Wireless Sensors for Brain Activity—A Survey
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 EE...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/9/12/2092 |
| _version_ | 1797545408700874752 |
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| author | Mahyar TajDini Volodymyr Sokolov Ievgeniia Kuzminykh Stavros Shiaeles Bogdan Ghita |
| author_facet | Mahyar TajDini Volodymyr Sokolov Ievgeniia Kuzminykh Stavros Shiaeles Bogdan Ghita |
| author_sort | Mahyar TajDini |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T14:15:21Z |
| format | Article |
| id | doaj.art-2e705e96695f4891b365c3cffeb07da2 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T14:15:21Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-2e705e96695f4891b365c3cffeb07da22023-11-20T23:51:08ZengMDPI AGElectronics2079-92922020-12-01912209210.3390/electronics9122092Wireless Sensors for Brain Activity—A SurveyMahyar TajDini0Volodymyr Sokolov1Ievgeniia Kuzminykh2Stavros Shiaeles3Bogdan Ghita4Department of Information and Cyber Security, Borys Grinchenko Kyiv University, 04212 Kyiv, UkraineDepartment of Information and Cyber Security, Borys Grinchenko Kyiv University, 04212 Kyiv, UkraineDepartment of Informatics, King’s College London, London WC2R 2ND, UKSchool of Computing, University of Portsmouth, Portsmouth PO1 2UP, UKSchool of Engineering, Computing and Mathematics, University of Plymouth, Plymouth PL4 8AA, UKOver 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.https://www.mdpi.com/2079-9292/9/12/2092brain waveEEG signalscognition studybrain-controlled gamesNeuroSkyOpenBCI |
| spellingShingle | Mahyar TajDini Volodymyr Sokolov Ievgeniia Kuzminykh Stavros Shiaeles Bogdan Ghita Wireless Sensors for Brain Activity—A Survey Electronics brain wave EEG signals cognition study brain-controlled games NeuroSky OpenBCI |
| title | Wireless Sensors for Brain Activity—A Survey |
| title_full | Wireless Sensors for Brain Activity—A Survey |
| title_fullStr | Wireless Sensors for Brain Activity—A Survey |
| title_full_unstemmed | Wireless Sensors for Brain Activity—A Survey |
| title_short | Wireless Sensors for Brain Activity—A Survey |
| title_sort | wireless sensors for brain activity a survey |
| topic | brain wave EEG signals cognition study brain-controlled games NeuroSky OpenBCI |
| url | https://www.mdpi.com/2079-9292/9/12/2092 |
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