Improving Motor Imagery-Based Brain-Computer Interfaces with Chebyshev Graph Neural Networks

This study aims to enhance the accuracy and generalization of motor imagery-based brain-computer interface (MI-BCI) systems using a novel frequency-based graph convolutional neural network technique called Chebyshev Graph Neural Networks (ChebGNNs). The study involves acquiring a substantial amount of motor imagery EEG signals and merging them with an open-source dataset to create a heterogeneous dataset. The study shows that ChebGNNs significantly improve the training and classification performance of the MI-BCI model. ChebGNNs outperform conventional frequency-based and time-domain techniques, achieving an average accuracy of 91.09%, 90.27%, and 89.24% on various datasets. The study demonstrates the promising potential of graph convolutional neural networks in improving the accuracy and generalization of MI-BCI systems. This enables a more effective capture of correlation information and local characteristics in signals, thereby enhancing the overall performance of the system.