A novel hybrid CNN-Transformer model for EEG Motor Imagery classification
Yaxin Ma, Yonghao Song, Fei Gao
Abstract
Motor imagery is one of the most popular brain-computer interface (BCI) paradigms with good potential to help disabled people. However, EEG decoding is still challenging because of its low signal-to-noise ratio, with which valuable features are difficult to perceive. In this paper, we propose a hybrid model that combines a convolutional neural network (CNN) with the Transformer for decoding motor imagery EEG signals. The CNN is used to extract local features, while the Transformer is utilized to perceive global dependencies. Moreover, we exploit spatial-spectral-temporal features to improve classification performance. To validate the effectiveness and superiority of the proposed method, we conduct experiments on the BCICIV dataset 2a and compare it with other efficient approaches. The results show that our algorithm outperforms the state-of-the-art methods, indicating that the CNN-Transformer model is a competing strategy.