Integrated Connectivity-based Stacking Ensemble Learning with GCNNs for EEG Representation

This study proposes a novel approach that com-bines stacking ensemble learning with Graph Convolutional Neural Networks (GCNNs) to enhance the classification accuracy of Motor Imagery (MI) tasks in supporting individuals with injuries or impairments, enabling more effective rehabilitation and assistance. The method integrates both structural and functional connectivity information to leverage the benefits of GCNNs and ensemble learning techniques. The BCI Com-petition IV-2a dataset is used for evaluation. The approach employs a stacked ensemble model consisting of nine baseline models and six combining meta-models, including Logistic Re-gression, Neural Networks, Support Vector Machines, Random Forest, K-Nearest Neighbor, and Gradient Boosting Machine. By leveraging information from both structural and functional connectivity, the GCNNs extract meaningful features from MI data, leading to improved classification accuracy. The stacking ensemble learning technique combines multiple GCNN models trained on different connectivity aspects, resulting in a robust and accurate classifier. The fusion of structural connectivity (ADJ-CNNM) capturing anatomical connections and functional connectivity (PLV-CNNM) measuring brain activity synchronization enables a comprehensive analysis of MI data. The proposed approach effectively captures both local and global connectivity patterns, addressing the challenges associated with MI data analysis. By considering both types of connectivity, a holistic understanding of the dynamics of the underlying brain network during MI tasks is achieved. Experimental results demonstrate the effectiveness of the proposed approach, achieving an accuracy of 86.23% with K-Nearest Neighbor as the meta-model. Comparisons with state-of-the-art and baseline methods on the same dataset validate the approach's superiority, emphasizing the importance of GCNNs and stacking ensemble learning for accurate MI task classification.