GM-VRC: Semantic Topological Data Ensemble Approach for EEG Signal Classification

• Published in: Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 1971 - 1975
• Main Authors: Reddy, Srikireddy Dhanunjay, Kumar Reddy, Tharun
• Format: Conference Proceeding
• Language: English
• Published: IEEE 14.04.2024
• Subjects: Artificial neural networks; Brain modeling; Depression; EEG; Electroencephalography; Graph Mapping; Robustness; Semantics; Signal processing; Topological Data Analysis; Vietoris-Rips Complex
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP48485.2024.10446927

Usage of Machine Learning (ML) models has been trending for automated screening of mental health. Electroencephalogram (EEG) signals, due to their non-invasive nature and ease of availability with low cost, are mostly recorded and used for diagnosis. Such signals however are non-stationary and also lie on a nonlinear manifold. Therefore, ML models may then struggle to discover the underlying connectivities in neurological disorders diagnosis using EEG. Topological studies can help in this context. However, there have been limited studies conducted on the application of Topological Data Analysis (TDA) for the purpose of characterizing and classifying EEG data. For the very first time, a Semantic Topological Ensemble approach is proposed through Graph Mapping and Vietoris-Rips Complex (GM-VRC) framework to improve the robustness of TDA features for depression classification. The proposed framework is assessed with the publicly available datasets containing Healthy Controls (HC) and Major Depressive Disorder (MDD) subjects. By comparing the test accuracies of the traditional TDA features analysis with baseline Deep Neural Network (DNN) and Graph Neural Network (GNN), improved performance with a mean gain of 9% is noticed with the proposed GM-VRC framework.