EEG Applications Research Articles

Projection Versus Prewhitening for EEG Interference Suppression

Suppression of strong, spatially correlated background interference is a challenge associated with electroencephalography (EEG) source localization problems. The most common way of dealing with such interference is through the use of a prewhitening transformation based on an estimate of the covariance of the interference plus noise. This approach is based on strong assumptions regarding temporal stationarity of the data, which do not commonly hold in EEG applications. In addition, prewhitening cannot typically be implemented directly due to ill conditioning of the covariance matrix, and ad hoc regularization is often necessary. Using both simulation examples and experiments involving real EEG data with auditory evoked responses, we demonstrate that a straightforward interference projection method is significantly more robust than prewhitening for EEG source localization.

Signal Quality of Titanium and Titanium Nitride Coated Dry Polymer Electrodes

Brain Computer Interfaces, mobile monitoring and Ambient Assisted Living are new fields of application for Electroencephalography (EEG). These technologies require sensors enabling fast and easy preparation as well as mobile and long-term application. Conventional Silver/Silver-Chloride (Ag/AgCl) electrodes are inadequate due to drawbacks arising from the need for electrolyte materials, e.g. extensive skin and electrode preparation, limited application time, and multiple error sources. Novel dry electrodes are intended to be applied without additional electrolyte materials and thus provide the technological base for new EEG applications.

Sleep Disorder Detection and Identification

Electroencephalogram (EEG) has been widely used for capturing the electrical human's brain activities for diagnosis and treatment purposes. One of the applications of EEG is to detect the sleep disorders include insomnia and stress-related disorder depend on the severity of the disorders. In this study, Ensemble Empirical Mode Decomposition (EEMD) method which is modified original Empirical Mode Decomposition (EMD) algorithm will be employed for recognition and identification of the EEG patterns and features. Besides that, ICA was applied to assist the process of isolating noise components and to explain the functions of different brain parts through the topographical scalp map. The application of ICA has shown to be an efficient tool for artifact extraction from EEG. Finally, with the combination of EEMD and ICA methods, the music stimulation has been proved that it can enhance the sleep quality of human compare to non-music stimulation.

Dry and Noncontact EEG Sensors for Mobile Brain–Computer Interfaces

Dry and noncontact electroencephalographic (EEG) electrodes, which do not require gel or even direct scalp coupling, have been considered as an enabler of practical, real-world, brain-computer interface (BCI) platforms. This study compares wet electrodes to dry and through hair, noncontact electrodes within a steady state visual evoked potential (SSVEP) BCI paradigm. The construction of a dry contact electrode, featuring fingered contact posts and active buffering circuitry is presented. Additionally, the development of a new, noncontact, capacitive electrode that utilizes a custom integrated, high-impedance analog front-end is introduced. Offline tests on 10 subjects characterize the signal quality from the different electrodes and demonstrate that acquisition of small amplitude, SSVEP signals is possible, even through hair using the new integrated noncontact sensor. Online BCI experiments demonstrate that the information transfer rate (ITR) with the dry electrodes is comparable to that of wet electrodes, completely without the need for gel or other conductive media. In addition, data from the noncontact electrode, operating on the top of hair, show a maximum ITR in excess of 19 bits/min at 100% accuracy (versus 29.2 bits/min for wet electrodes and 34.4 bits/min for dry electrodes), a level that has never been demonstrated before. The results of these experiments show that both dry and noncontact electrodes, with further development, may become a viable tool for both future mobile BCI and general EEG applications.

