Hybrid Genetic Algorithm for Clustering IC Topographies of EEGs

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 a...

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Veröffentlicht in:Brain topography Jg. 36; H. 3; S. 338 - 349
Hauptverfasser: Munilla, Jorge, Al-Safi, Haedar E. S., Ortiz, Andrés, Luque, Juan L.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.05.2023
Springer Nature B.V
Schlagworte:
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
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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Handling Editor: Ramesh Srinivasan.
ISSN:0896-0267
1573-6792
1573-6792
DOI:10.1007/s10548-023-00947-y