Automatic Artifact Removal from Electroencephalogram Data Based on A Priori Artifact Information.
Electroencephalogram (EEG) is susceptible to various nonneural physiological artifacts. Automatic artifact removal from EEG data remains a key challenge for extracting relevant information from brain activities. To adapt to variable subjects and EEG acquisition environments, this paper presents an a...
| Publicado en: | BioMed Research International Vol. 2015; pp. 1 - 9 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Wiley-Blackwell
8/25/2015
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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=109322383&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109322383 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/25/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109322383 109322383 NLM26380294 10.1155/2015/720450 NLM26380294 PMC4562337 109322383 ppf: 1 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Automatic Artifact Removal from Electroencephalogram Data Based on A Priori Artifact Information. aug: au: Zhang, Chi Tong, Li Zeng, Ying Jiang, Jingfang Bu, Haibing Yan, Bin Li, Jianxin affil: China National Digital Switching System Engineering and Technological Research Center, Zhengzhou 450002, China sug: ab: Electroencephalogram (EEG) is susceptible to various nonneural physiological artifacts. Automatic artifact removal from EEG data remains a key challenge for extracting relevant information from brain activities. To adapt to variable subjects and EEG acquisition environments, this paper presents an automatic online artifact removal method based on a priori artifact information. The combination of discrete wavelet transform and independent component analysis (ICA), wavelet-ICA, was utilized to separate artifact components. The artifact components were then automatically identified using a priori artifact information, which was acquired in advance. Subsequently, signal reconstruction without artifact components was performed to obtain artifact-free signals. The results showed that, using this automatic online artifact removal method, there were statistical significant improvements of the classification accuracies in both two experiments, namely, motor imagery and emotion recognition. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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