Clinical applicability of EEG in differential diagnosis of cognitive impairment and dementia

One of the main tasks in the diagnostic work up of cognitive impairment and dementia is to differentiate between the various causes but the current criteria for diagnosis of the most prevalent forms of dementia are of different accuracy and up to 10% of cases are difficult to diagnose with certainty (Knopman DS et al Neurology 2001;56:1143-53.) We present a diagnostic method which can provide support for differential diagnosis into one of the following eight categories: Normal (NRM), Alzheimer's disease (AD), stable Mild Cognitive Impairment (stable for > 24 months = sMCI), Progressive MCI (converting to dementia within 24 months = pMCI), Vascular Dementia (VaD), Lewy-body/Parkinson's (DLBP), depression (DPR) and Frontal Lobe Dementia (FLD). 1050 EEG registrations have been collected into an EEG database. All participants are in the age range of 50 - 90 years. The individuals in the clinical groups were diagnosed at a memory clinic and the NRM individuals were sought from the community. They were not demented and showed no signs of cognitive impairment. Statistical pattern recognition (SPR) was used to create classifiers using selected features from 1120 features of the EEG registration. For all binary comparisons of two distinct groups, a total of 28 comparisons, a numeric index from 0-100 was established to describe the likelihood of an individual belonging to one of the two groups being compared. The diagnostic accuracy of the 28 comparisons all fall within the range 73%-97%. The accuracy is estimated using ten-fold cross validation. Thus it is possible to create a table with the results of the 28 classifications and use it to predict which group a given individual most likely belongs to. Clinical examples illustrate how this technique is applied for differential diagnosis. Following a more substantial clinical validation and an easy access to the methodology, we expect this application of clinical EEG in support for differential diagnosis of dementia to become a realistic first step in the full clinical workup of patients with cognitive impairment and dementia. The underlying technology is well known, widely available and inexpensive in relation to other imaging techniques.

An Energy-Efficient Biomedical Signal Processing Platform

This paper presents an energy-efficient processing platform for wearable sensor nodes, designed to support diverse biological signals and algorithms. The platform features a 0.5 V-1.0 V 16-bit microcontroller, SRAM, and accelerators for biomedical signal processing. Voltage scaling and block-level power gating allow optimizing energy efficiency under applications of varying complexity. Programmable accelerators support numerous usage scenarios and perform signal processing tasks at 133 to 215× lower energy than the general-purpose CPU. When running complete EEG and EKG applications using both CPU and accelerators, the platform achieves 10.2× and 11.5× energy reduction respectively compared to CPU-only implementations.

Clinical applicability of EEG in evaluating the possibility of a progress of mild cognitive impairment

Clinical applicability of EEG in evaluating the possibility of a progress of mild cognitive impairment

Clinical electrophysiologic assessments and mild traumatic brain injury: State-of-the-science and implications for clinical practice

Conventional and quantitative electroencephalography (EEG and qEEG, respectively) may enhance clinical diagnosis and treatment planning provided to persons with mild traumatic brain injury (mTBI) and postconcussive symptoms. Effective and appropriate use of EEG and qEEG in this context requires expert-level knowledge of these technologies, mTBI, and the differential diagnosis for postconcussive symptoms. A practical and brief review from the perspective of a clinician–scientist engaged principally in the care and study of persons with mTBI therefore may be of use and value to other clinicians and scientists interested in these matters. Toward that end, this article offers an overview of the current applications of conventional EEG and qEEG to the study and clinical evaluation of persons with mTBI. The clinical case definition of TBI, the differential diagnosis of post-injury neuropsychiatric disturbances, and the typical course of recovery following mTBI are reviewed. With this background and context, the strengths and limitations of the literature describing EEG and qEEG studies in this population are considered. The implications of this review on the applications of these electrophysiologic assessments to the clinical evaluation of persons with mTBI and postconcussive symptoms are then considered. Finally, suggestions are offered regarding the design of future studies using these technologies in this population. Although this review may be of interest and value to professionals engaged in clinical or research electrophysiology in their daily work, it is intended to serve more immediately the needs of clinicians less familiar with these types of clinical electrophysiologic assessments.

