电子学报2025，Vol.53Issue(3)：941-950,10.DOI:10.12263/DZXB.20240885
基于Dempster-Shafer证据推理的EEG-fNIRS运动想象分类决策层融合方法
Decision-Level Fusion of EEG-fNIRS for Motor Imagery Classification Based on Dempster-Shafer Evidence Reasoning
摘要
Abstract
To address the issues of low spatial resolution and susceptibility to noise in traditional single-modality brain-computer interface(BCI)technologies based on electroencephalography(EEG),an increasing number of studies have focused on BCI research that combines EEG signals with functional near-infrared spectroscopy(fNIRS)signals.However,integrating these two heterogeneous signals poses challenges.This paper proposes an innovative end-to-end signal fusion method based on deep learning and evidence theory for motor imagery(MI)classification.The spatiotemporal feature infor-mation of EEG signals is extracted using dual-scale temporal convolution and depth wise separable convolution,with a hy-brid attention module introduced to enhance the network's ability to perceive important features.For fNIRS signals,spatial convolution across all channels explores activation differences between different brain regions,while parallel temporal con-volution and gated recurrent unit(GRU)capture richer temporal feature information.During the decision fusion stage,the decision outputs obtained from decoding each signal are first utilized to estimate uncertainty using Dirichlet distribution pa-rameter estimation.Subsequently,Dempster-Shafer theory(DST)is employed for dual-layer reasoning,effectively merging evidence from the two basic belief assignment(BBA)methods and different modalities to obtain the decoding results.The proposed model is evaluated on the publicly available TU-Berlin-A dataset,achieving an average accuracy of 83.26%,which represents a 3.78 percentage points improvement compared to the state-of-the-art research.This provides new ideas and approaches for fusion studies based on EEG and fNIRS signals.关键词
混合脑机接口(BCI)/运动想象(MI)/深度学习/Dempster-Shafer理论/功能近红外光谱(fNIRS)信号/脑电信号(EEG)信号Key words
hybrid brain-computer interface(BCI)/motor imagery(MI)/deep learning/Dempster-Shafer theory(DST)/functional near-infrared spectroscopy(fNIRS)signal/electroencephalography(EEG)signal分类
信息技术与安全科学引用本文复制引用
康冉斓,李玉榕,史武翔,李吉祥..基于Dempster-Shafer证据推理的EEG-fNIRS运动想象分类决策层融合方法[J].电子学报,2025,53(3):941-950,10.基金项目
国家自然科学基金(No.62373108) National Natural Science Foundation of China(No.62373108) （No.62373108）
