Development of a generative model of magnetoencephalography noise that enables brain signal extraction from single-epoch data.
We presented a method of rejecting sensor-specific and environmental noise during magnetoencephalography (MEG) measurement that enables the extraction of brain signals from single-epoch data. The method assumes a parametric generative model of MEG data. The model's optimal parameters were determined...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 51; no. 8; pp. 937 - 952 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Aug2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | We presented a method of rejecting sensor-specific and environmental noise during magnetoencephalography (MEG) measurement that enables the extraction of brain signals from single-epoch data. The method assumes a parametric generative model of MEG data. The model's optimal parameters were determined from single-epoch data, and noise reduction was performed by the decomposition of data within the optimal model. We confirmed our method's validity through multiple experiments. Moreover, we compared our method's performance with that of several previous noise-reduction methods. Finally, we confirmed that the proposed method followed by spatial filtering reduced noise more efficiently. |
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