Adaptive graph convolutional neural network incorporating ECG for individualized motor imagery EEG classification

The recognition of motor imagery electroencephalogram (EEG) signals, which non-invasively capture the macroscopic electrical activity of the brain, is critical for medical rehabilitation and intelligent control. In these applications, reliable prediction is essential due to the safety risks associated with misclassification. However, existing methods often suffer from limited generalization in cross-subject scenarios caused by substantial inter-subject variability. To address this challenge, this work develops adaptive modeling strategies to improve robust cross-subject recognition performance.

We propose a Hybrid Adaptive Domain Graph Convolutional Network (HAD-GCN) to enhance decoding performance through multi-level adaptability. At the spatial level, an adaptive generator synthesizes electrocardiogram (ECG) signals, which record the electrical activity of the heart, from EEG signals and concatenates them within a connected graph structure, thereby mitigating the limitations of non-invasive data acquisition. At the temporal level, an adaptive splitter selects the most suitable time-frequency domain processing method for each subject's signal and routes the data into the corresponding branches for feature extraction.

Accuracy and the Kappa coefficient, which are widely adopted in motor imagery research, are used as evaluation metrics. Cross-subject experiments conducted on the Mixed dataset and the BCI Competition IV-2a dataset achieve accuracies of 83.10% ± 6.54% and 74.81% ± 8.97%, respectively, with corresponding Kappa values of 0.778 ± 0.06 and 0.655 ± 0.09.

Experimental results demonstrate that HAD-GCN significantly improves cross-subject classification performance and prediction reliability while maintaining strong generalization capabilities. The proposed multi-level adaptive approach consistently enhances classification accuracy for individual subjects, highlighting its potential for practical applications in EEG-based technologies. Our code is available at https://github.com/chuanlaiair/HAD-GCN.