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

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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.

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Authors and Affiliations

  1. The Mind Research Network, 1101 Yale Blvd. NE, Albuquerque, NM, 87131, USA

    David A. Bridwell, Srinivas Rachakonda, Rogers F. Silva & Vince D. Calhoun

  2. Department of ECE, University of New Mexico, Albuquerque, NM, 87131, USA

    Rogers F. Silva & Vince D. Calhoun

  3. Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06510, USA

    Godfrey D. Pearlson & Vince D. Calhoun

  4. Department of Neurobiology, Yale University School of Medicine, New Haven, CT, 06510, USA

    Godfrey D. Pearlson

  5. Olin Neuropsychiatry Research Center, Hartford Healthcare Corporation, Hartford, CT, 06106, USA

    Godfrey D. Pearlson

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  1. David A. Bridwell
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  2. Srinivas Rachakonda
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  3. Rogers F. Silva
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  4. Godfrey D. Pearlson
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  5. Vince D. Calhoun
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Correspondence to David A. Bridwell.

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This is one of several papers published together in Brain Topography on the “Special Issue: Multisubject decomposition of EEG - methods and applications”.

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Bridwell, D.A., Rachakonda, S., Silva, R.F. et al. Spatiospectral Decomposition of Multi-subject EEG: Evaluating Blind Source Separation Algorithms on Real and Realistic Simulated Data. Brain Topogr 31, 47–61 (2018). https://doi.org/10.1007/s10548-016-0479-1

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