Kernel-based Nonlinear Manifold Learning for EEG-based Functional Connectivity Analysis and Channel Selection with Application to Alzheimer's Disease

Dynamical causal, and cross-frequency coupling analysis using the electroencephalogram (EEG) has gained significant attention for diagnosing and characterizing neurological disorders. Selecting important EEG channels is crucial for reducing computational complexity in implementing these methods and improving classification accuracy. In neuroscience, measures of (dis) similarity between EEG channels are often used as functional connectivity (FC) features, and important channels are selected via feature selection. Developing a generic measure of (dis) similarity is important for FC analysis and channel selection. In this study, learning of (dis) similarity information within the EEG is achieved using kernel-based nonlinear manifold learning. The focus is on FC changes and, thereby, EEG channel selection. Isomap and Gaussian Process Latent Variable Model (Isomap-GPLVM) are employed for this purpose. The resulting kernel (dis) similarity matrix is used as a novel measure of linear and nonlinear FC between EEG channels. The analysis of EEG from healthy controls (HC) and patients with mild to moderate Alzheimer's disease (AD) are presented as a case study. Classification results are compared with other commonly used FC measures. Our analysis shows significant differences in FC between bipolar channels of the occipital region and other regions (i.e. parietal, centro-parietal, and fronto-central) be-tween AD and HC groups. Furthermore, our results indicate that FC changes between channels along the fronto-parietal region and the rest of the EEG are important in diagnosing AD. Our results and its relation to func-tional networks are consistent with those obtained from previous studies using fMRI, resting-state fMRI and EEG. & COPY; 2023 The Author(s). Published by Elsevier Ltd on behalf of IBRO. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

Dynamical causal and cross-frequency coupling analysis using EEG has gained attention for diagnosing neurological disorders. Developing a measure of similarity is crucial for functional connectivity analysis and channel selection. In this study, kernel-based nonlinear manifold learning is used to learn similarity information within the EEG, and the resulting similarity matrix is used to measure linear and nonlinear functional connectivity between EEG channels. The analysis of EEG from healthy controls and patients with Alzheimer's disease shows significant differences in functional connectivity and highlights the importance of specific channel changes in diagnosing AD. The results are consistent with previous studies using fMRI, resting-state fMRI, and EEG.