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 |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104081480&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104081480 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Aug2013 vid: 51 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104081480 NLM23657832 2012188601 10.1007/s11517-013-1069-y NLM23657832 104081480 ppf: 937 ppct: 15 formats: fmt: @attributes: type: P tig: atl: Development of a generative model of magnetoencephalography noise that enables brain signal extraction from single-epoch data. aug: au: Uno, Yutaka Amano, Kaoru Takeda, Tsunehiro affil: Department of Complexity Science and Engineering, The University of Tokyo, Tokyo, Japan, yutaka.uno@brain.riken.jp. sug: subj: Brain Physiology Diagnosis, Neurologic Methods Signal Processing, Computer Assisted Algorithms Probability Human Models, Theoretical ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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