEG) changes could be detected on the amplitude-integrated EEG monitor during induced hypoglycaemia in newborn lambs, and also to determine the accuracy of continuously measured interstitial glucose in this situation. Needle electrodes were placed in the P3-P4, O1-O2 montages. The interstitial glucose sensor was placed subcutaneously. After 30 min baseline recordings, hypoglycaemia was induced by insulin infusion and blood glucose levels were monitored every 5 min. The infusion was adjusted to reduce blood glucose levels by 0.5 mmol/l every 15 min and then maintain a blood glucose level <1.0 mmol/l for 4 h. EEG parameters analysed included amplitude, continuity and spectral edge frequency. The interstitial and blood glucose levels were compared. All lambs (n = 15, aged 3-11 days) became hypoglycaemic, with median blood glucose levels falling from 6.5 to 1.0 mmol/l, p < 0.0001. There were no detectable changes in any of the measured EEG parameters related to hypoglycaemia, although seizures occurred in 2 lambs. There was moderate agreement between the intermittent blood glucose and continuous interstitial glucose measurements in the baseline, decline, and hypoglycaemia periods (mean difference -0.7 mmol/l, 95% confidence interval, CI, -2.8 to 1.4 mmol/l). However, agreement was poor during reversal of hypoglycaemia (mean difference 4.5 mmol/l, 95% CI -1.1 to 10.7 mmol/l). The cot-side EEG may not be a useful clinical tool in the detection of neurological changes induced by hypoglycaemia. However, continuous interstitial glucose monitoring may be useful in the management of babies at risk of hypoglycaemia. (c) 2008 S. Karger AG, Basel.

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

PubMed

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

2015-01-01

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

Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals.

PubMed

Engemann, Denis A; Gramfort, Alexandre

2015-03-01

Magnetoencephalography and electroencephalography (M/EEG) measure non-invasively the weak electromagnetic fields induced by post-synaptic neural currents. The estimation of the spatial covariance of the signals recorded on M/EEG sensors is a building block of modern data analysis pipelines. Such covariance estimates are used in brain-computer interfaces (BCI) systems, in nearly all source localization methods for spatial whitening as well as for data covariance estimation in beamformers. The rationale for such models is that the signals can be modeled by a zero mean Gaussian distribution. While maximizing the Gaussian likelihood seems natural, it leads to a covariance estimate known as empirical covariance (EC). It turns out that the EC is a poor estimate of the true covariance when the number of samples is small. To address this issue the estimation needs to be regularized. The most common approach downweights off-diagonal coefficients, while more advanced regularization methods are based on shrinkage techniques or generative models with low rank assumptions: probabilistic PCA (PPCA) and factor analysis (FA). Using cross-validation all of these models can be tuned and compared based on Gaussian likelihood computed on unseen data. We investigated these models on simulations, one electroencephalography (EEG) dataset as well as magnetoencephalography (MEG) datasets from the most common MEG systems. First, our results demonstrate that different models can be the best, depending on the number of samples, heterogeneity of sensor types and noise properties. Second, we show that the models tuned by cross-validation are superior to models with hand-selected regularization. Hence, we propose an automated solution to the often overlooked problem of covariance estimation of M/EEG signals. The relevance of the procedure is demonstrated here for spatial whitening and source localization of MEG signals. Copyright © 2015 Elsevier Inc. All rights reserved.

Quantitative EEG during REM and NREM sleep in combat-exposed veterans with and without Posttraumatic Stress Disorder

PubMed Central

Cohen, Daniel J.; Begley, Amy; Alman, Jennie J.; Cashmere, J. David; Pietrone, Regina N.; Seres, Robert J.; Germain, Anne

2012-01-01

Summary Sleep disturbances are a hallmark feature of posttraumatic stress disorder (PTSD), and associated with poor clinical outcomes. Few studies have examined sleep quantitative electroencephalography (qEEG), a technique able to detect subtle differences polysomnography does not capture. We hypothesized greater high-frequency qEEG would reflect “hyperarousal” in in combat veterans with PTSD (n=16) compared to veterans without PTSD (n=13). EEG power in traditional EEG frequency bands was computed for artifact-free sleep epochs across an entire night. Correlations were performed between qEEG and ratings of PTSD symptoms and combat exposure. The groups did not differ significantly in whole night qEEG measures for either REM or NREM. Non-significant medium effect sizes suggest less REM beta (opposite to our hypothesis), less REM and NREM sigma, and more NREM gamma in combat veterans with PTSD. Positive correlations were found between combat exposure and NREM beta (PTSD group only), and REM and NREM sigma (non-PTSD group only). Results did not support global hyperarousal in PTSD as indexed by increased beta qEEG activity. The correlation of sigma activity with combat exposure in those without PTSD, and the non-significant trend towards less sigma activity during both REM and NREM sleep in combat veterans with PTSD suggests that differential information processing during sleep may characterize combat-exposed military veterans with and without PTSD. PMID:22845675

Compliant head probe for positioning electroencephalography electrodes and near-infrared spectroscopy optodes

NASA Astrophysics Data System (ADS)

Giacometti, Paolo; Diamond, Solomon G.

2013-02-01

A noninvasive head probe that combines near-infrared spectroscopy (NIRS) and electroencephalography (EEG) for simultaneous measurement of neural dynamics and hemodynamics in the brain is presented. It is composed of a compliant expandable mechanism that accommodates a wide range of head size variation and an elastomeric web that maintains uniform sensor contact pressure on the scalp as the mechanism expands and contracts. The design is intended to help maximize optical and electrical coupling and to maintain stability during head movement. Positioning electrodes at the inion, nasion, central, and preauricular fiducial locations mechanically shapes the probe to place 64 NIRS optodes and 65 EEG electrodes following the 10-5 scalp coordinates. The placement accuracy, precision, and scalp pressure uniformity of the sensors are evaluated. A root-mean-squared (RMS) positional precision of 0.89±0.23 mm, percent arc subdivision RMS accuracy of 0.19±0.15%, and mean normal force on the scalp of 2.28±0.88 N at 5 mm displacement were found. Geometric measurements indicate that the probe will accommodate the full range of adult head sizes. The placement accuracy, precision, and uniformity of sensor contact pressure of the proposed head probe are important determinants of data quality in noninvasive brain monitoring with simultaneous NIRS-EEG.

Multichannel wearable system dedicated for simultaneous electroencephalography/near-infrared spectroscopy real-time data acquisitions

NASA Astrophysics Data System (ADS)

Lareau, Etienne; Lesage, Frederic; Pouliot, Philippe; Nguyen, Dang; Le Lan, Jerome; Sawan, Mohamad

2011-09-01

Functional neuroimaging is becoming a valuable tool in cognitive research and clinical applications. The clinical context brings specific constraints that include the requirement of a high channel count to cover the whole head, high sensitivity for single event detection, and portability for long-term bedside monitoring. For epilepsy and stroke monitoring, the combination of electroencephalography (EEG) and functional near-infrared spectroscopy (NIRS) is expected to provide useful clinical information, and efforts have been deployed to create prototypes able to simultaneously acquire both measurement modalities. However, to the best of our knowledge, existing systems lack portability, NIRS sensitivity, or have low channel count. We present a battery-powered, portable system with potentially up to 32 EEG channels, 32 NIRS light sources, and 32 detectors. Avalanche photodiodes allow for high NIRS sensitivity and the autonomy of the system is over 24 h. A reduced channel count prototype with 8 EEG channels, 8 sources, and 8 detectors was tested on phantoms. Further validation was done on five healthy adults using a visual stimulation protocol to detect local hemodynamic changes and visually evoked potentials. Results show good concordance with literature regarding functional activations and suggest sufficient performance for clinical use, provided some minor adjustments were made.

Graph theory network function in Parkinson's disease assessed with electroencephalography.

PubMed

Utianski, Rene L; Caviness, John N; van Straaten, Elisabeth C W; Beach, Thomas G; Dugger, Brittany N; Shill, Holly A; Driver-Dunckley, Erika D; Sabbagh, Marwan N; Mehta, Shyamal; Adler, Charles H; Hentz, Joseph G

2016-05-01

To determine what differences exist in graph theory network measures derived from electroencephalography (EEG), between Parkinson's disease (PD) patients who are cognitively normal (PD-CN) and matched healthy controls; and between PD-CN and PD dementia (PD-D). EEG recordings were analyzed via graph theory network analysis to quantify changes in global efficiency and local integration. This included minimal spanning tree analysis. T-tests and correlations were used to assess differences between groups and assess the relationship with cognitive performance. Network measures showed increased local integration across all frequency bands between control and PD-CN; in contrast, decreased local integration occurred in PD-D when compared to PD-CN in the alpha1 frequency band. Differences found in PD-MCI mirrored PD-D. Correlations were found between network measures and assessments of global cognitive performance in PD. Our results reveal distinct patterns of band and network measure type alteration and breakdown for PD, as well as with cognitive decline in PD. These patterns suggest specific ways that interaction between cortical areas becomes abnormal and contributes to PD symptoms at various stages. Graph theory analysis by EEG suggests that network alteration and breakdown are robust attributes of PD cortical dysfunction pathophysiology. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

BLINKER: Automated Extraction of Ocular Indices from EEG Enabling Large-Scale Analysis.

PubMed

Kleifges, Kelly; Bigdely-Shamlo, Nima; Kerick, Scott E; Robbins, Kay A

2017-01-01

Electroencephalography (EEG) offers a platform for studying the relationships between behavioral measures, such as blink rate and duration, with neural correlates of fatigue and attention, such as theta and alpha band power. Further, the existence of EEG studies covering a variety of subjects and tasks provides opportunities for the community to better characterize variability of these measures across tasks and subjects. We have implemented an automated pipeline (BLINKER) for extracting ocular indices such as blink rate, blink duration, and blink velocity-amplitude ratios from EEG channels, EOG channels, and/or independent components (ICs). To illustrate the use of our approach, we have applied the pipeline to a large corpus of EEG data (comprising more than 2000 datasets acquired at eight different laboratories) in order to characterize variability of certain ocular indicators across subjects. We also investigate dependence of ocular indices on task in a shooter study. We have implemented our algorithms in a freely available MATLAB toolbox called BLINKER. The toolbox, which is easy to use and can be applied to collections of data without user intervention, can automatically discover which channels or ICs capture blinks. The tools extract blinks, calculate common ocular indices, generate a report for each dataset, dump labeled images of the individual blinks, and provide summary statistics across collections. Users can run BLINKER as a script or as a plugin for EEGLAB. The toolbox is available at https://github.com/VisLab/EEG-Blinks. User documentation and examples appear at http://vislab.github.io/EEG-Blinks/.

[Are subcortical signs in the EEG a reliable indication of brain stem displacement and impaction processes by intracranial space-occupying processes? A comparative computer tomography-electroencephalography study].

PubMed

Zettler, H; Järisch, M; Leonhard, T

1985-01-01

Within the scope of an elektroencephalographic-computertomographic comperative study carried out in 430 patients, the concurrence of secondary brain stem damage due to mass displacement and herniation processes and parroxysmal generalised slow activity in the EEG ("intermittant frontal delta rhythms", "projected discharges", "subcortical signs") in intracranial space-occupying processes were studied among others. The occurrence of the EEG pattern was independent of the presence of brain stem displacements in about 20 and 25 per cent, respectively, of the 152 patients with supratentorial space occupations. The absence of the characteristics on 80 per cent of the patients with clear CT criteria for a secondary brain stem impairment shows that it is not suitable as a warning sign of an imminent intracranial decompensation and that in particular from the non-occurrence in the EEG no contribution to the operative risk and to the choice of the time of the operation can be derived. A relation between the occurrence of paroxysmal slow activity and the acuity of the course of the disease or the degree of malignity of cerebral tumours could not be verified. Possible causes of the inconstant occurrence of this EEG pattern in brain stem alterations are discussed.

Spatial and temporal EEG dynamics of dual-task driving performance

PubMed Central

2011-01-01

Background Driver distraction is a significant cause of traffic accidents. The aim of this study is to investigate Electroencephalography (EEG) dynamics in relation to distraction during driving. To study human cognition under a specific driving task, simulated real driving using virtual reality (VR)-based simulation and designed dual-task events are built, which include unexpected car deviations and mathematics questions. Methods We designed five cases with different stimulus onset asynchrony (SOA) to investigate the distraction effects between the deviations and equations. The EEG channel signals are first converted into separated brain sources by independent component analysis (ICA). Then, event-related spectral perturbation (ERSP) changes of the EEG power spectrum are used to evaluate brain dynamics in time-frequency domains. Results Power increases in the theta and beta bands are observed in relation with distraction effects in the frontal cortex. In the motor area, alpha and beta power suppressions are also observed. All of the above results are consistently observed across 15 subjects. Additionally, further analysis demonstrates that response time and multiple cortical EEG power both changed significantly with different SOA. Conclusions This study suggests that theta power increases in the frontal area is related to driver distraction and represents the strength of distraction in real-life situations. PMID:21332977

Neuroligin 3 R451C mutation alters electroencephalography spectral activity in an animal model of autism spectrum disorders.

PubMed

Liu, Jackie J; Grace, Kevin P; Horner, Richard L; Cortez, Miguel A; Shao, Yiwen; Jia, Zhengping

2017-04-07

Human studies demonstrate that sleep impairment is a concurrent comorbidity of autism spectrum disorders (ASD), but its etiology remains largely uncertain. One of the prominent theories of ASD suggests that an imbalance in synaptic excitation/inhibition may contribute to various aspects of ASD, including sleep impairments. Following the identification of Nlgn3 R451C mutation in patients with ASD, its effects on synaptic transmission and social behaviours have been examined extensively in the mouse model. However, the contributory role of this mutation to sleep impairments in ASD remains unknown. In this study, we showed that Nlgn3 R451C knock-in mice, an established genetic model for ASD, exhibited normal duration and distribution of sleep/wake states but significantly altered electroencephalography (EEG) power spectral profiles for wake and sleep.

Neural signatures of economic parameters during decision-making: a functional MRI (FMRI), electroencephalography (EEG) and autonomic monitoring study.

PubMed

Minati, Ludovico; Grisoli, Marina; Franceschetti, Silvana; Epifani, Francesca; Granvillano, Alice; Medford, Nick; Harrison, Neil A; Piacentini, Sylvie; Critchley, Hugo D

2012-01-01

Adaptive behaviour requires an ability to obtain rewards by choosing between different risky options. Financial gambles can be used to study effective decision-making experimentally, and to distinguish processes involved in choice option evaluation from outcome feedback and other contextual factors. Here, we used a paradigm where participants evaluated 'mixed' gambles, each presenting a potential gain and a potential loss and an associated variable outcome probability. We recorded neural responses using autonomic monitoring, electroencephalography (EEG) and functional neuroimaging (fMRI), and used a univariate, parametric design to test for correlations with the eleven economic parameters that varied across gambles, including expected value (EV) and amount magnitude. Consistent with behavioural economic theory, participants were risk-averse. Gamble evaluation generated detectable autonomic responses, but only weak correlations with outcome uncertainty were found, suggesting that peripheral autonomic feedback does not play a major role in this task. Long-latency stimulus-evoked EEG potentials were sensitive to expected gain and expected value, while alpha-band power reflected expected loss and amount magnitude, suggesting parallel representations of distinct economic qualities in cortical activation and central arousal. Neural correlates of expected value representation were localized using fMRI to ventromedial prefrontal cortex, while the processing of other economic parameters was associated with distinct patterns across lateral prefrontal, cingulate, insula and occipital cortices including default-mode network and early visual areas. These multimodal data provide complementary evidence for distributed substrates of choice evaluation across multiple, predominantly cortical, brain systems wherein distinct regions are preferentially attuned to specific economic features. Our findings extend biologically-plausible models of risky decision-making while providing potential biomarkers of economic representations that can be applied to the study of deficits in motivational behaviour in neurological and psychiatric patients.

An Integrated Model of Emotional Problems, Beta Power of Electroencephalography, and Low Frequency of Heart Rate Variability after Childhood Trauma in a Non-Clinical Sample: A Path Analysis Study.

PubMed

Jin, Min Jin; Kim, Ji Sun; Kim, Sungkean; Hyun, Myoung Ho; Lee, Seung-Hwan

2017-01-01

Childhood trauma is known to be related to emotional problems, quantitative electroencephalography (EEG) indices, and heart rate variability (HRV) indices in adulthood, whereas directions among these factors have not been reported yet. This study aimed to evaluate pathway models in young and healthy adults: (1) one with physiological factors first and emotional problems later in adulthood as results of childhood trauma and (2) one with emotional problems first and physiological factors later. A total of 103 non-clinical volunteers were included. Self-reported psychological scales, including the Childhood Trauma Questionnaire (CTQ), State-Trait Anxiety Inventory, Beck Depression Inventory, and Affective Lability Scale were administered. For physiological evaluation, EEG record was performed during resting eyes closed condition in addition to the resting-state HRV, and the quantitative power analyses of eight EEG bands and three HRV components were calculated in the frequency domain. After a normality test, Pearson's correlation analysis to make path models and path analyses to examine them were conducted. The CTQ score was significantly correlated with depression, state and trait anxiety, affective lability, and HRV low-frequency (LF) power. LF power was associated with beta2 (18-22 Hz) power that was related to affective lability. Affective lability was associated with state anxiety, trait anxiety, and depression. Based on the correlation and the hypothesis, two models were composed: a model with pathways from CTQ score to affective lability, and a model with pathways from CTQ score to LF power. The second model showed significantly better fit than the first model (AIC model1  = 63.403 > AIC model2  = 46.003), which revealed that child trauma could affect emotion, and then physiology. The specific directions of relationships among emotions, the EEG, and HRV in adulthood after childhood trauma was discussed.

Therapeutic Effects of Rivastigmine and Alfa-Lipoic Acid Combination in the Charles Bonnet Syndrome: Electroencephalography Correlates.

PubMed

Hanoglu, Lutfu; Yildiz, Sultan; Polat, Burcu; Demirci, Sema; Tavli, Ahmet Mithat; Yilmaz, Nesrin; Yulug, Burak

2016-01-01

Charles Bonnet Syndrome (CBS) is a rare clinical condition which is characterized by complex hallucinations in visually impaired patients. The pathophysiology of this disorder remains largely unknown, and there is still no proven treatment for this disease. In our study, we aimed to investigate the neural activity through Electroencephalography (EEG) power and evaluate the effect of rivastigmine in combination with alpha-lipoic acid on hallucination in two CBS patients with diabetic retinopathy. EEG data was recorded with standard routine EEG protocols for both patients in our electrophysiological research laboratory (REMER Clinical Electrophysiology and Neuromodulation Research and Application Laboratory) with Brain Vision Recorder (Brainproduct, Munich, Germany). All spectral analyses were processed by BrainVision Analyzer 2 (Brainproduct, Munich, Germany, 2.0.4 Version) in 128 Hz sample rates and the EEG recording and analysis was performed before the administration of rivastigmine (4.5 mg/daily and five patch daily for the first and second patients, respectively) in combination with alpha-lipoic acid (600 mg/daily) for both patients while they were not hallucinated during the time period recordings. Based on our measurement protocol, we have compared the patients in the study group with the three control subjects who were found to be normal except of visual disturbances secondary to significant diabetic retinopathy. Highest theta power values were found in right occipital and left temporo-parietal regions for first and second CBS patients, respectively. Additionally, power spectra were lower in two cases as compared to their control groups in the alpha band for all electrodes. We have also shown that acid rivastigmine in combination with alpha-lipoic exerted significant anti-hallucinatory efficiency. Our present findings could support the hypothesis that increased activation of specific areas in the source monitoring system plays an important role in the pathogenesis of CBS. In addition, rivastigmine in combination with alpha-lipoic acid could be a new valuable option for CBS patients.

An Integrated Model of Emotional Problems, Beta Power of Electroencephalography, and Low Frequency of Heart Rate Variability after Childhood Trauma in a Non-Clinical Sample: A Path Analysis Study

PubMed Central

Jin, Min Jin; Kim, Ji Sun; Kim, Sungkean; Hyun, Myoung Ho; Lee, Seung-Hwan

2018-01-01

Childhood trauma is known to be related to emotional problems, quantitative electroencephalography (EEG) indices, and heart rate variability (HRV) indices in adulthood, whereas directions among these factors have not been reported yet. This study aimed to evaluate pathway models in young and healthy adults: (1) one with physiological factors first and emotional problems later in adulthood as results of childhood trauma and (2) one with emotional problems first and physiological factors later. A total of 103 non-clinical volunteers were included. Self-reported psychological scales, including the Childhood Trauma Questionnaire (CTQ), State–Trait Anxiety Inventory, Beck Depression Inventory, and Affective Lability Scale were administered. For physiological evaluation, EEG record was performed during resting eyes closed condition in addition to the resting-state HRV, and the quantitative power analyses of eight EEG bands and three HRV components were calculated in the frequency domain. After a normality test, Pearson’s correlation analysis to make path models and path analyses to examine them were conducted. The CTQ score was significantly correlated with depression, state and trait anxiety, affective lability, and HRV low-frequency (LF) power. LF power was associated with beta2 (18–22 Hz) power that was related to affective lability. Affective lability was associated with state anxiety, trait anxiety, and depression. Based on the correlation and the hypothesis, two models were composed: a model with pathways from CTQ score to affective lability, and a model with pathways from CTQ score to LF power. The second model showed significantly better fit than the first model (AICmodel1 = 63.403 > AICmodel2 = 46.003), which revealed that child trauma could affect emotion, and then physiology. The specific directions of relationships among emotions, the EEG, and HRV in adulthood after childhood trauma was discussed. PMID:29403401

Cortical and Subcortical Coordination of Visual Spatial Attention Revealed by Simultaneous EEG-fMRI Recording.

PubMed

Green, Jessica J; Boehler, Carsten N; Roberts, Kenneth C; Chen, Ling-Chia; Krebs, Ruth M; Song, Allen W; Woldorff, Marty G

2017-08-16

Visual spatial attention has been studied in humans with both electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) individually. However, due to the intrinsic limitations of each of these methods used alone, our understanding of the systems-level mechanisms underlying attentional control remains limited. Here, we examined trial-to-trial covariations of concurrently recorded EEG and fMRI in a cued visual spatial attention task in humans, which allowed delineation of both the generators and modulators of the cue-triggered event-related oscillatory brain activity underlying attentional control function. The fMRI activity in visual cortical regions contralateral to the cued direction of attention covaried positively with occipital gamma-band EEG, consistent with activation of cortical regions representing attended locations in space. In contrast, fMRI activity in ipsilateral visual cortical regions covaried inversely with occipital alpha-band oscillations, consistent with attention-related suppression of the irrelevant hemispace. Moreover, the pulvinar nucleus of the thalamus covaried with both of these spatially specific, attention-related, oscillatory EEG modulations. Because the pulvinar's neuroanatomical geometry makes it unlikely to be a direct generator of the scalp-recorded EEG, these covariational patterns appear to reflect the pulvinar's role as a regulatory control structure, sending spatially specific signals to modulate visual cortex excitability proactively. Together, these combined EEG/fMRI results illuminate the dynamically interacting cortical and subcortical processes underlying spatial attention, providing important insight not realizable using either method alone. SIGNIFICANCE STATEMENT Noninvasive recordings of changes in the brain's blood flow using functional magnetic resonance imaging and electrical activity using electroencephalography in humans have individually shown that shifting attention to a location in space produces spatially specific changes in visual cortex activity in anticipation of a stimulus. The mechanisms controlling these attention-related modulations of sensory cortex, however, are poorly understood. Here, we recorded these two complementary measures of brain activity simultaneously and examined their trial-to-trial covariations to gain insight into these attentional control mechanisms. This multi-methodological approach revealed the attention-related coordination of visual cortex modulation by the subcortical pulvinar nucleus of the thalamus while also disentangling the mechanisms underlying the attentional enhancement of relevant stimulus input and those underlying the concurrent suppression of irrelevant input. Copyright © 2017 the authors 0270-6474/17/377803-08$15.00/0.

Single-channel in-ear-EEG detects the focus of auditory attention to concurrent tone streams and mixed speech.

PubMed

Fiedler, Lorenz; Wöstmann, Malte; Graversen, Carina; Brandmeyer, Alex; Lunner, Thomas; Obleser, Jonas

2017-06-01

Conventional, multi-channel scalp electroencephalography (EEG) allows the identification of the attended speaker in concurrent-listening ('cocktail party') scenarios. This implies that EEG might provide valuable information to complement hearing aids with some form of EEG and to install a level of neuro-feedback. To investigate whether a listener's attentional focus can be detected from single-channel hearing-aid-compatible EEG configurations, we recorded EEG from three electrodes inside the ear canal ('in-Ear-EEG') and additionally from 64 electrodes on the scalp. In two different, concurrent listening tasks, participants (n  =  7) were fitted with individualized in-Ear-EEG pieces and were either asked to attend to one of two dichotically-presented, concurrent tone streams or to one of two diotically-presented, concurrent audiobooks. A forward encoding model was trained to predict the EEG response at single EEG channels. Each individual participants' attentional focus could be detected from single-channel EEG response recorded from short-distance configurations consisting only of a single in-Ear-EEG electrode and an adjacent scalp-EEG electrode. The differences in neural responses to attended and ignored stimuli were consistent in morphology (i.e. polarity and latency of components) across subjects. In sum, our findings show that the EEG response from a single-channel, hearing-aid-compatible configuration provides valuable information to identify a listener's focus of attention.

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

Magnetocardiography and magnetoencephalography measurements at room temperature using tunnel magneto-resistance sensors

NASA Astrophysics Data System (ADS)

Fujiwara, Kosuke; Oogane, Mikihiko; Kanno, Akitake; Imada, Masahiro; Jono, Junichi; Terauchi, Takashi; Okuno, Tetsuo; Aritomi, Yuuji; Morikawa, Masahiro; Tsuchida, Masaaki; Nakasato, Nobukazu; Ando, Yasuo

2018-02-01

Magnetocardiography (MCG) and magnetoencephalography (MEG) signals were detected at room temperature using tunnel magneto-resistance (TMR) sensors. TMR sensors developed with low-noise amplifier circuits detected the MCG R wave without averaging, and the QRS complex was clearly observed with averaging at a high signal-to-noise ratio. Spatial mapping of the MCG was also achieved. Averaging of MEG signals triggered by electroencephalography (EEG) clearly observed the phase inversion of the alpha rhythm with a correlation coefficient as high as 0.7 between EEG and MEG.

Estimating cognitive workload using wavelet entropy-based features during an arithmetic task.

PubMed

Zarjam, Pega; Epps, Julien; Chen, Fang; Lovell, Nigel H

2013-12-01

Electroencephalography (EEG) has shown promise as an indicator of cognitive workload; however, precise workload estimation is an ongoing research challenge. In this investigation, seven levels of workload were induced using an arithmetic task, and the entropy of wavelet coefficients extracted from EEG signals is shown to distinguish all seven levels. For a subject-independent multi-channel classification scheme, the entropy features achieved high accuracy, up to 98% for channels from the frontal lobes, in the delta frequency band. This suggests that a smaller number of EEG channels in only one frequency band can be deployed for an effective EEG-based workload classification system. Together with analysis based on phase locking between channels, these results consistently suggest increased synchronization of neural responses for higher load levels. Copyright © 2013 Elsevier Ltd. All rights reserved.

A Unified Fisher's Ratio Learning Method for Spatial Filter Optimization.

