Hybrid Genetic Algorithm for Clustering IC Topographies of EEGs

• Published in: Brain topography Vol. 36; no. 3; pp. 338 - 349
• Main Authors: Munilla, Jorge, Al-Safi, Haedar E. S., Ortiz, Andrés, Luque, Juan L.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2023; Springer Nature B.V
• Subjects: Algorithms; Biomedical and Life Sciences; Biomedicine; Brain; Cluster Analysis; Clustering; EEG; Electroencephalography; Electroencephalography - methods; Event-related potentials; Genetic algorithms; Humans; Neurology; Neurosciences; Original Paper; Psychiatry; Signal Processing, Computer-Assisted; GA; Clustering; EEG; ICA
• ISSN: 0896-0267; 1573-6792; 1573-6792
• DOI: 10.1007/s10548-023-00947-y

Clustering of independent component (IC) topographies of Electroencephalograms (EEG) is an effective way to find brain-generated IC processes associated with a population of interest, particularly for those cases where event-related potential features are not available. This paper proposes a novel algorithm for the clustering of these IC topographies and compares its results with the most currently used clustering algorithms. In this study, 32-electrode EEG signals were recorded at a sampling rate of 500 Hz for 48 participants. EEG signals were pre-processed and IC topographies computed using the AMICA algorithm. The algorithm implements a hybrid approach where genetic algorithms are used to compute more accurate versions of the centroids and the final clusters after a pre-clustering phase based on spectral clustering. The algorithm automatically selects the optimum number of clusters by using a fitness function that involves local-density along with compactness and separation criteria. Specific internal validation metrics adapted to the use of the absolute correlation coefficient as the similarity measure are defined for the benchmarking process. Assessed results across different ICA decompositions and groups of subjects show that the proposed clustering algorithm significantly outperforms the (baseline) clustering algorithms provided by the software EEGLAB, including CORRMAP.