An investigation of in-ear sensing for motor task classification.

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.