MEG Signal Denoising Based on Time-Shift PCA

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MEG Signal Denoising Based on Time-Shift PCA
We present a method for removing environmental noise from physiological recordings such as Magnetoencephalography (MEG) for which noise-sensitive reference channels are available. Sensor signals are projected on a subspace spanned by the reference channels augmented by time-shifted and/or nonlinearly transformed versions of the same, and the projections are removed to obtain “clean” sensor signals. The method compensates for scalar, convolutional or non-linear mismatches between sensor and reference channels by synthesizing, for each reference/sensor pair, a filter that is optimal in a least-squares sense for removal of the artifact. The method was tested with synthetic and real MEG data, typically removing up to 98% of noise variance. It offers an alternative to bulky and costly magnetic shielding (multiple layers of aluminium and mu-metal) for present scientific and medical applications and future developments such as brainmachine interfaces (BMI).
Alain de Cheveigné, Jonathan Le Roux, Jonat
Added 02 Jun 2010
Updated 02 Jun 2010
Type Conference
Year 2007
Authors Alain de Cheveigné, Jonathan Le Roux, Jonathan Z. Simon
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