University of Washington

Samu Taulu

Eric T Larson

Eric D Larson

Embodiments disclosed herein directed to systems, apparatuses, and methods for the suppression of artifacts in non-invasive electromagnetic recordings. A data set, such as an electroencephalography (EEG) or magnetoencephalography (MEG) data set may be recorded by a number of sensors. The data set may include contributions from a signal of interest and from artifacts. The contribution of artifacts may be suppressed by splitting the data set into projected data and residual data based on a spatial model of the signal of interest. The projected data may contain contributions from the signal of interest and artifacts, while the residual data may primarily contain contributions from artifacts. The projected and residual data may be compared to remove or reduce the contribution of artifacts from the projected data.

Apparatuses, systems, and methods for suppression of artifacts in non-invasive electromagnetic recordings

A method for suppressing sensor noise in a spatially oversampled sensor array includes receiving spatially oversampled multi-channel sensor data from a region of interest and building a spatial model from the data for essential spatial degrees of freedom. The method further includes decomposing the data into the underlying spatial model to obtain associated amplitude components containing a mixture of original temporal waveforms of the data and, for each channel of the multi-channel sensor, estimating time-domain amplitude components using cross-validation. Next, for each channel, based on the estimated time-domain amplitude components, sensor noise and/or artifacts for that channel are identified. Finally, for each channel, the identified sensor noise and/or artifacts can be suppressed from the data.

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Seattle, WA, United States of America

Samu Taulu