Spatiospectral Decomposition of Multi-subject EEG: EvaluatingBlind Source Separation Algorithms on Real and RealisticSimulated Data

Abstract

Electroencephalographic (EEG) oscillationspredominantly appear with periods between 1 s (1 Hz) and20 ms (50 Hz), and are subdivided into distinct frequencybands which appear to correspond to distinct cognitiveprocesses. A variety of blind source separation (BSS)approaches have been developed and implemented withinthe past few decades, providing an improved isolation ofthese distinct processes. Within the present study, wedemonstrate the feasibility of multi-subject BSS forderiving distinct EEG spatiospectral maps. Multi-subjectspatiospectral EEG decompositions were implementedusing the EEGIFT toolbox (http://mialab.mrn.org/software/eegift/) with real and realistic simulated datasets (thesimulation code is available at http://mialab.mrn.org/software/simeeg). Twelve different decomposition algorithmswere evaluated. Within the simulated data, WASOBI andCOMBI appeared to be the best performing algorithms, asthey decomposed the four sources across a range ofcomponent numbers and noise levels. RADICAL ICA,ERBM, INFOMAX ICA, ICA EBM, FAST ICA, andJADE OPAC decomposed a subset of sources within asmaller range of component numbers and noise levels.INFOMAX ICA, FAST ICA, WASOBI, and COMBIgenerated the largest number of stable sources within thereal dataset and provided partially distinct views ofunderlying spatiospectral maps. We recommend the multisubjectBSS approach and the selected algorithms for furtherstudies examining distinct spatiospectral networkswithin healthy and clinical populations.Keywords Blind source separation Multi-subjectdecomposition Resting EEG Simulated EEG Wavelets IC