An improved model using convolutional sliding window-attention network for motor imagery EEG classification - Huang, Yuxuan; Zheng, Jianxu; Xu, Binxing; Li, Xuhang; Liu, Yu; Wang, Zijian; Feng, Hua; Cao, Shiqi (Frontiers in Neuroscience, 2023)
The classification model of Motor Imagery-based Electroencephalogram (MI-EEG) is a new humancomputer interface pattern and a new neural rehabilitation assessment method for diseases such as Parkinson's and stroke. However, existing MI-EEG models often suffer from insufficient richness of spatiotemporal feature extraction, learning ability, and dynamic selection ability. To solve these problems, this work proposed a convolutional sliding window-attention network (CSANet) model composed of novel spatiotemporal convolution, sliding window, and two-stage attention blocks. The model outperformed existing state-of-the-art (SOTA) models in within-and between-individual classification tasks on commonly used MI-EEG datasets BCI-2a and Physionet MI-EEG, with classification accuracies improved by 4.22% and 2.02%, respectively. The experimental results also demonstrated that the proposed type token, sliding window, and local and global multi-head selfattention mechanisms can significantly improve the model's ability to construct, learn, and adaptively select multi-scale spatiotemporal features in MI-EEG signals, and accurately identify electroencephalogram signals in the unilateral motor area. This work provided a novel and accurate classification model for MI-EEG brain-computer interface tasks and proposed a feasible neural rehabilitation assessment scheme based on the model, which could promote the further development and application of MI-EEG methods in neural rehabilitation.