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Brain Topogr. 2018 Jan;31(1):47-61. doi: 10.1007/s10548-016-0479-1. Epub 2016 Feb 24.

Spatiospectral Decomposition of Multi-subject EEG: Evaluating Blind Source Separation Algorithms on Real and Realistic Simulated Data.

Author information

1
The Mind Research Network, 1101 Yale Blvd. NE, Albuquerque, NM, 87131, USA. dbridwell@mrn.org.
2
The Mind Research Network, 1101 Yale Blvd. NE, Albuquerque, NM, 87131, USA.
3
Department of ECE, University of New Mexico, Albuquerque, NM, 87131, USA.
4
Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06510, USA.
5
Department of Neurobiology, Yale University School of Medicine, New Haven, CT, 06510, USA.
6
Olin Neuropsychiatry Research Center, Hartford Healthcare Corporation, Hartford, CT, 06106, USA.

Abstract

Electroencephalographic (EEG) oscillations predominantly appear with periods between 1 s (1 Hz) and 20 ms (50 Hz), and are subdivided into distinct frequency bands which appear to correspond to distinct cognitive processes. A variety of blind source separation (BSS) approaches have been developed and implemented within the past few decades, providing an improved isolation of these distinct processes. Within the present study, we demonstrate the feasibility of multi-subject BSS for deriving distinct EEG spatiospectral maps. Multi-subject spatiospectral EEG decompositions were implemented using the EEGIFT toolbox ( http://mialab.mrn.org/software/eegift/ ) with real and realistic simulated datasets (the simulation code is available at http://mialab.mrn.org/software/simeeg ). Twelve different decomposition algorithms were evaluated. Within the simulated data, WASOBI and COMBI appeared to be the best performing algorithms, as they decomposed the four sources across a range of component numbers and noise levels. RADICAL ICA, ERBM, INFOMAX ICA, ICA EBM, FAST ICA, and JADE OPAC decomposed a subset of sources within a smaller range of component numbers and noise levels. INFOMAX ICA, FAST ICA, WASOBI, and COMBI generated the largest number of stable sources within the real dataset and provided partially distinct views of underlying spatiospectral maps. We recommend the multi-subject BSS approach and the selected algorithms for further studies examining distinct spatiospectral networks within healthy and clinical populations.

KEYWORDS:

Blind source separation; ICA; Multi-subject decomposition; Resting EEG; Simulated EEG; Wavelets

PMID:
26909688
PMCID:
PMC4996763
[Available on 2019-01-01]
DOI:
10.1007/s10548-016-0479-1
[Indexed for MEDLINE]

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