A Computationally Efficient Method for Hybrid EEG-fNIRS BCI Based on the Pearson Correlation.
A hybrid brain computer interface (BCI) system considered here is a combination of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). EEG-fNIRS signals are simultaneously recorded to achieve high motor imagery task classification. This integration helps to achieve better...
| Publicado en: | BioMed Research International pp. 1 - 14 |
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| Autores principales: | , , |
| Formato: | algorithm equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
8/19/2020
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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=145196810&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145196810 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/19/2020 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 145196810 145196810 145196810 10.1155/2020/1838140 145196810 ppf: 1 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Computationally Efficient Method for Hybrid EEG-fNIRS BCI Based on the Pearson Correlation. aug: au: Hasan, Mustafa A. H. Khan, Muhammad U. Mishra, Deepti affil: Department of Mechatronics Engineering, Atilim University, Ankara, Turkey sug: subj: User-Computer Interface Electroencephalography Methods Spectroscopy, Near-Infrared Human Pearson's Correlation Coefficient Electronic Publications Guided Imagery Descriptive Statistics Brain Physiology ab: A hybrid brain computer interface (BCI) system considered here is a combination of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). EEG-fNIRS signals are simultaneously recorded to achieve high motor imagery task classification. This integration helps to achieve better system performance, but at the cost of an increase in system complexity and computational time. In hybrid BCI studies, channel selection is recognized as the key element that directly affects the system's performance. In this paper, we propose a novel channel selection approach using the Pearson product-moment correlation coefficient, where only highly correlated channels are selected from each hemisphere. Then, four different statistical features are extracted, and their different combinations are used for the classification through KNN and Tree classifiers. As far as we know, there is no report available that explored the Pearson product-moment correlation coefficient for hybrid EEG-fNIRS BCI channel selection. The results demonstrate that our hybrid system significantly reduces computational burden while achieving a classification accuracy with high reliability comparable to the existing literature. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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