Journal Article

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

Stenroos, Matti, Mäkelä, Niko, Sarvas, Jukka, Ilmoniemi, Risto J.

2018

Overview

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. [Display omitted] •A modified RAP-MUSIC method to locate multiple brain sources in EEG/MEG is proposed.•The new method, TRAP-MUSIC, solves a hidden deficiency in the original algorithm.•TRAP-MUSIC applies a recursive dimension reduction of the signal-space estimate.•The improvements in performance come without any computational cost.

• Publisher: Elsevier Inc,Elsevier Limited
• Subject: Algorithms / EEG / Electroencephalography / Hypothesis testing / Inverse methods / Linear algebra / Localization / Magnetoencephalography / MEG / Multiple sources / Noise / Parameter estimation / Rap music / Sensors / Source localization / Topography