基于双分支网络的动态知识蒸馏运动想象脑电解码OA

To address low decoding accuracy and high computational complexity in multi-task motor imagery EEG decoding models,a dual-branch network-based dynamic knowledge distillation model is proposed.The principle of"multi-scale temporal fusion first,followed by unified spatial processing",is followed in this architecture and a multi-scale spatiotemporal feature extraction module is designed.A dual-branch structure combining Transformer and BiLSTM are employed as the teacher model,which effectively integrates the Transformer's strength in global feature representation with the BiLSTM's advantage in fine-grained temporal modeling,thereby enhancing the decoding accuracy of motor imagery EEG signals.During the knowledge distillation phase,a lightweight EEGNet is utilized as the student model.A dual-branch dynamic knowledge distillation strategy is proposed,where the optimal branch is adaptively selected to generate soft labels.This enables the student model to learn EEG feature representations adaptable to different individuals,allowing it to achieve performance close to that of the teacher model while maintaining a compact size.Experimental validation on the BCI Competition IV Dataset 2a demonstrates the effectiveness of the proposed method.The dual-branch teacher model achieves an average decoding accuracy of 91.32%for multi-task motor imagery.After knowledge distillation,the student model attains an average EEG decoding accuracy of 88.85%,representing an improvement of 6.63%compared to the student model without distillation.Furthermore,compared to the teacher model,the number of parameters in the student model is reduced by 99.78%,and the computational load is decreased by 89.02%.The results indicate that the proposed method maintains high EEG decoding accuracy while significantly reducing computational complexity,providing a practical and lightweight solution for brain-computer interface systems in resource-constrained environments.