Optimizing magnetometers arrays and analysis pipelines for multivariate pattern analysis

Background Multivariate pattern analysis (MVPA) has proven an excellent tool in cognitive neuroscience used M/EEG, and MRI. It also holds a strong promise when applied to optically-pumped magnetometer-based magnetoencephalography. New method To optimize OPM-MEG systems for MVPA experiments this study examines data from a conventional MEG magnetometer array, focusing on appropriate noise reduction techniques for magnetometers. We also determined the least required number of sensors needed for robust MVPA for image categorization experiments. Results We found that the use of signal space separation (SSS) significantly lowered the classification accuracy considering a sub-array of 102 magnetometers or a sub-array of 204 gradiometers. We also found that classification accuracy did not improve when going beyond 30 sensors irrespective of whether SSS has been applied. Comparison with existing methods The power spectra of data filtered with SSS has a substantially higher noise floor that data cleaned with SSP or HFC. Consequently, the MVPA decoding results obtained from the SSS-filtered data are significantly lower compared to all other methods employed. Conclusions When designing an MEG system based on SQUID magnetometers optimized for multivariate analysis for image categorization experiments, about 30 magnetometers are sufficient. We advise against applying SSS filters to data from MEG and OPM systems prior to performing MVPA as this method, albeit reducing low-frequency external noise contributions, also introduces an increase in broadband noise. We recommend employing noise reduction techniques that either decrease or maintain the noise floor of the data like signal-space projection, homogeneous field correction and gradient noise reduction. Highlights A sensor array of about 30 sensors is sufficient for multivariate pattern analysis using conventional MEG magnetometers for image classification. Using signal space separation filter on magnetometer data prior to multivariate pattern analysis might reduce classification accuracy due to an increase in white noise in the data contributed by the algorithm. When performing multivariate data analysis, other noise reduction approaches that diminish the contribution of external noise sources and reduce the variance of the data are advisable such as synthetic gradiometers, signal space projection or homogeneous field correction.