PubMed

Li, Xinyang; Guan, Cuntai; Zhang, Haihong; Ang, Kai Keng

To detect the mental task of interest, spatial filtering has been widely used to enhance the spatial resolution of electroencephalography (EEG). However, the effectiveness of spatial filtering is undermined due to the significant nonstationarity of EEG. Based on regularization, most of the conventional stationary spatial filter design methods address the nonstationarity at the cost of the interclass discrimination. Moreover, spatial filter optimization is inconsistent with feature extraction when EEG covariance matrices could not be jointly diagonalized due to the regularization. In this paper, we propose a novel framework for a spatial filter design. With Fisher's ratio in feature space directly used as the objective function, the spatial filter optimization is unified with feature extraction. Given its ratio form, the selection of the regularization parameter could be avoided. We evaluate the proposed method on a binary motor imagery data set of 16 subjects, who performed the calibration and test sessions on different days. The experimental results show that the proposed method yields improvement in classification performance for both single broadband and filter bank settings compared with conventional nonunified methods. We also provide a systematic attempt to compare different objective functions in modeling data nonstationarity with simulation studies.To detect the mental task of interest, spatial filtering has been widely used to enhance the spatial resolution of electroencephalography (EEG). However, the effectiveness of spatial filtering is undermined due to the significant nonstationarity of EEG. Based on regularization, most of the conventional stationary spatial filter design methods address the nonstationarity at the cost of the interclass discrimination. Moreover, spatial filter optimization is inconsistent with feature extraction when EEG covariance matrices could not be jointly diagonalized due to the regularization. In this paper, we propose a novel framework for a spatial filter design. With Fisher's ratio in feature space directly used as the objective function, the spatial filter optimization is unified with feature extraction. Given its ratio form, the selection of the regularization parameter could be avoided. We evaluate the proposed method on a binary motor imagery data set of 16 subjects, who performed the calibration and test sessions on different days. The experimental results show that the proposed method yields improvement in classification performance for both single broadband and filter bank settings compared with conventional nonunified methods. We also provide a systematic attempt to compare different objective functions in modeling data nonstationarity with simulation studies.

Selective mutism and abnormal electroencephalography (EEG) tracings.

PubMed

Politi, Keren; Kivity, Sara; Goldberg-Stern, Hadassa; Halevi, Ayelet; Shuper, Avinoam

2011-11-01

Epileptic discharges are not considered a part of the clinical picture of selective mutism, and electroencephalography is generally not recommended in its work-up. This report describes 6 children with selective mutism who were found to have a history of epilepsy and abnormal interictal or subclinical electroencephalography recordings. Two of them had benign epilepsy of childhood with centro-temporal spikes. The mutism was not related in time to the presence of active seizures. While seizures could be controlled in all children by medications, the mutism resolved only in 1. Although the discharges could be coincidental, they might represent a co-morbidity of selective mutism or even play a role in its pathogenesis. Selective mutism should be listed among the psychiatric disorders that may be associated with electroencephalographic abnormalities. It can probably be regarded as a symptom of a more complicated organic brain disorder.

Exploration of Lower Frequency EEG Dynamics and Cortical Alpha Asymmetry in Long-term Rajyoga Meditators

PubMed Central

Sharma, Kanishka; Chandra, Sushil; Dubey, Ashok Kumar

2018-01-01

Background: Rajyoga meditation is taught by Prajapita Brahmakumaris World Spiritual University (Brahmakumaris) and has been followed by more than one million followers across the globe. However, rare studies were conducted on physiological aspects of rajyoga meditation using electroencephalography (EEG). Band power and cortical asymmetry were not studied with Rajyoga meditators. Aims: This study aims to investigate the effect of regular meditation practice on EEG brain dynamics in low-frequency bands of long-term Rajyoga meditators. Settings and Design: Subjects were matched for age in both groups. Lower frequency EEG bands were analyzed in resting and during meditation. Materials and Methods: Twenty-one male long-term meditators (LTMs) and same number of controls were selected to participate in study as par inclusion criteria. Semi high-density EEG was recorded before and during meditation in LTM group and resting in control group. The main outcome of the study was spectral power of alpha and theta bands and cortical (hemispherical) asymmetry calculated using band power. Statistical Analysis: One-way ANOVA was performed to find the significant difference between EEG spectral properties of groups. Pearson's Chi-square test was used to find difference among demographics data. Results: Results reveal high-band power in alpha and theta spectra in meditators. Cortical asymmetry calculated through EEG power was also found to be high in frontal as well as parietal channels. However, no correlation was seen between the experience of meditation (years, hours) practice and EEG indices. Conclusion: Overall findings indicate contribution of smaller frequencies (alpha and theta) while maintaining meditative experience. This suggests a positive impact of meditation on frontal and parietal areas of brain, involved in the processes of regulation of selective and sustained attention as well as provide evidence about their involvement in emotion and cognitive processing. PMID:29343928

Evaluation of cerebral maturation by visual and quantitative analysis of resting electroencephalography in children with primary nocturnal enuresis.

PubMed

Hallioğlu, O; Ozge, A; Comelekoglu, U; Topaloglu, A K; Kanik, A; Duzovali, O; Yilgor, E

2001-10-01

This study was undertaken to evaluate resting electroencephalographic (EEG) changes and their relations to cerebral maturation in children with primary nocturnal enuresis. Cerebral maturation is known to be important in the pathogenesis of this disorder. Twenty-five right-handed patients with primary nocturnal enuresis, aged 6 to 14 years, and 23 age- and sex-matched healthy children were included in this cross-sectional case-control study. The abnormalities detected using such techniques as hemispheral asymmetry, regional differences, and hyperventilation response in addition to visual and quantitative EEG analysis were examined statistically by multivariate analysis. A decrease in alpha activity in the left (dominant hemisphere) temporal lobe and in the frontal lobes bilaterally and an increase in delta activity in the right temporal region were observed. We concluded that insufficient cerebral maturation is an important factor in the pathogenesis of primary nocturnal enuresis, and EEG, as a noninvasive and inexpensive method, could be used in evaluating cerebral maturation.

Transcranial direct current stimulation and power spectral parameters: a tDCS/EEG co-registration study

PubMed Central

Mangia, Anna L.; Pirini, Marco; Cappello, Angelo

2014-01-01

Transcranial direct current stimulation (tDCS) delivers low electric currents to the brain through the scalp. Constant electric currents induce shifts in neuronal membrane excitability, resulting in secondary changes in cortical activity. Concomitant electroencephalography (EEG) monitoring during tDCS can provide valuable information on the tDCS mechanisms of action. This study examined the effects of anodal tDCS on spontaneous cortical activity in a resting brain to disclose possible modulation of spontaneous oscillatory brain activity. EEG activity was measured in ten healthy subjects during and after a session of anodal stimulation of the postero-parietal cortex to detect the tDCS-induced alterations. Changes in the theta, alpha, beta, and gamma power bands were investigated. Three main findings emerged: (1) an increase in theta band activity during the first minutes of stimulation; (2) an increase in alpha and beta power during and after stimulation; (3) a widespread activation in several brain regions. PMID:25147519

Sensor Level Functional Connectivity Topography Comparison Between Different References Based EEG and MEG.

PubMed

Huang, Yunzhi; Zhang, Junpeng; Cui, Yuan; Yang, Gang; Liu, Qi; Yin, Guangfu

2018-01-01

Sensor-level functional connectivity topography (sFCT) contributes significantly to our understanding of brain networks. sFCT can be constructed using either electroencephalography (EEG) or magnetoencephalography (MEG). Here, we compared sFCT within the EEG modality and between EEG and MEG modalities. We first used simulations to look at how different EEG references-including the Reference Electrode Standardization Technique (REST), average reference (AR), linked mastoids (LM), and left mastoid references (LR)-affect EEG-based sFCT. The results showed that REST decreased the reference effects on scalp EEG recordings, making REST-based sFCT closer to the ground truth (sFCT based on ideal recordings). For the inter-modality simulation comparisons, we compared each type of EEG-sFCT with MEG-sFCT using three metrics to quantize the differences: Relative Error (RE), Overlap Rate (OR), and Hamming Distance (HD). When two sFCTs are similar, RE and HD are low, while OR is high. Results showed that among all reference schemes, EEG-and MEG-sFCT were most similar when the EEG was REST-based and the EEG and MEG were recorded simultaneously. Next, we analyzed simultaneously recorded MEG and EEG data from publicly available face-recognition experiments using a similar procedure as in the simulations. The results showed (1) if MEG-sFCT is the standard, REST-and LM-based sFCT provided results closer to this standard in the terms of HD; (2) REST-based sFCT and MEG-sFCT had the highest similarity in terms of RE; (3) REST-based sFCT had the most overlapping edges with MEG-sFCT in terms of OR. This study thus provides new insights into the effect of different reference schemes on sFCT and the similarity between MEG and EEG in terms of sFCT.

Automated EEG entropy measurements in coma, vegetative state/unresponsive wakefulness syndrome and minimally conscious state

PubMed Central

Gosseries, Olivia; Schnakers, Caroline; Ledoux, Didier; Vanhaudenhuyse, Audrey; Bruno, Marie-Aurélie; Demertzi, Athéna; Noirhomme, Quentin; Lehembre, Rémy; Damas, Pierre; Goldman, Serge; Peeters, Erika; Moonen, Gustave; Laureys, Steven

Summary Monitoring the level of consciousness in brain-injured patients with disorders of consciousness is crucial as it provides diagnostic and prognostic information. Behavioral assessment remains the gold standard for assessing consciousness but previous studies have shown a high rate of misdiagnosis. This study aimed to investigate the usefulness of electroencephalography (EEG) entropy measurements in differentiating unconscious (coma or vegetative) from minimally conscious patients. Left fronto-temporal EEG recordings (10-minute resting state epochs) were prospectively obtained in 56 patients and 16 age-matched healthy volunteers. Patients were assessed in the acute (≤1 month post-injury; n=29) or chronic (>1 month post-injury; n=27) stage. The etiology was traumatic in 23 patients. Automated online EEG entropy calculations (providing an arbitrary value ranging from 0 to 91) were compared with behavioral assessments (Coma Recovery Scale-Revised) and outcome. EEG entropy correlated with Coma Recovery Scale total scores (r=0.49). Mean EEG entropy values were higher in minimally conscious (73±19; mean and standard deviation) than in vegetative/unresponsive wakefulness syndrome patients (45±28). Receiver operating characteristic analysis revealed an entropy cut-off value of 52 differentiating acute unconscious from minimally conscious patients (sensitivity 89% and specificity 90%). In chronic patients, entropy measurements offered no reliable diagnostic information. EEG entropy measurements did not allow prediction of outcome. User-independent time-frequency balanced spectral EEG entropy measurements seem to constitute an interesting diagnostic – albeit not prognostic – tool for assessing neural network complexity in disorders of consciousness in the acute setting. Future studies are needed before using this tool in routine clinical practice, and these should seek to improve automated EEG quantification paradigms in order to reduce the remaining false negative and false positive findings. PMID:21693085

Second Language Research Using Magnetoencephalography: A Review

ERIC Educational Resources Information Center

Schmidt, Gwen L.; Roberts, Timothy P. L.

2009-01-01

In this review we show how magnetoencephalography (MEG) is a constructive tool for language research and review MEG findings in second language (L2) research. MEG is the magnetic analog of electroencephalography (EEG), and its primary advantage over other cross-sectional (e.g. magnetic resonance imaging, or positron emission tomography) functional…

Data-Driven User Feedback: An Improved Neurofeedback Strategy considering the Interindividual Variability of EEG Features.

PubMed

Han, Chang-Hee; Lim, Jeong-Hwan; Lee, Jun-Hak; Kim, Kangsan; Im, Chang-Hwan

2016-01-01

It has frequently been reported that some users of conventional neurofeedback systems can experience only a small portion of the total feedback range due to the large interindividual variability of EEG features. In this study, we proposed a data-driven neurofeedback strategy considering the individual variability of electroencephalography (EEG) features to permit users of the neurofeedback system to experience a wider range of auditory or visual feedback without a customization process. The main idea of the proposed strategy is to adjust the ranges of each feedback level using the density in the offline EEG database acquired from a group of individuals. Twenty-two healthy subjects participated in offline experiments to construct an EEG database, and five subjects participated in online experiments to validate the performance of the proposed data-driven user feedback strategy. Using the optimized bin sizes, the number of feedback levels that each individual experienced was significantly increased to 139% and 144% of the original results with uniform bin sizes in the offline and online experiments, respectively. Our results demonstrated that the use of our data-driven neurofeedback strategy could effectively increase the overall range of feedback levels that each individual experienced during neurofeedback training.

Prognostic value of amplitude-integrated electroencephalography in neonates with hypernatremic dehydration.

PubMed

Tekgunduz, Kadir Şerafettin; Caner, Ibrahim; Eras, Zeynep; Taştekin, Ayhan; Tan, Huseyin; Dinlen, Nurdan

2014-05-01

Hypernatremic dehydration in neonates is a condition that develops due to inadequate fluid intake and it may lead to cerebral damage. We aimed to determine whether there was an association between serum sodium levels on admission and aEEG patterns and prognosis, as well as any association between aEEG findings and survival rates and long-term prognosis. The present study included all term infants hospitalized for hypernatremic dehydration in between January 2010 and May 2011. Infants were monitored by aEEG. At 2 years of age, we performed a detailed evaluation to assess the impact of hypernatremic dehydration on the neurodevelopmental outcome. Twenty-one infants were admitted to the neonatal intensive care unit for hypernatremic dehydration. A correlation was found between increased serum sodium levels and aEEG abnormalities. Neurodevelopmental assessment was available for 17 of the 21 infants. The results revealed that hypernatremic dehydration did not adversely affect the long-term outcomes. The follow-up of newborns after discharge is key to determine the risks associated with hypernatremic dehydration. Our results suggest that hypernatremic dehydration had no impact on the long-term outcome. In addition, continuous aEEG monitoring could provide information regarding early prognosis and mortality.

Data-Driven User Feedback: An Improved Neurofeedback Strategy considering the Interindividual Variability of EEG Features

PubMed Central

Lim, Jeong-Hwan; Lee, Jun-Hak; Kim, Kangsan

2016-01-01

It has frequently been reported that some users of conventional neurofeedback systems can experience only a small portion of the total feedback range due to the large interindividual variability of EEG features. In this study, we proposed a data-driven neurofeedback strategy considering the individual variability of electroencephalography (EEG) features to permit users of the neurofeedback system to experience a wider range of auditory or visual feedback without a customization process. The main idea of the proposed strategy is to adjust the ranges of each feedback level using the density in the offline EEG database acquired from a group of individuals. Twenty-two healthy subjects participated in offline experiments to construct an EEG database, and five subjects participated in online experiments to validate the performance of the proposed data-driven user feedback strategy. Using the optimized bin sizes, the number of feedback levels that each individual experienced was significantly increased to 139% and 144% of the original results with uniform bin sizes in the offline and online experiments, respectively. Our results demonstrated that the use of our data-driven neurofeedback strategy could effectively increase the overall range of feedback levels that each individual experienced during neurofeedback training. PMID:27631005

Mouse epileptic seizure detection with multiple EEG features and simple thresholding technique

NASA Astrophysics Data System (ADS)

Tieng, Quang M.; Anbazhagan, Ashwin; Chen, Min; Reutens, David C.

2017-12-01

Objective. Epilepsy is a common neurological disorder characterized by recurrent, unprovoked seizures. The search for new treatments for seizures and epilepsy relies upon studies in animal models of epilepsy. To capture data on seizures, many applications require prolonged electroencephalography (EEG) with recordings that generate voluminous data. The desire for efficient evaluation of these recordings motivates the development of automated seizure detection algorithms. Approach. A new seizure detection method is proposed, based on multiple features and a simple thresholding technique. The features are derived from chaos theory, information theory and the power spectrum of EEG recordings and optimally exploit both linear and nonlinear characteristics of EEG data. Main result. The proposed method was tested with real EEG data from an experimental mouse model of epilepsy and distinguished seizures from other patterns with high sensitivity and specificity. Significance. The proposed approach introduces two new features: negative logarithm of adaptive correlation integral and power spectral coherence ratio. The combination of these new features with two previously described features, entropy and phase coherence, improved seizure detection accuracy significantly. Negative logarithm of adaptive correlation integral can also be used to compute the duration of automatically detected seizures.

Approach and withdrawal motivation in schizophrenia: an examination of frontal brain asymmetric activity.

PubMed

Horan, William P; Wynn, Jonathan K; Mathis, Ian; Miller, Gregory A; Green, Michael F

2014-01-01

Although motivational disturbances are common in schizophrenia, their neurophysiological and psychological basis is poorly understood. This electroencephalography (EEG) study examined the well-established motivational direction model of asymmetric frontal brain activity in schizophrenia. According to this model, relative left frontal activity in the resting EEG reflects enhanced approach motivation tendencies, whereas relative right frontal activity reflects enhanced withdrawal motivation tendencies. Twenty-five schizophrenia outpatients and 25 healthy controls completed resting EEG assessments of frontal asymmetry in the alpha frequency band (8-12 Hz), as well as a self-report measure of behavioral activation and inhibition system (BIS/BAS) sensitivity. Patients showed an atypical pattern of differences from controls. On the EEG measure patients failed to show the left lateralized activity that was present in controls, suggesting diminished approach motivation. On the self-report measure, patients reported higher BIS sensitivity than controls, which is typically interpreted as heightened withdrawal motivation. EEG asymmetry scores did not significantly correlate with BIS/BAS scores or with clinical symptom ratings among patients. The overall pattern suggests a motivational disturbance in schizophrenia characterized by elements of both diminished approach and elevated withdrawal tendencies.

EFFECTIVE INDICES FOR MONITORING MENTAL WORKLOAD WHILE PERFORMING MULTIPLE TASKS.

PubMed

Hsu, Bin-Wei; Wang, Mao-Jiun J; Chen, Chi-Yuan; Chen, Fang

2015-08-01

This study identified several physiological indices that can accurately monitor mental workload while participants performed multiple tasks with the strategy of maintaining stable performance and maximizing accuracy. Thirty male participants completed three 10-min. simulated multitasks: MATB (Multi-Attribute Task Battery) with three workload levels. Twenty-five commonly used mental workload measures were collected, including heart rate, 12 HRV (heart rate variability), 10 EEG (electroencephalography) indices (α, β, θ, α/θ, θ/β from O1-O2 and F4-C4), and two subjective measures. Analyses of index sensitivity showed that two EEG indices, θ and α/θ (F4-C4), one time-domain HRV-SDNN (standard deviation of inter-beat intervals), and four frequency-domain HRV: VLF (very low frequency), LF (low frequency), %HF (percentage of high frequency), and LF/HF were sensitive to differentiate high workload. EEG α/θ (F4-C4) and LF/HF were most effective for monitoring high mental workload. LF/HF showed the highest correlations with other physiological indices. EEG α/θ (F4-C4) showed strong correlations with subjective measures across different mental workload levels. Operation strategy would affect the sensitivity of EEG α (F4-C4) and HF.

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

Spatial and Temporal EEG-fMRI Changes During Preictal and Postictal Phases in a Patient With Posttraumatic Epilepsy.

PubMed

Storti, Silvia F; Del Felice, Alessandra; Formaggio, Emanuela; Boscolo Galazzo, Ilaria; Bongiovanni, Luigi G; Cerini, Roberto; Fiaschi, Antonio; Manganotti, Paolo

2015-07-01

The combined use of electroencephalography (EEG) and functional magnetic resonance imaging (EEG-fMRI) in epilepsy allows the noninvasive hemodynamic characterization of epileptic discharge-related neuronal activations. The aim of this study was to investigate pathophysiologic mechanisms underlying epileptic activity by exploring the spatial and temporal distribution of fMRI signal modifications during seizure in a single patient with posttraumatic epilepsy. EEG and fMRI data were acquired during two scanning sessions: a spontaneous critical episode was observed during the first, and interictal events were recorded during the second. The EEG-fMRI data were analyzed using the general linear model (GLM). Blood oxygenation level-dependent (BOLD) localization derived from the preictal and artifact-free postictal phase was concordant with the BOLD localization of the interictal epileptiform discharges identified in the second session, pointing to a left perilesional mesiofrontal area. Of note, BOLD signal modifications were already visible several seconds before seizure onset. In brief, BOLD activations from the preictal, postictal, and interictal epileptiform discharge analysis appear to be concordant with the clinically driven localization hypothesis, whereas a widespread network of activations is detected during the ictal phase in a partial seizure. © EEG and Clinical Neuroscience Society (ECNS) 2014.

Microstates in resting-state EEG: current status and future directions.

PubMed

Khanna, Arjun; Pascual-Leone, Alvaro; Michel, Christoph M; Farzan, Faranak

2015-02-01

Electroencephalography (EEG) is a powerful method of studying the electrophysiology of the brain with high temporal resolution. Several analytical approaches to extract information from the EEG signal have been proposed. One method, termed microstate analysis, considers the multichannel EEG recording as a series of quasi-stable "microstates" that are each characterized by a unique topography of electric potentials over the entire channel array. Because this technique simultaneously considers signals recorded from all areas of the cortex, it is capable of assessing the function of large-scale brain networks whose disruption is associated with several neuropsychiatric disorders. In this review, we first introduce the method of EEG microstate analysis. We then review studies that have discovered significant changes in the resting-state microstate series in a variety of neuropsychiatric disorders and behavioral states. We discuss the potential utility of this method in detecting neurophysiological impairments in disease and monitoring neurophysiological changes in response to an intervention. Finally, we discuss how the resting-state microstate series may reflect rapid switching among neural networks while the brain is at rest, which could represent activity of resting-state networks described by other neuroimaging modalities. We conclude by commenting on the current and future status of microstate analysis, and suggest that EEG microstates represent a promising neurophysiological tool for understanding and assessing brain network dynamics on a millisecond timescale in health and disease. Copyright © 2014 Elsevier Ltd. All rights reserved.

Microstates in Resting-State EEG: Current Status and Future Directions

PubMed Central

Khanna, Arjun; Pascual-Leone, Alvaro; Michel, Christoph M.; Farzan, Faranak

2015-01-01

Electroencephalography (EEG) is a powerful method of studying the electrophysiology of the brain with high temporal resolution. Several analytical approaches to extract information from the EEG signal have been proposed. One method, termed microstate analysis, considers the multichannel EEG recording as a series of quasi-stable “microstates” that are each characterized by a unique topography of electric potentials over the entire channel array. Because this technique simultaneously considers signals recorded from all areas of the cortex, it is capable of assessing the function of large-scale brain networks whose disruption is associated with several neuropsychiatric disorders. In this review, we first introduce the method of EEG microstate analysis. We then review studies that have discovered significant changes in the resting-state microstate series in a variety of neuropsychiatric disorders and behavioral states. We discuss the potential utility of this method in detecting neurophysiological impairments in disease and monitoring neurophysiological changes in response to an intervention. Finally, we discuss how the resting-state microstate series may reflect rapid switching among neural networks while the brain is at rest, which could represent activity of resting-state networks described by other neuroimaging modalities. We conclude by commenting on the current and future status of microstate analysis, and suggest that EEG microstates represent a promising neurophysiological tool for understanding and assessing brain network dynamics on a millisecond timescale in health and disease. PMID:25526823

Protocol Design Challenges in the Detection of Awareness in Aware Subjects Using EEG Signals.

PubMed

Henriques, J; Gabriel, D; Grigoryeva, L; Haffen, E; Moulin, T; Aubry, R; Pazart, L; Ortega, J-P

2016-10-01

Recent studies have evidenced serious difficulties in detecting covert awareness with electroencephalography-based techniques both in unresponsive patients and in healthy control subjects. This work reproduces the protocol design in two recent mental imagery studies with a larger group comprising 20 healthy volunteers. The main goal is assessing if modifications in the signal extraction techniques, training-testing/cross-validation routines, and hypotheses evoked in the statistical analysis, can provide solutions to the serious difficulties documented in the literature. The lack of robustness in the results advises for further search of alternative protocols more suitable for machine learning classification and of better performing signal treatment techniques. Specific recommendations are made using the findings in this work. © EEG and Clinical Neuroscience Society (ECNS) 2014.

Utility of Neurodiagnostic Studies in the Diagnosis of Autoimmune Encephalitis in Children.

PubMed

Albert, Dara V; Pluto, Charles P; Weber, Amanda; Vidaurre, Jorge; Barbar-Smiley, Fatima; Abdul Aziz, Rabheh; Driest, Kyla; Bout-Tabaku, Sharon; Ruess, Lynne; Rusin, Jerome A; Morgan-Followell, Bethanie

2016-02-01

Autoimmune encephalitis is currently a clinical diagnosis without widely accepted diagnostic criteria, often leading to a delay in diagnosis. The utility of magnetic resonance imaging (MRI) and electroencephalography (EEG) in this disease is unknown. The objective of this study was to identify disease-specific patterns of neurodiagnostic studies (MRI and EEG) for autoimmune encephalitis in children. We completed a retrospective chart review of encephalopathic patients seen at a large pediatric hospital over a four year interval. Clinical presentation, autoantibody status, and MRI and EEG findings were identified and compared. Individuals with autoantibodies were considered "definite" cases, whereas those without antibodies or those with only thyroperoxidase antibodies were characterized as "suspected." Eighteen patients met the inclusion criteria and autoantibodies were identified in nine of these. The patients with definite autoimmune encephalitis had MRI abnormalities within limbic structures, most notably the anteromedial temporal lobes (56%). Only individuals with suspected disease had nontemporal lobe cortical lesions. Sixteen patients had an EEG and 13 (81%) of these were abnormal. The most common findings were abnormal background rhythm (63%), generalized slowing (50%), focal slowing (43%), and focal epileptiform discharges (31%). Sleep spindle abnormalities occurred in 38% of patients. There were no specific differences in the EEG findings between the definite and suspected cases. Focal EEG findings only correlated with a focal lesion on MRI in a single definite case. Pediatric patients with definite autoimmune encephalitis have a narrow spectrum of MRI abnormalities. Conversely, EEG abnormalities are mostly nonspecific. All patients in our cohort had abnormalities on one or both of these neurodiagnostic studies. Copyright © 2016 Elsevier Inc. All rights reserved.

Toward an Attention-Based Diagnostic Tool for Patients With Locked-in Syndrome.

PubMed

Lesenfants, Damien; Habbal, Dina; Chatelle, Camille; Soddu, Andrea; Laureys, Steven; Noirhomme, Quentin

2018-03-01

Electroencephalography (EEG) has been proposed as a supplemental tool for reducing clinical misdiagnosis in severely brain-injured populations helping to distinguish conscious from unconscious patients. We studied the use of spectral entropy as a measure of focal attention in order to develop a motor-independent, portable, and objective diagnostic tool for patients with locked-in syndrome (LIS), answering the issues of accuracy and training requirement. Data from 20 healthy volunteers, 6 LIS patients, and 10 patients with a vegetative state/unresponsive wakefulness syndrome (VS/UWS) were included. Spectral entropy was computed during a gaze-independent 2-class (attention vs rest) paradigm, and compared with EEG rhythms (delta, theta, alpha, and beta) classification. Spectral entropy classification during the attention-rest paradigm showed 93% and 91% accuracy in healthy volunteers and LIS patients respectively. VS/UWS patients were at chance level. EEG rhythms classification reached a lower accuracy than spectral entropy. Resting-state EEG spectral entropy could not distinguish individual VS/UWS patients from LIS patients. The present study provides evidence that an EEG-based measure of attention could detect command-following in patients with severe motor disabilities. The entropy system could detect a response to command in all healthy subjects and LIS patients, while none of the VS/UWS patients showed a response to command using this system.

Attentional Selection in a Cocktail Party Environment Can Be Decoded from Single-Trial EEG

PubMed Central

O'Sullivan, James A.; Power, Alan J.; Mesgarani, Nima; Rajaram, Siddharth; Foxe, John J.; Shinn-Cunningham, Barbara G.; Slaney, Malcolm; Shamma, Shihab A.; Lalor, Edmund C.

2015-01-01

How humans solve the cocktail party problem remains unknown. However, progress has been made recently thanks to the realization that cortical activity tracks the amplitude envelope of speech. This has led to the development of regression methods for studying the neurophysiology of continuous speech. One such method, known as stimulus-reconstruction, has been successfully utilized with cortical surface recordings and magnetoencephalography (MEG). However, the former is invasive and gives a relatively restricted view of processing along the auditory hierarchy, whereas the latter is expensive and rare. Thus it would be extremely useful for research in many populations if stimulus-reconstruction was effective using electroencephalography (EEG), a widely available and inexpensive technology. Here we show that single-trial (≈60 s) unaveraged EEG data can be decoded to determine attentional selection in a naturalistic multispeaker environment. Furthermore, we show a significant correlation between our EEG-based measure of attention and performance on a high-level attention task. In addition, by attempting to decode attention at individual latencies, we identify neural processing at ∼200 ms as being critical for solving the cocktail party problem. These findings open up new avenues for studying the ongoing dynamics of cognition using EEG and for developing effective and natural brain–computer interfaces. PMID:24429136

Analysis of connectivity in NeuCube spiking neural network models trained on EEG data for the understanding of functional changes in the brain: A case study on opiate dependence treatment.

