ST-GCN: A Spatiotemporal Graph Convolution Neural Network for EEG Motor Imagery Signal Decoding

Motor imagery (MI) is a mental process extensively used in the experimental paradigm for brain-computer interfaces (BCIs) across various basic science and clinical research studies. Despite its widespread use, accurately decoding intentions from MI poses significant challenges due to the complex nature of brain patterns and the limited sample sizes typically available for machine learning. This paper introduces a Spatiotemporal Graph Neural Network (ST-GCN) designed for MI classification. First, the spatial-temporal convolution layer is used to extract features from raw EEG data, where mixed depthwise convolution extracts temporal features, followed by spatial filtering convolution that decomposes the EEG signal. A graph convolution module employing the max relative aggregator is then utilized to explore the relationships between the spatially decomposed EEG components. In the final step, under the combined supervision of cross-entropy and our proposed channel selection loss, the ST-GCN achieves feature extraction that enhances interclass dispersion and intraclass compactness. We compare ST-GCN with several benchmark EEG decoding methods on two MI datasets: the BCI Competition III Dataset IVa and the BCI Competition IV Dataset 1. ST-GCN outperforms the deep learning benchmark methods by achieving an accuracy of 78.11% and 71.94%, respectively, in 10-fold cross-validation.