ArticleJournal of neuroscience methods2024
Optimizing magnetometers arrays and analysis pipelines for multivariate pattern analysis.
Article in Journal of neuroscience methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Enhanced Neural Decoding With Optically Pumped Magnetometer MEG Using Multivariate Pattern Analysis.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2026Article
- Anatomically Veridical On-Scalp Sensor Topographies.The European journal of neuroscience · 2025Article
- Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations.Diagnostics (Basel, Switzerland) · 2025Review
- Optimizing magnetometers arrays and analysis pipelines for multivariate pattern analysis.Journal of neuroscience methods · 2024Article
- Spatiotemporal Properties of Common Semantic Categories for Words and Pictures.Journal of cognitive neuroscience · 2024Article
- Decoding N400m Evoked Component: A Tutorial on Multivariate Pattern Analysis for OP-MEG Data.Bioengineering (Basel, Switzerland) · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
backgroundMultivariate pattern analysis (MVPA) has proven an excellent tool in cognitive neuroscience. It also holds a strong promise when applied to optically-pumped magnetometer-based magnetoencephalography. NEW
methodTo 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 determined the least required number of sensors needed for robust MVPA for image categorization experiments.
resultsWe found that the use of signal space separation (SSS) without a proper regularization 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
methodsThe power spectra of data filtered with SSS has a substantially higher noise floor that data cleaned with SSP or HFC. Consequently, MVPA decoding results obtained from the SSS-filtered data are significantly lower compared to all other methods employed.
conclusionsWhen designing 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 without a proper regularization 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.
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