A new parameter tuning approach for enhanced motor imagery EEG signal classification.
A brain-computer interface (BCI) system allows direct communication between the brain and the external world. Common spatial pattern (CSP) has been used effectively for feature extraction of data used in BCI systems. However, many studies show that the performance of a BCI system using CSP largely d...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 56; no. 10; pp. 1861 - 1875 |
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| Autores principales: | , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2018
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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=131861022&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 131861022 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Oct2018 vid: 56 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 131861022 131861022 NLM29616456 131861022 10.1007/s11517-018-1821-4 NLM29616456 131861022 ppf: 1861 ppct: 14 formats: fmt: @attributes: type: P tig: atl: A new parameter tuning approach for enhanced motor imagery EEG signal classification. aug: au: Kumar, Shiu Sharma, Alok affil: Department of Electronics, Instrumentation & Control Engineering, School of Electrical & Electronics Engineering, Fiji National University, Samabula, Fiji sug: subj: Motor Activity Physiology Guided Imagery Signal Processing, Computer Assisted Electroencephalography Reproducibility of Results Algorithms Brain-Computer Interfaces Human ab: A brain-computer interface (BCI) system allows direct communication between the brain and the external world. Common spatial pattern (CSP) has been used effectively for feature extraction of data used in BCI systems. However, many studies show that the performance of a BCI system using CSP largely depends on the filter parameters. The filter parameters that yield most discriminating information vary from subject to subject and manually tuning of the filter parameters is a difficult and time-consuming exercise. In this paper, we propose a new automated filter tuning approach for motor imagery electroencephalography (EEG) signal classification, which automatically and flexibly finds the filter parameters for optimal performance. We have evaluated the performance of our proposed method on two public benchmark datasets. Compared to the existing conventional CSP approach, our method reduces the average classification error rate by 2.89% and 3.61% for BCI Competition III dataset IVa and BCI Competition IV dataset I, respectively. Moreover, our proposed approach also achieved lowest average classification error rate compared to state-of-the-art methods studied in this paper. Thus, our proposed method can be potentially used for developing improved BCI systems, which can assist people with disabilities to recover their environmental control. It can also be used for enhanced disease recognition such as epileptic seizure detection using EEG signals. Graphical abstract ᅟ. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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