Local Subspace Pruning (LSP) for Multichannel Data Denoising release_dvk3rnrdljeezacoicp6qa73la

Abstract

This paper proposes a simple algorithm to remove noise and artifact from multichannel data. Data are processed trial by trial: for each trial the covariance matrix of the trial is diagonalized together with that of the full data to reveal the subspace that is -- locally -- most excentric relative to other trials. That subspace is then projected out from the data of that trial. This algorithm addresses a fundamental limitation of standard linear analysis methods (e.g. ICA) that assume that brain and artifact are linearly separable within the data. That assumption fails if there are more sources, including noise and brain sources, than data channels. The algorithm captializes on the fact that, if enough of those sources are temporally sparse, linear separation may succeed locally in time. The paper explains the rationale, describes the algorithm, and evaluates the outcome using synthetic and real brain data.
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