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Wireless Sensors for Brain Activity—A Survey

Authors: Mahyar TajDini; Volodymyr Sokolov; Ievgeniia Kuzminykh; Stavros Shiaeles; Bogdan Ghita;

Wireless Sensors for Brain Activity—A Survey

Abstract

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.

Countries
United Kingdom, Ukraine
Keywords

/dk/atira/pure/subjectarea/asjc/2200/2207, /dk/atira/pure/subjectarea/asjc/1700/1705, Computer Networks and Communications, /dk/atira/pure/subjectarea/asjc/2200/2208, /dk/atira/pure/subjectarea/asjc/1700/1708, OpenBCI, NeuroSky, 004, Brain-controlled games, Control and Systems Engineering, Hardware and Architecture, Signal Processing, EEG signals, Scopus, Web of Science, Brain wave, Cognition study, Electrical and Electronic Engineering, /dk/atira/pure/subjectarea/asjc/1700/1711

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    popularity
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    Top 10%
    influence
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    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
28
Top 10%
Top 10%
Top 10%
Green
gold