Miri Maliheh, Abootalebi Vahid, Saeedi-Sourck Hamid, Van De Ville Dimitri, Behjat Hamid
Department of Electrical Engineering, Yazd University, Yazd, Iran.
Neuro-X Institute, EPFL, Geneva, Switzerland.
J Med Signals Sens. 2025 Mar 13;15:7. doi: 10.4103/jmss.jmss_63_24. eCollection 2025.
Accurate classification of electroencephalogram (EEG) signals is challenging given the nonlinear and nonstationary nature of the data as well as subject-dependent variations. Graph signal processing (GSP) has shown promising results in the analysis of brain imaging data.
In this article, a GSP-based approach is presented that exploits instantaneous amplitude and phase coupling between EEG time series to decode motor imagery (MI) tasks. A graph spectral representation of the Hilbert-transformed EEG signals is obtained, in which simultaneous diagonalization of covariance matrices provides the basis of a subspace that differentiates two classes of right hand and right foot MI tasks. To determine the most discriminative subspace, an exploratory analysis was conducted in the spectral domain of the graphs by ranking the graph frequency components using a feature selection method. The selected features are fed into a binary support vector machine that predicts the label of the test trials.
The performance of the proposed approach was evaluated on brain-computer interface competition III (IVa) dataset.
Experimental results reflect that brain functional connectivity graphs derived using the instantaneous amplitude and phase of the EEG signals show comparable performance with the best results reported on these data in the literature, indicating the efficiency of the proposed method compared to the state-of-the-art methods.
鉴于脑电图（EEG）信号数据的非线性和非平稳特性以及个体差异，对其进行准确分类具有挑战性。图信号处理（GSP）在脑成像数据分析中已显示出有前景的结果。
本文提出一种基于GSP的方法，该方法利用EEG时间序列之间的瞬时幅度和相位耦合来解码运动想象（MI）任务。获得经希尔伯特变换的EEG信号的图谱表示，其中协方差矩阵的同时对角化提供了区分右手和右脚MI任务两类的子空间基础。为了确定最具判别力的子空间，通过使用特征选择方法对图频率分量进行排序，在图的谱域中进行了探索性分析。所选特征被输入到二元支持向量机中，以预测测试试验的标签。
在脑机接口竞赛III（IVa）数据集上评估了所提出方法的性能。
实验结果表明，利用EEG信号的瞬时幅度和相位导出的脑功能连接图表现出与文献中报道的这些数据的最佳结果相当的性能，表明与现有方法相比，所提方法的有效性。
