电子科技2026，Vol.39Issue(5)：30-39,10.DOI:10.16180/j.cnki.issn1007-7820.2026.05.004
基于混合脑机接口多模态特征图融合的认知工作负荷识别
Cognitive Workload Recognition Based on Multimodal Feature Map Fusion of Hybrid Brain Computer Interface
摘要
Abstract
The recognition task of cognitive workload holds significant importance in the field of brain-computer interfaces.Existing methods rely on single-modal electroencephalogram data and use shallow graph neural networks,which limits the performance of cognitive workload recognition.Traditional feature fusion methods struggle to effec-tively combine the high spatial resolution of near-infrared spectroscopy with the high temporal resolution of EEG sig-nals.This study proposes a novel architecture based on LGCN-DATF(Local Graph Convolutional Dual-Channel A-daptive Transformer Feature Map Fusion)aiming to simulate the complexity of dynamic changes between different brain regions.It captures the real-time connection status between signal channels by introducing a dynamic adjacency matrix.A unique brain graph learning module is designed,which is conducive to a deeper understanding and predic-tion of the dynamic changes in cognitive workload.The proposed model is tested under two training strategies.The re-sults show that the accuracy rates of subject-dependent training in mental arithmetic and working memory tasks are 87.3%and 89.1%,respectively,while the accuracy rates of subject-independent training are 68.5%and 55.6%,respec-tively.These results verify that the proposed model can effectively recognize cognitive workload in complex environments.关键词
深度学习/脑电图/功能性近红外光谱/图卷积/动态邻接矩阵/认知工作负荷/特征图融合/工作记忆Key words
deep learning/EEG/fNIRS/GCN/dynamic adjacency matrix/cognitive workload/feature map fu-sion/working memory分类
信息技术与安全科学引用本文复制引用
詹志远,陈利,张恒千,尹钟..基于混合脑机接口多模态特征图融合的认知工作负荷识别[J].电子科技,2026,39(5):30-39,10.基金项目
国家自然科学基金(61703277) （61703277）
上海青年科技英才扬帆计划(17YF1427000)National Natural Science Foundation of China(61703277) （17YF1427000）
Shanghai Sailing Program(17YF1427000) （17YF1427000）
