Sparse time artifact removal

Background: Muscle artifacts and electrode noise are an obstacle to interpretation of EEG and other electrophysiological signals. They are often channel-specific and do not fully benefit from component analysis techniques such as ICA, and their presence reduces the dimensionality needed by those techniques. Their high-frequency content may mask or masquerade as gamma band cortical activity. New method: The sparse time artifact removal (STAR) algorithm removes artifacts that are sparse in space and time. The time axis is partitioned into an artifact-free and an artifact-contaminated part, and the correlation structure of the data is estimated from the covariance matrix of the artifact-free part. Artifacts are then corrected by projection of each channel onto the subspace spanned by the other channels. Results: The method is evaluated with both simulated and real data, and found to be highly effective in removing or attenuating typical channel-specific artifacts. Comparison with existing methods: In contrast to the widespread practice of trial removal or channel removal or interpolation, very few data are lost. In contrast to ICA or other linear techniques, processing is local in time and affects only the artifact part, so most of the data are identical to the unprocessed data and the full dimensionality of the data is preserved. Conclusions: STAR complements other linear component analysis techniques, and can enhance their ability to discover weak sources of interest by increasing the number of effective noise-free channels. (C) 2016 The Author. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.