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Journal of Applied Sciences
  Year: 2016 | Volume: 16 | Issue: 4 | Page No.: 138-145
DOI: 10.3923/jas.2016.138.145
 
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Prosthetic Arm Control with Statistical Features of EEG signals using K-star Algorithm

V.V. Ramalingam and S. Mohan

Abstract:
This study presents an approach with a set of statistical features for the classification of various limb movements using K-star algorithm. The way of representing the characteristics of electroencephalogram (EEG) signals through the features are the most prominent one and it plays a vital role in the classification systems. The classification will be perfect when the sample is simplified through the feature extraction and feature selection process. In the present study, there are four categories of EEG signals recorded from healthy volunteers with finger open, finger close, wrist clockwise and wrist counter clockwise movements. The prominent statistical features were obtained from the EEG signals. The K-star algorithm was used to detect the changes in the EEG signals. The output of the classifier confirmed that the proposed K-star algorithm has potential to classify the EEG signals of the different hand movements.
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How to cite this article:

V.V. Ramalingam and S. Mohan, 2016. Prosthetic Arm Control with Statistical Features of EEG signals using K-star Algorithm. Journal of Applied Sciences, 16: 138-145.

DOI: 10.3923/jas.2016.138.145

URL: https://scialert.net/abstract/?doi=jas.2016.138.145

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