An investigation of in-ear sensing for motor task classification

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Article number066010
Journal / PublicationJournal of Neural Engineering
Volume17
Issue number6
Online published19 Nov 2020
Publication statusPublished - Nov 2020

Abstract

Objective. Our study aims to investigate the feasibility of in-ear sensing for human–computer interface. Approach. We first measured the agreement between in-ear biopotential and scalp-electroencephalogram (EEG) signals by channel correlation and power spectral density analysis. Then we applied EEG compact network (EEGNet) for the classification of a two-class motor task using in-ear electrophysiological signals. Main results. The best performance using in-ear biopotential with global reference reached an average accuracy of 70.22% (cf 92.61%accuracy using scalp-EEG signals), but the performance in-ear biopotential with near-ear reference was poor. Significance. Our results suggest in-ear sensing would be a viable human–computer interface for movement prediction, but careful consideration should be given to the position of the reference electrode.

Research Area(s)

  • electroencephalography, human–computer interface, in-ear sensing, wearable device

Citation Format(s)

An investigation of in-ear sensing for motor task classification. / Wu, Xiaoli; Zhang, Wenhui; Fu, Zhibo; Cheung, Roy T H; Chan, Rosa H M.

In: Journal of Neural Engineering, Vol. 17, No. 6, 066010, 11.2020.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review