REEG-BTCNet: A Novel Framework for EEG-based Motor Imagery Classification

Motor imagery (MI) based on Electroencephalogram (EEG) analysis is a common used paradigm in BCI. Previous works to classify MI have obtained promising classification results. However, there still exit some challenges: 1) Although the deep learning (DL) models are the mainstream methods to solve MI classification, with their depths increasing, the accuracy gets saturated then degrades rapidly. 2) The complex and changeable relationship of the time sequence in the EEG signals makes it difficult to model. In this paper, we propose REEG-BTCNet, a novel framework that achieves outstanding accuracy with stronger robustness for EEG-based motor imagery classification. The REEG-BTCNet consists of residual compact convolution (RCV) module and bi-directional temporal convolution (BTCN) module. Specifically, the RCV module consists of convolution with residual connection to learn high-level task specific EEG feature. The BTCN module consists of a window splitting module and various bi-directional temporal convolutions blocks to model the temporal information from the MI-EEG signals. The proposed model outperforms the current state-of-the-art techniques in the BCI Competition IV-2a dataset with an accuracy of 86.15% for the subject-dependent modes. For reproducibility, the code for this research and the trained models will be released on GitHub.