The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Applications of the signal space separation method
Abstract
The reliability of biomagnetic measurements is traditionally challenged by external interference signals, movement artifacts, and comparison problems caused by different positions of the subjects or different sensor configurations. The Signal Space Separation method (SSS) idealizes magnetic multichannel signals by transforming them into device-independent idealized channels representing the measured data in uncorrelated form. The transformation has separate components for the biomagnetic and external interference signals, and thus, the biomagnetic signals can be reconstructed simply by leaving out the contribution of the external interference. The foundation of SSS is a basis spanning all multichannel signals of magnetic origin. It is based on Maxwell's equations and the geometry of the sensor array only, with the assumption that the sensors are located in a current free volume. SSS is demonstrated to provide suppression of external interference signals, standardization of different positions of the subject, standardization of different sensor configurations, compensation for distortions caused by movement of the subject (even a subject containing magnetic impurities), suppression of sporadic sensor artifacts, a tool for fine calibration of the device, extraction of biomagnetic DC fields, and an aid for realizing an active compensation system. Thus, SSS removes many limitations of traditional biomagnetic measurements.
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Cites
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A subfemtotesla multichannel atomic magnetometer
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On MEG forward modelling using multipolar expansions
Jerbi, K., Mosher, J. C., Baillet, S., & Leahy, R. M. (2002) · Physics in Medicine and Biology · doi:10.1088/0031-9155/47/4/301 · Cited by 103
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Cited by
Brookes, M. J., Leggett, J., Rea, M., Hill, R. M., Holmes, N., Boto, E., & Bowtell, R. (2022) · Trends in Neurosciences · doi:10.1016/j.tins.2022.05.008 · Cited by 412
MEG: An Introduction to Methods
(2010) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195307238.001.0001 · Cited by 408
Predictive top-down integration of prior knowledge during speech perception
Sohoglu, E., Peelle, J. E., Carlyon, R. P., & Davis, M. H. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.5069-11.2012 · Cited by 388
On-scalp MEG system utilizing an actively shielded array of optically-pumped magnetometers
Iivanainen, J., Zetter, R., Grön, M., Hakkarainen, K., & Parkkonen, L. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.03.022 · Cited by 269
Holmes, N., Leggett, J., Boto, E., Roberts, G., Hill, R. M., Tierney, T. M., Shah, V., Barnes, G. R., Brookes, M. J., & Bowtell, R. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.07.028 · Cited by 250
Metadata
- Authors
- Taulu, S., Simola, J., & Kajola, M.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2005-09
- Volume
- 53
- Issue
- 9
- Pages
- 3359-3372
- ISSN
- 1053-587X (ISSN-L)
- OpenAlex
- W2106628957
- Cited by
- 548
- References
- 5
- FWCI
- 3.42
- Type
- journal article
- Language
- en
- Open access
- closed
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