Motor imagery, P300 and error-related EEG-based robot arm movement control for rehabilitation purpose.
The paper proposes a novel approach toward EEG-driven position control of a robot arm by utilizing motor imagery, P300 and error-related potentials (ErRP) to align the robot arm with desired target position. In the proposed scheme, the users generate motor imagery signals to control the motion of th...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 52; no. 12; pp. 1007 - 1018 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Dec2014
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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=103853458&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103853458 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Dec2014 vid: 52 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 103853458 NLM25266261 2012794282 10.1007/s11517-014-1204-4 NLM25266261 103853458 ppf: 1007 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Motor imagery, P300 and error-related EEG-based robot arm movement control for rehabilitation purpose. aug: au: Bhattacharyya, Saugat Konar, Amit Tibarewala, D N affil: School of Bioscience and Engineering, Jadavpur University, Kolkata, 700032, India, saugatbhattacharyya@live.com. sug: subj: Brain-Computer Interfaces Electroencephalography Methods Imagination Rehabilitation Equipment and Supplies Robotics Equipment and Supplies Adult Arm Algorithms Task Performance and Analysis Young Adult Adult: 19-44 years ab: The paper proposes a novel approach toward EEG-driven position control of a robot arm by utilizing motor imagery, P300 and error-related potentials (ErRP) to align the robot arm with desired target position. In the proposed scheme, the users generate motor imagery signals to control the motion of the robot arm. The P300 waveforms are detected when the user intends to stop the motion of the robot on reaching the goal position. The error potentials are employed as feedback response by the user. On detection of error the control system performs the necessary corrections on the robot arm. Here, an AdaBoost-Support Vector Machine (SVM) classifier is used to decode the 4-class motor imagery and an SVM is used to decode the presence of P300 and ErRP waveforms. The average steady-state error, peak overshoot and settling time obtained for our proposed approach is 0.045, 2.8% and 44 s, respectively, and the average rate of reaching the target is 95%. The results obtained for the proposed control scheme make it suitable for designs of prosthetics in rehabilitative applications. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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