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 language | English |
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Article number | 66010 |
Number of pages | 11 |
Journal | Journal of Neural Engineering |
Volume | 17 |
Issue number | 6 |
DOIs | |
Publication status | Published - 2020 |
Keywords
- computer interfaces
- ear
- electroencephalography
- wearable technology