An investigation of in-ear sensing for motor task classification

Xiaoli Wu, Wenhui Zhang, Zhibo Fu, Roy T. H. Cheung, Rosa H. M. Chan

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

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.
Original languageEnglish
Article number66010
Number of pages11
JournalJournal of Neural Engineering
Volume17
Issue number6
DOIs
Publication statusPublished - 2020

Keywords

  • computer interfaces
  • ear
  • electroencephalography
  • wearable technology

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