Truncated RAP-MUSIC (TRAP-MUSIC) for MEG and EEG source localization

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date

2018-02-15

Department

Department of Neuroscience and Biomedical Engineering
Department of Neuroscience and Biomedical Engineering

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Mcode

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Language

en

Pages

11
73-83

Series

NeuroImage, Volume 167

Abstract

Electrically active brain regions can be located applying MUltiple SIgnal Classification (MUSIC) on magneto- or electroencephalographic (MEG; EEG) data. We introduce a new MUSIC method, called truncated recursively-applied-and-projected MUSIC (TRAP-MUSIC). It corrects a hidden deficiency of the conventional RAP-MUSIC algorithm, which prevents estimation of the true number of brain-signal sources accurately. The correction is done by applying a sequential dimension reduction to the signal-subspace projection. We show that TRAP-MUSIC significantly improves the performance of MUSIC-type localization; in particular, it successfully and robustly locates active brain regions and estimates their number. We compare TRAP-MUSIC and RAP-MUSIC in simulations with varying key parameters, e.g., signal-to-noise ratio, correlation between source time-courses, and initial estimate for the dimension of the signal space. In addition, we validate TRAP-MUSIC with measured MEG data. We suggest that with the proposed TRAP-MUSIC method, MUSIC-type localization could become more reliable and suitable for various online and offline MEG and EEG applications.

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Keywords

EEG, Electroencephalography, Inverse methods, Magnetoencephalography, MEG, Multiple sources, Source localization, Neurology, Cognitive Neuroscience, 3112 Neurosciences

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DOI

10.1016/j.neuroimage.2017.11.013

Citation

Mäkelä , N , Stenroos , M , Sarvas , J & Ilmoniemi , R J 2018 , ' Truncated RAP-MUSIC (TRAP-MUSIC) for MEG and EEG source localization ' , NeuroImage , vol. 167 , pp. 73-83 . https://doi.org/10.1016/j.neuroimage.2017.11.013

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https://urn.fi/URN:NBN:fi:aalto-202002212243

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[article-cris] Perustieteiden korkeakoulu / SCI