Spatio-spectral filters for low-density surface electromyographic signal classification.
In this paper, we proposed to utilize a novel spatio-spectral filter, common spatio-spectral pattern (CSSP), to improve the classification accuracy in identifying intended motions based on low-density surface electromyography (EMG). Five able-bodied subjects and a transradial amputee participated in...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 51; no. 5; pp. 547 - 556 |
|---|---|
| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
May2013
|
| 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=109855815&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109855815 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: May2013 vid: 51 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 109855815 NLM23385330 2012084104 10.1007/s11517-012-1024-3 NLM23385330 109855815 ppf: 547 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Spatio-spectral filters for low-density surface electromyographic signal classification. aug: au: Huang, Gan Zhang, Zhiguo Zhang, Dingguo Zhu, Xiangyang affil: State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University, Shanghai, 200240, China, huanggan1982@gmail.com. sug: subj: Electromyography Methods Signal Processing, Computer Assisted Algorithms Amputees Forearm Physiology Hand Physiology Human Movement Physiology Wrist Joint Physiology ab: In this paper, we proposed to utilize a novel spatio-spectral filter, common spatio-spectral pattern (CSSP), to improve the classification accuracy in identifying intended motions based on low-density surface electromyography (EMG). Five able-bodied subjects and a transradial amputee participated in an experiment of eight-task wrist and hand motion recognition. Low-density (six channels) surface EMG signals were collected on forearms. Since surface EMG signals are contaminated by large amount of noises from various sources, the performance of the conventional time-domain feature extraction method is limited. The CSSP method is a classification-oriented optimal spatio-spectral filter, which is capable of separating discriminative information from noise and, thus, leads to better classification accuracy. The substantially improved classification accuracy of the CSSP method over the time-domain and other methods is observed in all five able-bodied subjects and verified via the cross-validation. The CSSP method can also achieve better classification accuracy in the amputee, which shows its potential use for functional prosthetic control. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|