A self-paced brain-computer interface for controlling a robot simulator: an online event labelling paradigm and an extended Kalman filter based algorithm for online training.
Due to the non-stationarity of EEG signals, online training and adaptation are essential to EEG based brain-computer interface (BCI) systems. Self-paced BCIs offer more natural human-machine interaction than synchronous BCIs, but it is a great challenge to train and adapt a self-paced BCI online bec...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 47; no. 3; pp. 257 - 266 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Mar2009
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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=105219822&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105219822 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Mar2009 vid: 47 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 105219822 105219822 NLM19225819 2010207068 10.1007/s11517-009-0459-7 NLM19225819 105219822 ppf: 257 ppct: 9 formats: fmt: @attributes: type: P tig: atl: A self-paced brain-computer interface for controlling a robot simulator: an online event labelling paradigm and an extended Kalman filter based algorithm for online training. aug: au: Tsui CS Gan JQ Roberts SJ Tsui, Chun Sing Louis Gan, John Q Roberts, Stephen J affil: School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO4 3SQ, UK sug: subj: Brain Physiology Brain-Computer Interfaces Communication Aids for Persons with Disabilities Algorithms Electroencephalography Methods Imagination Learning Online Systems Robotics Signal Processing, Computer Assisted ab: Due to the non-stationarity of EEG signals, online training and adaptation are essential to EEG based brain-computer interface (BCI) systems. Self-paced BCIs offer more natural human-machine interaction than synchronous BCIs, but it is a great challenge to train and adapt a self-paced BCI online because the user's control intention and timing are usually unknown. This paper proposes a novel motor imagery based self-paced BCI paradigm for controlling a simulated robot in a specifically designed environment which is able to provide user's control intention and timing during online experiments, so that online training and adaptation of the motor imagery based self-paced BCI can be effectively investigated. We demonstrate the usefulness of the proposed paradigm with an extended Kalman filter based method to adapt the BCI classifier parameters, with experimental results of online self-paced BCI training with four subjects. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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