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

Brain Topography
David A BridwellVince D Calhoun

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 numb...Continue Reading

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Citations

Sep 7, 2017·Brain Topography·René LabounekJiří Jan
Mar 12, 2020·Journal of Clinical Medicine·Federico GennaroEling D de Bruin
Oct 25, 2017·Brain Topography·René J Huster, Liisa Raud
Apr 11, 2018·Frontiers in Human Neuroscience·David A BridwellVince D Calhoun
May 14, 2018··Marcel FajkusRadek Martinek

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Methods Mentioned

BETA
PCA

Software Mentioned

EVD
ERICA
EFICA
INFOMAX
RADICAL
SIMBEC
INFOMAX ICA
Matlab
ICA EBM ICA
ICASSO

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