Journal article
Optimizing magnetometers arrays and analysis pipelines for multivariate pattern analysis
- Abstract:
-
Background: Multivariate 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 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 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) 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 methods: The 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.
Conclusions: When 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, pdf, 720.2KB, Terms of use)
-
- Publisher copy:
- 10.1016/j.jneumeth.2024.110279
Authors
- Funder identifier:
- https://ror.org/029chgv08
- Grant:
- 207550/Z/17/Z
- Funder identifier:
- https://ror.org/0439y7842
- Grant:
- EP/T001046/1
- Funder identifier:
- https://ror.org/0187kwz08
- Grant:
- NIHR203316
- Funder identifier:
- https://ror.org/00cwqg982
- Grant:
- BB/R018723/1
- Publisher:
- Elsevier
- Journal:
- Journal of Neuroscience Methods More from this journal
- Volume:
- 412
- Article number:
- 110279
- Publication date:
- 2024-09-17
- Acceptance date:
- 2024-09-09
- DOI:
- EISSN:
-
1872-678X
- ISSN:
-
0165-0270
- Pmid:
-
39265820
- Language:
-
English
- Keywords:
- Pubs id:
-
2041557
- Local pid:
-
pubs:2041557
- Deposit date:
-
2026-05-15
- ARK identifier:
Terms of use
- Copyright holder:
- Published by Elsevier B.V.
- Copyright date:
- 2024
- Rights statement:
- © 2024 Published by Elsevier B.V.
- Notes:
- The author accepted manuscript (AAM) of this paper has been made available under the University of Oxford's Open Access Publications Policy, and a CC BY public copyright licence has been applied.
- Licence:
- CC Attribution (CC BY)
If you are the owner of this record, you can report an update to it here: Report update to this record