A Dry EEG-System for Scientific Research and Brain–Computer Interfaces

Although it ranks among the oldest tools in neuroscientific research, electroencephalography (EEG) still forms the method of choice in a wide variety of clinical and research applications. In the context of brain–computer interfacing (BCI), EEG recently has become a tool to enhance human–machine interaction. EEG could be employed in a wider range of environments, especially for the use of BCI systems in a clinical context or at the homes of patients. However, the application of EEG in these contexts is impeded by the cumbersome preparation of the electrodes with conductive gel that is necessary to lower the impedance between electrodes and scalp. Dry electrodes could provide a solution to this barrier and allow for EEG applications outside the laboratory. In addition, dry electrodes may reduce the time needed for neurological exams in clinical practice. This study evaluates a prototype of a three-channel dry electrode EEG system, comparing it to state-of-the-art conventional EEG electrodes. Two experimental paradigms were used: first, event-related potentials (ERP) were investigated with a variant of the oddball paradigm. Second, features of the frequency domain were compared by a paradigm inducing occipital alpha. Furthermore, both paradigms were used to evaluate BCI classification accuracies of both EEG systems. Amplitude and temporal structure of ERPs as well as features in the frequency domain did not differ significantly between the EEG systems. BCI classification accuracies were equally high in both systems when the frequency domain was considered. With respect to the oddball classification accuracy, there were slight differences between the wet and dry electrode systems. We conclude that the tested dry electrodes were capable to detect EEG signals with good quality and that these signals can be used for research or BCI applications. Easy to handle electrodes may help to foster the use of EEG among a wider range of potential users.

The Application of EEG related

Abstract The development of information and electronic technology to promote the convergence of various disciplines, EEG evolved from biological information, combined with information technology, extend the application of many related, that many have about the application of EEG Mature, there are many applications of EEG are related to research, this paper related to the application of EEG summary.

FMRI Artefact Rejection and Sleep Scoring Toolbox

We started writing the “fMRI artefact rejection and sleep scoring toolbox”, or “FA𝕊T”, to process our sleep EEG-fMRI data, that is, the simultaneous recording of electroencephalographic and functional magnetic resonance imaging data acquired while a subject is asleep. FA𝕊T tackles three crucial issues typical of this kind of data: (1) data manipulation (viewing, comparing, chunking, etc.) of long continuous M/EEG recordings, (2) rejection of the fMRI-induced artefact in the EEG signal, and (3) manual sleep-scoring of the M/EEG recording. Currently, the toolbox can efficiently deal with these issues via a GUI, SPM8 batching system or hand-written script. The tools developed are, of course, also useful for other EEG applications, for example, involving simultaneous EEG-fMRI acquisition, continuous EEG eye-balling, and manipulation. Even though the toolbox was originally devised for EEG data, it will also gracefully handle MEG data without any problem. “FA𝕊T” is developed in Matlab as an add-on toolbox for SPM8 and, therefore, internally uses its SPM8-meeg data format. “FA𝕊T” is available for free, under the GNU-GPL.

P5-27 Basal ganglia and cerebellum in the motor timing prediction task: Effective connectivity

transform was performed and potential of its features was tested. The dataset consisted of EEG channels from polysomnographic recordings of 12 healthy term newborns. From each recording artifact-free parts corresponding to active and quiet sleep were selected. As processing of this type of signal is a complex multilevel procedure, we focused our attention on feature extraction. After the application of constant segmentation each segment was represented by its features, derived after the application of discrete WT. Set of extracted features comprised statistical parameters as well as total and relative sub-band energies. We explored how these features change for different behavioral states, and how their values changed with the position of electrodes. Also, as parameters of WT various types of mother wavelets were tested. For final classification the same classifier was used through the study in order to obtain comparable results. As feature extraction is important for automatic classification, obtained results concerning WT parameters and appropriate features may provide a reference for developing or enhancing algorithms in this field of EEG application. Acknowledgement: This research was supported by Grant Nos. 1ET101210512 and MSM6840770012.