PubMed

Capecci, Elisa; Kasabov, Nikola; Wang, Grace Y

2015-08-01

The paper presents a methodology for the analysis of functional changes in brain activity across different conditions and different groups of subjects. This analysis is based on the recently proposed NeuCube spiking neural network (SNN) framework and more specifically on the analysis of the connectivity of a NeuCube model trained with electroencephalography (EEG) data. The case study data used to illustrate this method is EEG data collected from three groups-subjects with opiate addiction, patients undertaking methadone maintenance treatment, and non-drug users/healthy control group. The proposed method classifies more accurately the EEG data than traditional statistical and artificial intelligence (AI) methods and can be used to predict response to treatment and dose-related drug effect. But more importantly, the method can be used to compare functional brain activities of different subjects and the changes of these activities as a result of treatment, which is a step towards a better understanding of both the EEG data and the brain processes that generated it. The method can also be used for a wide range of applications, such as a better understanding of disease progression or aging. Copyright © 2015 Elsevier Ltd. All rights reserved.

Modulation of EEG spectral edge frequency during patterned pneumatic oral stimulation in preterm infants

PubMed Central

Song, Dongli; Jegatheesan, Priya; Weiss, Sunshine; Govindaswami, Balaji; Wang, Jingyan; Lee, Jaehoon; Oder, Austin; Barlow, Steven M

2014-01-01

Background Stimulation of the nervous system plays a central role in brain development and neurodevelopmental outcome. Thalamocortical and corticocortical development is diminished in premature infants and correlated to electroencephalography (EEG) progression. The purpose of this study was to determine the effects of orocutaneous stimulation on the modulation of spectral edge frequency, fc=90% (SEF-90) derived from EEG recordings in preterm infants. Methods Twenty two preterm infants were randomized to experimental and control conditions. Pulsed orocutaneous stimulation was presented during gavage feedings begun at around 32 weeks postmenstrual age (PMA). The SEF-90 was derived from 2-channel EEG recordings. Results Compared to the control condition, the pulsed orocutaneous stimulation produced a significant reorganization of SEF-90 in the left (p = 0.005) and right (p < 0.0001) hemispheres. Notably, the left and right hemisphere showed a reversal in the polarity of frequency shift, demonstrating hemispheric asymmetry in the frequency domain. Pulsed orocutaneous stimulation also produced a significant pattern of short term cortical adaptation and a long term neural adaptation manifest as a 0.5 Hz elevation in SEF-90 after repeated stimulation sessions. Conclusion This is the first study to demonstrate the modulating effects of a servo-controlled oral somatosensory input on the spectral features of EEG activity in preterm infants. PMID:24129553

Modulation of EEG spectral edge frequency during patterned pneumatic oral stimulation in preterm infants.

PubMed

Song, Dongli; Jegatheesan, Priya; Weiss, Sunshine; Govindaswami, Balaji; Wang, Jingyan; Lee, Jaehoon; Oder, Austin; Barlow, Steven M

2014-01-01

Stimulation of the nervous system plays a central role in brain development and neurodevelopmental outcomes. Thalamocortical and corticocortical development is diminished in premature infants and correlated to electroencephalography (EEG) progression. The purpose of this study was to determine the effects of orocutaneous stimulation on the modulation of spectral edge frequency fc = 90% (SEF-90), which is derived from EEG recordings in preterm infants. A total of 22 preterm infants were randomized to experimental and control conditions. Pulsed orocutaneous stimulation was presented during gavage feedings begun at ~32 wk postmenstrual age. The SEF-90 was derived from two-channel EEG recordings. Compared with the control condition, the pulsed orocutaneous stimulation produced a significant reorganization of SEF-90 in the left (P = 0.005) and right (P < 0.0001) hemispheres. Notably, the left and right hemispheres showed a reversal in the polarity of frequency shift, demonstrating hemispheric asymmetry in the frequency domain. Pulsed orocutaneous stimulation also produced a significant pattern of short-term cortical adaptation and a long-term neural adaptation manifested as a 0.5 Hz elevation in SEF-90 after repeated stimulation sessions. This is the first study to demonstrate the modulating effects of a servo-controlled oral somatosensory input on the spectral features of EEG activity in preterm infants.

Guidelines for intraoperative neuromonitoring using raw (analog or digital waveforms) and quantitative electroencephalography: a position statement by the American Society of Neurophysiological Monitoring.

PubMed

Isley, Michael R; Edmonds, Harvey L; Stecker, Mark

2009-12-01

Electroencephalography (EEG) is one of the oldest and most commonly utilized modalities for intraoperative neuromonitoring. Historically, interest in the EEG patterns associated with anesthesia is as old as the discovery of the EEG itself. The evolution of its intraoperative use was also expanded to include monitoring for assessing cortical perfusion and oxygenation during a variety of vascular, cardiac, and neurosurgical procedures. Furthermore, a number of quantitative or computer-processed algorithms have also been developed to aid in its visual representation and interpretation. The primary clinical outcomes for which modern EEG technology has made significant intraoperative contributions include: (1) recognizing and/or preventing perioperative ischemic insults, and (2) monitoring of brain function for anesthetic drug administration in order to determine depth of anesthesia (and level of consciousness), including the tailoring of drug levels to achieve a predefined neural effect (e.g., burst suppression). While the accelerated development of microprocessor technologies has fostered an extraordinarily rapid growth in the use of intraoperative EEG, there is still no universal adoption of a monitoring technique(s) or of criteria for its neural end-point(s) by anesthesiologists, surgeons, neurologists, and neurophysiologists. One of the most important limitations to routine intraoperative use of EEG may be the lack of standardization of methods, alarm criteria, and recommendations related to its application. Lastly, refinements in technology and signal processing can be expected to advance the usefulness of the intraoperative EEG for both anesthetic and surgical management of patients. This paper is the position statement of the American Society of Neurophysiological Monitoring. It is the practice guidelines for the intraoperative use of raw (analog and digital) and quantitative EEG. The following recommendations are based on trends in the current scientific and clinical literature and meetings, guidelines published by other organizations, expert opinion, and public review by the members of the American Society of Neurophysiological Monitoring. This document may not include all possible methodologies and interpretative criteria, nor do the authors and their sponsor intentionally exclude any new alternatives. The use of the techniques reviewed in these guidelines may reduce perioperative neurological morbidity and mortality. This position paper summarizes commonly used protocols for recording and interpreting the intraoperative use of EEG. Furthermore, the American Society of Neurophysiological Monitoring recognizes this as primarily an educational service.

The effect of hypobaric hypoxia on multichannel EEG signal complexity.

PubMed

Papadelis, Christos; Kourtidou-Papadeli, Chrysoula; Bamidis, Panagiotis D; Maglaveras, Nikos; Pappas, Konstantinos

2007-01-01

The objective of this study was the development and evaluation of nonlinear electroencephalography parameters which assess hypoxia-induced EEG alterations, and describe the temporal characteristics of different hypoxic levels' residual effect upon the brain electrical activity. Multichannel EEG, pO2, pCO2, ECG, and respiration measurements were recorded from 10 subjects exposed to three experimental conditions (100% oxygen, hypoxia, recovery) at three-levels of reduced barometric pressure. The mean spectral power of EEG under each session and altitude were estimated for the standard bands. Approximate Entropy (ApEn) of EEG segments was calculated, and the ApEn's time-courses were smoothed by a moving average filter. On the smoothed diagrams, parameters were defined. A significant increase in total power and power of theta and alpha bands was observed during hypoxia. Visual interpretation of ApEn time-courses revealed a characteristic pattern (decreasing during hypoxia and recovering after oxygen re-administration). The introduced qEEG parameters S1 and K1 distinguished successfully the three hypoxic conditions. The introduced parameters based on ApEn time-courses are assessing reliably and effectively the different hypoxic levels. ApEn decrease may be explained by neurons' functional isolation due to hypoxia since decreased complexity corresponds to greater autonomy of components, although this interpretation should be further supported by electrocorticographic animal studies. The introduced qEEG parameters seem to be appropriate for assessing the hypoxia-related neurophysiological state of patients in the hyperbaric chambers in the treatment of decompression sickness, carbon dioxide poisoning, and mountaineering.

Continuous EEG-fMRI in Pre-Surgical Evaluation of a Patient with Symptomatic Seizures: Bold Activation Linked to Interictal Epileptic Discharges Caused by Cavernoma.

PubMed

Avesani, M; Formaggio, E; Milanese, F; Baraldo, A; Gasparini, A; Cerini, R; Bongiovanni, L G; Pozzi Mucelli, R; Fiaschi, A; Manganotti, P

2008-04-07

We used continuous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) to identify the linkage between the "epileptogenic" and the "irritative" area in a patient with symptomatic epilepsy (cavernoma, previously diagnosed and surgically treated), i.e. a patient with a well known "epileptogenic area", and to increase the possibility of a non invasive pre-surgical evaluation of drug-resistant epilepsies. A compatible MRI system was used (EEG with 29 scalp electrodes and two electrodes for ECG and EMG) and signals were recorded with a 1.5 Tesla MRI scanner. After the recording session and MRI artifact removal, EEG data were analyzed offline and used as paradigms in fMRI study. Activation (EEG sequences with interictal slow-spiked-wave activity) and rest (sequences of normal EEG) conditions were compared to identify the potential resulting focal increase in BOLD signal and to consider if this is spatially linked to the interictal focus used as a paradigm and to the lesion. We noted an increase in the BOLD signal in the left neocortical temporal region, laterally and posteriorly to the poro-encephalic cavity (residual of cavernoma previously removed), that is around the "epileptogenic area". In our study "epileptogenic" and "irritative" areas were connected with each other. Combined EEG-fMRI may become routine in clinical practice for a better identification of an irritative and lesional focus in patients with symptomatic drug-resistant epilepsy.

Recovering TMS-evoked EEG responses masked by muscle artifacts.

PubMed

Mutanen, Tuomas P; Kukkonen, Matleena; Nieminen, Jaakko O; Stenroos, Matti; Sarvas, Jukka; Ilmoniemi, Risto J

2016-10-01

Combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) often suffers from large muscle artifacts. Muscle artifacts can be removed using signal-space projection (SSP), but this can make the visual interpretation of the remaining EEG data difficult. We suggest to use an additional step after SSP that we call source-informed reconstruction (SIR). SSP-SIR improves substantially the signal quality of artifactual TMS-EEG data, causing minimal distortion in the neuronal signal components. In the SSP-SIR approach, we first project out the muscle artifact using SSP. Utilizing an anatomical model and the remaining signal, we estimate an equivalent source distribution in the brain. Finally, we map the obtained source estimate onto the original signal space, again using anatomical information. This approach restores the neuronal signals in the sensor space and interpolates EEG traces onto the completely rejected channels. The introduced algorithm efficiently suppresses TMS-related muscle artifacts in EEG while retaining well the neuronal EEG topographies and signals. With the presented method, we can remove muscle artifacts from TMS-EEG data and recover the underlying brain responses without compromising the readability of the signals of interest. Copyright © 2016 Elsevier Inc. All rights reserved.

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.

Hidden pattern discovery on epileptic EEG with 1-D local binary patterns and epileptic seizures detection by grey relational analysis.

PubMed

Kaya, Yılmaz

2015-09-01

This paper proposes a novel approach to detect epilepsy seizures by using Electroencephalography (EEG), which is one of the most common methods for the diagnosis of epilepsy, based on 1-Dimension Local Binary Pattern (1D-LBP) and grey relational analysis (GRA) methods. The main aim of this paper is to evaluate and validate a novel approach, which is a computer-based quantitative EEG analyzing method and based on grey systems, aimed to help decision-maker. In this study, 1D-LBP, which utilizes all data points, was employed for extracting features in raw EEG signals, Fisher score (FS) was employed to select the representative features, which can also be determined as hidden patterns. Additionally, GRA is performed to classify EEG signals through these Fisher scored features. The experimental results of the proposed approach, which was employed in a public dataset for validation, showed that it has a high accuracy in identifying epileptic EEG signals. For various combinations of epileptic EEG, such as A-E, B-E, C-E, D-E, and A-D clusters, 100, 96, 100, 99.00 and 100% were achieved, respectively. Also, this work presents an attempt to develop a new general-purpose hidden pattern determination scheme, which can be utilized for different categories of time-varying signals.

EEG entropy measures indicate decrease of cortical information processing in Disorders of Consciousness.

PubMed

Thul, Alexander; Lechinger, Julia; Donis, Johann; Michitsch, Gabriele; Pichler, Gerald; Kochs, Eberhard F; Jordan, Denis; Ilg, Rüdiger; Schabus, Manuel

2016-02-01

Clinical assessments that rely on behavioral responses to differentiate Disorders of Consciousness are at times inapt because of some patients' motor disabilities. To objectify patients' conditions of reduced consciousness the present study evaluated the use of electroencephalography to measure residual brain activity. We analyzed entropy values of 18 scalp EEG channels of 15 severely brain-damaged patients with clinically diagnosed Minimally-Conscious-State (MCS) or Unresponsive-Wakefulness-Syndrome (UWS) and compared the results to a sample of 24 control subjects. Permutation entropy (PeEn) and symbolic transfer entropy (STEn), reflecting information processes in the EEG, were calculated for all subjects. Participants were tested on a modified active own-name paradigm to identify correlates of active instruction following. PeEn showed reduced local information content in the EEG in patients, that was most pronounced in UWS. STEn analysis revealed altered directed information flow in the EEG of patients, indicating impaired feed-backward connectivity. Responses to auditory stimulation yielded differences in entropy measures, indicating reduced information processing in MCS and UWS. Local EEG information content and information flow are affected in Disorders of Consciousness. This suggests local cortical information capacity and feedback information transfer as neural correlates of consciousness. The utilized EEG entropy analyses were able to relate to patient groups with different Disorders of Consciousness. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Decoding Individual Finger Movements from One Hand Using Human EEG Signals

PubMed Central

Gonzalez, Jania; Ding, Lei

2014-01-01

Brain computer interface (BCI) is an assistive technology, which decodes neurophysiological signals generated by the human brain and translates them into control signals to control external devices, e.g., wheelchairs. One problem challenging noninvasive BCI technologies is the limited control dimensions from decoding movements of, mainly, large body parts, e.g., upper and lower limbs. It has been reported that complicated dexterous functions, i.e., finger movements, can be decoded in electrocorticography (ECoG) signals, while it remains unclear whether noninvasive electroencephalography (EEG) signals also have sufficient information to decode the same type of movements. Phenomena of broadband power increase and low-frequency-band power decrease were observed in EEG in the present study, when EEG power spectra were decomposed by a principal component analysis (PCA). These movement-related spectral structures and their changes caused by finger movements in EEG are consistent with observations in previous ECoG study, as well as the results from ECoG data in the present study. The average decoding accuracy of 77.11% over all subjects was obtained in classifying each pair of fingers from one hand using movement-related spectral changes as features to be decoded using a support vector machine (SVM) classifier. The average decoding accuracy in three epilepsy patients using ECoG data was 91.28% with the similarly obtained features and same classifier. Both decoding accuracies of EEG and ECoG are significantly higher than the empirical guessing level (51.26%) in all subjects (p<0.05). The present study suggests the similar movement-related spectral changes in EEG as in ECoG, and demonstrates the feasibility of discriminating finger movements from one hand using EEG. These findings are promising to facilitate the development of BCIs with rich control signals using noninvasive technologies. PMID:24416360

Usefulness of Ilae 2010 classification in Mexican epilepsy patients.

PubMed

Leyva, Ildefonso Rodríguez; Gómez, Juan Francisco Hernández; Enríquez, Fernando Cortés; Sierra, Juan Francisco Hernández

2017-05-15

Advances in neuroimaging, genomics, and molecular biology have improved the understanding of the pathogenesis of epilepsy. That is why the International League Against Epilepsy (ILAE) has created a new classification system. The present study aims to evaluate the association between epilepsy cases classified by the ILAE 2010 classification proposal, electroencephalography (EEG), and magnetic resonance imaging brain findings (MRI). Prospective cross-sectional design of 277 cases of epilepsy seen at the Epilepsy Clinic, Hospital Central "Dr. Ignacio Morones Prieto", were compared with the ILAE classification based on the etiology and clinical manifestations and their MRI and EEG findings. Cochran, Mantell, Haenzel test with significance p<0.05. MRI findings were associated with the etiology of the ILAE classification. According to EEG findings, the structural-metabolic etiology patients had more dysfunctional reports than genetic or unknown etiology patients (p<0.05). The adoption of the ILAE classification is recommended, as it can provide useful guidance towards the etiology of cases of epilepsy even when brain MRIs and EEGs are not available. Copyright © 2017 Elsevier B.V. All rights reserved.

Diagnostic Performance and Utility of Quantitative EEG Analyses in Delirium: Confirmatory Results From a Large Retrospective Case-Control Study.

PubMed

Fleischmann, Robert; Tränkner, Steffi; Bathe-Peters, Rouven; Rönnefarth, Maria; Schmidt, Sein; Schreiber, Stephan J; Brandt, Stephan A

2018-03-01

The lack of objective disease markers is a major cause of misdiagnosis and nonstandardized approaches in delirium. Recent studies conducted in well-selected patients and confined study environments suggest that quantitative electroencephalography (qEEG) can provide such markers. We hypothesize that qEEG helps remedy diagnostic uncertainty not only in well-defined study cohorts but also in a heterogeneous hospital population. In this retrospective case-control study, EEG power spectra of delirious patients and age-/gender-matched controls (n = 31 and n = 345, respectively) were fitted in a linear model to test their performance as binary classifiers. We subsequently evaluated the diagnostic performance of the best classifiers in control samples with normal EEGs (n = 534) and real-world samples including pathologic findings (n = 4294). Test reliability was estimated through split-half analyses. We found that the combination of spectral power at F3-P4 at 2 Hz (area under the curve [AUC] = .994) and C3-O1 at 19 Hz (AUC = .993) provided a sensitivity of 100% and a specificity of 99% to identify delirious patients among normal controls. These classifiers also yielded a false positive rate as low as 5% and increased the pretest probability of being delirious by 57% in an unselected real-world sample. Split-half reliabilities were .98 and .99, respectively. This retrospective study yielded preliminary evidence that qEEG provides excellent diagnostic performance to identify delirious patients even outside confined study environments. It furthermore revealed reduced beta power as a novel specific finding in delirium and that a normal EEG excludes delirium. Prospective studies including parameters of pretest probability and delirium severity are required to elaborate on these promising findings.

Time-series analysis of sleep wake stage of rat EEG using time-dependent pattern entropy

NASA Astrophysics Data System (ADS)

Ishizaki, Ryuji; Shinba, Toshikazu; Mugishima, Go; Haraguchi, Hikaru; Inoue, Masayoshi

2008-05-01

We performed electroencephalography (EEG) for six male Wistar rats to clarify temporal behaviors at different levels of consciousness. Levels were identified both by conventional sleep analysis methods and by our novel entropy method. In our method, time-dependent pattern entropy is introduced, by which EEG is reduced to binary symbolic dynamics and the pattern of symbols in a sliding temporal window is considered. A high correlation was obtained between level of consciousness as measured by the conventional method and mean entropy in our entropy method. Mean entropy was maximal while awake (stage W) and decreased as sleep deepened. These results suggest that time-dependent pattern entropy may offer a promising method for future sleep research.

Prediction of fatigue-related driver performance from EEG data by deep Riemannian model.

PubMed

Hajinoroozi, Mehdi; Jianqiu Zhang; Yufei Huang

2017-07-01

Prediction of the drivers' drowsy and alert states is important for safety purposes. The prediction of drivers' drowsy and alert states from electroencephalography (EEG) using shallow and deep Riemannian methods is presented. For shallow Riemannian methods, the minimum distance to Riemannian mean (mdm) and Log-Euclidian metric are investigated, where it is shown that Log-Euclidian metric outperforms the mdm algorithm. In addition the SPDNet, a deep Riemannian model, that takes the EEG covariance matrix as the input is investigated. It is shown that SPDNet outperforms all tested shallow and deep classification methods. Performance of SPDNet is 6.02% and 2.86% higher than the best performance by the conventional Euclidian classifiers and shallow Riemannian models, respectively.

Mobile Device Applications for the Visualization of Functional Connectivity Networks and EEG Electrodes: iBraiN and iBraiNEEG

PubMed Central

Rojas, Gonzalo M.; Fuentes, Jorge A.; Gálvez, Marcelo

2016-01-01

Multiple functional MRI (fMRI)-based functional connectivity networks were obtained by Yeo et al. (2011), and the visualization of these complex networks is a difficult task. Also, the combination of functional connectivity networks determined by fMRI with electroencephalography (EEG) data could be a very useful tool. Mobile devices are becoming increasingly common among users, and for this reason, we describe here two applications for Android and iOS mobile devices: one that shows in an interactive way the seven Yeo functional connectivity networks, and another application that shows the relative position of 10–20 EEG electrodes with Yeo’s seven functional connectivity networks. PMID:27807416

Clinical utility of EEG in diagnosing and monitoring epilepsy in adults.

PubMed

Tatum, W O; Rubboli, G; Kaplan, P W; Mirsatari, S M; Radhakrishnan, K; Gloss, D; Caboclo, L O; Drislane, F W; Koutroumanidis, M; Schomer, D L; Kasteleijn-Nolst Trenite, D; Cook, Mark; Beniczky, S

2018-05-01

Electroencephalography (EEG) remains an essential diagnostic tool for people with epilepsy (PWE). The International Federation of Clinical Neurophysiology produces new guidelines as an educational service for clinicians to address gaps in knowledge in clinical neurophysiology. The current guideline was prepared in response to gaps present in epilepsy-related neurophysiological assessment and is not intended to replace sound clinical judgement in the care of PWE. Furthermore, addressing specific pathophysiological conditions of the brain that produce epilepsy is of primary importance though is beyond the scope of this guideline. Instead, our goal is to summarize the scientific evidence for the utility of EEG when diagnosing and monitoring PWE. Copyright © 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Evaluation of Electroencephalography Source Localization Algorithms with Multiple Cortical Sources.

PubMed

Bradley, Allison; Yao, Jun; Dewald, Jules; Richter, Claus-Peter

2016-01-01

Source localization algorithms often show multiple active cortical areas as the source of electroencephalography (EEG). Yet, there is little data quantifying the accuracy of these results. In this paper, the performance of current source density source localization algorithms for the detection of multiple cortical sources of EEG data has been characterized. EEG data were generated by simulating multiple cortical sources (2-4) with the same strength or two sources with relative strength ratios of 1:1 to 4:1, and adding noise. These data were used to reconstruct the cortical sources using current source density (CSD) algorithms: sLORETA, MNLS, and LORETA using a p-norm with p equal to 1, 1.5 and 2. Precision (percentage of the reconstructed activity corresponding to simulated activity) and Recall (percentage of the simulated sources reconstructed) of each of the CSD algorithms were calculated. While sLORETA has the best performance when only one source is present, when two or more sources are present LORETA with p equal to 1.5 performs better. When the relative strength of one of the sources is decreased, all algorithms have more difficulty reconstructing that source. However, LORETA 1.5 continues to outperform other algorithms. If only the strongest source is of interest sLORETA is recommended, while LORETA with p equal to 1.5 is recommended if two or more of the cortical sources are of interest. These results provide guidance for choosing a CSD algorithm to locate multiple cortical sources of EEG and for interpreting the results of these algorithms.

Evaluation of Electroencephalography Source Localization Algorithms with Multiple Cortical Sources

PubMed Central

Bradley, Allison; Yao, Jun; Dewald, Jules; Richter, Claus-Peter

2016-01-01

Background Source localization algorithms often show multiple active cortical areas as the source of electroencephalography (EEG). Yet, there is little data quantifying the accuracy of these results. In this paper, the performance of current source density source localization algorithms for the detection of multiple cortical sources of EEG data has been characterized. Methods EEG data were generated by simulating multiple cortical sources (2–4) with the same strength or two sources with relative strength ratios of 1:1 to 4:1, and adding noise. These data were used to reconstruct the cortical sources using current source density (CSD) algorithms: sLORETA, MNLS, and LORETA using a p-norm with p equal to 1, 1.5 and 2. Precision (percentage of the reconstructed activity corresponding to simulated activity) and Recall (percentage of the simulated sources reconstructed) of each of the CSD algorithms were calculated. Results While sLORETA has the best performance when only one source is present, when two or more sources are present LORETA with p equal to 1.5 performs better. When the relative strength of one of the sources is decreased, all algorithms have more difficulty reconstructing that source. However, LORETA 1.5 continues to outperform other algorithms. If only the strongest source is of interest sLORETA is recommended, while LORETA with p equal to 1.5 is recommended if two or more of the cortical sources are of interest. These results provide guidance for choosing a CSD algorithm to locate multiple cortical sources of EEG and for interpreting the results of these algorithms. PMID:26809000

The association of mothers' and fathers' insomnia symptoms with school-aged children's sleep assessed by parent report and in-home sleep-electroencephalography.

PubMed

Urfer-Maurer, Natalie; Weidmann, Rebekka; Brand, Serge; Holsboer-Trachsler, Edith; Grob, Alexander; Weber, Peter; Lemola, Sakari

2017-10-01

Sleep plays an essential role for children's well-being. Because children's sleep is associated with parental sleep patterns, it must be considered in the family context. As a first aim of the present study, we test whether parental insomnia symptoms are related to children's in-home sleep-electroencephalography (EEG). Second, we examine the association between parental insomnia symptoms and maternal and paternal perception of children's sleep using actor-partner interdependence models. A total of 191 healthy children enrolled in public school and aged 7-12 years took part in the study. Ninety-six were formerly very preterm born children. Children underwent in-home sleep-EEG, and parents reported children's sleep-related behavior by using the German version of the Children's Sleep Habits Questionnaire. Further, parents completed the Insomnia Severity Index to report their own insomnia symptoms. Maternal but not paternal insomnia symptoms were related to less children's EEG-derived total sleep time, more stage 2 sleep, less slow wave sleep, later sleep onset time, and later awakening time. Mothers' and fathers' own insomnia symptoms were related to their reports of children's bedtime resistance, sleep duration, sleep anxiety, night wakings, and/or daytime sleepiness. Moreover, maternal insomnia symptoms were associated with paternal reports of children's bedtime resistance, sleep anxiety, and sleep-disordered breathing. The associations between parental insomnia symptoms and parents' perception of children's sleep could not be explained by children's objectively measured sleep. Mothers' insomnia symptoms and children's objective sleep patterns are associated. Moreover, the parents' own insomnia symptoms might bias their perception of children's sleep-related behavior problems. Copyright © 2017 Elsevier B.V. All rights reserved.

Mapping of electrophysiological response to transcranial infrared laser stimulation on the human brain in vivo measured by electroencephalography (Conference Presentation)

NASA Astrophysics Data System (ADS)

Wang, Xinlong; Reddy, Divya Dhandapani; Gonzalez-Lima, F.; Liu, Hanli

2017-02-01

Transcranial infrared laser stimulation (TILS) is a non-destructive and non-thermal photobiomodulation therapy or process on the human brain; TILS uses infrared light from lasers or LEDs and has gained increased recognition for its beneficial effects on a variety of neurological and psychological conditions. While the mechanism of TILS has been assumed to stem from cytochrome-c-oxidase (CCO), which is the last enzyme in the electron transportation chain and is the primary photoacceptor, no literature is found to report electrophysiological response to TILS. In this study, a 64-channel electroencephalography (EEG) system was employed to monitor electrophysiological activities from 15 healthy human participants before, during and after TILS. A placebo experimental protocol was also applied for rigorous comparison. After recording a 3-minute baseline, we applied a 1064-nm laser with a power of 3.5W on the right forehead of each human participant for 8 minutes, followed by a 5-minute recovery period. In 64-channel EEG data analysis, we utilized several methods (root mean square, principal component analysis followed by independent component analysis, permutation conditional mutual information, and time-frequency wavelet analysis) to reveal differences in electrophysiological response to TILS between the stimulated versus placebo group. The analyzed results were further investigated using general linear model and paired t-test to reveal statistically meaningful responses induced by TILS. Moreover, this study will provide spatial mapping of human electrophysiological and possibly neural network responses to TILS for first time, indicating the potential of EEG to be an effective method for monitoring neurological improvement induced by TILS.

