Next Article in Journal
A Multimodal User Interface for an Assistive Robotic Shopping Cart
Next Article in Special Issue
Explainable Convolutional Neural Network to Investigate Age-Related Changes in Multi-Order Functional Connectivity
Previous Article in Journal
A Benchmark of Popular Indoor 3D Reconstruction Technologies: Comparison of ARCore and RTAB-Map
Review

Wireless Sensors for Brain Activity—A Survey

1
Department of Information and Cyber Security, Borys Grinchenko Kyiv University, 04212 Kyiv, Ukraine
2
Department of Informatics, King’s College London, London WC2R 2ND, UK
3
Department of Infocommunication Engineering, Kharkiv National University of Radio Electronics, 61166 Kharkiv, Ukraine
4
School of Computing, University of Portsmouth, Portsmouth PO1 2UP, UK
5
School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth PL4 8AA, UK
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(12), 2092; https://doi.org/10.3390/electronics9122092
Received: 23 October 2020 / Revised: 19 November 2020 / Accepted: 27 November 2020 / Published: 8 December 2020
(This article belongs to the Special Issue Advanced Technologies and Challenges in Brain Machine Interface)
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. View Full-Text
Keywords: brain wave; EEG signals; cognition study; brain-controlled games; NeuroSky; OpenBCI brain wave; EEG signals; cognition study; brain-controlled games; NeuroSky; OpenBCI
MDPI and ACS Style

TajDini, M.; Sokolov, V.; Kuzminykh, I.; Shiaeles, S.; Ghita, B. Wireless Sensors for Brain Activity—A Survey. Electronics 2020, 9, 2092. https://doi.org/10.3390/electronics9122092

AMA Style

TajDini M, Sokolov V, Kuzminykh I, Shiaeles S, Ghita B. Wireless Sensors for Brain Activity—A Survey. Electronics. 2020; 9(12):2092. https://doi.org/10.3390/electronics9122092

Chicago/Turabian Style

TajDini, Mahyar, Volodymyr Sokolov, Ievgeniia Kuzminykh, Stavros Shiaeles, and Bogdan Ghita. 2020. "Wireless Sensors for Brain Activity—A Survey" Electronics 9, no. 12: 2092. https://doi.org/10.3390/electronics9122092

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop