Abstract

(1) Background: Accurately decoding motor imagery (MI) tasks is a prerequisite for creating a MI-based brain-computer interface (BCI). However, low signal-to-noise ratio and non-stationarity of EEG signals present a huge challenge for the classification of MI-EEG signals, restricting the extensive development of the BCI industry.''' '''(2) Methods: In this paper, we propose a novel deep learning model CTANet that integrates both channel and temporal attention mechanisms into a convolutional neural network to improve the classification accuracy of the MI-BCI systems. The model is constituted first by three serially connected temporal, spatial, and temporal convolution layers to extract features from the brain signals, with an efficient channel attention module inserted between the second and the third convolutional layers to highlight useful feature channels. Subsequently, the time segment for task decoding is partitioned into several time windows, and a variance layer is employed for computing the logarithmic variance of each window. Next, a multi-head attention mechanism is adopted to extract temporal dependency of features from different windows. Finally, a fully connected layer is used for classifying MI-EEG signals. (3) Results: The performance of the proposed model was evaluated on two publicly available BCI datasets and compared with the state-of-the-art methods. The experimental results show that for dataset BCIC-IV2a, our network achieved classification accuracies of 81.17% and 84.33% in inter-session and intra-session scenarios respectively, whereas for dataset OpenBMI, our network achieved classification accuracy of 73.06% and 77.59% in inter-session and intra-session scenarios respectively. (4) Conclusions: These results outperform state-of-the-art networks, indicating significant potential of the proposed model CTANet in MI decoding.