Independent component analysis for unaveraged single-trial MEG data decomposition and single-dipole source localization

This paper presents a novel method for decomposing and localizing unaveraged single-trial magnetoencephalographic data based on the independent component analysis (ICA) approach associated with pre- and post-processing techniques. In the pre-processing stage, recorded single-trial raw data are 6rst decomposed into uncorrelated signals with the reduction ofhigh-power additive noise. In the stage ofsource separation, the decorrelated source signals are f decomposed into independent source components. In the post-processing stage, we perform a source localization procedure to seek a single-dipole map ofdecomposed individual source components, e.g., evoked responses. The 6rst results ofapplying the proposed robust ICA approach to single-trial data with phantom and auditory evoked 6eld tasks indicate the following. (1) A source signal is successfully extracted from unaveraged single-trial phantom data. The accuracy of dipole estimation f the decomposed source is even better than that oftaking the average oftotal trials. (2) Not only the behavior and location ofindividual neuronal sources can be obtained but also the activity strength (amplitude) ofevoked responses corresponding to a stimulation trial can be obtained and visualized. Moreover, the dynamics ofindividual neuronal sources, such as the

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