Motor Imagery Classification from Few Shot EEG Data Using CNN-GRU Hybrid Model with Domain Adaptation

Abstract

Brain-computer interface (BCI) is technology to control computers directly using the signal from brain and is expected to have various applications in rehabilitation and daily life support, especially for people with severe motor dysfunction. Low computational cost and high generalization performance are important for the widespread use of BCI. In this study, we propose the CNN-GRU hybrid model to reduce computational cost and improve generalization performance using domain adaptation. We investigated the discrimination performance of opening/closing either the left or right hand using the dataset OpenBMI as target domain data and the other four datasets (Physionet, Kaya2018, Meng2019, and Stieger2021) as source domain data. The proposed frameworks achieve an accuracy of 72.2%. At the same time, 32 out of 54 participants reached the accuracy of 70%, BCI control, which assumes binary classification.