Scalable tensor factorizations for incomplete data

The problem of incomplete data – i.e., data with missing or unknown values – in multi-way arrays is ubiquitous in biomedical signal processing, network traffic analysis, bibliometrics, social network analysis, chemometrics, computer vision, communication networks, etc. We consider the problem of how to factorize data sets with missing values with the goal of capturing the underlying latent structure of the data and possibly reconstructing missing values (i.e., tensor completion). We focus on one of the most well-known tensor factorizations that captures multi-linear structure, CANDECOMP/PARAFAC (CP). In the presence of missing data, CP can be formulated as a weighted least squares problem that models only the known entries. We develop an algorithm called CP-WOPT (CP Weighted OPTimization) that uses a first-order optimization approach to solve the weighted least squares problem. Based on extensive numerical experiments, our algorithm is shown to successfully factorize tensors with noise and up to 99% missing data. A unique aspect of our approach is that it scales to sparse large-scale data, e.g., 1000 × 1000 × 1000 with five million known entries (0.5% dense). We further demonstrate the usefulness of CP-WOPT on two real-world applications: a novel EEG (electroencephalogram) application where missing data is frequently encountered due to disconnections of electrodes and the problem of modeling computer network traffic where data may be absent due to the expense of the data collection process.

Clinical applicability of EEG in differential diagnosis of dementia

Clinical applicability of EEG in differential diagnosis of dementia

Wearable Electroencephalography

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

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

ObjectiveTo determine the extent and implications of internal human electroencephalography (EEG) research conducted by the tobacco industry.MethodsThis study analysed internal documents that describe the results of human EEG studies conducted...

Graph Analysis and Visualization for Brain Function Characterization Using EEG Data

Over the past few years, there has been an increased interest in studying the underlying neural mechanism of cognitive brain activity as well as in diagnosing certain pathologies. Noninvasive imaging modalities such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and dynamic signal acquisition techniques such as quantitative electroencephalography (EEG) have been vastly used to estimate cortical connectivity and identify functional interdependencies among synchronized brain lobes. In this area, graph‐theoretic concepts and tools are used to describe large scale brain networks while performing cognitive tasks or to characterize certain neuropathologies. Such tools can be of particular value in basic neuroscience and can be potential candidates for future inclusion in a clinical setting. This paper discusses the application of the high time resolution EEG to resolve interdependence patterns using both linear and nonlinear techniques. The network formed by the statistical dependencies between the activations of distinct and often well separated neuronal populations is further analyzed using a number of graph theoretic measures capable of capturing and quantifying its structure and summarizing the information that it contains. Finally, graph visualization reveals the hidden structure of the networks and amplifies human understanding. A number of possible applications of the graph theoretic approach are also listed. A freely available standalone brain visualization tool to benefit the healthcare engineering community is also provided (http://www.ics.forth.gr/bmi/tools.html).

A New Multimodal Neuroimaging Method Integrating Simultaneously Recorded EEG and MEG

