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dc.contributor.authorTajDini, M
dc.contributor.authorSokolov, V
dc.contributor.authorKuzminykh, I
dc.contributor.authorShiaeles, S
dc.contributor.authorGhita, B
dc.date.accessioned2021-05-18T10:50:48Z
dc.date.available2021-05-18T10:50:48Z
dc.date.issued2020-12-08
dc.identifier.issn1450-5843
dc.identifier.issn2079-9292
dc.identifier.otherARTN 2092
dc.identifier.urihttp://hdl.handle.net/10026.1/17128
dc.description.abstract

<jats:p>Over the last decade, the area of electroencephalography (EEG) witnessed a progressive move from high-end large measurement devices, relying on accurate construction and providing high sensitivity, to miniature hardware, more specifically wireless wearable EEG devices. While accurate, traditional EEG systems need a complex structure and long periods of application time, unwittingly causing discomfort and distress on the users. Given their size and price, aside from their lower sensitivity and narrower spectrum band(s), wearable EEG devices may be used regularly by individuals for continuous collection of user data from non-medical environments. This allows their usage for diverse, nontraditional, non-medical applications, including cognition, BCI, education, and gaming. Given the reduced need for standardization or accuracy, the area remains a rather incipient one, mostly driven by the emergence of new devices that represent the critical link of the innovation chain. In this context, the aim of this study is to provide a holistic assessment of the consumer-grade EEG devices for cognition, BCI, education, and gaming, based on the existing products, the success of their underlying technologies, as benchmarked by the undertaken studies, and their integration with current applications across the four areas. Beyond establishing a reference point, this review also provides the critical and necessary systematic guidance for non-medical EEG research and development efforts at the start of their investigation.</jats:p>

dc.format.extent2092-2092
dc.languageen
dc.language.isoen
dc.publisherMDPI AG
dc.subjectbrain wave
dc.subjectEEG signals
dc.subjectcognition study
dc.subjectbrain-controlled games
dc.subjectNeuroSky
dc.subjectOpenBCI
dc.titleWireless Sensors for Brain Activity—A Survey
dc.typejournal-article
dc.typeReview
dc.typeJournal
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000602048000001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue12
plymouth.volume9
plymouth.publication-statusPublished online
plymouth.journalElectronics
dc.identifier.doi10.3390/electronics9122092
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dcterms.dateAccepted2020-11-27
dc.rights.embargodate2021-6-8
dc.identifier.eissn2079-9292
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.3390/electronics9122092
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2020-12-08
rioxxterms.typeJournal Article/Review


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