Driving behavior recognition using EEG data from a simulated car-following experiment.

PubMed

Yang, Liu; Ma, Rui; Zhang, H Michael; Guan, Wei; Jiang, Shixiong

2018-07-01

Driving behavior recognition is the foundation of driver assistance systems, with potential applications in automated driving systems. Most prevailing studies have used subjective questionnaire data and objective driving data to classify driving behaviors, while few studies have used physiological signals such as electroencephalography (EEG) to gather data. To bridge this gap, this paper proposes a two-layer learning method for driving behavior recognition using EEG data. A simulated car-following driving experiment was designed and conducted to simultaneously collect data on the driving behaviors and EEG data of drivers. The proposed learning method consists of two layers. In Layer I, two-dimensional driving behavior features representing driving style and stability were selected and extracted from raw driving behavior data using K-means and support vector machine recursive feature elimination. Five groups of driving behaviors were classified based on these two-dimensional driving behavior features. In Layer II, the classification results from Layer I were utilized as inputs to generate a k-Nearest-Neighbor classifier identifying driving behavior groups using EEG data. Using independent component analysis, a fast Fourier transformation, and linear discriminant analysis sequentially, the raw EEG signals were processed to extract two core EEG features. Classifier performance was enhanced using the adaptive synthetic sampling approach. A leave-one-subject-out cross validation was conducted. The results showed that the average classification accuracy for all tested traffic states was 69.5% and the highest accuracy reached 83.5%, suggesting a significant correlation between EEG patterns and car-following behavior. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2015-08-01

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

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

PubMed Central

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

2015-01-01

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

Sparsity enables estimation of both subcortical and cortical activity from MEG and EEG

PubMed Central

Krishnaswamy, Pavitra; Obregon-Henao, Gabriel; Ahveninen, Jyrki; Khan, Sheraz; Iglesias, Juan Eugenio; Hämäläinen, Matti S.; Purdon, Patrick L.

2017-01-01

Subcortical structures play a critical role in brain function. However, options for assessing electrophysiological activity in these structures are limited. Electromagnetic fields generated by neuronal activity in subcortical structures can be recorded noninvasively, using magnetoencephalography (MEG) and electroencephalography (EEG). However, these subcortical signals are much weaker than those generated by cortical activity. In addition, we show here that it is difficult to resolve subcortical sources because distributed cortical activity can explain the MEG and EEG patterns generated by deep sources. We then demonstrate that if the cortical activity is spatially sparse, both cortical and subcortical sources can be resolved with M/EEG. Building on this insight, we develop a hierarchical sparse inverse solution for M/EEG. We assess the performance of this algorithm on realistic simulations and auditory evoked response data, and show that thalamic and brainstem sources can be correctly estimated in the presence of cortical activity. Our work provides alternative perspectives and tools for characterizing electrophysiological activity in subcortical structures in the human brain. PMID:29138310

W. Grey Walter, pioneer in the electroencephalogram, robotics, cybernetics, artificial intelligence.

PubMed

Bladin, Peter F

2006-02-01

With the announcement by William Lennox at the 1935 London International Neurology Congress of the use of electroencephalography in the study of epilepsy, it became evident that a new and powerful technique for the investigation of seizures had been discovered. William Grey Walter, a young researcher finishing his post-graduate studies at Cambridge, was selected to construct and study the EEG in clinical neurology at the Maudsley Hospital, London. His hugely productive pioneering career in the use of EEG would eventually lead to groundbreaking work in other fields --the emerging sciences of robotics, cybernetics, and early work in artificial intelligence. In this historical note his pioneering work in the fields of clinical neurophysiology is documented, both in the areas of epileptology and tumour detection. His landmark contributions to clinical neurophysiology are worthy of documentation.

Neurophysiological Studies of Auditory Verbal Hallucinations

PubMed Central

Ford, Judith M.; Dierks, Thomas; Fisher, Derek J.; Herrmann, Christoph S.; Hubl, Daniela; Kindler, Jochen; Koenig, Thomas; Mathalon, Daniel H.; Spencer, Kevin M.; Strik, Werner; van Lutterveld, Remko

2012-01-01

We discuss 3 neurophysiological approaches to study auditory verbal hallucinations (AVH). First, we describe “state” (or symptom capture) studies where periods with and without hallucinations are compared “within” a patient. These studies take 2 forms: passive studies, where brain activity during these states is compared, and probe studies, where brain responses to sounds during these states are compared. EEG (electroencephalography) and MEG (magnetoencephalography) data point to frontal and temporal lobe activity, the latter resulting in competition with external sounds for auditory resources. Second, we discuss “trait” studies where EEG and MEG responses to sounds are recorded from patients who hallucinate and those who do not. They suggest a tendency to hallucinate is associated with competition for auditory processing resources. Third, we discuss studies addressing possible mechanisms of AVH, including spontaneous neural activity, abnormal self-monitoring, and dysfunctional interregional communication. While most studies show differences in EEG and MEG responses between patients and controls, far fewer show symptom relationships. We conclude that efforts to understand the pathophysiology of AVH using EEG and MEG have been hindered by poor anatomical resolution of the EEG and MEG measures, poor assessment of symptoms, poor understanding of the phenomenon, poor models of the phenomenon, decoupling of the symptoms from the neurophysiology due to medications and comorbidites, and the possibility that the schizophrenia diagnosis breeds truer than the symptoms it comprises. These problems are common to studies of other psychiatric symptoms and should be considered when attempting to understand the basic neural mechanisms responsible for them. PMID:22368236

Stimulus-dependent spiking relationships with the EEG

PubMed Central

Snyder, Adam C.

2015-01-01

The development and refinement of noninvasive techniques for imaging neural activity is of paramount importance for human neuroscience. Currently, the most accessible and popular technique is electroencephalography (EEG). However, nearly all of what we know about the neural events that underlie EEG signals is based on inference, because of the dearth of studies that have simultaneously paired EEG recordings with direct recordings of single neurons. From the perspective of electrophysiologists there is growing interest in understanding how spiking activity coordinates with large-scale cortical networks. Evidence from recordings at both scales highlights that sensory neurons operate in very distinct states during spontaneous and visually evoked activity, which appear to form extremes in a continuum of coordination in neural networks. We hypothesized that individual neurons have idiosyncratic relationships to large-scale network activity indexed by EEG signals, owing to the neurons' distinct computational roles within the local circuitry. We tested this by recording neuronal populations in visual area V4 of rhesus macaques while we simultaneously recorded EEG. We found substantial heterogeneity in the timing and strength of spike-EEG relationships and that these relationships became more diverse during visual stimulation compared with the spontaneous state. The visual stimulus apparently shifts V4 neurons from a state in which they are relatively uniformly embedded in large-scale network activity to a state in which their distinct roles within the local population are more prominent, suggesting that the specific way in which individual neurons relate to EEG signals may hold clues regarding their computational roles. PMID:26108954

Validation of a smartphone-based EEG among people with epilepsy: A prospective study

PubMed Central

McKenzie, Erica D.; Lim, Andrew S. P.; Leung, Edward C. W.; Cole, Andrew J.; Lam, Alice D.; Eloyan, Ani; Nirola, Damber K.; Tshering, Lhab; Thibert, Ronald; Garcia, Rodrigo Zepeda; Bui, Esther; Deki, Sonam; Lee, Liesly; Clark, Sarah J.; Cohen, Joseph M.; Mantia, Jo; Brizzi, Kate T.; Sorets, Tali R.; Wahlster, Sarah; Borzello, Mia; Stopczynski, Arkadiusz; Cash, Sydney S.; Mateen, Farrah J.

2017-01-01

Our objective was to assess the ability of a smartphone-based electroencephalography (EEG) application, the Smartphone Brain Scanner-2 (SBS2), to detect epileptiform abnormalities compared to standard clinical EEG. The SBS2 system consists of an Android tablet wirelessly connected to a 14-electrode EasyCap headset (cost ~ 300 USD). SBS2 and standard EEG were performed in people with suspected epilepsy in Bhutan (2014–2015), and recordings were interpreted by neurologists. Among 205 participants (54% female, median age 24 years), epileptiform discharges were detected on 14% of SBS2 and 25% of standard EEGs. The SBS2 had 39.2% sensitivity (95% confidence interval (CI) 25.8%, 53.9%) and 94.8% specificity (95% CI 90.0%, 97.7%) for epileptiform discharges with positive and negative predictive values of 0.71 (95% CI 0.51, 0.87) and 0.82 (95% CI 0.76, 0.89) respectively. 31% of focal and 82% of generalized abnormalities were identified on SBS2 recordings. Cohen’s kappa (κ) for the SBS2 EEG and standard EEG for the epileptiform versus non-epileptiform outcome was κ = 0.40 (95% CI 0.25, 0.55). No safety or tolerability concerns were reported. Despite limitations in sensitivity, the SBS2 may become a viable supportive test for the capture of epileptiform abnormalities, and extend EEG access to new, especially resource-limited, populations at a reduced cost. PMID:28367974

A single camera photogrammetry system for multi-angle fast localization of EEG electrodes.

PubMed

Qian, Shuo; Sheng, Yang

2011-11-01

Photogrammetry has become an effective method for the determination of electroencephalography (EEG) electrode positions in three dimensions (3D). Capturing multi-angle images of the electrodes on the head is a fundamental objective in the design of photogrammetry system for EEG localization. Methods in previous studies are all based on the use of either a rotating camera or multiple cameras, which are time-consuming or not cost-effective. This study aims to present a novel photogrammetry system that can realize simultaneous acquisition of multi-angle head images in a single camera position. Aligning two planar mirrors with the angle of 51.4°, seven views of the head with 25 electrodes are captured simultaneously by the digital camera placed in front of them. A complete set of algorithms for electrode recognition, matching, and 3D reconstruction is developed. It is found that the elapsed time of the whole localization procedure is about 3 min, and camera calibration computation takes about 1 min, after the measurement of calibration points. The positioning accuracy with the maximum error of 1.19 mm is acceptable. Experimental results demonstrate that the proposed system provides a fast and cost-effective method for the EEG positioning.

Sustained Attention in Real Classroom Settings: An EEG Study.

PubMed

Ko, Li-Wei; Komarov, Oleksii; Hairston, W David; Jung, Tzyy-Ping; Lin, Chin-Teng

2017-01-01

Sustained attention is a process that enables the maintenance of response persistence and continuous effort over extended periods of time. Performing attention-related tasks in real life involves the need to ignore a variety of distractions and inhibit attention shifts to irrelevant activities. This study investigates electroencephalography (EEG) spectral changes during a sustained attention task within a real classroom environment. Eighteen healthy students were instructed to recognize as fast as possible special visual targets that were displayed during regular university lectures. Sorting their EEG spectra with respect to response times, which indicated the level of visual alertness to randomly introduced visual stimuli, revealed significant changes in the brain oscillation patterns. The results of power-frequency analysis demonstrated a relationship between variations in the EEG spectral dynamics and impaired performance in the sustained attention task. Across subjects and sessions, prolongation of the response time was preceded by an increase in the delta and theta EEG powers over the occipital region, and decrease in the beta power over the occipital and temporal regions. Meanwhile, implementation of the complex attention task paradigm into a real-world classroom setting makes it possible to investigate specific mutual links between brain activities and factors that cause impaired behavioral performance, such as development and manifestation of classroom mental fatigue. The findings of the study set a basis for developing a system capable of estimating the level of visual attention during real classroom activities by monitoring changes in the EEG spectra.

Sustained Attention in Real Classroom Settings: An EEG Study

PubMed Central

Ko, Li-Wei; Komarov, Oleksii; Hairston, W. David; Jung, Tzyy-Ping; Lin, Chin-Teng

2017-01-01

Sustained attention is a process that enables the maintenance of response persistence and continuous effort over extended periods of time. Performing attention-related tasks in real life involves the need to ignore a variety of distractions and inhibit attention shifts to irrelevant activities. This study investigates electroencephalography (EEG) spectral changes during a sustained attention task within a real classroom environment. Eighteen healthy students were instructed to recognize as fast as possible special visual targets that were displayed during regular university lectures. Sorting their EEG spectra with respect to response times, which indicated the level of visual alertness to randomly introduced visual stimuli, revealed significant changes in the brain oscillation patterns. The results of power-frequency analysis demonstrated a relationship between variations in the EEG spectral dynamics and impaired performance in the sustained attention task. Across subjects and sessions, prolongation of the response time was preceded by an increase in the delta and theta EEG powers over the occipital region, and decrease in the beta power over the occipital and temporal regions. Meanwhile, implementation of the complex attention task paradigm into a real-world classroom setting makes it possible to investigate specific mutual links between brain activities and factors that cause impaired behavioral performance, such as development and manifestation of classroom mental fatigue. The findings of the study set a basis for developing a system capable of estimating the level of visual attention during real classroom activities by monitoring changes in the EEG spectra. PMID:28824396

BLINKER: Automated Extraction of Ocular Indices from EEG Enabling Large-Scale Analysis

PubMed Central

Kleifges, Kelly; Bigdely-Shamlo, Nima; Kerick, Scott E.; Robbins, Kay A.

2017-01-01

Electroencephalography (EEG) offers a platform for studying the relationships between behavioral measures, such as blink rate and duration, with neural correlates of fatigue and attention, such as theta and alpha band power. Further, the existence of EEG studies covering a variety of subjects and tasks provides opportunities for the community to better characterize variability of these measures across tasks and subjects. We have implemented an automated pipeline (BLINKER) for extracting ocular indices such as blink rate, blink duration, and blink velocity-amplitude ratios from EEG channels, EOG channels, and/or independent components (ICs). To illustrate the use of our approach, we have applied the pipeline to a large corpus of EEG data (comprising more than 2000 datasets acquired at eight different laboratories) in order to characterize variability of certain ocular indicators across subjects. We also investigate dependence of ocular indices on task in a shooter study. We have implemented our algorithms in a freely available MATLAB toolbox called BLINKER. The toolbox, which is easy to use and can be applied to collections of data without user intervention, can automatically discover which channels or ICs capture blinks. The tools extract blinks, calculate common ocular indices, generate a report for each dataset, dump labeled images of the individual blinks, and provide summary statistics across collections. Users can run BLINKER as a script or as a plugin for EEGLAB. The toolbox is available at https://github.com/VisLab/EEG-Blinks. User documentation and examples appear at http://vislab.github.io/EEG-Blinks/. PMID:28217081

A comparative study of electrical potential sensors and Ag/AgCl electrodes for characterising spontaneous and event related electroencephalagram signals.

PubMed

Fatoorechi, M; Parkinson, J; Prance, R J; Prance, H; Seth, A K; Schwartzman, D J

2015-08-15

Electroencephalography (EEG) is still a widely used imaging tool that combines high temporal resolution with a relatively low cost. Ag/AgCl metal electrodes have been the gold standard for non-invasively monitoring electrical brain activity. Although reliable, these electrodes have multiple drawbacks: they suffer from noise, such as offset potential drift, and usability issues, for example, difficult skin preparation and cross-coupling of adjacent electrodes. In order to tackle these issues a prototype Electric Potential Sensor (EPS) device based on an auto-zero operational amplifier was developed and evaluated. The EPS is a novel active ultrahigh impedance capacitively coupled sensor. The absence of 1/f noise makes the EPS ideal for use with signal frequencies of ∼10Hz or less. A comprehensive study was undertaken to compare neural signals recorded by the EPS with a standard commercial EEG system. Quantitatively, highly similar signals were observed between the EPS and EEG sensors for both free running and evoked brain activity with cross correlations of higher than 0.9 between the EPS and a standard benchmark EEG system. These studies comprised measurements of both free running EEG and Event Related Potentials (ERPs) from a commercial EEG system and EPS. The EPS provides a promising alternative with many added benefits compared to standard EEG sensors, including reduced setup time and elimination of sensor cross-coupling. In the future the scalability of the EPS will allow the implementation of a whole head ultra-dense EPS array. Copyright © 2015 Elsevier B.V. All rights reserved.

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

PubMed

Ding, Lei; Yuan, Han

2013-04-01

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

Resting State EEG-based biometrics for individual identification using convolutional neural networks.

PubMed

Lan Ma; Minett, James W; Blu, Thierry; Wang, William S-Y

2015-08-01

Biometrics is a growing field, which permits identification of individuals by means of unique physical features. Electroencephalography (EEG)-based biometrics utilizes the small intra-personal differences and large inter-personal differences between individuals' brainwave patterns. In the past, such methods have used features derived from manually-designed procedures for this purpose. Another possibility is to use convolutional neural networks (CNN) to automatically extract an individual's best and most unique neural features and conduct classification, using EEG data derived from both Resting State with Open Eyes (REO) and Resting State with Closed Eyes (REC). Results indicate that this CNN-based joint-optimized EEG-based Biometric System yields a high degree of accuracy of identification (88%) for 10-class classification. Furthermore, rich inter-personal difference can be found using a very low frequency band (0-2Hz). Additionally, results suggest that the temporal portions over which subjects can be individualized is less than 200 ms.

[Electroencephalography (EEG) recording techniques and artifact detection in early premature babies].

PubMed

Wallois, F; Vecchierini, M-F; Héberlé, C; Walls-Esquivel, E

2007-01-01

EEG recording techniques in early premature babies are not very different from those used for full-term neonates. Here, we emphasise the most important points: asepsis precautions, full knowledge of the clinical data and drug therapies, the fundamental role of a well-trained technician in supervising the EEG recording and monitoring the baby. The best electrode positions, the most informative montages and their standardisation between neurophysiological laboratories, are suggested. Artifact detection constitutes an important aspect of EEG signal analysis in preterm babies of less than 30 weeks. It is obviously necessary to discriminate between meaningful information and artefacts. The complexity of the signal in neonates makes artifact detection difficult. We present some characteristic features and describe some methods for eliminating them. We underline the positive aspect of some artifacts and their clinical use. We emphasise the crucial role of the technicians.

Single-channel EEG-based mental fatigue detection based on deep belief network.

PubMed

Pinyi Li; Wenhui Jiang; Fei Su

2016-08-01

Mental fatigue has a pernicious influence on road and work place safety as well as a negative symptom of many acute and chronic illnesses, since the ability of concentrating, responding and judging quickly decreases during the fatigue or drowsiness stage. Electroencephalography (EEG) has been proven to be a robust physiological indicator of human cognitive state over the last few decades. But most existing EEG-based fatigue detection methods have poor performance in accuracy. This paper proposed a single-channel EEG-based mental fatigue detection method based on Deep Belief Network (DBN). The fused nonliear features from specified sub-bands and dynamic analysis, a total of 21 features are extracted as the input of the DBN to discriminate three classes of mental state including alert, slight fatigue and severe fatigue. Experimental results show the good performance of the proposed model comparing with those state-of-art methods.

EEG-MEG Integration Enhances the Characterization of Functional and Effective Connectivity in the Resting State Network

PubMed Central

Mideksa, Kidist Gebremariam; Anwar, Abdul Rauf; Stephani, Ulrich; Deuschl, Günther; Freitag, Christine M.; Siniatchkin, Michael

2015-01-01

At the sensor level many aspects, such as spectral power, functional and effective connectivity as well as relative-power-ratio ratio (RPR) and spatial resolution have been comprehensively investigated through both electroencephalography (EEG) and magnetoencephalography (MEG). Despite this, differences between both modalities have not yet been systematically studied by direct comparison. It remains an open question as to whether the integration of EEG and MEG data would improve the information obtained from the above mentioned parameters. Here, EEG (64-channel system) and MEG (275 sensor system) were recorded simultaneously in conditions with eyes open (EO) and eyes closed (EC) in 29 healthy adults. Spectral power, functional and effective connectivity, RPR, and spatial resolution were analyzed at five different frequency bands (delta, theta, alpha, beta and gamma). Networks of functional and effective connectivity were described using a spatial filter approach called the dynamic imaging of coherent sources (DICS) followed by the renormalized partial directed coherence (RPDC). Absolute mean power at the sensor level was significantly higher in EEG than in MEG data in both EO and EC conditions. At the source level, there was a trend towards a better performance of the combined EEG+MEG analysis compared with separate EEG or MEG analyses for the source mean power, functional correlation, effective connectivity for both EO and EC. The network of coherent sources and the spatial resolution were similar for both the EEG and MEG data if they were analyzed separately. Results indicate that the combined approach has several advantages over the separate analyses of both EEG and MEG. Moreover, by a direct comparison of EEG and MEG, EEG was characterized by significantly higher values in all measured parameters in both sensor and source level. All the above conclusions are specific to the resting state task and the specific analysis used in this study to have general conclusion multi-center studies would be helpful. PMID:26509448

Integrating EEG and fMRI in epilepsy.

PubMed

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

2011-02-14

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

The Use of Electroencephalography in Language Production Research: A Review

PubMed Central

Ganushchak, Lesya Y.; Christoffels, Ingrid K.; Schiller, Niels O.

2011-01-01

Speech production long avoided electrophysiological experiments due to the suspicion that potential artifacts caused by muscle activity of overt speech may lead to a bad signal-to-noise ratio in the measurements. Therefore, researchers have sought to assess speech production by using indirect speech production tasks, such as tacit or implicit naming, delayed naming, or meta-linguistic tasks, such as phoneme-monitoring. Covert speech may, however, involve different processes than overt speech production. Recently, overt speech has been investigated using electroencephalography (EEG). As the number of papers published is rising steadily, this clearly indicates the increasing interest and demand for overt speech research within the field of cognitive neuroscience of language. Our main goal here is to review all currently available results of overt speech production involving EEG measurements, such as picture naming, Stroop naming, and reading aloud. We conclude that overt speech production can be successfully studied using electrophysiological measures, for instance, event-related brain potentials (ERPs). We will discuss possible relevant components in the ERP waveform of speech production and aim to address the issue of how to interpret the results of ERP research using overt speech, and whether the ERP components in language production are comparable to results from other fields. PMID:21909333

Revealing hidden states in visual working memory using electroencephalography

PubMed Central

Wolff, Michael J.; Ding, Jacqueline; Myers, Nicholas E.; Stokes, Mark G.

2015-01-01

It is often assumed that information in visual working memory (vWM) is maintained via persistent activity. However, recent evidence indicates that information in vWM could be maintained in an effectively “activity-silent” neural state. Silent vWM is consistent with recent cognitive and neural models, but poses an important experimental problem: how can we study these silent states using conventional measures of brain activity? We propose a novel approach that is analogous to echolocation: using a high-contrast visual stimulus, it may be possible to drive brain activity during vWM maintenance and measure the vWM-dependent impulse response. We recorded electroencephalography (EEG) while participants performed a vWM task in which a randomly oriented grating was remembered. Crucially, a high-contrast, task-irrelevant stimulus was shown in the maintenance period in half of the trials. The electrophysiological response from posterior channels was used to decode the orientations of the gratings. While orientations could be decoded during and shortly after stimulus presentation, decoding accuracy dropped back close to baseline in the delay. However, the visual evoked response from the task-irrelevant stimulus resulted in a clear re-emergence in decodability. This result provides important proof-of-concept for a promising and relatively simple approach to decode “activity-silent” vWM content using non-invasive EEG. PMID:26388748

Multichannel continuous electroencephalography-functional near-infrared spectroscopy recording of focal seizures and interictal epileptiform discharges in human epilepsy: a review

PubMed Central

Peng, Ke; Pouliot, Philippe; Lesage, Frédéric; Nguyen, Dang Khoa

2016-01-01

Abstract. Functional near-infrared spectroscopy (fNIRS) has emerged as a promising neuroimaging technique as it allows noninvasive and long-term monitoring of cortical hemodynamics. Recent work by our group and others has revealed the potential of fNIRS, combined with electroencephalography (EEG), in the context of human epilepsy. Hemodynamic brain responses attributed to epileptic events, such as seizures and interictal epileptiform discharges (IEDs), are routinely observed with a good degree of statistical significance and in concordance with clinical presentation. Recording done with over 100 channels allows sufficiently large coverage of the epileptic focus and other areas. Three types of seizures have been documented: frontal lobe seizures, temporal lobe seizures, and posterior seizures. Increased oxygenation was observed in the epileptic focus in most cases, while rapid but similar hemodynamic variations were identified in the contralateral homologous region. While investigating IEDs, it was shown that their hemodynamic effect is observable with fNIRS, that their response is associated with significant (inhibitive) nonlinearities, and that the sensitivity and specificity of fNIRS to localize the epileptic focus can be estimated in a sample of 40 patients. This paper first reviews recent EEG-fNIRS developments in epilepsy research and then describes applications to the study of focal seizures and IEDs. PMID:26958576

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

PubMed

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

2017-07-01

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

Modeling EEG Waveforms with Semi-Supervised Deep Belief Nets: Fast Classification and Anomaly Measurement

PubMed Central

Wulsin, D. F.; Gupta, J. R.; Mani, R.; Blanco, J. A.; Litt, B.

2011-01-01

Clinical electroencephalography (EEG) records vast amounts of human complex data yet is still reviewed primarily by human readers. Deep Belief Nets (DBNs) are a relatively new type of multi-layer neural network commonly tested on two-dimensional image data, but are rarely applied to times-series data such as EEG. We apply DBNs in a semi-supervised paradigm to model EEG waveforms for classification and anomaly detection. DBN performance was comparable to standard classifiers on our EEG dataset, and classification time was found to be 1.7 to 103.7 times faster than the other high-performing classifiers. We demonstrate how the unsupervised step of DBN learning produces an autoencoder that can naturally be used in anomaly measurement. We compare the use of raw, unprocessed data—a rarity in automated physiological waveform analysis—to hand-chosen features and find that raw data produces comparable classification and better anomaly measurement performance. These results indicate that DBNs and raw data inputs may be more effective for online automated EEG waveform recognition than other common techniques. PMID:21525569

Localization Accuracy of Distributed Inverse Solutions for Electric and Magnetic Source Imaging of Interictal Epileptic Discharges in Patients with Focal Epilepsy.

PubMed

Heers, Marcel; Chowdhury, Rasheda A; Hedrich, Tanguy; Dubeau, François; Hall, Jeffery A; Lina, Jean-Marc; Grova, Christophe; Kobayashi, Eliane

2016-01-01

Distributed inverse solutions aim to realistically reconstruct the origin of interictal epileptic discharges (IEDs) from noninvasively recorded electroencephalography (EEG) and magnetoencephalography (MEG) signals. Our aim was to compare the performance of different distributed inverse solutions in localizing IEDs: coherent maximum entropy on the mean (cMEM), hierarchical Bayesian implementations of independent identically distributed sources (IID, minimum norm prior) and spatially coherent sources (COH, spatial smoothness prior). Source maxima (i.e., the vertex with the maximum source amplitude) of IEDs in 14 EEG and 19 MEG studies from 15 patients with focal epilepsy were analyzed. We visually compared their concordance with intracranial EEG (iEEG) based on 17 cortical regions of interest and their spatial dispersion around source maxima. Magnetic source imaging (MSI) maxima from cMEM were most often confirmed by iEEG (cMEM: 14/19, COH: 9/19, IID: 8/19 studies). COH electric source imaging (ESI) maxima co-localized best with iEEG (cMEM: 8/14, COH: 11/14, IID: 10/14 studies). In addition, cMEM was less spatially spread than COH and IID for ESI and MSI (p < 0.001 Bonferroni-corrected post hoc t test). Highest positive predictive values for cortical regions with IEDs in iEEG could be obtained with cMEM for MSI and with COH for ESI. Additional realistic EEG/MEG simulations confirmed our findings. Accurate spatially extended sources, as found in cMEM (ESI and MSI) and COH (ESI) are desirable for source imaging of IEDs because this might influence surgical decision. Our simulations suggest that COH and IID overestimate the spatial extent of the generators compared to cMEM.

