Recognition of regions of stroke injury using multi-modal frequency features of electroencephalogram

• Published in: Frontiers in neuroscience Vol. 18; p. 1404816
• Main Authors: Jin, Yan, Li, Jing, Fan, Zhuyao, Hua, Xian, Wang, Ting, Du, Shunlan, Xi, Xugang, Li, Lihua
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 10-06-2024
• Subjects: EEG; feature fusion; functional connectivity; Neuroscience; regions of brain injury; stroke; wavelet packet transform; feature fusion; functional connectivity; wavelet packet transform; regions of brain injury; stroke; EEG
• ISSN: 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2024.1404816

Nowadays, increasingly studies are attempting to analyze strokes in advance. The identification of brain damage areas is essential for stroke rehabilitation.
We proposed Electroencephalogram (EEG) multi-modal frequency features to classify the regions of stroke injury. The EEG signals were obtained from stroke patients and healthy subjects, who were divided into right-sided brain injury group, left-sided brain injury group, bilateral brain injury group, and healthy controls. First, the wavelet packet transform was used to perform a time-frequency analysis of the EEG signal and extracted a set of features (denoted as WPT features). Then, to explore the nonlinear phase coupling information of the EEG signal, phase-locked values (PLV) and partial directed correlations (PDC) were extracted from the brain network, and the brain network produced a second set of features noted as functional connectivity (FC) features. Furthermore, we fused the extracted multiple features and used the resnet50 convolutional neural network to classify the fused multi-modal (WPT + FC) features.
The classification accuracy of our proposed methods was up to 99.75%.
The proposed multi-modal frequency features can be used as a potential indicator to distinguish regions of brain injury in stroke patients, and are potentially useful for the optimization of decoding algorithms for brain-computer interfaces.