Electromyogram (EMG) Feature Reduction Using Mutual Components Analysis for Multifunction Prosthetic Fingers Control

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Conference Proceeding
12th International Conference on Control, Automation, Robotics & Vision, 2012, pp. 1535 - 1539
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Surface Electromyogram (EMG) signals are usually utilized as a control source for multifunction powered prostheses. A challenge that arises with the current demands of such prostheses is the ability to accurately control a large number of individual and combined ngers movements and to do so in a computationally efcient manner. As a response to such a challenge, we present a combined feature selection and projection algorithm, denoted as Mutual Components Analysis (MCA). The proposed MCA algorithm extends the well-known Principal Components Analysis (PCA) by pruning the noisy and redundant features before projecting the data. To implement the feature selection step, the mutual information concept is utilized to implement a new information gain evaluation function. The performance and signicance of the proposed MCA is demonstrated on EMG datasets collected for the purpose of this research from eight subjects with eight electrodes attached on their forearm. Fifteen classes of ngers movements where considered in this paper with MCA achieving >95% accuracy on average across all subjects.
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