dorsal/arxiv
View SchemaThe Signal Space Separation method
| Authors | Samu Taulu, Matti Kajola, Juha Simola |
|---|---|
| Categories | |
| ArXiv ID | physics/0401166 |
| URL | https://arxiv.org/abs/physics/0401166 |
Abstract
Multichannel measurement with hundreds of channels essentially covers all measurable degrees of freedom of a curl and source free vector field, like the magnetic field in a volume free of current sources (e.g. in magnetoencephalography, MEG). A functional expansion solution of Laplace's equation enables one to separate signals arising from the sphere enclosing the interesting sources, e.g. the currents in the brain, from the rest of the signals. The signal space separation (SSS) is accomplished by calculating individual basis vectors for each term of the functional expansion solution to create a signal basis covering all measurable signal vectors. Any signal vector has a unique SSS decomposition with separate coefficients for the interesting signals and signals coming from outside the interesting volume. Thus, SSS basis provides an elegant method to remove external disturbances, and to transform the interesting signals to virtual sensor configurations. SSS can also be used in compensating the movements of the subject and removing the artefacts caused by magnetized particles in the subject.
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"abstract": "Multichannel measurement with hundreds of channels essentially covers all\nmeasurable degrees of freedom of a curl and source free vector field, like the\nmagnetic field in a volume free of current sources (e.g. in\nmagnetoencephalography, MEG). A functional expansion solution of Laplace\u0027s\nequation enables one to separate signals arising from the sphere enclosing the\ninteresting sources, e.g. the currents in the brain, from the rest of the\nsignals. The signal space separation (SSS) is accomplished by calculating\nindividual basis vectors for each term of the functional expansion solution to\ncreate a signal basis covering all measurable signal vectors. Any signal vector\nhas a unique SSS decomposition with separate coefficients for the interesting\nsignals and signals coming from outside the interesting volume. Thus, SSS basis\nprovides an elegant method to remove external disturbances, and to transform\nthe interesting signals to virtual sensor configurations. SSS can also be used\nin compensating the movements of the subject and removing the artefacts caused\nby magnetized particles in the subject.",
"arxiv_id": "physics/0401166",
"authors": [
"Samu Taulu",
"Matti Kajola",
"Juha Simola"
],
"categories": [
"physics.ins-det",
"physics.med-ph"
],
"title": "The Signal Space Separation method",
"url": "https://arxiv.org/abs/physics/0401166"
},
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