Mind the gap: State-of-the-art technologies and applications for EEG-based brain-computer interfaces
Author(s)
Portillo-Lara, Roberto
Tahirbegi, Bogachan
Chapman, Christopher AR
Goding, Josef A
Green, Rylie A
Type
Journal Article
Abstract
Brain–computer interfaces (BCIs) provide bidirectional communication between the brain and output devices that translate user intent into function. Among the different brain imaging techniques used to operate BCIs, electroencephalography (EEG) constitutes the preferred method of choice, owing to its relative low cost, ease of use, high temporal resolution, and noninvasiveness. In recent years, significant progress in wearable technologies and computational intelligence has greatly enhanced the performance and capabilities of EEG-based BCIs (eBCIs) and propelled their migration out of the laboratory and into real-world environments. This rapid translation constitutes a paradigm shift in human–machine interaction that will deeply transform different industries in the near future, including healthcare and wellbeing, entertainment, security, education, and marketing. In this contribution, the state-of-the-art in wearable biosensing is reviewed, focusing on the development of novel electrode interfaces for long term and noninvasive EEG monitoring. Commercially available EEG platforms are surveyed, and a comparative analysis is presented based on the benefits and limitations they provide for eBCI development. Emerging applications in neuroscientific research and future trends related to the widespread implementation of eBCIs for medical and nonmedical uses are discussed. Finally, a commentary on the ethical, social, and legal concerns associated with this increasingly ubiquitous technology is provided, as well as general recommendations to address key issues related to mainstream consumer adoption.
Date Issued
2021-09-01
Date Acceptance
2021-05-19
Citation
APL Bioengineering, 2021, 5 (3), pp.1-16
ISSN
2473-2877
Publisher
AIP Publishing LLC
Start Page
1
End Page
16
Journal / Book Title
APL Bioengineering
Volume
5
Issue
3
Copyright Statement
© 2021 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://
creativecommons.org/licenses/by/4.0/)
creativecommons.org/licenses/by/4.0/)
License URL
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Commission of the European Communities
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000674713900001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
EP/R004498/1
771985
Subjects
Science & Technology
Technology
Engineering, Biomedical
Engineering
MOTOR IMAGERY
FEATURE-EXTRACTION
MACHINE INTERFACE
WAVELET TRANSFORM
NEURAL-NETWORKS
DRY ELECTRODE
BCI
CLASSIFICATION
MOVEMENT
SIGNALS
Publication Status
Published
Article Number
ARTN 031507
Date Publish Online
2021-07-20