Discrete Scale Invariance of Human Large EEG Voltage Deflections is More Prominent in Waking than Sleep Stage 2.

PubMed

Zorick, Todd; Mandelkern, Mark A

2015-01-01

Electroencephalography (EEG) is typically viewed through the lens of spectral analysis. Recently, multiple lines of evidence have demonstrated that the underlying neuronal dynamics are characterized by scale-free avalanches. These results suggest that techniques from statistical physics may be used to analyze EEG signals. We utilized a publicly available database of fourteen subjects with waking and sleep stage 2 EEG tracings per subject, and observe that power-law dynamics of critical-state neuronal avalanches are not sufficient to fully describe essential features of EEG signals. We hypothesized that this could reflect the phenomenon of discrete scale invariance (DSI) in EEG large voltage deflections (LVDs) as being more prominent in waking consciousness. We isolated LVDs, and analyzed logarithmically transformed LVD size probability density functions (PDF) to assess for DSI. We find evidence of increased DSI in waking, as opposed to sleep stage 2 consciousness. We also show that the signatures of DSI are specific for EEG LVDs, and not a general feature of fractal simulations with similar statistical properties to EEG. Removing only LVDs from waking EEG produces a reduction in power in the alpha and beta frequency bands. These findings may represent a new insight into the understanding of the cortical dynamics underlying consciousness.

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

PubMed Central

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

2014-01-01

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

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

PubMed

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

2014-05-30

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

A Customizable and Expandable Electroencephalography (EEG) Data Collection System

DTIC Science & Technology

2016-03-01

devices, including Emotiv Systems3 and Advanced Brain Monitoring,4 as well as open source alternatives such as OpenBCI.5 These products generally...Analysis, Inc and ACI Society; 2006. p. 91–101. 3. Emotiv . San Francisco (CA): Emotiv , Inc; c2011 – 2015 [accessed 2015 Mar 6]. http://emotiv.com/. 4

The Use of Computer Networks in Data Gathering and Data Analysis.

ERIC Educational Resources Information Center

Yost, Michael; Bremner, Fred

This document describes the review, analysis, and decision-making process that Trinity University, Texas, went through to develop the three-part computer network that they use to gather and analyze EEG (electroencephalography) and EKG (electrocardiogram) data. The data are gathered in the laboratory on a PDP-1124, an analog minicomputer. Once…

Resting-state EEG power and coherence vary between migraine phases.

PubMed

Cao, Zehong; Lin, Chin-Teng; Chuang, Chun-Hsiang; Lai, Kuan-Lin; Yang, Albert C; Fuh, Jong-Ling; Wang, Shuu-Jiun

2016-12-01

Migraine is characterized by a series of phases (inter-ictal, pre-ictal, ictal, and post-ictal). It is of great interest whether resting-state electroencephalography (EEG) is differentiable between these phases. We compared resting-state EEG energy intensity and effective connectivity in different migraine phases using EEG power and coherence analyses in patients with migraine without aura as compared with healthy controls (HCs). EEG power and isolated effective coherence of delta (1-3.5 Hz), theta (4-7.5 Hz), alpha (8-12.5 Hz), and beta (13-30 Hz) bands were calculated in the frontal, central, temporal, parietal, and occipital regions. Fifty patients with episodic migraine (1-5 headache days/month) and 20 HCs completed the study. Patients were classified into inter-ictal, pre-ictal, ictal, and post-ictal phases (n = 22, 12, 8, 8, respectively), using 36-h criteria. Compared to HCs, inter-ictal and ictal patients, but not pre- or post-ictal patients, had lower EEG power and coherence, except for a higher effective connectivity in fronto-occipital network in inter-ictal patients (p < .05). Compared to data obtained from the inter-ictal group, EEG power and coherence were increased in the pre-ictal group, with the exception of a lower effective connectivity in fronto-occipital network (p < .05). Inter-ictal and ictal patients had decreased EEG power and coherence relative to HCs, which were "normalized" in the pre-ictal or post-ictal groups. Resting-state EEG power density and effective connectivity differ between migraine phases and provide an insight into the complex neurophysiology of migraine.

The spectrum of epilepsy and electroencephalographic abnormalities due to SHANK3 loss-of-function mutations.

PubMed

Holder, J Lloyd; Quach, Michael M

2016-10-01

The coincidence of autism with epilepsy is 27% in those individuals with intellectual disability. 1 Individuals with loss-of-function mutations in SHANK3 have intellectual disability, autism, and variably, epilepsy. 2-5 The spectrum of seizure semiologies and electroencephalography (EEG) abnormalities has never been investigated in detail. With the recent report that SHANK3 mutations are present in approximately 2% of individuals with moderate to severe intellectual disabilities and 1% of individuals with autism, determining the spectrum of seizure semiologies and electrographic abnormalities will be critical for medical practitioners to appropriately counsel the families of patients with SHANK3 mutations. A retrospective chart review was performed of all individuals treated at the Blue Bird Circle Clinic for Child Neurology who have been identified as having either a chromosome 22q13 microdeletion encompassing SHANK3 or a loss-of-function mutation in SHANK3 identified through whole-exome sequencing. For each subject, the presence or absence of seizures, seizure semiology, frequency, age of onset, and efficacy of therapy were determined. Electroencephalography studies were reviewed by a board certified neurophysiologist. Neuroimaging was reviewed by both a board certified pediatric neuroradiologist and child neurologist. There is a wide spectrum of seizure semiologies, frequencies, and severity in individuals with SHANK3 mutations. There are no specific EEG abnormalities found in our cohort, and EEG abnormalities were present in individuals diagnosed with epilepsy and those without history of a clinical seizure. All individuals with a mutation in SHANK3 should be evaluated for epilepsy due to the high prevalence of seizures in this population. The most common semiology is atypical absence seizure, which can be challenging to identify due to comorbid intellectual disability in individuals with SHANK3 mutations; however, no consistent seizure semiology, neuroimaging findings, or EEG findings were present in the majority of individuals with SHANK3 mutations. Wiley Periodicals, Inc. © 2016 International League Against Epilepsy.

Assessment of mental stress effects on prefrontal cortical activities using canonical correlation analysis: an fNIRS-EEG study.

PubMed

Al-Shargie, Fares; Tang, Tong Boon; Kiguchi, Masashi

2017-05-01

This paper presents an investigation about the effects of mental stress on prefrontal cortex (PFC) subregions using simultaneous measurement of functional Near-Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG) signals. The aim is to explore canonical correlation analysis (CCA) technique to study the relationship among the bi-modality signals in mental stress assessment, and how we could fuse the signals for better accuracy in stress detection. Twenty-five male healthy subjects participated in the study while performing mental arithmetic task under control and stress (under time pressure with negative feedback) conditions. The fusion of brain signals acquired by fNIRS-EEG was performed at feature-level using CCA by maximizing the inter-subject covariance across modalities. The CCA result discovered the associations across the modalities and estimated the components responsible for these associations. The experiment results showed that mental stress experienced by this cohort of subjects is subregion specific and localized to the right ventrolateral PFC subregion. These suggest the right ventrolateral PFC as a suitable candidate region to extract biomarkers as performance indicators of neurofeedback training in stress coping.

Single-channel in-ear-EEG detects the focus of auditory attention to concurrent tone streams and mixed speech

NASA Astrophysics Data System (ADS)

Fiedler, Lorenz; Wöstmann, Malte; Graversen, Carina; Brandmeyer, Alex; Lunner, Thomas; Obleser, Jonas

2017-06-01

Objective. Conventional, multi-channel scalp electroencephalography (EEG) allows the identification of the attended speaker in concurrent-listening (‘cocktail party’) scenarios. This implies that EEG might provide valuable information to complement hearing aids with some form of EEG and to install a level of neuro-feedback. Approach. To investigate whether a listener’s attentional focus can be detected from single-channel hearing-aid-compatible EEG configurations, we recorded EEG from three electrodes inside the ear canal (‘in-Ear-EEG’) and additionally from 64 electrodes on the scalp. In two different, concurrent listening tasks, participants (n  =  7) were fitted with individualized in-Ear-EEG pieces and were either asked to attend to one of two dichotically-presented, concurrent tone streams or to one of two diotically-presented, concurrent audiobooks. A forward encoding model was trained to predict the EEG response at single EEG channels. Main results. Each individual participants’ attentional focus could be detected from single-channel EEG response recorded from short-distance configurations consisting only of a single in-Ear-EEG electrode and an adjacent scalp-EEG electrode. The differences in neural responses to attended and ignored stimuli were consistent in morphology (i.e. polarity and latency of components) across subjects. Significance. In sum, our findings show that the EEG response from a single-channel, hearing-aid-compatible configuration provides valuable information to identify a listener’s focus of attention.

A pipeline VLSI design of fast singular value decomposition processor for real-time EEG system based on on-line recursive independent component analysis.

PubMed

Huang, Kuan-Ju; Shih, Wei-Yeh; Chang, Jui Chung; Feng, Chih Wei; Fang, Wai-Chi

2013-01-01

This paper presents a pipeline VLSI design of fast singular value decomposition (SVD) processor for real-time electroencephalography (EEG) system based on on-line recursive independent component analysis (ORICA). Since SVD is used frequently in computations of the real-time EEG system, a low-latency and high-accuracy SVD processor is essential. During the EEG system process, the proposed SVD processor aims to solve the diagonal, inverse and inverse square root matrices of the target matrices in real time. Generally, SVD requires a huge amount of computation in hardware implementation. Therefore, this work proposes a novel design concept for data flow updating to assist the pipeline VLSI implementation. The SVD processor can greatly improve the feasibility of real-time EEG system applications such as brain computer interfaces (BCIs). The proposed architecture is implemented using TSMC 90 nm CMOS technology. The sample rate of EEG raw data adopts 128 Hz. The core size of the SVD processor is 580×580 um(2), and the speed of operation frequency is 20MHz. It consumes 0.774mW of power during the 8-channel EEG system per execution time.

Simultaneous ocular and muscle artifact removal from EEG data by exploiting diverse statistics.

PubMed

Chen, Xun; Liu, Aiping; Chen, Qiang; Liu, Yu; Zou, Liang; McKeown, Martin J

2017-09-01

Electroencephalography (EEG) recordings are frequently contaminated by both ocular and muscle artifacts. These are normally dealt with separately, by employing blind source separation (BSS) techniques relying on either second-order or higher-order statistics (SOS & HOS respectively). When HOS-based methods are used, it is usually in the setting of assuming artifacts are statistically independent to the EEG. When SOS-based methods are used, it is assumed that artifacts have autocorrelation characteristics distinct from the EEG. In reality, ocular and muscle artifacts do not completely follow the assumptions of strict temporal independence to the EEG nor completely unique autocorrelation characteristics, suggesting that exploiting HOS or SOS alone may be insufficient to remove these artifacts. Here we employ a novel BSS technique, independent vector analysis (IVA), to jointly employ HOS and SOS simultaneously to remove ocular and muscle artifacts. Numerical simulations and application to real EEG recordings were used to explore the utility of the IVA approach. IVA was superior in isolating both ocular and muscle artifacts, especially for raw EEG data with low signal-to-noise ratio, and also integrated usually separate SOS and HOS steps into a single unified step. Copyright © 2017 Elsevier Ltd. All rights reserved.

Ongoing slow oscillatory phase modulates speech intelligibility in cooperation with motor cortical activity.

PubMed

Onojima, Takayuki; Kitajo, Keiichi; Mizuhara, Hiroaki

2017-01-01

Neural oscillation is attracting attention as an underlying mechanism for speech recognition. Speech intelligibility is enhanced by the synchronization of speech rhythms and slow neural oscillation, which is typically observed as human scalp electroencephalography (EEG). In addition to the effect of neural oscillation, it has been proposed that speech recognition is enhanced by the identification of a speaker's motor signals, which are used for speech production. To verify the relationship between the effect of neural oscillation and motor cortical activity, we measured scalp EEG, and simultaneous EEG and functional magnetic resonance imaging (fMRI) during a speech recognition task in which participants were required to recognize spoken words embedded in noise sound. We proposed an index to quantitatively evaluate the EEG phase effect on behavioral performance. The results showed that the delta and theta EEG phase before speech inputs modulated the participant's response time when conducting speech recognition tasks. The simultaneous EEG-fMRI experiment showed that slow EEG activity was correlated with motor cortical activity. These results suggested that the effect of the slow oscillatory phase was associated with the activity of the motor cortex during speech recognition.

Use of Electroencephalography Brain-Computer Interface Systems as a Rehabilitative Approach for Upper Limb Function After a Stroke: A Systematic Review.

PubMed

Monge-Pereira, Esther; Ibañez-Pereda, Jaime; Alguacil-Diego, Isabel M; Serrano, Jose I; Spottorno-Rubio, María P; Molina-Rueda, Francisco

2017-09-01

Brain-computer interface (BCI) systems have been suggested as a promising tool for neurorehabilitation. However, to date, there is a lack of homogeneous findings. Furthermore, no systematic reviews have analyzed the degree of validation of these interventions for upper limb (UL) motor rehabilitation poststroke. The study aims were to compile all available studies that assess an UL intervention based on an electroencephalography (EEG) BCI system in stroke; to analyze the methodological quality of the studies retrieved; and to determine the effects of these interventions on the improvement of motor abilities. TYPE: This was a systematic review. Searches were conducted in PubMed, PEDro, Embase, Cumulative Index to Nursing and Allied Health, Web of Science, and Cochrane Central Register of Controlled Trial from inception to September 30, 2015. This systematic review compiles all available studies that assess UL intervention based on an EEG-BCI system in patients with stroke, analyzing their methodological quality using the Critical Review Form for Quantitative Studies, and determining the grade of recommendation of these interventions for improving motor abilities as established by the Oxford Centre for Evidence-based Medicine. The articles were selected according to the following criteria: studies evaluating an EEG-based BCI intervention; studies including patients with a stroke and hemiplegia, regardless of lesion origin or temporal evolution; interventions using an EEG-based BCI to restore functional abilities of the affected UL, regardless of the interface used or its combination with other therapies; and studies using validated tools to evaluate motor function. After the literature search, 13 articles were included in this review: 4 studies were randomized controlled trials; 1 study was a controlled study; 4 studies were case series studies; and 4 studies were case reports. The methodological quality of the included papers ranged from 6 to 15, and the level of evidence varied from 1b to 5. The articles included in this review involved a total of 141 stroke patients. This systematic review suggests that BCI interventions may be a promising rehabilitation approach in subjects with stroke. II. Copyright © 2017 American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved.

Name that tune: decoding music from the listening brain.

PubMed

Schaefer, Rebecca S; Farquhar, Jason; Blokland, Yvonne; Sadakata, Makiko; Desain, Peter

2011-05-15

In the current study we use electroencephalography (EEG) to detect heard music from the brain signal, hypothesizing that the time structure in music makes it especially suitable for decoding perception from EEG signals. While excluding music with vocals, we classified the perception of seven different musical fragments of about three seconds, both individually and cross-participants, using only time domain information (the event-related potential, ERP). The best individual results are 70% correct in a seven-class problem while using single trials, and when using multiple trials we achieve 100% correct after six presentations of the stimulus. When classifying across participants, a maximum rate of 53% was reached, supporting a general representation of each musical fragment over participants. While for some music stimuli the amplitude envelope correlated well with the ERP, this was not true for all stimuli. Aspects of the stimulus that may contribute to the differences between the EEG responses to the pieces of music are discussed. Copyright © 2010 Elsevier Inc. All rights reserved.

Drowsiness/alertness algorithm development and validation using synchronized EEG and cognitive performance to individualize a generalized model

PubMed Central

Johnson, Robin R.; Popovic, Djordje P.; Olmstead, Richard E.; Stikic, Maja; Levendowski, Daniel J.; Berka, Chris

2011-01-01

A great deal of research over the last century has focused on drowsiness/alertness detection, as fatigue-related physical and cognitive impairments pose a serious risk to public health and safety. Available drowsiness/alertness detection solutions are unsatisfactory for a number of reasons: 1) lack of generalizability, 2) failure to address individual variability in generalized models, and/or 3) they lack a portable, un-tethered application. The current study aimed to address these issues, and determine if an individualized electroencephalography (EEG) based algorithm could be defined to track performance decrements associated with sleep loss, as this is the first step in developing a field deployable drowsiness/alertness detection system. The results indicated that an EEG-based algorithm, individualized using a series of brief "identification" tasks, was able to effectively track performance decrements associated with sleep deprivation. Future development will address the need for the algorithm to predict performance decrements due to sleep loss, and provide field applicability. PMID:21419826

Drowsiness/alertness algorithm development and validation using synchronized EEG and cognitive performance to individualize a generalized model.

PubMed

Johnson, Robin R; Popovic, Djordje P; Olmstead, Richard E; Stikic, Maja; Levendowski, Daniel J; Berka, Chris

2011-05-01

A great deal of research over the last century has focused on drowsiness/alertness detection, as fatigue-related physical and cognitive impairments pose a serious risk to public health and safety. Available drowsiness/alertness detection solutions are unsatisfactory for a number of reasons: (1) lack of generalizability, (2) failure to address individual variability in generalized models, and/or (3) lack of a portable, un-tethered application. The current study aimed to address these issues, and determine if an individualized electroencephalography (EEG) based algorithm could be defined to track performance decrements associated with sleep loss, as this is the first step in developing a field deployable drowsiness/alertness detection system. The results indicated that an EEG-based algorithm, individualized using a series of brief "identification" tasks, was able to effectively track performance decrements associated with sleep deprivation. Future development will address the need for the algorithm to predict performance decrements due to sleep loss, and provide field applicability. Copyright © 2011 Elsevier B.V. All rights reserved.

[Training cortical signals by means of a BMI-EEG system, its evolution and intervention. A case report].

PubMed

Monge-Pereira, E; Casatorres Perez-Higueras, I; Fernandez-Gonzalez, P; Ibanez-Pereda, J; Serrano, J I; Molina-Rueda, F

2017-04-16

In the last years, new technologies such as the brain-machine interfaces (BMI) have been incorporated in the rehabilitation process of subjects with stroke. These systems are able to detect motion intention, analyzing the cortical signals using different techniques such as the electroencephalography (EEG). This information could guide different interfaces such as robotic devices, electrical stimulation or virtual reality. A 40 years-old man with stroke with two months from the injury participated in this study. We used a BMI based on EEG. The subject's motion intention was analyzed calculating the event-related desynchronization. The upper limb motor function was evaluated with the Fugl-Meyer Assessment and the participant's satisfaction was evaluated using the QUEST 2.0. The intervention using a physical therapist as an interface was carried out without difficulty. The BMI systems detect cortical changes in a subacute stroke subject. These changes are coherent with the evolution observed using the Fugl-Meyer Assessment.

A Fast, Open EEG Classification Framework Based on Feature Compression and Channel Ranking

PubMed Central

Han, Jiuqi; Zhao, Yuwei; Sun, Hongji; Chen, Jiayun; Ke, Ang; Xu, Gesen; Zhang, Hualiang; Zhou, Jin; Wang, Changyong

2018-01-01

Superior feature extraction, channel selection and classification methods are essential for designing electroencephalography (EEG) classification frameworks. However, the performance of most frameworks is limited by their improper channel selection methods and too specifical design, leading to high computational complexity, non-convergent procedure and narrow expansibility. In this paper, to remedy these drawbacks, we propose a fast, open EEG classification framework centralized by EEG feature compression, low-dimensional representation, and convergent iterative channel ranking. First, to reduce the complexity, we use data clustering to compress the EEG features channel-wise, packing the high-dimensional EEG signal, and endowing them with numerical signatures. Second, to provide easy access to alternative superior methods, we structurally represent each EEG trial in a feature vector with its corresponding numerical signature. Thus, the recorded signals of many trials shrink to a low-dimensional structural matrix compatible with most pattern recognition methods. Third, a series of effective iterative feature selection approaches with theoretical convergence is introduced to rank the EEG channels and remove redundant ones, further accelerating the EEG classification process and ensuring its stability. Finally, a classical linear discriminant analysis (LDA) model is employed to classify a single EEG trial with selected channels. Experimental results on two real world brain-computer interface (BCI) competition datasets demonstrate the promising performance of the proposed framework over state-of-the-art methods. PMID:29713262

Epilepsy analytic system with cloud computing.

PubMed

Shen, Chia-Ping; Zhou, Weizhi; Lin, Feng-Seng; Sung, Hsiao-Ya; Lam, Yan-Yu; Chen, Wei; Lin, Jeng-Wei; Pan, Ming-Kai; Chiu, Ming-Jang; Lai, Feipei

2013-01-01

Biomedical data analytic system has played an important role in doing the clinical diagnosis for several decades. Today, it is an emerging research area of analyzing these big data to make decision support for physicians. This paper presents a parallelized web-based tool with cloud computing service architecture to analyze the epilepsy. There are many modern analytic functions which are wavelet transform, genetic algorithm (GA), and support vector machine (SVM) cascaded in the system. To demonstrate the effectiveness of the system, it has been verified by two kinds of electroencephalography (EEG) data, which are short term EEG and long term EEG. The results reveal that our approach achieves the total classification accuracy higher than 90%. In addition, the entire training time accelerate about 4.66 times and prediction time is also meet requirements in real time.

Assessing neuro-motor recovery in a stroke survivor with high-resolution EEG, robotics and Virtual Reality.

PubMed

Comani, Silvia; Schinaia, Lorenzo; Tamburro, Gabriella; Velluto, Lucia; Sorbi, Sandro; Conforto, Silvia; Guarnieri, Biancamaria

2015-01-01

One post-stroke patient underwent neuro-motor rehabilitation of one upper limb with a novel system combining a passive robotic device, Virtual Reality training applications and high resolution electroencephalography (HR-EEG). The outcome of the clinical tests and the evaluation of the kinematic parameters recorded with the robotic device concurred to highlight an improved motor recovery of the impaired limb despite the age of the patient, his compromised motor function, and the start of rehabilitation at the 3rd week post stroke. The time frequency and functional source analysis of the HR-EEG signals permitted to quantify the functional changes occurring in the brain in association with the rehabilitation motor tasks, and to highlight the recovery of the neuro-motor function.

Moderate unconjugated hyperbilirubinemia causes a transient but delayed suppression of amplitude-integrated electroencephalographic activity in preterm infants.

PubMed

Ter Horst, Hendrik J; Bos, Arend F; Duijvendijk, Jildou; Hulzebos, Christian V

2012-01-01

Unconjugated hyperbilirubinemia occurs frequently in preterm infants and may result in bilirubin encephalopathy. Amplitude-integrated electroencephalography (aEEG) is used to evaluate brain function in newborns. To investigate the influence of total serum bilirubin (TSB) on the aEEG amplitude of preterm infants and to evaluate aEEG as a noninvasive method to identify acute bilirubin encephalopathy. We performed a prospective observational study of 34 infants with a gestational age (GA) of 26-31 6/7 weeks. Infants had aEEG recordings on the 1st-5th, 8th and 15th day after birth. Infants with asphyxia, intraventricular hemorrhage >grade I or circulatory insufficiency were excluded. aEEG was evaluated by calculating the mean 5th, 50th and 95th centiles of the aEEG amplitudes. TSB peaked on the 4th day after birth. There was no synchronous relationship between TSB and aEEG amplitudes. The 5th, 50th, and 95th aEEG amplitude centiles on the 8th day correlated negatively with the TSB peak value (r = -0.37, p = 0.048; r = -0.60, p = 0.001; r = -0.44, p = 0.017, respectively), irrespective of GA. The 5th and 50th aEEG amplitude centiles increased with increasing GA (r = 0.45, p < 0.001, and r = 0.26, p < 0.001, respectively) and postnatal age (r = 0.25, p < 0.001, and r = 0.16, p = 0.023, respectively). TSB had no direct effect on aEEG amplitudes in preterm infants. There is, however, a delayed effect on electrocerebral activity in the 2nd week after birth. Copyright © 2012 S. Karger AG, Basel.

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

PubMed

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

2016-09-01

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

Seizure clusters and adverse events during pre-surgical video-EEG monitoring with a slow anti-epileptic drug (AED) taper.

PubMed

Di Gennaro, Giancarlo; Picardi, Angelo; Sparano, Antonio; Mascia, Addolorata; Meldolesi, Giulio N; Grammaldo, Liliana G; Esposito, Vincenzo; Quarato, Pier P

2012-03-01

To evaluate the efficiency and safety of pre-surgical video-EEG monitoring with a slow anti-epileptic drug (AED) taper and a rescue benzodiazepine protocol. Fifty-four consecutive patients with refractory focal epilepsy who underwent pre-surgical video-electroencephalography (EEG) monitoring during the year 2010 were included in the study. Time to first seizure, duration of monitoring, incidence of 4-h and 24-h seizure clustering, secondarily generalised tonic-clonic seizures (sGTCS), status epilepticus, falls and cardiac asystole were evaluated. A total of 190 seizures were recorded. Six (11%) patients had 4-h clusters and 21 (39%) patients had 24-h clusters. While 15 sGTCS were recorded in 14 patients (26%), status epilepticus did not occur and no seizure was complicated with cardiac asystole. Epileptic falls with no significant injuries occurred in three patients. The mean time to first seizure was 3.3days and the time to conclude video-EEG monitoring averaged 6days. Seizure clustering was common during pre-surgical video-EEG monitoring, although serious adverse events were rare with a slow AED tapering and a rescue benzodiazepine protocol. Slow AED taper pre-surgical video-EEG monitoring is fairly safe when performed in a highly specialised and supervised hospital setting. Copyright © 2011 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

A Within-subjects Experimental Protocol to Assess the Effects of Social Input on Infant EEG.

PubMed

St John, Ashley M; Kao, Katie; Chita-Tegmark, Meia; Liederman, Jacqueline; Grieve, Philip G; Tarullo, Amanda R

2017-05-03

Despite the importance of social interactions for infant brain development, little research has assessed functional neural activation while infants socially interact. Electroencephalography (EEG) power is an advantageous technique to assess infant functional neural activation. However, many studies record infant EEG only during one baseline condition. This protocol describes a paradigm that is designed to comprehensively assess infant EEG activity in both social and nonsocial contexts as well as tease apart how different types of social inputs differentially relate to infant EEG. The within-subjects paradigm includes four controlled conditions. In the nonsocial condition, infants view objects on computer screens. The joint attention condition involves an experimenter directing the infant's attention to pictures. The joint attention condition includes three types of social input: language, face-to-face interaction, and the presence of joint attention. Differences in infant EEG between the nonsocial and joint attention conditions could be due to any of these three types of input. Therefore, two additional conditions (one with language input while the experimenter is hidden behind a screen and one with face-to-face interaction) were included to assess the driving contextual factors in patterns of infant neural activation. Representative results demonstrate that infant EEG power varied by condition, both overall and differentially by brain region, supporting the functional nature of infant EEG power. This technique is advantageous in that it includes conditions that are clearly social or nonsocial and allows for examination of how specific types of social input relate to EEG power. This paradigm can be used to assess how individual differences in age, affect, socioeconomic status, and parent-infant interaction quality relate to the development of the social brain. Based on the demonstrated functional nature of infant EEG power, future studies should consider the role of EEG recording context and design conditions that are clearly social or nonsocial.

Brain Dynamics in Predicting Driving Fatigue Using a Recurrent Self-Evolving Fuzzy Neural Network.

PubMed

Liu, Yu-Ting; Lin, Yang-Yin; Wu, Shang-Lin; Chuang, Chun-Hsiang; Lin, Chin-Teng

2016-02-01

This paper proposes a generalized prediction system called a recurrent self-evolving fuzzy neural network (RSEFNN) that employs an on-line gradient descent learning rule to address the electroencephalography (EEG) regression problem in brain dynamics for driving fatigue. The cognitive states of drivers significantly affect driving safety; in particular, fatigue driving, or drowsy driving, endangers both the individual and the public. For this reason, the development of brain-computer interfaces (BCIs) that can identify drowsy driving states is a crucial and urgent topic of study. Many EEG-based BCIs have been developed as artificial auxiliary systems for use in various practical applications because of the benefits of measuring EEG signals. In the literature, the efficacy of EEG-based BCIs in recognition tasks has been limited by low resolutions. The system proposed in this paper represents the first attempt to use the recurrent fuzzy neural network (RFNN) architecture to increase adaptability in realistic EEG applications to overcome this bottleneck. This paper further analyzes brain dynamics in a simulated car driving task in a virtual-reality environment. The proposed RSEFNN model is evaluated using the generalized cross-subject approach, and the results indicate that the RSEFNN is superior to competing models regardless of the use of recurrent or nonrecurrent structures.

