NDLI: Toward non-hair-bearing brain-computer interfaces for neurocognitive lapse detection

Content Provider	IEEE Xplore Digital Library
Author	Chun-Shu Wei Yu-Te Wang Chin-Teng Lin Tzyy-Ping Jung
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci. & Eng., UCSD, La Jolla, CA, USA (Yu-Te Wang) \|\| Dept. of Electr. Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan (Chin-Teng Lin) \|\| Dept. of Bioeng., Univ. of California, San Diego, La Jolla, CA, USA (Chun-Shu Wei; Tzyy-Ping Jung)
Abstract	Recent advances in mobile electroencephalogram (EEG) acquisition based on dry electrodes have started moving Brain-Computer Interface (BCI) applications from well-controlled laboratory settings to real-world environments. However, the application mechanisms and high impedance of dry electrodes over the hair-covered areas remain challenging for everyday use of BCI. In addition, whole-scalp recordings are not always necessary or applicable due to various practical constrains. Therefore, alternative montages for EEG recordings to meet the everyday needs are in-demand. Inspired by our previous work on measuring non-hair-bearing steady state visual evoked potentials for BCI applications, this study explores the feasibility and efficacy of detecting cognitive lapses of participants based on EEG signals collected from the non-hair-bearing areas. Study results suggest that informative EEG features associated with lapses could be assessed from non-hair-bearing areas with comparable accuracy obtained from the whole-scalp EEG. The design principles, validation processes and promising findings reported in this study may enable and/or facilitate numerous BCI applications in real-world environments.
Starting Page	6638
Ending Page	6641
File Size	1173364
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7319915
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electroencephalography Electrodes Correlation Feature extraction Accuracy Brain-computer interfaces Scalp
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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