Event Abstract Back to Event A New Multimodal Neuroimaging Method Integrating Simultaneously Recorded EEG and MEG Jong-Ho Choi1*, Young-Jin Jung2, June-Sic Kim3, Chun-Kee Chung3, Hyun-Kyo Jung1 and Chang-Hwan Im2 1 Seoul National University, School of Electrical Engineering, Republic of Korea 2 Yonsei University, Department of Biomedical Engineering, Republic of Korea 3 Seoul National University College of Medicine, MEG Center, Department of Neurosurgery, Republic of Korea Accurate reconstruction of neural current source distribution has been a crucial issue for many EEG and MEG applications such as epileptogenic source localization. Since MEG cannot reliably detect the radially oriented sources, many studies have assumed that the MEG data are generated only from the tangential components of neuronal sources. In contrast, EEG is sensitive to both tangential and radial components of sources. In this study, we propose a new multimodal neuroimaging method integrating simultaneously recorded EEG and MEG, considering the directional sensitivity characteristics of EEG and MEG. To take account of directional sensitivity of EEG and MEG, we decompose the sensor and source spaces into radial and tangential components and develop an explicit formulation which reflects the difference in the directional sensitivity of EEG and MEG without any assumption on the tangential component of MEG data. The radial direction at a source location is defined as the orientation along which the calculated MEG data is minimized. Since the cortical current is oriented normal to the cortical surface, the tangential direction can be uniquely defined by the normal and radial direction. On the sensor space, the EEG or MEG data is decomposed by the radial and tangential components which are generated by the radial and tangential components of the source respectively. The extensive computer simulation supports that the new combined method enhances the overall accuracy and brings major improvement when the radial component of source patches is dominant. We also apply our method to practical EEG and MEG data recorded simultaneously for an epilepsy patient whose surgical outcome was successful. Compared to source locations obtained from EEG, MEG and conventional combined method, the source distribution acquired from the new methods shows the best coincidence with the resection areas. Conference: Biomag 2010 - 17th International Conference on Biomagnetism , Dubrovnik, Croatia, 28 Mar - 1 Apr, 2010. Presentation Type: Poster Presentation Topic: Epilepsy Citation: Choi J, Jung Y, Kim J, Chung C, Jung H and Im C (2010). A New Multimodal Neuroimaging Method Integrating Simultaneously Recorded EEG and MEG. Front. Neurosci. Conference Abstract: Biomag 2010 - 17th International Conference on Biomagnetism . doi: 10.3389/conf.fnins.2010.06.00324 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 06 Apr 2010; Published Online: 06 Apr 2010. * Correspondence: Jong-Ho Choi, Seoul National University, School of Electrical Engineering, Seoul, Republic of Korea, cjhaha@snu.ac.kr Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Jong-Ho Choi Young-Jin Jung June-Sic Kim Chun-Kee Chung Hyun-Kyo Jung Chang-Hwan Im Google Jong-Ho Choi Young-Jin Jung June-Sic Kim Chun-Kee Chung Hyun-Kyo Jung Chang-Hwan Im Google Scholar Jong-Ho Choi Young-Jin Jung June-Sic Kim Chun-Kee Chung Hyun-Kyo Jung Chang-Hwan Im PubMed Jong-Ho Choi Young-Jin Jung June-Sic Kim Chun-Kee Chung Hyun-Kyo Jung Chang-Hwan Im Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Prospects for Clinical Applications of Transcranial Magnetic Stimulation and Real-Time EEG in Epilepsy

Recent advances in methods for transcranial magnetic stimulation (TMS) enable its coupling to real-time EEG (TMS-EEG). Although TMS-EEG is applied largely in neurophysiology research, there are prospects for its use in clinical TMS practice, particularly in epilepsy where EEG is already in wide use, and where TMS is emerging as a diagnostic and therapeutic tool. In diagnostic applications, TMS-EEG may provide a useful measure of cortical excitability at baseline or after antiepileptic treatment. For therapeutic purposes, TMS-EEG may be of use in selection of appropriate TMS strength outside of the motor cortex where the threshold for cortical activation is more apparent with the aid of EEG. In other realistic clinical applications, TMS-EEG may be of use in real-time monitoring for epileptiform activity in vulnerable populations where TMS may trigger seizures, or as a component of a responsive neurostimulation setup in which TMS timing is determined by underlying EEG activity. Future trials and evolution of TMS-EEG methods are likely to provide answers as to the actual clinical value of TMS-EEG.

Source Localization of EEG/MEG Data by Correlating Columns of ICA and Lead Field Matrices

Independent components analysis (ICA) has previously been used to denoise EEG/magnetoencephalography (MEG) signals before performing neural source localization. Source localization is then performed using a method such as beamforming or dipole fitting. Here we show how ICA can also be used as a source localization method, negating the need for beamforming and dipole fitting. This type of approach is valid whenever an estimate of the forward (mixing) model for all putative source locations is available, which includes EEG and MEG applications. The proposed method consists of estimating the forward model using the laws of physics, estimating a second forward model using ICA, and then correlating the columns of the matrices that represent the two forward models. We show that, when synthetic data are used, the proposed localization method produces a smaller localization error than several alternatives. We also show localization results for real auditory-evoked MEG data.