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

High-throughput ocular artifact reduction in multichannel electroencephalography (EEG) using component subspace projection.

PubMed

Ma, Junshui; Bayram, Sevinç; Tao, Peining; Svetnik, Vladimir

2011-03-15

After a review of the ocular artifact reduction literature, a high-throughput method designed to reduce the ocular artifacts in multichannel continuous EEG recordings acquired at clinical EEG laboratories worldwide is proposed. The proposed method belongs to the category of component-based methods, and does not rely on any electrooculography (EOG) signals. Based on a concept that all ocular artifact components exist in a signal component subspace, the method can uniformly handle all types of ocular artifacts, including eye-blinks, saccades, and other eye movements, by automatically identifying ocular components from decomposed signal components. This study also proposes an improved strategy to objectively and quantitatively evaluate artifact reduction methods. The evaluation strategy uses real EEG signals to synthesize realistic simulated datasets with different amounts of ocular artifacts. The simulated datasets enable us to objectively demonstrate that the proposed method outperforms some existing methods when no high-quality EOG signals are available. Moreover, the results of the simulated datasets improve our understanding of the involved signal decomposition algorithms, and provide us with insights into the inconsistency regarding the performance of different methods in the literature. The proposed method was also applied to two independent clinical EEG datasets involving 28 volunteers and over 1000 EEG recordings. This effort further confirms that the proposed method can effectively reduce ocular artifacts in large clinical EEG datasets in a high-throughput fashion. Copyright © 2011 Elsevier B.V. All rights reserved.

A Comparative Study on the Detection of Covert Attention in Event-Related EEG and MEG Signals to Control a BCI

PubMed Central

Reichert, Christoph; Dürschmid, Stefan; Heinze, Hans-Jochen; Hinrichs, Hermann

2017-01-01

In brain-computer interface (BCI) applications the detection of neural processing as revealed by event-related potentials (ERPs) is a frequently used approach to regain communication for people unable to interact through any peripheral muscle control. However, the commonly used electroencephalography (EEG) provides signals of low signal-to-noise ratio, making the systems slow and inaccurate. As an alternative noninvasive recording technique, the magnetoencephalography (MEG) could provide more advantageous electrophysiological signals due to a higher number of sensors and the magnetic fields not being influenced by volume conduction. We investigated whether MEG provides higher accuracy in detecting event-related fields (ERFs) compared to detecting ERPs in simultaneously recorded EEG, both evoked by a covert attention task, and whether a combination of the modalities is advantageous. In our approach, a detection algorithm based on spatial filtering is used to identify ERP/ERF components in a data-driven manner. We found that MEG achieves higher decoding accuracy (DA) compared to EEG and that the combination of both further improves the performance significantly. However, MEG data showed poor performance in cross-subject classification, indicating that the algorithm's ability for transfer learning across subjects is better in EEG. Here we show that BCI control by covert attention is feasible with EEG and MEG using a data-driven spatial filter approach with a clear advantage of the MEG regarding DA but with a better transfer learning in EEG. PMID:29085279

Electroencephalography leads placed by nontechnologists using a template system produce signals equal in quality to technologist-applied, collodion disk leads.

PubMed

Kolls, Brad J; Olson, Daiwai M; Gallentine, William B; Skeen, Mark B; Skidmore, Christopher T; Sinha, Saurabh R

2012-02-01

The purpose of this study was to compare the quality of the electroencephalographic (EEG) data obtained with a BraiNet template in a practical use setting, to that obtained with standard 10/20 spaced, technologist-applied, collodion-based disk leads. Pairs of 8-hour blocks of EEG data were prospectively collected from 32 patients with a Glasgow coma score of ≤9 and clinical concern for underlying nonconvulsive status epilepticus over a 6-month period in the Neurocritical Care Unit at the Duke University Medical Center. The studies were initiated with the BraiNet template system applied by critical care nurse practitioners or physicians, followed by standard, collodion leads applied by registered technologists using the 10/20 system of placement. Impedances were measured at the beginning and end of each block recorded and variance in impedance, mean impedance, and the largest differences in impedances found within a given lead set were compared. Physicians experienced in reading EEG performed a masked review of the EEG segments obtained to assess the subjective quality of the recordings obtained with the templates. We found no clinically significant differences in the impedance measures. There was a 3-hour reduction in the time required to initiate EEG recording using the templates (P < 0.001). There was no difference in the overall subjective quality distributions for template-applied versus technologist-applied EEG leads. The templates were also found to be well accepted by the primary users in the intensive care unit. The findings suggest that the EEG data obtained with this approach are comparable with that obtained by registered technologist-applied leads and represents a possible solution to the growing clinical need for continuous EEG recording availability in the critical care setting.

Somatosensory-evoked spikes on electroencephalography (EEG): longitudinal clinical and EEG aspects in 313 children.

PubMed

Fonseca, Lineu Corrêa; Tedrus, Gloria M A S

2012-01-01

Somatosensory-evoked spikes (ESp) are high-voltage potentials registered on the EEG, which accompany each of the percussions on the feet or hands. The objective of this research was to study the longitudinal clinical and EEG aspects of children with ESp. A total of 313 children, 53.7% male, showing ESp on the EEG and with an average initial age of 6.82 (range from 2 to 14 years) were followed for a mean period of 35.7 months. In the initial evaluation, 118 (37.7%) had a history of nonfebrile epileptic seizures (ES). Epileptiform activity (EA) was observed on the EEG in 61% and showed a significantly greater occurrence in children with ES than in those without (P = .000). Of the 118 showing seizures from the start, 53 (44.9%) continued to have seizures; of the 195 without seizures at the start, only 13 (6.67%) developed them. Thus, only 66 (21.1%) children showed ES during the follow-up. ESp disappeared in 237 (75.7%) cases and EA in 221 (70.6%). In the children with ES, it was found that the presence of EA on the first EEG did not indicate continuation of the ES throughout the remaining period, while the 13 children who presented their first ES in a later period showed a greater occurrence of EA on the initial EEG than those who did not develop ES (P = .001). Evidence of brain injury was observed in 43 (13.7%) children and was associated with a greater continuity of the ES during the study (P = .018). ESp, EA, and ES tend to disappear, suggesting an age-dependent phenomenon. The finding of ESp, particularly in the absence of any evidence of brain injury, indicates a low association with ES and benign outcome.

Beamformer source analysis and connectivity on concurrent EEG and MEG data during voluntary movements.

PubMed

Muthuraman, Muthuraman; Hellriegel, Helge; Hoogenboom, Nienke; Anwar, Abdul Rauf; Mideksa, Kidist Gebremariam; Krause, Holger; Schnitzler, Alfons; Deuschl, Günther; Raethjen, Jan

2014-01-01

Electroencephalography (EEG) and magnetoencephalography (MEG) are the two modalities for measuring neuronal dynamics at a millisecond temporal resolution. Different source analysis methods, to locate the dipoles in the brain from which these dynamics originate, have been readily applied to both modalities alone. However, direct comparisons and possible advantages of combining both modalities have rarely been assessed during voluntary movements using coherent source analysis. In the present study, the cortical and sub-cortical network of coherent sources at the finger tapping task frequency (2-4 Hz) and the modes of interaction within this network were analysed in 15 healthy subjects using a beamformer approach called the dynamic imaging of coherent sources (DICS) with subsequent source signal reconstruction and renormalized partial directed coherence analysis (RPDC). MEG and EEG data were recorded simultaneously allowing the comparison of each of the modalities separately to that of the combined approach. We found the identified network of coherent sources for the finger tapping task as described in earlier studies when using only the MEG or combined MEG+EEG whereas the EEG data alone failed to detect single sub-cortical sources. The signal-to-noise ratio (SNR) level of the coherent rhythmic activity at the tapping frequency in MEG and combined MEG+EEG data was significantly higher than EEG alone. The functional connectivity analysis revealed that the combined approach had more active connections compared to either of the modalities during the finger tapping (FT) task. These results indicate that MEG is superior in the detection of deep coherent sources and that the SNR seems to be more vital than the sensitivity to theoretical dipole orientation and the volume conduction effect in the case of EEG.

Beamformer Source Analysis and Connectivity on Concurrent EEG and MEG Data during Voluntary Movements

PubMed Central

Muthuraman, Muthuraman; Hellriegel, Helge; Hoogenboom, Nienke; Anwar, Abdul Rauf; Mideksa, Kidist Gebremariam; Krause, Holger; Schnitzler, Alfons; Deuschl, Günther; Raethjen, Jan

2014-01-01

Electroencephalography (EEG) and magnetoencephalography (MEG) are the two modalities for measuring neuronal dynamics at a millisecond temporal resolution. Different source analysis methods, to locate the dipoles in the brain from which these dynamics originate, have been readily applied to both modalities alone. However, direct comparisons and possible advantages of combining both modalities have rarely been assessed during voluntary movements using coherent source analysis. In the present study, the cortical and sub-cortical network of coherent sources at the finger tapping task frequency (2–4 Hz) and the modes of interaction within this network were analysed in 15 healthy subjects using a beamformer approach called the dynamic imaging of coherent sources (DICS) with subsequent source signal reconstruction and renormalized partial directed coherence analysis (RPDC). MEG and EEG data were recorded simultaneously allowing the comparison of each of the modalities separately to that of the combined approach. We found the identified network of coherent sources for the finger tapping task as described in earlier studies when using only the MEG or combined MEG+EEG whereas the EEG data alone failed to detect single sub-cortical sources. The signal-to-noise ratio (SNR) level of the coherent rhythmic activity at the tapping frequency in MEG and combined MEG+EEG data was significantly higher than EEG alone. The functional connectivity analysis revealed that the combined approach had more active connections compared to either of the modalities during the finger tapping (FT) task. These results indicate that MEG is superior in the detection of deep coherent sources and that the SNR seems to be more vital than the sensitivity to theoretical dipole orientation and the volume conduction effect in the case of EEG. PMID:24618596

[EEG-adjusted target-controlled infusion : Propofol target concentration with different doses of remifentanil].

PubMed

Büttner, N; Schultz, B; Grouven, U; Schultz, A

2010-02-01

The aim of this study was to examine to what extent the use of electroencephalography (EEG) monitoring leads to an adaptation of the target-controlled infusion (TCI) concentration of propofol during propofol anaesthesia with different doses of remifentanil. With ethics committee approval 60 patients (27-69 years old) with American Society of Anesthesiologists classification (ASA) I-III received anaesthestics with propofol (TCI, Diprifusor, AstraZeneca, Wedel, Deutschland) and 0.2, 0.4, or 0.6 microg/kg body weight remifentanil, respectively (groups 1-3). Anaesthesia was maintained at a level of deep hypnosis (EEG stages D(2)/E(0), EEG monitor: Narcotrend, version 2.0/5.0, manufacturer: MT MonitorTechnik, Bad Bramstedt, Germany). During the steady state the propofol concentration in groups 1-3 was 3.02+/-0.86, 1.93+/-0.53 and 1.60+/-0.55 microg/ml, respectively (p<0.001). Women had a higher propofol consumption than men (p<0.05). Dreams during anaesthesia were more often reported by women than by men (p<0.05). The need for postoperative analgesia decreased with an increasing intraoperative remifentanil dose (p<0.05). The study demonstrates that remifentanil has both analgetic and hypnotic effects. With increasing remifentanil dose the propofol requirement decreased and in this context EEG monitoring is useful to adapt the target concentrations of propofol to the patients' age and gender.

EEG correlates of P300-based brain-computer interface (BCI) performance in people with amyotrophic lateral sclerosis

NASA Astrophysics Data System (ADS)

Mak, Joseph N.; McFarland, Dennis J.; Vaughan, Theresa M.; McCane, Lynn M.; Tsui, Phillippa Z.; Zeitlin, Debra J.; Sellers, Eric W.; Wolpaw, Jonathan R.

2012-04-01

The purpose of this study was to identify electroencephalography (EEG) features that correlate with P300-based brain-computer interface (P300 BCI) performance in people with amyotrophic lateral sclerosis (ALS). Twenty people with ALS used a P300 BCI spelling application in copy-spelling mode. Three types of EEG features were found to be good predictors of P300 BCI performance: (1) the root-mean-square amplitude and (2) the negative peak amplitude of the event-related potential to target stimuli (target ERP) at Fz, Cz, P3, Pz, and P4; and (3) EEG theta frequency (4.5-8 Hz) power at Fz, Cz, P3, Pz, P4, PO7, PO8 and Oz. A statistical prediction model that used a subset of these features accounted for >60% of the variance in copy-spelling performance (p < 0.001, mean R2 = 0.6175). The correlations reflected between-subject, rather than within-subject, effects. The results enhance understanding of performance differences among P300 BCI users. The predictors found in this study might help in: (1) identifying suitable candidates for long-term P300 BCI operation; (2) assessing performance online. Further work on within-subject effects needs to be done to establish whether P300 BCI user performance could be improved by optimizing one or more of these EEG features.

The Positive Effects of Trait Emotional Intelligence during a Performance Review Discussion – A Psychophysiological Study

PubMed Central

Salminen, Mikko; Ravaja, Niklas

2017-01-01

Performance review discussions of real manager–subordinate pairs were examined in two studies to investigate the effects of trait emotional intelligence (EI) on dyad member’s felt and expressed emotions. Altogether there were 84 managers and 122 subordinates in two studies using 360 measured and self-reported trait EI. Facial electromyography, and frontal electroencephalography (EEG) asymmetry were collected continuously. Manager’s high trait EI was related to increased positive valence emotional facial expressions in the dyad during the discussions. The managers also had more EEG frontal asymmetry indicating approach motivation, than the subordinates. In addition, actor and partner effects and actor × partner interactions, and interactions between the role and actor or partner effect of trait EI were observed. Both actor and partner trait EI were related to more positive self-reported emotional valence. The results imply that trait EI has a role in organizational social interaction. PMID:28400747

[Musical-verbal hallucinations and their correlation with electroencephalography and PET findings. Case report].

PubMed

Erkwoh, R; Ebel, H; Kachel, F; Reiche, W; Ringelstein, E B; Zimmermann, J; Büll, U; Sass, H

1992-03-01

A case is reported of an organic auditory hallucinosis and depressive episode in a man aged 52. A new finding is the correlation of the musical hallucinosis to a central nervous disorder as shown by pathological sphenoidal EEG and 18FDG-PET-examination and not to acquired peripheral hearing deficit as often reported before.

Gastroesophageal Reflux in Neurologically Impaired Children: What Are the Risk Factors?

PubMed

Kim, Seung; Koh, Hong; Lee, Joon Soo

2017-03-15

Neurologically impaired patients frequently suffer from gastrointestinal tract problems, such as gastroesophageal reflux disease (GERD). In this study, we aimed to define the risk factors for GERD in neurologically impaired children. From May 2006 to March 2014, 101 neurologically impaired children who received 24-hour esophageal pH monitoring at Severance Children's Hospital were enrolled in the study. The esophageal pH finding and the clinical characteristics of the patients were analyzed. The reflux index was higher in patients with abnormal electroencephalography (EEG) results than in those with normal EEG results (p=0.027). Mitochondrial disease was associated with a higher reflux index than were epileptic disorders or cerebral palsy (p=0.009). Patient gender, feeding method, scoliosis, tracheostomy, and baclofen use did not lead to statistical differences in reflux index. Age of onset of neurological impairment was inversely correlated with DeMeester score and reflux index. Age at the time of examination, the duration of the disease, and the number of antiepileptic drugs were not correlated with GER severity. Early-onset neurological impairment, abnormal EEG results, and mitochondrial disease are risk factors for severe GERD.

Different spatio-temporal electroencephalography features drive the successful decoding of binaural and monaural cues for sound localization.

PubMed

Bednar, Adam; Boland, Francis M; Lalor, Edmund C

2017-03-01

The human ability to localize sound is essential for monitoring our environment and helps us to analyse complex auditory scenes. Although the acoustic cues mediating sound localization have been established, it remains unknown how these cues are represented in human cortex. In particular, it is still a point of contention whether binaural and monaural cues are processed by the same or distinct cortical networks. In this study, participants listened to a sequence of auditory stimuli from different spatial locations while we recorded their neural activity using electroencephalography (EEG). The stimuli were presented over a loudspeaker array, which allowed us to deliver realistic, free-field stimuli in both the horizontal and vertical planes. Using a multivariate classification approach, we showed that it is possible to decode sound source location from scalp-recorded EEG. Robust and consistent decoding was shown for stimuli that provide binaural cues (i.e. Left vs. Right stimuli). Decoding location when only monaural cues were available (i.e. Front vs. Rear and elevational stimuli) was successful for a subset of subjects and showed less consistency. Notably, the spatio-temporal pattern of EEG features that facilitated decoding differed based on the availability of binaural and monaural cues. In particular, we identified neural processing of binaural cues at around 120 ms post-stimulus and found that monaural cues are processed later between 150 and 200 ms. Furthermore, different spatial activation patterns emerged for binaural and monaural cue processing. These spatio-temporal dissimilarities suggest the involvement of separate cortical mechanisms in monaural and binaural acoustic cue processing. © 2017 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

EEG source space analysis of the supervised factor analytic approach for the classification of multi-directional arm movement

NASA Astrophysics Data System (ADS)

Shenoy Handiru, Vikram; Vinod, A. P.; Guan, Cuntai

2017-08-01

Objective. In electroencephalography (EEG)-based brain-computer interface (BCI) systems for motor control tasks the conventional practice is to decode motor intentions by using scalp EEG. However, scalp EEG only reveals certain limited information about the complex tasks of movement with a higher degree of freedom. Therefore, our objective is to investigate the effectiveness of source-space EEG in extracting relevant features that discriminate arm movement in multiple directions. Approach. We have proposed a novel feature extraction algorithm based on supervised factor analysis that models the data from source-space EEG. To this end, we computed the features from the source dipoles confined to Brodmann areas of interest (BA4a, BA4p and BA6). Further, we embedded class-wise labels of multi-direction (multi-class) source-space EEG to an unsupervised factor analysis to make it into a supervised learning method. Main Results. Our approach provided an average decoding accuracy of 71% for the classification of hand movement in four orthogonal directions, that is significantly higher (>10%) than the classification accuracy obtained using state-of-the-art spatial pattern features in sensor space. Also, the group analysis on the spectral characteristics of source-space EEG indicates that the slow cortical potentials from a set of cortical source dipoles reveal discriminative information regarding the movement parameter, direction. Significance. This study presents evidence that low-frequency components in the source space play an important role in movement kinematics, and thus it may lead to new strategies for BCI-based neurorehabilitation.

Sensor Level Functional Connectivity Topography Comparison Between Different References Based EEG and MEG

PubMed Central

Huang, Yunzhi; Zhang, Junpeng; Cui, Yuan; Yang, Gang; Liu, Qi; Yin, Guangfu

2018-01-01

Sensor-level functional connectivity topography (sFCT) contributes significantly to our understanding of brain networks. sFCT can be constructed using either electroencephalography (EEG) or magnetoencephalography (MEG). Here, we compared sFCT within the EEG modality and between EEG and MEG modalities. We first used simulations to look at how different EEG references—including the Reference Electrode Standardization Technique (REST), average reference (AR), linked mastoids (LM), and left mastoid references (LR)—affect EEG-based sFCT. The results showed that REST decreased the reference effects on scalp EEG recordings, making REST-based sFCT closer to the ground truth (sFCT based on ideal recordings). For the inter-modality simulation comparisons, we compared each type of EEG-sFCT with MEG-sFCT using three metrics to quantize the differences: Relative Error (RE), Overlap Rate (OR), and Hamming Distance (HD). When two sFCTs are similar, RE and HD are low, while OR is high. Results showed that among all reference schemes, EEG-and MEG-sFCT were most similar when the EEG was REST-based and the EEG and MEG were recorded simultaneously. Next, we analyzed simultaneously recorded MEG and EEG data from publicly available face-recognition experiments using a similar procedure as in the simulations. The results showed (1) if MEG-sFCT is the standard, REST—and LM-based sFCT provided results closer to this standard in the terms of HD; (2) REST-based sFCT and MEG-sFCT had the highest similarity in terms of RE; (3) REST-based sFCT had the most overlapping edges with MEG-sFCT in terms of OR. This study thus provides new insights into the effect of different reference schemes on sFCT and the similarity between MEG and EEG in terms of sFCT. PMID:29867395

Propofol Anesthesia and Sleep: A High-Density EEG Study

PubMed Central

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

2011-01-01

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

Automated Classification and Removal of EEG Artifacts With SVM and Wavelet-ICA.

PubMed

Sai, Chong Yeh; Mokhtar, Norrima; Arof, Hamzah; Cumming, Paul; Iwahashi, Masahiro

2018-05-01

Brain electrical activity recordings by electroencephalography (EEG) are often contaminated with signal artifacts. Procedures for automated removal of EEG artifacts are frequently sought for clinical diagnostics and brain-computer interface applications. In recent years, a combination of independent component analysis (ICA) and discrete wavelet transform has been introduced as standard technique for EEG artifact removal. However, in performing the wavelet-ICA procedure, visual inspection or arbitrary thresholding may be required for identifying artifactual components in the EEG signal. We now propose a novel approach for identifying artifactual components separated by wavelet-ICA using a pretrained support vector machine (SVM). Our method presents a robust and extendable system that enables fully automated identification and removal of artifacts from EEG signals, without applying any arbitrary thresholding. Using test data contaminated by eye blink artifacts, we show that our method performed better in identifying artifactual components than did existing thresholding methods. Furthermore, wavelet-ICA in conjunction with SVM successfully removed target artifacts, while largely retaining the EEG source signals of interest. We propose a set of features including kurtosis, variance, Shannon's entropy, and range of amplitude as training and test data of SVM to identify eye blink artifacts in EEG signals. This combinatorial method is also extendable to accommodate multiple types of artifacts present in multichannel EEG. We envision future research to explore other descriptive features corresponding to other types of artifactual components.

Presurgical EEG-fMRI in a complex clinical case with seizure recurrence after epilepsy surgery

PubMed Central

Zhang, Jing; Liu, Qingzhu; Mei, Shanshan; Zhang, Xiaoming; Wang, Xiaofei; Liu, Weifang; Chen, Hui; Xia, Hong; Zhou, Zhen; Li, Yunlin

2013-01-01

Epilepsy surgery has improved over the last decade, but non-seizure-free outcome remains at 10%–40% in temporal lobe epilepsy (TLE) and 40%–60% in extratemporal lobe epilepsy (ETLE). This paper reports a complex multifocal case. With a normal magnetic resonance imaging (MRI) result and nonlocalizing electroencephalography (EEG) findings (bilateral TLE and ETLE, with more interictal epileptiform discharges [IEDs] in the right frontal and temporal regions), a presurgical EEG-functional MRI (fMRI) was performed before the intraoperative intracranial EEG (icEEG) monitoring (icEEG with right hemispheric coverage). Our previous EEG-fMRI analysis results (IEDs in the left hemisphere alone) were contradictory to the EEG and icEEG findings (IEDs in the right frontal and temporal regions). Thus, the EEG-fMRI data were reanalyzed with newly identified IED onsets and different fMRI model options. The reanalyzed EEG-fMRI findings were largely concordant with those of EEG and icEEG, and the failure of our previous EEG-fMRI analysis may lie in the inaccurate identification of IEDs and wrong usage of model options. The right frontal and temporal regions were resected in surgery, and dual pathology (hippocampus sclerosis and focal cortical dysplasia in the extrahippocampal region) was found. The patient became seizure-free for 3 months, but his seizures restarted after antiepileptic drugs (AEDs) were stopped. The seizures were not well controlled after resuming AEDs. Postsurgical EEGs indicated that ictal spikes in the right frontal and temporal regions reduced, while those in the left hemisphere became prominent. This case suggested that (1) EEG-fMRI is valuable in presurgical evaluation, but requires caution; and (2) the intact seizure focus in the remaining brain may cause the non-seizure-free outcome. PMID:23926432

Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset.

PubMed

Shin, Jaeyoung; von Lühmann, Alexander; Kim, Do-Won; Mehnert, Jan; Hwang, Han-Jeong; Müller, Klaus-Robert

2018-02-13

We provide an open access multimodal brain-imaging dataset of simultaneous electroencephalography (EEG) and near-infrared spectroscopy (NIRS) recordings. Twenty-six healthy participants performed three cognitive tasks: 1) n-back (0-, 2- and 3-back), 2) discrimination/selection response task (DSR) and 3) word generation (WG) tasks. The data provided includes: 1) measured data, 2) demographic data, and 3) basic analysis results. For n-back (dataset A) and DSR tasks (dataset B), event-related potential (ERP) analysis was performed, and spatiotemporal characteristics and classification results for 'target' versus 'non-target' (dataset A) and symbol 'O' versus symbol 'X' (dataset B) are provided. Time-frequency analysis was performed to show the EEG spectral power to differentiate the task-relevant activations. Spatiotemporal characteristics of hemodynamic responses are also shown. For the WG task (dataset C), the EEG spectral power and spatiotemporal characteristics of hemodynamic responses are analyzed, and the potential merit of hybrid EEG-NIRS BCIs was validated with respect to classification accuracy. We expect that the dataset provided will facilitate performance evaluation and comparison of many neuroimaging analysis techniques.

Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset

PubMed Central

Shin, Jaeyoung; von Lühmann, Alexander; Kim, Do-Won; Mehnert, Jan; Hwang, Han-Jeong; Müller, Klaus-Robert

2018-01-01

We provide an open access multimodal brain-imaging dataset of simultaneous electroencephalography (EEG) and near-infrared spectroscopy (NIRS) recordings. Twenty-six healthy participants performed three cognitive tasks: 1) n-back (0-, 2- and 3-back), 2) discrimination/selection response task (DSR) and 3) word generation (WG) tasks. The data provided includes: 1) measured data, 2) demographic data, and 3) basic analysis results. For n-back (dataset A) and DSR tasks (dataset B), event-related potential (ERP) analysis was performed, and spatiotemporal characteristics and classification results for ‘target’ versus ‘non-target’ (dataset A) and symbol ‘O’ versus symbol ‘X’ (dataset B) are provided. Time-frequency analysis was performed to show the EEG spectral power to differentiate the task-relevant activations. Spatiotemporal characteristics of hemodynamic responses are also shown. For the WG task (dataset C), the EEG spectral power and spatiotemporal characteristics of hemodynamic responses are analyzed, and the potential merit of hybrid EEG-NIRS BCIs was validated with respect to classification accuracy. We expect that the dataset provided will facilitate performance evaluation and comparison of many neuroimaging analysis techniques. PMID:29437166

Increase of EEG Spectral Theta Power Indicates Higher Risk of the Development of Severe Cognitive Decline in Parkinson’s Disease after 3 Years

PubMed Central

Cozac, Vitalii V.; Chaturvedi, Menorca; Hatz, Florian; Meyer, Antonia; Fuhr, Peter; Gschwandtner, Ute

2016-01-01

Objective: We investigated quantitative electroencephalography (qEEG) and clinical parameters as potential risk factors of severe cognitive decline in Parkinson’s disease. Methods: We prospectively investigated 37 patients with Parkinson’s disease at baseline and follow-up (after 3 years). Patients had no severe cognitive impairment at baseline. We used a summary score of cognitive tests as the outcome at follow-up. At baseline we assessed motor, cognitive, and psychiatric factors; qEEG variables [global relative median power (GRMP) spectra] were obtained by a fully automated processing of high-resolution EEG (256-channels). We used linear regression models with calculation of the explained variance to evaluate the relation of baseline parameters with cognitive deterioration. Results: The following baseline parameters significantly predicted severe cognitive decline: GRMP theta (4–8 Hz), cognitive task performance in executive functions and working memory. Conclusions: Combination of neurocognitive tests and qEEG improves identification of patients with higher risk of cognitive decline in PD. PMID:27965571

Exploring differences between left and right hand motor imagery via spatio-temporal EEG microstate.

