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Controlling Prosthetic Limb Movements Using EEG Signals

Controlling Prosthetic Limb Movements Using EEG Signals

V. V. Ramalingam, Mohan S., V. Sugumaran, Vani V., B. Rebecca Jeya Vadhanam
Copyright: © 2017 |Pages: 23
ISBN13: 9781522508892|ISBN10: 1522508899|EISBN13: 9781522508908
DOI: 10.4018/978-1-5225-0889-2.ch008
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MLA

Ramalingam, V. V., et al. "Controlling Prosthetic Limb Movements Using EEG Signals." Multi-Core Computer Vision and Image Processing for Intelligent Applications, edited by Mohan S. and Vani V., IGI Global, 2017, pp. 211-233. https://doi.org/10.4018/978-1-5225-0889-2.ch008

APA

Ramalingam, V. V., S., M., Sugumaran, V., V., V., & Vadhanam, B. R. (2017). Controlling Prosthetic Limb Movements Using EEG Signals. In M. S. & V. V. (Eds.), Multi-Core Computer Vision and Image Processing for Intelligent Applications (pp. 211-233). IGI Global. https://doi.org/10.4018/978-1-5225-0889-2.ch008

Chicago

Ramalingam, V. V., et al. "Controlling Prosthetic Limb Movements Using EEG Signals." In Multi-Core Computer Vision and Image Processing for Intelligent Applications, edited by Mohan S. and Vani V., 211-233. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-0889-2.ch008

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Abstract

This chapter focuses on replacing natural arms with artificial arms with movement controlled by EEG signals. The selected features were classified using C4.5 decision tree algorithm, best first decision tree algorithm, Naïve Bayes algorithm, Bayes net algorithm, K star algorithm and ripple down rule learner algorithm. The results of statistical and histogram features are discussed and conclusions of the study are presented.

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