Motor imagery, P300 and error-related EEG-based robot arm movement control for rehabilitation purpose View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2014-12

AUTHORS

Saugat Bhattacharyya, Amit Konar, D. N. Tibarewala

ABSTRACT

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. More... »

PAGES

1007-1017

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11517-014-1204-4

DOI

http://dx.doi.org/10.1007/s11517-014-1204-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1031876041

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/25266261


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