A centered sliding-window neighborhood subspace projection method for suppressing temporally sparse artifacts in OPM-MEG

Magnetoencephalography (MEG) based on optically pumped magnetometers (OPM) offers new opportunities for brain research, yet its recordings are highly susceptible to various noise interferences. Noise sources such as sensor slippage, cable movement, muscle activity, or head movement can cause sudden spikes or fluctuations with significantly higher amplitudes than normal signals in certain channels over specific time periods, manifesting as sparse artifacts in the time domain. These temporally sparse artifacts can significantly distort signal waveforms and severely interfere with subsequent data analysis. To address this issue, this study proposes a novel method called Centered Sliding-Window Neighborhood Subspace Projection (CSNSP) for effectively suppressing temporally sparse artifacts and improving signal quality. This method captures the intrinsic patterns of the MEG signals by identifying the intersection of the common signal subspaces between artifact-free segments and artifact-contaminated segments in the time domain. Based on this intersection, a signal subspace is constructed, and the artifact-contaminated segments are projected onto it to isolate and correct the artifact effects, achieving more accurate signal restoration. The CSNSP method is evaluated with both simulated and real data, and found to be highly effective in removing or attenuating temporally sparse artifacts. The method demonstrates strong robustness, unaffected by either the number of contaminated channels or the severity of the artifacts.