Hybrid Genetic Algorithm for Clustering IC Topographies of EEGs

Abstract

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.

Keywords: Clustering; EEG; GA; ICA.

Fig. 1

Workflow applied to EEG data. It also indicates the sections where the different steps are addressed throughout the paper

Fig. 2

Scalp maps of ICs with different correlation coefficients with an IC template

Fig. 3

Silhouette graph for ICLabel

Fig. 4

Results using CORRMAP

Fig. 5

Results using Kmeans of EEGLAB

Fig. 6

Flowchart of the proposed clustering algorithm

Fig. 7

Results using the proposed method

Fig. 8

AMICA shows great stability when labels assigned by ICLabel are analyzed. a Boxplot of the labels. b ${\chi }^2$ scores and the corresponding p-values for the distribution of the assigned labels (the green dot indicates the decomposition used in the previous section)