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

运动想象脑电(Motor Imagery Electroencephalogram,MI-EEG)已经应用在脑机接口(Brain Computer Interface,BCI)中,能帮助上下肢功能障碍的患者进行康复训练.然而,现有技术对MI-EEG低效的解码性能和对MI-EEG过度依赖预处理的方式限制了BCI的广泛发展.提出了一种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 EEG Decoding Method,MMFTSF).MMFTSF使用时空卷积网络提取MI-EEG中浅层信息特征,使用多头概率稀疏自注意力机制关注MI-EEG中最具有价值的信息特征,使用时间卷积网络提取MI-EEG高维时间特征,使用带有softmax分类器的全连接层对MI-EEG进行分类,并利用基于卷积的滑动窗口和空间信息增强模块进一步提升MI-EEG解码性能.在公开的BCI竞赛数据集IV-2a上进行验证.实验结果表明,MMFTSF在数据集上达到89.03%的解码准确度,在MI-EEG分类任务中具有理想的分类性能.

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.