Literature DB >> 20659857

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

Alexander Casson1, David Yates, Shelagh Smith, John Duncan, Esther Rodriguez-Villegas.   

Abstract

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.

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Year:  2010        PMID: 20659857     DOI: 10.1109/MEMB.2010.936545

Source DB:  PubMed          Journal:  IEEE Eng Med Biol Mag        ISSN: 0739-5175


  35 in total

1.  Compressive sensing scalp EEG signals: implementations and practical performance.

Authors:  Amir M Abdulghani; Alexander J Casson; Esther Rodriguez-Villegas
Journal:  Med Biol Eng Comput       Date:  2011-09-27       Impact factor: 2.602

2.  Optimal features for online seizure detection.

Authors:  Lojini Logesparan; Alexander J Casson; Esther Rodriguez-Villegas
Journal:  Med Biol Eng Comput       Date:  2012-04-03       Impact factor: 2.602

3.  The impact of signal normalization on seizure detection using line length features.

Authors:  Lojini Logesparan; Esther Rodriguez-Villegas; Alexander J Casson
Journal:  Med Biol Eng Comput       Date:  2015-05-16       Impact factor: 2.602

Review 4.  Wearable EEG and beyond.

Authors:  Alexander J Casson
Journal:  Biomed Eng Lett       Date:  2019-01-04

5.  Comparison of dry and gel based electrodes for p300 brain-computer interfaces.

Authors:  Christoph Guger; Gunther Krausz; Brendan Z Allison; Guenter Edlinger
Journal:  Front Neurosci       Date:  2012-05-07       Impact factor: 4.677

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

Authors:  Thorsten Oliver Zander; Moritz Lehne; Klas Ihme; Sabine Jatzev; Joao Correia; Christian Kothe; Bernd Picht; Femke Nijboer
Journal:  Front Neurosci       Date:  2011-05-26       Impact factor: 4.677

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

Authors:  Lun-De Liao; Chi-Yu Chen; I-Jan Wang; Sheng-Fu Chen; Shih-Yu Li; Bo-Wei Chen; Jyh-Yeong Chang; Chin-Teng Lin
Journal:  J Neuroeng Rehabil       Date:  2012-01-28       Impact factor: 4.262

8.  Nanopower Integrated Gaussian Mixture Model Classifier for Epileptic Seizure Prediction.

Authors:  Vassilis Alimisis; Georgios Gennis; Konstantinos Touloupas; Christos Dimas; Nikolaos Uzunoglu; Paul P Sotiriadis
Journal:  Bioengineering (Basel)       Date:  2022-04-05

9.  Smart Helmet: Wearable Multichannel ECG and EEG.

Authors:  Wilhelm Von Rosenberg; Theerasak Chanwimalueang; Valentin Goverdovsky; David Looney; David Sharp; Danilo P Mandic
Journal:  IEEE J Transl Eng Health Med       Date:  2016-11-01       Impact factor: 3.316

10.  Classifying Multi-Level Stress Responses From Brain Cortical EEG in Nurses and Non-Health Professionals Using Machine Learning Auto Encoder.

Authors:  Ashlesha Akella; Avinash Kumar Singh; Daniel Leong; Sara Lal; Phillip Newton; Roderick Clifton-Bligh; Craig Steven Mclachlan; Sylvia Maria Gustin; Shamona Maharaj; Ty Lees; Zehong Cao; Chin-Teng Lin
Journal:  IEEE J Transl Eng Health Med       Date:  2021-05-05       Impact factor: 3.316

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