南京大学学报（自然科学版）2024，Vol.60Issue(1)：65-75,11.DOI:10.13232/j.cnki.jnju.2024.01.007
多模型融合的时空特征运动想象脑电解码方法
Multi-model fusion temporal-spatial feature motor imagery electroencephalogram decoding method
摘要
Abstract
Motor imagery electroencephalogram(MI-EEG)has been applied in brain computer interface(BCI)to assist patients with upper and lower limb dysfunction in rehabilitation training.However,the limited decoding performance of MI-EEG and over-reliance on pre-processing are restricting the broad growth of brain computer interface(BCI).We propose a multi-model fusion temporal-spatial feature motor imagery electroencephalogram decoding method(MMFTSF).The MMFTSF uses temporal-spatial convolutional networks to extract shallow features,multi-head probsparse self-attention mechanism to focus on the most valuable features,temporal convolutional networks to extract high-dimensional temporal features,fully connected layer with softmax classifier for classification,and convolutional-based sliding window and spatial information enhancement module to further improve decoding performance from MI-EEG.Experimental results have shown that the proposed reaches 89.03％on public BCI competition IV-2a dataset,which demonstrate MMFTSF has ideal classification performance on MI-EEG.关键词
概率稀疏注意力/运动想象/卷积神经网络/时间卷积网络Key words
probsparse self-attention/motor imagery/convolutional neural networks/temporal convolutional networks分类
电子信息工程引用本文复制引用
凌六一,李卫校,冯彬..多模型融合的时空特征运动想象脑电解码方法[J].南京大学学报（自然科学版）,2024,60(1):65-75,11.基金项目
安徽理工大学环境友好材料与职业健康研究院(芜湖)研发专项(ALW2022YF06),安徽高校协同创新项目(GXXT-2022-053) （芜湖）