PubMed

Liu, Weifeng; Liu, Xiaoming; Dai, Ruomeng; Tang, Xiaoying

2017-12-01

EEG-based motor imagery is very useful in brain-computer interface. How to identify the imaging movement is still being researched. Electroencephalography (EEG) microstates reflect the spatial configuration of quasi-stable electrical potential topographies. Different microstates represent different brain functions. In this paper, microstate method was used to process the EEG-based motor imagery to obtain microstate. The single-trial EEG microstate sequences differences between two motor imagery tasks - imagination of left and right hand movement were investigated. The microstate parameters - duration, time coverage and occurrence per second as well as the transition probability of the microstate sequences were obtained with spatio-temporal microstate analysis. The results were shown significant differences (P < 0.05) with paired t-test between the two tasks. Then these microstate parameters were used as features and a linear support vector machine (SVM) was utilized to classify the two tasks with mean accuracy 89.17%, superior performance compared to the other methods. These indicate that the microstate can be a promising feature to improve the performance of the brain-computer interface classification.

Spatio-Temporal EEG Models for Brain Interfaces

PubMed Central

Gonzalez-Navarro, P.; Moghadamfalahi, M.; Akcakaya, M.; Erdogmus, D.

2016-01-01

Multichannel electroencephalography (EEG) is widely used in non-invasive brain computer interfaces (BCIs) for user intent inference. EEG can be assumed to be a Gaussian process with unknown mean and autocovariance, and the estimation of parameters is required for BCI inference. However, the relatively high dimensionality of the EEG feature vectors with respect to the number of labeled observations lead to rank deficient covariance matrix estimates. In this manuscript, to overcome ill-conditioned covariance estimation, we propose a structure for the covariance matrices of the multichannel EEG signals. Specifically, we assume that these covariances can be modeled as a Kronecker product of temporal and spatial covariances. Our results over the experimental data collected from the users of a letter-by-letter typing BCI show that with less number of parameter estimations, the system can achieve higher classification accuracies compared to a method that uses full unstructured covariance estimation. Moreover, in order to illustrate that the proposed Kronecker product structure could enable shortening the BCI calibration data collection sessions, using Cramer-Rao bound analysis on simulated data, we demonstrate that a model with structured covariance matrices will achieve the same estimation error as a model with no covariance structure using fewer labeled EEG observations. PMID:27713590

Channels selection using independent component analysis and scalp map projection for EEG-based driver fatigue classification.

PubMed

Rifai Chai; Naik, Ganesh R; Sai Ho Ling; Tran, Yvonne; Craig, Ashley; Nguyen, Hung T

2017-07-01

This paper presents a classification of driver fatigue with electroencephalography (EEG) channels selection analysis. The system employs independent component analysis (ICA) with scalp map back projection to select the dominant of EEG channels. After channel selection, the features of the selected EEG channels were extracted based on power spectral density (PSD), and then classified using a Bayesian neural network. The results of the ICA decomposition with the back-projected scalp map and a threshold showed that the EEG channels can be reduced from 32 channels into 16 dominants channels involved in fatigue assessment as chosen channels, which included AF3, F3, FC1, FC5, T7, CP5, P3, O1, P4, P8, CP6, T8, FC2, F8, AF4, FP2. The result of fatigue vs. alert classification of the selected 16 channels yielded a sensitivity of 76.8%, specificity of 74.3% and an accuracy of 75.5%. Also, the classification results of the selected 16 channels are comparable to those using the original 32 channels. So, the selected 16 channels is preferable for ergonomics improvement of EEG-based fatigue classification system.

Functional coupling of sensorimotor and associative areas during a catching ball task: a qEEG coherence study

PubMed Central

2012-01-01

Background Catching an object is a complex movement that involves not only programming but also effective motor coordination. Such behavior is related to the activation and recruitment of cortical regions that participates in the sensorimotor integration process. This study aimed to elucidate the cortical mechanisms involved in anticipatory actions when performing a task of catching an object in free fall. Methods Quantitative electroencephalography (qEEG) was recorded using a 20-channel EEG system in 20 healthy right-handed participants performed the catching ball task. We used the EEG coherence analysis to investigate subdivisions of alpha (8-12 Hz) and beta (12-30 Hz) bands, which are related to cognitive processing and sensory-motor integration. Results Notwithstanding, we found the main effects for the factor block; for alpha-1, coherence decreased from the first to sixth block, and the opposite effect occurred for alpha-2 and beta-2, with coherence increasing along the blocks. Conclusion It was concluded that to perform successfully our task, which involved anticipatory processes (i.e. feedback mechanisms), subjects exhibited a great involvement of sensory-motor and associative areas, possibly due to organization of information to process visuospatial parameters and further catch the falling object. PMID:22364485

Predictive role of brain connectivity for resective surgery in Lennox-Gastaut syndrome.

PubMed

Hur, Yun Jung; Kim, Heung Dong

2016-08-01

Callosotomy can reveal hidden primary epileptogenic areas in Lennox-Gastaut syndrome (LGS). We studied the significance of causal connectivity for identifying hidden epileptogenic areas in preoperative electroencephalography (EEG) and for making a decision regarding resective surgery. We enrolled 18 LGS patients who underwent corpus callosotomy. Eight patients with unilateral epileptogenicity on post-callosotomy EEG underwent resective surgery (group A). Ten patients with independent bilateral epileptogenicity did not undergo resective surgery (group B). We analyzed generalized epileptiform discharges on pre-callosotomy EEG via direct directed transfer function (dDTF) and partial directed coherence (PDC). All regions exhibiting unilaterality in group A and bilaterality identified by dDTF or PDC in group B were concordant with the lateralization of the irritative zone on post-callosotomy EEG and with the localization of the resective areas, except for one patient in group A. The regions identified by dDTF exhibited high concordance rates with the resective areas in patients with good outcomes. Causal connectivity methods showed good concordance with hidden epileptogenic areas, and its concordance was associated with the prognosis of surgical outcome. This study provides evidence that causal connectivity methods can be helpful in deciding which type of surgery will be suitable for an LGS patient. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Combining Cryptography with EEG Biometrics

PubMed Central

Kazanavičius, Egidijus; Woźniak, Marcin

2018-01-01

Cryptographic frameworks depend on key sharing for ensuring security of data. While the keys in cryptographic frameworks must be correctly reproducible and not unequivocally connected to the identity of a user, in biometric frameworks this is different. Joining cryptography techniques with biometrics can solve these issues. We present a biometric authentication method based on the discrete logarithm problem and Bose-Chaudhuri-Hocquenghem (BCH) codes, perform its security analysis, and demonstrate its security characteristics. We evaluate a biometric cryptosystem using our own dataset of electroencephalography (EEG) data collected from 42 subjects. The experimental results show that the described biometric user authentication system is effective, achieving an Equal Error Rate (ERR) of 0.024.

Combining Cryptography with EEG Biometrics.

PubMed

Damaševičius, Robertas; Maskeliūnas, Rytis; Kazanavičius, Egidijus; Woźniak, Marcin

2018-01-01

Cryptographic frameworks depend on key sharing for ensuring security of data. While the keys in cryptographic frameworks must be correctly reproducible and not unequivocally connected to the identity of a user, in biometric frameworks this is different. Joining cryptography techniques with biometrics can solve these issues. We present a biometric authentication method based on the discrete logarithm problem and Bose-Chaudhuri-Hocquenghem (BCH) codes, perform its security analysis, and demonstrate its security characteristics. We evaluate a biometric cryptosystem using our own dataset of electroencephalography (EEG) data collected from 42 subjects. The experimental results show that the described biometric user authentication system is effective, achieving an Equal Error Rate (ERR) of 0.024.

Using theta and alpha band power to assess cognitive workload in multitasking environments.

PubMed

Puma, Sébastien; Matton, Nadine; Paubel, Pierre-V; Raufaste, Éric; El-Yagoubi, Radouane

2018-01-01

Cognitive workload is of central importance in the fields of human factors and ergonomics. A reliable measurement of cognitive workload could allow for improvements in human machine interface designs and increase safety in several domains. At present, numerous studies have used electroencephalography (EEG) to assess cognitive workload, reporting the rise in cognitive workload to be associated with increases in theta band power and decreases in alpha band power. However, results have been inconsistent with some failing to reach the required level of significance. We hypothesized that the lack of consistency could be related to individual differences in task performance and/or to the small sample sizes in most EEG studies. In the present study we used EEG to assess the increase in cognitive workload occurring in a multitasking environment while taking into account differences in performance. Twenty participants completed a task commonly used in airline pilot recruitment, which included an increasing number of concurrent sub-tasks to be processed from one to four. Subjective ratings, performances scores, pupil size and EEG signals were recorded. Results showed that increases in EEG alpha and theta band power reflected increases in the involvement of cognitive resources for the completion of one to three subtasks in a multitasking environment. These values reached a ceiling when performances dropped. Consistent differences in levels of alpha and theta band power were associated to levels of task performance: highest performance was related to lowest band power. Copyright © 2017 Elsevier B.V. All rights reserved.

The Application of Electro- and Magneto-Encephalography in Tinnitus Research – Methods and Interpretations

PubMed Central

Adjamian, Peyman

2014-01-01

In recent years, there has been a significant increase in the use of electroencephalography (EEG) and magnetoencephalography (MEG) to investigate changes in oscillatory brain activity associated with tinnitus with many conflicting results. Current view of the underlying mechanism of tinnitus is that it results from changes in brain activity in various structures of the brain as a consequence of sensory deprivation. This in turn gives rise to increased spontaneous activity and/or synchrony in the auditory centers but also involves modulation from non-auditory processes from structures of the limbic and paralimbic system. Some of the neural changes associated with tinnitus may be assessed non-invasively in human beings with MEG and EEG (M/EEG) in ways, which are superior to animal studies and other non-invasive imaging techniques. However, both MEG and EEG have their limitations and research results can be misinterpreted without appropriate consideration of these limitations. In this article, I intend to provide a brief review of these techniques, describe what the recorded signals reflect in terms of the underlying neural activity, and their strengths and limitations. I also discuss some pertinent methodological issues involved in tinnitus-related studies and conclude with suggestions to minimize possible discrepancies between results. The overall message is that while MEG and EEG are extremely useful techniques, the interpretation of results from tinnitus studies requires much caution given the individual variability in oscillatory activity and the limits of these techniques. PMID:25431567

Integration of EEG lead placement templates into traditional technologist-based staffing models reduces costs in continuous video-EEG monitoring service.

PubMed

Kolls, Brad J; Lai, Amy H; Srinivas, Anang A; Reid, Robert R

2014-06-01

The purpose of this study was to determine the relative cost reductions within different staffing models for continuous video-electroencephalography (cvEEG) service by introducing a template system for 10/20 lead application. We compared six staffing models using decision tree modeling based on historical service line utilization data from the cvEEG service at our center. Templates were integrated into technologist-based service lines in six different ways. The six models studied were templates for all studies, templates for intensive care unit (ICU) studies, templates for on-call studies, templates for studies of ≤ 24-hour duration, technologists for on-call studies, and technologists for all studies. Cost was linearly related to the study volume for all models with the "templates for all" model incurring the lowest cost. The "technologists for all" model carried the greatest cost. Direct cost comparison shows that any introduction of templates results in cost savings, with the templates being used for patients located in the ICU being the second most cost efficient and the most practical of the combined models to implement. Cost difference between the highest and lowest cost models under the base case produced an annual estimated savings of $267,574. Implementation of the ICU template model at our institution under base case conditions would result in a $205,230 savings over our current "technologist for all" model. Any implementation of templates into a technologist-based cvEEG service line results in cost savings, with the most significant annual savings coming from using the templates for all studies, but the most practical implementation approach with the second highest cost reduction being the template used in the ICU. The lowered costs determined in this work suggest that a template-based cvEEG service could be supported at smaller centers with significantly reduced costs and could allow for broader use of cvEEG patient monitoring.

A Novel Application of Multiscale Entropy in Electroencephalography to Predict the Efficacy of Acetylcholinesterase Inhibitor in Alzheimer's Disease

PubMed Central

Tsai, Ping-Huang; Liu, Fang-Chun; Tsao, Jenho; Wang, Yung-Hung; Lo, Men-Tzung

2015-01-01

Alzheimer's disease (AD) is the most common form of dementia. According to one hypothesis, AD is caused by the reduced synthesis of the neurotransmitter acetylcholine. Therefore, acetylcholinesterase (AChE) inhibitors are considered to be an effective therapy. For clinicians, however, AChE inhibitors are not a predictable treatment for individual patients. We aimed to disclose the difference by biosignal processing. In this study, we used multiscale entropy (MSE) analysis, which can disclose the embedded information in different time scales, in electroencephalography (EEG), in an attempt to predict the efficacy of AChE inhibitors. Seventeen newly diagnosed AD patients were enrolled, with an initial minimental state examination (MMSE) score of 18.8 ± 4.5. After 12 months of AChE inhibitor therapy, 7 patients were responsive and 10 patients were nonresponsive. The major difference between these two groups is Slope 2 (MSE6 to 20). The area below the receiver operating characteristic (ROC) curve of Slope 2 is 0.871 (95% CI = 0.69–1). The sensitivity is 85.7% and the specificity is 60%, whereas the cut-off value of Slope 2 is −0.024. Therefore, MSE analysis of EEG signals, especially Slope 2, provides a potential tool for predicting the efficacy of AChE inhibitors prior to therapy. PMID:26120358

Altered slow wave activity in major depressive disorder with hypersomnia: a high density EEG pilot study

PubMed Central

Plante, David T.; Landsness, Eric C.; Peterson, Michael J.; Goldstein, Michael R.; Wanger, Tim; Guokas, Jeff J.; Tononi, Giulio; Benca, Ruth M.

2012-01-01

Hypersomnolence in major depressive disorder (MDD) plays an important role in the natural history of the disorder, but the basis of hypersomnia in MDD is poorly understood. Slow wave activity (SWA) has been associated with sleep homeostasis, as well as sleep restoration and maintenance, and may be altered in MDD. Therefore, we conducted a post-hoc study that utilized high density electroencephalography (hdEEG) to test the hypothesis that MDD subjects with hypersomnia (HYS+) would have decreased SWA relative to age and sex-matched MDD subjects without hypersomnia (HYS−) and healthy controls (n=7 for each group). After correcting for multiple comparisons using statistical non-parametric mapping, HYS+ subjects demonstrated significantly reduced parieto-occipital all-night SWA relative to HYS− subjects. Our results suggest hypersomnolence may be associated with topographic reductions in SWA in MDD. Further research using adequately powered prospective design is indicated to confirm these findings. PMID:22512951

Evaluation of multiple comparison correction procedures in drug assessment studies using LORETA maps.

PubMed

Alonso, Joan Francesc; Romero, Sergio; Mañanas, Miguel Ángel; Rojas, Mónica; Riba, Jordi; Barbanoj, Manel José

2015-10-01

The identification of the brain regions involved in the neuropharmacological action is a potential procedure for drug development. These regions are commonly determined by the voxels showing significant statistical differences after comparing placebo-induced effects with drug-elicited effects. LORETA is an electroencephalography (EEG) source imaging technique frequently used to identify brain structures affected by the drug. The aim of the present study was to evaluate different methods for the correction of multiple comparisons in the LORETA maps. These methods which have been commonly used in neuroimaging and also simulated studies have been applied on a real case of pharmaco-EEG study where the effects of increasing benzodiazepine doses on the central nervous system measured by LORETA were investigated. Data consisted of EEG recordings obtained from nine volunteers who received single oral doses of alprazolam 0.25, 0.5, and 1 mg, and placebo in a randomized crossover double-blind design. The identification of active regions was highly dependent on the selected multiple test correction procedure. The combined criteria approach known as cluster mass was useful to reveal that increasing drug doses led to higher intensity and spread of the pharmacologically induced changes in intracerebral current density.

Modulation of Electrophysiology by Transcranial Direct Current Stimulation in Psychiatric Disorders: A Systematic Review.

PubMed

Kim, Minah; Kwak, Yoo Bin; Lee, Tae Young; Kwon, Jun Soo

2018-04-27

Transcranial direct current stimulation (tDCS) is a non-invasive neuromodulation technique increasingly used to relieve symptoms of psychiatric disorders. Electrophysiologic markers, such as electroencephalography (EEG) and event-related potentials (ERP), have high temporal resolution sensitive to detect plastic changes of the brain associated with symptomatic improvement following tDCS application. We performed systematic review to identify electrophysiological markers that reflect tDCS effects on plastic brain changes in psychiatric disorders. A total of 638 studies were identified by searching PubMed, Embase, psychINFPO. Of these, 21 full-text articles were assessed eligible and included in the review. Although the reviewed studies were heterogeneous in their choices of tDCS protocols, targeted electrophysiological markers, and disease entities, their results strongly support EEG/ERPs to sensitively reflect plastic brain changes and the associated symptomatic improvement following tDCS. EEG/ERPs may serve a potent tool in revealing the mechanisms underlying psychiatric symptoms, as well as in localizing the brain area targeted for stimulation. Future studies in each disease entities employing consistent tDCS protocols and electrophysiological markers would be necessary in order to substantiate and further elaborate the findings of studies included in the present systematic review.

Automatic Seizure Detection Based on Morphological Features Using One-Dimensional Local Binary Pattern on Long-Term EEG.

PubMed

Shanir, P P Muhammed; Khan, Kashif Ahmad; Khan, Yusuf Uzzaman; Farooq, Omar; Adeli, Hojjat

2017-12-01

Epileptic neurological disorder of the brain is widely diagnosed using the electroencephalography (EEG) technique. EEG signals are nonstationary in nature and show abnormal neural activity during the ictal period. Seizures can be identified by analyzing and obtaining features of EEG signal that can detect these abnormal activities. The present work proposes a novel morphological feature extraction technique based on the local binary pattern (LBP) operator. LBP provides a unique decimal value to a sample point by weighing the binary outcomes after thresholding the neighboring samples with the present sample point. These LBP values assist in capturing the rising and falling edges of the EEG signal, thus providing a morphologically featured discriminating pattern for epilepsy detection. In the present work, the variability in the LBP values is measured by calculating the sum of absolute difference of the consecutive LBP values. Interquartile range is calculated over the preprocessed EEG signal to provide dispersion measure in the signal. For classification purpose, K-nearest neighbor classifier is used, and the performance is evaluated on 896.9 hours of data from CHB-MIT continuous EEG database. Mean accuracy of 99.7% and mean specificity of 99.8% is obtained with average false detection rate of 0.47/h and sensitivity of 99.2% for 136 seizures.

Hybrid EEG-fNIRS-Based Eight-Command Decoding for BCI: Application to Quadcopter Control.

PubMed

Khan, Muhammad Jawad; Hong, Keum-Shik

2017-01-01

In this paper, a hybrid electroencephalography-functional near-infrared spectroscopy (EEG-fNIRS) scheme to decode eight active brain commands from the frontal brain region for brain-computer interface is presented. A total of eight commands are decoded by fNIRS, as positioned on the prefrontal cortex, and by EEG, around the frontal, parietal, and visual cortices. Mental arithmetic, mental counting, mental rotation, and word formation tasks are decoded with fNIRS, in which the selected features for classification and command generation are the peak, minimum, and mean ΔHbO values within a 2-s moving window. In the case of EEG, two eyeblinks, three eyeblinks, and eye movement in the up/down and left/right directions are used for four-command generation. The features in this case are the number of peaks and the mean of the EEG signal during 1 s window. We tested the generated commands on a quadcopter in an open space. An average accuracy of 75.6% was achieved with fNIRS for four-command decoding and 86% with EEG for another four-command decoding. The testing results show the possibility of controlling a quadcopter online and in real-time using eight commands from the prefrontal and frontal cortices via the proposed hybrid EEG-fNIRS interface.

Detecting Large-Scale Brain Networks Using EEG: Impact of Electrode Density, Head Modeling and Source Localization

PubMed Central

Liu, Quanying; Ganzetti, Marco; Wenderoth, Nicole; Mantini, Dante

2018-01-01

Resting state networks (RSNs) in the human brain were recently detected using high-density electroencephalography (hdEEG). This was done by using an advanced analysis workflow to estimate neural signals in the cortex and to assess functional connectivity (FC) between distant cortical regions. FC analyses were conducted either using temporal (tICA) or spatial independent component analysis (sICA). Notably, EEG-RSNs obtained with sICA were very similar to RSNs retrieved with sICA from functional magnetic resonance imaging data. It still remains to be clarified, however, what technological aspects of hdEEG acquisition and analysis primarily influence this correspondence. Here we examined to what extent the detection of EEG-RSN maps by sICA depends on the electrode density, the accuracy of the head model, and the source localization algorithm employed. Our analyses revealed that the collection of EEG data using a high-density montage is crucial for RSN detection by sICA, but also the use of appropriate methods for head modeling and source localization have a substantial effect on RSN reconstruction. Overall, our results confirm the potential of hdEEG for mapping the functional architecture of the human brain, and highlight at the same time the interplay between acquisition technology and innovative solutions in data analysis. PMID:29551969

Noninvasive EEG correlates of overground and stair walking.

PubMed

Brantley, Justin A; Luu, Trieu Phat; Ozdemir, Recep; Zhu, Fangshi; Winslow, Anna T; Huang, Helen; Contreras-Vidal, Jose L

2016-08-01

Automated walking intention detection remains a challenge in lower-limb neuroprosthetic systems. Here, we assess the feasibility of extracting motor intent from scalp electroencephalography (EEG). First, we evaluated the corticomuscular coherence between central EEG electrodes (C1, Cz, C2) and muscles of the shank and thigh during walking on level ground and stairs. Second, we trained decoders to predict the linear envelope of the surface electromyogram (EMG). We observed significant EEG-led corticomuscular coupling between electrodes and sEMG (tibialis anterior) in the high delta (3-4 Hz) and low theta (4-5 Hz) frequency bands during level walking, indicating efferent signaling from the cortex to peripheral motor neurons. The coherence was increased between EEG and vastus lateralis and tibialis anterior in the delta band (<; 2 Hz) during stair ascent, indicating a task specific modulation in corticomuscular coupling. However, EMG was the leading signal for biceps femoris and gastrocnemius coherence during stair ascent, possibly representing afferent feedback loops from periphery to the motor cortex. Decoder validation showed that EEG signals contained information about the sEMG patterns during over ground walking, however, the accuracy of the predicted sEMG patterns decreased during the stair condition. Overall, these initial findings support the feasibility of integrating sEMG and EEG into a hybrid decoder for volitional control of lower limb neuroprostheses.

Wavelet-Based Artifact Identification and Separation Technique for EEG Signals during Galvanic Vestibular Stimulation

PubMed Central

Adib, Mani; Cretu, Edmond

2013-01-01

We present a new method for removing artifacts in electroencephalography (EEG) records during Galvanic Vestibular Stimulation (GVS). The main challenge in exploiting GVS is to understand how the stimulus acts as an input to brain. We used EEG to monitor the brain and elicit the GVS reflexes. However, GVS current distribution throughout the scalp generates an artifact on EEG signals. We need to eliminate this artifact to be able to analyze the EEG signals during GVS. We propose a novel method to estimate the contribution of the GVS current in the EEG signals at each electrode by combining time-series regression methods with wavelet decomposition methods. We use wavelet transform to project the recorded EEG signal into various frequency bands and then estimate the GVS current distribution in each frequency band. The proposed method was optimized using simulated signals, and its performance was compared to well-accepted artifact removal methods such as ICA-based methods and adaptive filters. The results show that the proposed method has better performance in removing GVS artifacts, compared to the others. Using the proposed method, a higher signal to artifact ratio of −1.625 dB was achieved, which outperformed other methods such as ICA-based methods, regression methods, and adaptive filters. PMID:23956786

Evaluation of a Compact Hybrid Brain-Computer Interface System

PubMed Central

Müller, Klaus-Robert; Schmitz, Christoph H.

2017-01-01

We realized a compact hybrid brain-computer interface (BCI) system by integrating a portable near-infrared spectroscopy (NIRS) device with an economical electroencephalography (EEG) system. The NIRS array was located on the subjects' forehead, covering the prefrontal area. The EEG electrodes were distributed over the frontal, motor/temporal, and parietal areas. The experimental paradigm involved a Stroop word-picture matching test in combination with mental arithmetic (MA) and baseline (BL) tasks, in which the subjects were asked to perform either MA or BL in response to congruent or incongruent conditions, respectively. We compared the classification accuracies of each of the modalities (NIRS or EEG) with that of the hybrid system. We showed that the hybrid system outperforms the unimodal EEG and NIRS systems by 6.2% and 2.5%, respectively. Since the proposed hybrid system is based on portable platforms, it is not confined to a laboratory environment and has the potential to be used in real-life situations, such as in neurorehabilitation. PMID:28373984

High-Frequency EEG Variations in Children with Autism Spectrum Disorder during Human Faces Visualization

PubMed Central

Reategui, Camille; Costa, Bruna Karen de Sousa; da Fonseca, Caio Queiroz; da Silva, Luana; Morya, Edgard

2017-01-01

Autism spectrum disorder (ASD) is a neuropsychiatric disorder characterized by the impairment in the social reciprocity, interaction/language, and behavior, with stereotypes and signs of sensory function deficits. Electroencephalography (EEG) is a well-established and noninvasive tool for neurophysiological characterization and monitoring of the brain electrical activity, able to identify abnormalities related to frequency range, connectivity, and lateralization of brain functions. This research aims to evidence quantitative differences in the frequency spectrum pattern between EEG signals of children with and without ASD during visualization of human faces in three different expressions: neutral, happy, and angry. Quantitative clinical evaluations, neuropsychological evaluation, and EEG of children with and without ASD were analyzed paired by age and gender. The results showed stronger activation in higher frequencies (above 30 Hz) in frontal, central, parietal, and occipital regions in the ASD group. This pattern of activation may correlate with developmental characteristics in the children with ASD. PMID:29018811

Evaluation of a Compact Hybrid Brain-Computer Interface System.

PubMed

Shin, Jaeyoung; Müller, Klaus-Robert; Schmitz, Christoph H; Kim, Do-Won; Hwang, Han-Jeong

2017-01-01

We realized a compact hybrid brain-computer interface (BCI) system by integrating a portable near-infrared spectroscopy (NIRS) device with an economical electroencephalography (EEG) system. The NIRS array was located on the subjects' forehead, covering the prefrontal area. The EEG electrodes were distributed over the frontal, motor/temporal, and parietal areas. The experimental paradigm involved a Stroop word-picture matching test in combination with mental arithmetic (MA) and baseline (BL) tasks, in which the subjects were asked to perform either MA or BL in response to congruent or incongruent conditions, respectively. We compared the classification accuracies of each of the modalities (NIRS or EEG) with that of the hybrid system. We showed that the hybrid system outperforms the unimodal EEG and NIRS systems by 6.2% and 2.5%, respectively. Since the proposed hybrid system is based on portable platforms, it is not confined to a laboratory environment and has the potential to be used in real-life situations, such as in neurorehabilitation.

