广西师范大学学报（自然科学版）2025，Vol.43Issue(3)：43-56,14.DOI:10.16088/j.issn.1001-6600.2024092401
基于复数协方差卷积神经网络的运动想象脑电信号解码方法
Complex-value Covariance-based Convolutional Neural Network for Decoding Motor Imagery-based EEG Signals
摘要
Abstract
To improve the classification performance of motor imagery(MI)tasks by deeply mining and using the characteristic information of electroencephalogram(EEG)signals has always been the focus of brain-computer interfaces(BCI)research.Because EEG feature space is highly dimensional and directly related to both amplitude and phase of EEG signals,how to simultaneously represent and utilize the information contained in amplitude and phase has become a difficult issue.To address this issue,a three-dimensional complex convolutional neural network based on complex-value covariance features is proposed.Firstly,complex-value covariance matrices related to different frequencies as MI-based EEG features is constructed.As a result,complex value can combine the amplitude and phase information of EEG signals together.Moreover,the covariance matrices can preserve multivariate information such as amplitude,phase,spatial locations,frequency,etc.required for classification.Secondly,a full complex convolutional neural network is designed for learning the covariance features and thus achieving high performance classification.Experimental results on two publicly available datasets show that the proposed method can achieve mean accuracies that are at least 2.49 and 1.85 percentage points higher than state-of-the-art methods.关键词
脑电信号/脑机接口/幅相信息融合/复数协方差特征/复值卷积神经网络/信息交互Key words
electroencephalogram/brain-computer interface/fusion of amplitude and phase information/complex covariance features/complex-valued convolutional neural network/information interactive分类
信息技术与安全科学引用本文复制引用
黄仁慧,张锐锋,文晓浩,闭金杰,黄守麟,李廷会..基于复数协方差卷积神经网络的运动想象脑电信号解码方法[J].广西师范大学学报（自然科学版）,2025,43(3):43-56,14.基金项目
国家自然科学基金(62466006) （62466006）
广西科技计划青年创新人才科研专项(桂科AD23026245) （桂科AD23026245）
