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.

客观的。我们的研究旨在调查人机界面入耳式传感的可行性。方法。我们首先通过通道相关性和功率谱密度分析测量了耳内生物电势和头皮脑电图 (EEG) 信号之间的一致性。然后，我们将 EEG 紧凑网络 (EEGNet) 用于使用耳内电生理信号对两类运动任务进行分类。主要结果。使用具有全局参考的入耳式生物电势的最佳性能达到了 70.22% 的平均准确度（比较使用头皮 EEG 信号的 92.61% 的准确度），但使用近耳参考的入耳式生物电势的性能较差。意义。我们的结果表明，入耳式传感将是一种可行的人机界面运动预测，但应仔细考虑参考电极的位置。