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

PubMed

Hansen, Sofie Therese; Hansen, Lars Kai

2017-03-01

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

Assessing the Minimum Number of Synchronization Triggers Necessary for Temporal Variance Compensation in Commercial Electroencephalography (EEG) Systems

DTIC Science & Technology

2012-09-01

by the ARL Translational Neuroscience Branch. It covers the Emotiv EPOC,6 Advanced Brain Monitoring (ABM) B-Alert X10,7 Quasar 8 DSI helmet-based...Systems; ARL-TR-5945; U.S. Army Research Laboratory: Aberdeen Proving Ground, MD, 2012 4 Ibid. 5 Ibid. 6 EPOC is a trademark of Emotiv . 7 B

How to Build a Hybrid Neurofeedback Platform Combining EEG and fMRI

PubMed Central

Mano, Marsel; Lécuyer, Anatole; Bannier, Elise; Perronnet, Lorraine; Noorzadeh, Saman; Barillot, Christian

2017-01-01

Multimodal neurofeedback estimates brain activity using information acquired with more than one neurosignal measurement technology. In this paper we describe how to set up and use a hybrid platform based on simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), then we illustrate how to use it for conducting bimodal neurofeedback experiments. The paper is intended for those willing to build a multimodal neurofeedback system, to guide them through the different steps of the design, setup, and experimental applications, and help them choose a suitable hardware and software configuration. Furthermore, it reports practical information from bimodal neurofeedback experiments conducted in our lab. The platform presented here has a modular parallel processing architecture that promotes real-time signal processing performance and simple future addition and/or replacement of processing modules. Various unimodal and bimodal neurofeedback experiments conducted in our lab showed high performance and accuracy. Currently, the platform is able to provide neurofeedback based on electroencephalography and functional magnetic resonance imaging, but the architecture and the working principles described here are valid for any other combination of two or more real-time brain activity measurement technologies. PMID:28377691

Electroencephalography Based Analysis of Working Memory Load and Affective Valence in an N-back Task with Emotional Stimuli

PubMed Central

Grissmann, Sebastian; Faller, Josef; Scharinger, Christian; Spüler, Martin; Gerjets, Peter

2017-01-01

Most brain-based measures of the electroencephalogram (EEG) are used in highly controlled lab environments and only focus on narrow mental states (e.g., working memory load). However, we assume that outside the lab complex multidimensional mental states are evoked. This could potentially create interference between EEG signatures used for identification of specific mental states. In this study, we aimed to investigate more realistic conditions and therefore induced a combination of working memory load and affective valence to reveal potential interferences in EEG measures. To induce changes in working memory load and affective valence, we used a paradigm which combines an N-back task (for working memory load manipulation) with a standard method to induce affect (affective pictures taken from the International Affective Picture System (IAPS) database). Subjective ratings showed that the experimental task was successful in inducing working memory load as well as affective valence. Additionally, performance measures were analyzed and it was found that behavioral performance decreased with increasing workload as well as negative valence, showing that affective valence can have an effect on cognitive processing. These findings are supported by changes in frontal theta and parietal alpha power, parameters used for measuring of working memory load in the EEG. However, these EEG measures are influenced by the negative valence condition as well and thereby show that detection of working memory load is sensitive to affective contexts. Unexpectedly, we did not find any effects for EEG measures typically used for affective valence detection (Frontal Alpha Asymmetry (FAA)). Therefore we assume that the FAA measure might not be usable if cognitive workload is induced simultaneously. We conclude that future studies should account for potential context-specifity of EEG measures. PMID:29311875

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

PubMed Central

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

2018-01-01

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

Human exposure to power frequency magnetic fields up to 7.6 mT: An integrated EEG/fMRI study.

PubMed

Modolo, Julien; Thomas, Alex W; Legros, Alexandre

2017-09-01

We assessed the effects of power-line frequency (60 Hz in North America) magnetic fields (MF) in humans using simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Twenty-five participants were enrolled in a pseudo-double-blind experiment involving "real" or "sham" exposure to sinusoidal 60 Hz MF exposures delivered using the gradient coil of an MRI scanner following two conditions: (i) 10 s exposures at 3 mT (10 repetitions); (ii) 2 s exposures at 7.6 mT (100 repetitions). Occipital EEG spectral power was computed in the alpha range (8-12 Hz, reportedly the most sensitive to MF exposure in the literature) with/without exposure. Brain functional activation was studied using fMRI blood oxygen level-dependent (BOLD, inversely correlated with EEG alpha power) maps. No significant effects were detected on occipital EEG alpha power during or post-exposure for any exposure condition. Consistent with EEG results, no effects were observed on fMRI BOLD maps in any brain region. Our results suggest that acute exposure (2-10 s) to 60 Hz MF from 3 to 7.6 mT (30,000 to 76,000 times higher than average public exposure levels for 60 Hz MF) does not induce detectable changes in EEG or BOLD signals. Combined with previous findings in which effects were observed on the BOLD signal after 1 h exposure to 3 mT, 60 Hz MF, this suggests that MF exposure in the low mT range (<10 mT) might require prolonged durations of exposure to induce detectable effects. Bioelectromagnetics. 38:425-435, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Electroencephalography Based Analysis of Working Memory Load and Affective Valence in an N-back Task with Emotional Stimuli.

PubMed

Grissmann, Sebastian; Faller, Josef; Scharinger, Christian; Spüler, Martin; Gerjets, Peter

2017-01-01

Most brain-based measures of the electroencephalogram (EEG) are used in highly controlled lab environments and only focus on narrow mental states (e.g., working memory load). However, we assume that outside the lab complex multidimensional mental states are evoked. This could potentially create interference between EEG signatures used for identification of specific mental states. In this study, we aimed to investigate more realistic conditions and therefore induced a combination of working memory load and affective valence to reveal potential interferences in EEG measures. To induce changes in working memory load and affective valence, we used a paradigm which combines an N-back task (for working memory load manipulation) with a standard method to induce affect (affective pictures taken from the International Affective Picture System (IAPS) database). Subjective ratings showed that the experimental task was successful in inducing working memory load as well as affective valence. Additionally, performance measures were analyzed and it was found that behavioral performance decreased with increasing workload as well as negative valence, showing that affective valence can have an effect on cognitive processing. These findings are supported by changes in frontal theta and parietal alpha power, parameters used for measuring of working memory load in the EEG. However, these EEG measures are influenced by the negative valence condition as well and thereby show that detection of working memory load is sensitive to affective contexts. Unexpectedly, we did not find any effects for EEG measures typically used for affective valence detection (Frontal Alpha Asymmetry (FAA)). Therefore we assume that the FAA measure might not be usable if cognitive workload is induced simultaneously. We conclude that future studies should account for potential context-specifity of EEG measures.

The effectiveness of a staff education program on the use of continuous EEG with patients in neuroscience intensive care units.

PubMed

Seiler, Lisa; Fields, Jennifer; Peach, Elizabeth; Zwerin, Suzanne; Savage, Christine

2012-04-01

Approximately a third of patients in neuroscience intensive care units (ICUs) experience subclinical seizures and, as a result, are at higher risk for poor outcomes. The use of continuous electroencephalography (cEEG) monitoring can help nurses detect seizure activity and initiate early prevention. Nurse competency in the use of cEEG is important to facilitate effective bedside monitoring. The objective of this study was to evaluate the effectiveness of a staff educational program aimed at improving the knowledge of nurses in the use of cEEG monitoring in adults. A quasi-experimental pretest/posttest 1-group design was utilized. Neuroscience ICU registered nurses, whose experience ranged from 2 months to 24 years, participated in the study. Participants completed a pretest on seizure knowledge and the use of cEEG monitoring. Participants received a 4-hour educational session on the use of cEEG monitoring. Immediately after the program and again 1 month later, they completed a posttest. Test scores improved significantly from pretest to the first posttest (t = -15.093, p < .001). Although there was a slight decline in the mean score from the posttest to the 1-month follow-up, posttest scores were significantly better than the pretest score (t = -12.42, df = 44, p < .001). Whereas years of experience correlated positively to the pretest score, after the intervention, no such correlation was evident. The results demonstrated that an educational program improved the competency of nurses in the use of cEEG with adult patients in a neuroscience ICU and that this knowledge was sustained over time. Further research is needed to demonstrate the effectiveness of this intervention in other settings.

[Neuromodulatory effects of bromazepam when individuals were exposed to a motor learning task: quantitative electroencephalography (qEEG)].

PubMed

Salles, José Inácio; Bastos, Victor Hugo; Cunha, Marlo; Machado, Dionis; Cagy, Maurício; Furtado, Vernon; Basile, Luis Fernando; Piedade, Roberto; Ribeiro, Pedro

2006-03-01

The sedative effects of bromazepam on cognitive and performance have been widely investigated. A number of different approaches have assessed the influence of bromazepam when individuals are engaged to a motor task. In this context, the present study aimed to investigate electrophysiological changes when individuals were exposed to a typewriting task after taking 6 mg of bromazepam. qEEG data were simultaneously recorded during the task. In particular, relative power in delta band (0.5-3.5 Hz) was analyzed. Time of execution and errors during the task were registered as behavioral variables. The experimental group, bromazepam 6 mg, showed a better motor performance and higher relative power than control individuals (placebo). These results suggest that the use of bromazepam reduces anxiety levels as expected and thus, produces an increment in motor performance.

Estimating the Integrated Information Measure Phi from High-Density Electroencephalography during States of Consciousness in Humans

PubMed Central

Kim, Hyoungkyu; Hudetz, Anthony G.; Lee, Joseph; Mashour, George A.; Lee, UnCheol; Avidan, Michael S.

2018-01-01

The integrated information theory (IIT) proposes a quantitative measure, denoted as Φ, of the amount of integrated information in a physical system, which is postulated to have an identity relationship with consciousness. IIT predicts that the value of Φ estimated from brain activities represents the level of consciousness across phylogeny and functional states. Practical limitations, such as the explosive computational demands required to estimate Φ for real systems, have hindered its application to the brain and raised questions about the utility of IIT in general. To achieve practical relevance for studying the human brain, it will be beneficial to establish the reliable estimation of Φ from multichannel electroencephalogram (EEG) and define the relationship of Φ to EEG properties conventionally used to define states of consciousness. In this study, we introduce a practical method to estimate Φ from high-density (128-channel) EEG and determine the contribution of each channel to Φ. We examine the correlation of power, frequency, functional connectivity, and modularity of EEG with regional Φ in various states of consciousness as modulated by diverse anesthetics. We find that our approximation of Φ alone is insufficient to discriminate certain states of anesthesia. However, a multi-dimensional parameter space extended by four parameters related to Φ and EEG connectivity is able to differentiate all states of consciousness. The association of Φ with EEG connectivity during clinically defined anesthetic states represents a new practical approach to the application of IIT, which may be used to characterize various physiological (sleep), pharmacological (anesthesia), and pathological (coma) states of consciousness in the human brain. PMID:29503611

Estimating the Integrated Information Measure Phi from High-Density Electroencephalography during States of Consciousness in Humans.

PubMed

Kim, Hyoungkyu; Hudetz, Anthony G; Lee, Joseph; Mashour, George A; Lee, UnCheol

2018-01-01

The integrated information theory (IIT) proposes a quantitative measure, denoted as Φ, of the amount of integrated information in a physical system, which is postulated to have an identity relationship with consciousness. IIT predicts that the value of Φ estimated from brain activities represents the level of consciousness across phylogeny and functional states. Practical limitations, such as the explosive computational demands required to estimate Φ for real systems, have hindered its application to the brain and raised questions about the utility of IIT in general. To achieve practical relevance for studying the human brain, it will be beneficial to establish the reliable estimation of Φ from multichannel electroencephalogram (EEG) and define the relationship of Φ to EEG properties conventionally used to define states of consciousness. In this study, we introduce a practical method to estimate Φ from high-density (128-channel) EEG and determine the contribution of each channel to Φ. We examine the correlation of power, frequency, functional connectivity, and modularity of EEG with regional Φ in various states of consciousness as modulated by diverse anesthetics. We find that our approximation of Φ alone is insufficient to discriminate certain states of anesthesia. However, a multi-dimensional parameter space extended by four parameters related to Φ and EEG connectivity is able to differentiate all states of consciousness. The association of Φ with EEG connectivity during clinically defined anesthetic states represents a new practical approach to the application of IIT, which may be used to characterize various physiological (sleep), pharmacological (anesthesia), and pathological (coma) states of consciousness in the human brain.

EEG artifacts reduction by multivariate empirical mode decomposition and multiscale entropy for monitoring depth of anaesthesia during surgery.

PubMed

Liu, Quan; Chen, Yi-Feng; Fan, Shou-Zen; Abbod, Maysam F; Shieh, Jiann-Shing

2017-08-01

Electroencephalography (EEG) has been widely utilized to measure the depth of anaesthesia (DOA) during operation. However, the EEG signals are usually contaminated by artifacts which have a consequence on the measured DOA accuracy. In this study, an effective and useful filtering algorithm based on multivariate empirical mode decomposition and multiscale entropy (MSE) is proposed to measure DOA. Mean entropy of MSE is used as an index to find artifacts-free intrinsic mode functions. The effect of different levels of artifacts on the performances of the proposed filtering is analysed using simulated data. Furthermore, 21 patients' EEG signals are collected and analysed using sample entropy to calculate the complexity for monitoring DOA. The correlation coefficients of entropy and bispectral index (BIS) results show 0.14 ± 0.30 and 0.63 ± 0.09 before and after filtering, respectively. Artificial neural network (ANN) model is used for range mapping in order to correlate the measurements with BIS. The ANN method results show strong correlation coefficient (0.75 ± 0.08). The results in this paper verify that entropy values and BIS have a strong correlation for the purpose of DOA monitoring and the proposed filtering method can effectively filter artifacts from EEG signals. The proposed method performs better than the commonly used wavelet denoising method. This study provides a fully adaptive and automated filter for EEG to measure DOA more accuracy and thus reduce risk related to maintenance of anaesthetic agents.

Hybrid ICA-Regression: Automatic Identification and Removal of Ocular Artifacts from Electroencephalographic Signals.

PubMed

Mannan, Malik M Naeem; Jeong, Myung Y; Kamran, Muhammad A

2016-01-01

Electroencephalography (EEG) is a portable brain-imaging technique with the advantage of high-temporal resolution that can be used to record electrical activity of the brain. However, it is difficult to analyze EEG signals due to the contamination of ocular artifacts, and which potentially results in misleading conclusions. Also, it is a proven fact that the contamination of ocular artifacts cause to reduce the classification accuracy of a brain-computer interface (BCI). It is therefore very important to remove/reduce these artifacts before the analysis of EEG signals for applications like BCI. In this paper, a hybrid framework that combines independent component analysis (ICA), regression and high-order statistics has been proposed to identify and eliminate artifactual activities from EEG data. We used simulated, experimental and standard EEG signals to evaluate and analyze the effectiveness of the proposed method. Results demonstrate that the proposed method can effectively remove ocular artifacts as well as it can preserve the neuronal signals present in EEG data. A comparison with four methods from literature namely ICA, regression analysis, wavelet-ICA (wICA), and regression-ICA (REGICA) confirms the significantly enhanced performance and effectiveness of the proposed method for removal of ocular activities from EEG, in terms of lower mean square error and mean absolute error values and higher mutual information between reconstructed and original EEG.

Hybrid ICA—Regression: Automatic Identification and Removal of Ocular Artifacts from Electroencephalographic Signals

PubMed Central

Mannan, Malik M. Naeem; Jeong, Myung Y.; Kamran, Muhammad A.

2016-01-01

Electroencephalography (EEG) is a portable brain-imaging technique with the advantage of high-temporal resolution that can be used to record electrical activity of the brain. However, it is difficult to analyze EEG signals due to the contamination of ocular artifacts, and which potentially results in misleading conclusions. Also, it is a proven fact that the contamination of ocular artifacts cause to reduce the classification accuracy of a brain-computer interface (BCI). It is therefore very important to remove/reduce these artifacts before the analysis of EEG signals for applications like BCI. In this paper, a hybrid framework that combines independent component analysis (ICA), regression and high-order statistics has been proposed to identify and eliminate artifactual activities from EEG data. We used simulated, experimental and standard EEG signals to evaluate and analyze the effectiveness of the proposed method. Results demonstrate that the proposed method can effectively remove ocular artifacts as well as it can preserve the neuronal signals present in EEG data. A comparison with four methods from literature namely ICA, regression analysis, wavelet-ICA (wICA), and regression-ICA (REGICA) confirms the significantly enhanced performance and effectiveness of the proposed method for removal of ocular activities from EEG, in terms of lower mean square error and mean absolute error values and higher mutual information between reconstructed and original EEG. PMID:27199714

Scalp EEG does not predict hemispherectomy outcome

PubMed Central

Greiner, Hansel M.; Park, Yong D.; Holland, Katherine; Horn, Paul S.; Byars, Anna W.; Mangano, Francesco T.; Smith, Joseph R.; Lee, Mark R.; Lee, Ki-Hyeong

2012-01-01

Background Functional hemispherectomy is effective in carefully selected patients, resulting in a reduction of seizure burden up to complete resolution, improvement of intellectual development, and developmental benefit despite possible additional neurological deficit. Despite apparent hemispheric pathology on brain magnetic resonance imaging (MRI) or other imaging tests, scalp electroencephalography (EEG) could be suggestive of bilateral ictal onset or even ictal onset contralateral to the dominant imaging abnormality. We aimed to investigate the role of scalp EEG lateralization pre-operatively in predicting outcome. Methods We retrospectively reviewed 54 patients who underwent hemispherectomy between 1991 and 2009 at Medical College of Georgia (1991–2006) and Cincinnati Children’s Hospital Medical Center (2006–2009) and had at least one year post-operative follow-up. All preoperative EEGs were reviewed, and classified as either lateralizing or nonlateralizing, for both ictal and interictal EEG recordings. Results Of 54 patients, 42 (78%) became seizure free. Twenty-four (44%) of 54 had a nonlateralizing ictal or interictal EEG. Further analysis was based on etiology of epilepsy, including malformation of cortical development (MCD), Rasmussen syndrome (RS), and stroke (CVA). EEG nonlateralization did not predict poor outcome in any of the etiology groups evaluated. Conclusion Scalp EEG abnormalities in contralateral or bilateral hemispheres do not, in isolation, predict a poor outcome from hemispherectomy. Results of other non-invasive and invasive evaluations should be used to determine candidacy. PMID:21813300

Mapping working memory retrieval in space and in time: A combined electroencephalography and electrocorticography approach.

PubMed

Zhang, Qiong; van Vugt, Marieke; Borst, Jelmer P; Anderson, John R

2018-07-01

In this study, we investigated the time course and neural correlates of the retrieval process underlying visual working memory. We made use of a rare dataset in which the same task was recorded using both scalp electroencephalography (EEG) and Electrocorticography (ECoG), respectively. This allowed us to examine with great spatial and temporal detail how the retrieval process works, and in particular how the medial temporal lobe (MTL) is involved. In each trial, participants judged whether a probe face had been among a set of recently studied faces. With a method that combines hidden semi-Markov models and multivariate pattern analysis, the neural signal was decomposed into a sequence of latent cognitive stages with information about their durations on a trial-by-trial basis. Analyzed separately, EEG and ECoG data yielded converging results on discovered stages and their interpretation, which reflected 1) a brief pre-attention stage, 2) encoding the stimulus, 3) retrieving the studied set, and 4) making a decision. Combining these stages with the high spatial resolution of ECoG suggested that activity in the temporal cortex reflected item familiarity in the retrieval stage; and that once retrieval is complete, there is active maintenance of the studied face set in the decision stage in the MTL. During this same period, the frontal cortex guides the decision by means of theta coupling with the MTL. These observations generalize previous findings on the role of MTL theta from long-term memory tasks to short-term memory tasks. Copyright © 2018 Elsevier Inc. All rights reserved.

Modulation of cortical activity in 2D versus 3D virtual reality environments: an EEG study.

PubMed

Slobounov, Semyon M; Ray, William; Johnson, Brian; Slobounov, Elena; Newell, Karl M

2015-03-01

There is a growing empirical evidence that virtual reality (VR) is valuable for education, training, entertaining and medical rehabilitation due to its capacity to represent real-life events and situations. However, the neural mechanisms underlying behavioral confounds in VR environments are still poorly understood. In two experiments, we examined the effect of fully immersive 3D stereoscopic presentations and less immersive 2D VR environments on brain functions and behavioral outcomes. In Experiment 1 we examined behavioral and neural underpinnings of spatial navigation tasks using electroencephalography (EEG). In Experiment 2, we examined EEG correlates of postural stability and balance. Our major findings showed that fully immersive 3D VR induced a higher subjective sense of presence along with enhanced success rate of spatial navigation compared to 2D. In Experiment 1 power of frontal midline EEG (FM-theta) was significantly higher during the encoding phase of route presentation in the 3D VR. In Experiment 2, the 3D VR resulted in greater postural instability and modulation of EEG patterns as a function of 3D versus 2D environments. The findings support the inference that the fully immersive 3D enriched-environment requires allocation of more brain and sensory resources for cognitive/motor control during both tasks than 2D presentations. This is further evidence that 3D VR tasks using EEG may be a promising approach for performance enhancement and potential applications in clinical/rehabilitation settings. Copyright © 2014 Elsevier B.V. All rights reserved.

Localizing seizure-onset zones in presurgical evaluation of drug-resistant epilepsy by electroencephalography/fMRI: effectiveness of alternative thresholding strategies.

PubMed

Hauf, M; Jann, K; Schindler, K; Scheidegger, O; Meyer, K; Rummel, C; Mariani, L; Koenig, T; Wiest, R

2012-10-01

Simultaneous EEG/fMRI is an effective noninvasive tool for identifying and localizing the SOZ in patients with focal epilepsy. In this study, we evaluated different thresholding strategies in EEG/fMRI for the assessment of hemodynamic responses to IEDs in the SOZ of drug-resistant epilepsy. Sixteen patients with focal epilepsy were examined by using simultaneous 92-channel EEG and BOLD fMRI. The temporal fluctuation of epileptiform signals on the EEG was extracted by independent component analysis to predict the hemodynamic responses to the IEDs. We applied 3 different threshold criteria to detect hemodynamic responses within the SOZ: 1) PA, 2) a fixed threshold at P < .05 corrected for multiple comparison (FWE), and 3) FAV (4000 ± 200 activated voxels within the brain). PA identified the SOZ in 9 of 16 patients; FWE resulted in concordant BOLD signal correlates in 11 of 16, and FAV in 13 of 16 patients. Hemodynamic responses were detected within the resected areas in 5 (PA), 6 (FWE), and 8 (FAV) of 10 patients who remained seizure-free after surgery. EEG/fMRI is a noninvasive tool for the presurgical work-up of patients with epilepsy, which can be performed during seizure-free periods and is complementary to the ictal electroclinical assessment. Our findings suggest that the effectiveness of EEG/fMRI in delineating the SOZ may be further improved by the additional use of alternative analysis strategies such as FAV.

EEG low-resolution brain electromagnetic tomography (LORETA) in Huntington's disease.

PubMed

Painold, Annamaria; Anderer, Peter; Holl, Anna K; Letmaier, Martin; Saletu-Zyhlarz, Gerda M; Saletu, Bernd; Bonelli, Raphael M

2011-05-01

Previous studies have shown abnormal electroencephalography (EEG) in Huntington's disease (HD). The aim of the present investigation was to compare quantitatively analyzed EEGs of HD patients and controls by means of low-resolution brain electromagnetic tomography (LORETA). Further aims were to delineate the sensitivity and utility of EEG LORETA in the progression of HD, and to correlate parameters of cognitive and motor impairment with neurophysiological variables. In 55 HD patients and 55 controls a 3-min vigilance-controlled EEG (V-EEG) was recorded during midmorning hours. Power spectra and intracortical tomography were computed by LORETA in seven frequency bands and compared between groups. Spearman rank correlations were based on V-EEG and psychometric data. Statistical overall analysis by means of the omnibus significance test demonstrated significant (p < 0.01) differences between HD patients and controls. LORETA theta, alpha and beta power were decreased from early to late stages of the disease. Only advanced disease stages showed a significant increase in delta power, mainly in the right orbitofrontal cortex. Correlation analyses revealed that a decrease of alpha and theta power correlated significantly with increasing cognitive and motor decline. LORETA proved to be a sensitive instrument for detecting progressive electrophysiological changes in HD. Reduced alpha power seems to be a trait marker of HD, whereas increased prefrontal delta power seems to reflect worsening of the disease. Motor function and cognitive function deteriorate together with a decrease in alpha and theta power. This data set, so far the largest in HD research, helps to elucidate remaining uncertainties about electrophysiological abnormalities in HD.

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.

Evaluation of Dry Sensors for Neonatal EEG Recordings.

PubMed

Fridman, Igor; Cordeiro, Malaika; Rais-Bahrami, Khodayar; McDonald, Neil J; Reese, James J; Massaro, An N; Conry, Joan A; Chang, Taeun; Soussou, Walid; Tsuchida, Tammy N

2016-04-01

Neonatal seizures are a common neurologic diagnosis in neonatal intensive care units, occurring in approximately 14,000 newborns annually in the United States. Although the only reliable means of detecting and treating neonatal seizures is with an electroencephalography (EEG) recording, many neonates do not receive an EEG or experience delays in getting them. Barriers to obtaining neonatal EEGs include (1) lack of skilled EEG technologists to apply conventional wet electrodes to delicate neonatal skin, (2) poor signal quality because of improper skin preparation and artifact, and (3) extensive time needed to apply electrodes. Dry sensors have the potential to overcome these obstacles but have not previously been evaluated on neonates. Sequential and simultaneous recordings with wet and dry sensors were performed for 1 hour on 27 neonates from 35 to 42.5 weeks postmenstrual age. Recordings were analyzed for correlation and amplitude and were reviewed by neurophysiologists. Performance of dry sensors on simulated vernix was examined. Analysis of dry and wet signals showed good time-domain correlation (reaching >0.8), given the nonsuperimposed sensor positions and similar power spectral density curves. Neurophysiologist reviews showed no statistically significant difference between dry and wet data on most clinically relevant EEG background and seizure patterns. There was no skin injury after 1 hour of dry sensor recordings. In contrast to wet electrodes, impedance and electrical artifact of dry sensors were largely unaffected by simulated vernix. Dry sensors evaluated in this study have the potential to provide high-quality, timely EEG recordings on neonates with less risk of skin injury.

Integrated approach to e-learning enhanced both subjective and objective knowledge of aEEG in a neonatal intensive care unit.

PubMed

Poon, W B; Tagamolila, V; Toh, Y P; Cheng, Z R

2015-03-01

Various meta-analyses have shown that e-learning is as effective as traditional methods of continuing professional education. However, there are some disadvantages to e-learning, such as possible technical problems, the need for greater self-discipline, cost involved in developing programmes and limited direct interaction. Currently, most strategies for teaching amplitude-integrated electroencephalography (aEEG) in neonatal intensive care units (NICUs) worldwide depend on traditional teaching methods. We implemented a programme that utilised an integrated approach to e-learning. The programme consisted of three sessions of supervised protected time e-learning in an NICU. The objective and subjective effectiveness of the approach was assessed through surveys administered to participants before and after the programme. A total of 37 NICU staff (32 nurses and 5 doctors) participated in the study. 93.1% of the participants appreciated the need to acquire knowledge of aEEG. We also saw a statistically significant improvement in the subjective knowledge score (p = 0.041) of the participants. The passing rates for identifying abnormal aEEG tracings (defined as ≥ 3 correct answers out of 5) also showed a statistically significant improvement (from 13.6% to 81.8%, p < 0.001). Among the participants who completed the survey, 96.0% felt the teaching was well structured, 77.8% felt the duration was optimal, 80.0% felt that they had learnt how to systematically interpret aEEGs, and 70.4% felt that they could interpret normal aEEG with confidence. An integrated approach to e-learning can help improve subjective and objective knowledge of aEEG.

Assessment of mental stress effects on prefrontal cortical activities using canonical correlation analysis: an fNIRS-EEG study

PubMed Central

Al-Shargie, Fares; Tang, Tong Boon; Kiguchi, Masashi

2017-01-01

This paper presents an investigation about the effects of mental stress on prefrontal cortex (PFC) subregions using simultaneous measurement of functional Near-Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG) signals. The aim is to explore canonical correlation analysis (CCA) technique to study the relationship among the bi-modality signals in mental stress assessment, and how we could fuse the signals for better accuracy in stress detection. Twenty-five male healthy subjects participated in the study while performing mental arithmetic task under control and stress (under time pressure with negative feedback) conditions. The fusion of brain signals acquired by fNIRS-EEG was performed at feature-level using CCA by maximizing the inter-subject covariance across modalities. The CCA result discovered the associations across the modalities and estimated the components responsible for these associations. The experiment results showed that mental stress experienced by this cohort of subjects is subregion specific and localized to the right ventrolateral PFC subregion. These suggest the right ventrolateral PFC as a suitable candidate region to extract biomarkers as performance indicators of neurofeedback training in stress coping. PMID:28663892