Cognitive Workload Recognition Based on Hybrid Brain-Computer Interface Channel Selection and Hierarchical Feature Fusion
Research on the recognition of cognitive workload based on electroencephalogram(EEG)and functional near-infrared spectrosco-py(fNIRS)physiological data has garnered significant attention in the field of brain-computer interfaces.However,the complex data acquisi-tion environment introduces uncontrollable effects on inter-channel data,severely limiting the accuracy and integrity of models simulating hu-man brain information transmission processes.Therefore,this paper proposes an improved dynamic graph attention-based channel selection method.The method utilizes attention scores returned by a Graph Attention Network(GAT)to select channels,thereby reducing environmen-tal interference and enhancing model robustness.Moreover,simple feature fusion can overlook the heterogeneity between different modalities,leading to the loss of critical information.To mitigate this,we designed a hierarchical feature fusion module.We validated our approach using two publicly available datasets provided by the Berlin Institute of Technology:a mental arithmetic task and an N-Back task.Employing a sub-ject-dependent training strategy and ten-fold cross-validation for each participant,our method achieved average accuracies of 85.44%and 91.72%,respectively.Compared to current state-of-the-art methods,our approach demonstrates certain advantages.The experimental results indicate that the proposed model effectively recognizes cognitive workload in complex data environments.Additionally,the proposed channel selection method is significant for reducing computational cost and eliminating irrelevant channels.
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