ST-GNN for EEG Motor Imagery Classification

Brain-computer interface (BCI) systems play an important role in medical applications such as stroke rehabilitation and neural prosthesis. These systems aim to decode the neural activity of the human brain measured using an Electroencephalogram (EEG). In this work, we consider the task of EEG-based motor imagery (intent) classification. Motor imagery (MI) refers to the imagination of the limb movement in the brain without actual action. Classification of motor imagery forms the basis for BCI-based prosthetic control. Existing approaches either use handcrafted features or features extracted from a deep neural network to interpret EEG-based MI. However, majority of the existing works fail to harness the functional connectivity within the brain that is captured using multiple EEG channels. In our work, we represent the input EEG signal as a graph where the nodes represent the EEG channels. The proposed approach uses a graph representation with a trainable weighted adjacency matrix to learn the optimal connectivity between nodes. Spatio-temporal features of the EEG signal are extracted via the proposed model that consists of a temporal convolution module and a graph convolution network. Experimental results and ablation study highlight the effectiveness of the proposed approach on the PhysioNet EEG motor movement and imagery dataset (EEG-MMIDB).