EEG-Based Motor Imagery Classification With Tuned Heuristic Fusion Graph Convolutional Network for Rehabilitation Training

Motor imagery-based brain-computer interfaces (MI-BCIs) hold significant promise for rehabilitation training in individuals with neurological impairments such as stroke and spinal cord injury (SCI). Achieving precise and robust lower limb movement prediction for each patient is crucial. However, the variability in MI response frequencies and brain activation patterns among subjects presents a great challenge to the generalizability of MI-BCIs. This paper proposes a Tuned Heuristic Fusion Graph Convolutional Network (THFGCN) for limb movement prediction in rehabilitation scenarios. THFGCN innovatively designs a learnable EEG frequency band tuned module and a heuristic space topology module. These two modules allow for the intricate extraction of both frequency and spatial topological features, utilizing graph adjacency matrices that encapsulate channel correlations and spatial relationships, hence fostering individualized analysis and enhanced generalizability across subjects. Furthermore, a spatio-temporal convolution module paired with a feature map attention mechanism is proposed to extract the critical spatio-temporal features of electroencephalogram (EEG) data. Validation experiments on the PhysioNet and LLM-BCImotion datasets against six mainstream methods demonstrate that THFGCN outperforms state-of-the-art methods, achieving 88.41% and 82.82% accuracy in the within-subject case, and 65.93% and 60.56% accuracy in the cross-subject case, respectively. Detailed frequency band weight and T-distributed Stochastic Neighbor Embedding visualization validate the effectiveness of proposed modules. Furthermore, feature interpretability analysis proves the extracted features’ profound MI task relevance, underlining THFGCN’s exceptional interpretability. Note to Practitioners—Motor imagery-based brain-computer interfaces (MI-BCIs) have shown great potential in rehabilitation training for patients with neurological disorders such as stroke and spinal cord injury (SCI), due to their capacity to promote neural plasticity and functional recovery. An accurate and generalized MI-BCI can accelerate the neural remodeling process in patients and promote the clinical application of BCI technology. In this paper, we propose a new graph convolutional network (GCN)-based MI classification framework to predict limb movements during rehabilitation training. This framework incorporates a frequency tuned topology module and a heuristic space topology module to extract private frequency-domain topological features and shared spatial topological features during MI across subjects. Our method improves the generalization ability to new subjects while ensuring accurate limb movement prediction for single subjects. We show the superiority of our method on two MI datasets, showing that the extracted features have effective physiological interpretability. These findings suggest that the proposed method is not only effective but also transparent, and may support future research in understanding neural mechanisms and optimizing rehabilitation strategies for motor-impaired populations.