Abstract
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) between 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 functional networks are consistent with those obtained from previous studies using fMRI, resting-state fMRI and EEG.
Original language | English |
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Pages (from-to) | 140-156 |
Number of pages | 17 |
Journal | Neuroscience |
Volume | 523 |
Early online date | 8 Jun 2023 |
DOIs | |
Publication status | Published - 15 Jul 2023 |
Bibliographical note
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Funder
The EEG data was funded by a grant from Alzheimer’s Research UK, grantreference number ARUK-PPG20114B-25.
Funding
RG and FH acknowledge Coventry University for the Trailblazer Ph.D. studentship. The EEG data was funded by a grant from Alzheimer's Research UK, grant reference number ARUK-PPG20114B-25. This is a summary of independent research carried out at the NIHR Sheffield Biomedical Research Centre (Translational Neuroscience). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. RG and FH acknowledge Coventry University for the Trailblazer Ph.D. studentship. The EEG data was funded by a grant from Alzheimer’s Research UK, grant reference number ARUK-PPG20114B-25. This is a summary of independent research carried out at the NIHR Sheffield Biomedical Research Centre (Translational Neuroscience). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.
Funders | Funder number |
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National Institute for Health and Care Research | |
Coventry University | |
Alzheimer’s Research UK | ARUK-PPG20114B-25 |
NIHR Sheffield Biomedical Research Centre |
Keywords
- Alzheimer's disease
- EEG
- channel selection
- manifold learning
- machine learning
- functional connectivity
ASJC Scopus subject areas
- General Neuroscience