Prosthetic arm control using Clonal Selection Classification Algorithm (CSCA) - a statistical learning approach

Abstract

Objectives: In monitoring brain activities, Electroencephalogram (EEG) signals play a significant role. As brain activities are many and highly dynamic in nature, processing of EEG signals is a challenging task. Since classification is more accurate when the pattern is simplified through representation by well performing features, feature extraction and selection play an important role in classification systems such as Clonal Selection Classification Algorithm (CSCA) algorithm. Methods/Analysis: This study is one such attempt to perform the prosthetic limb movements using EEG signals. In this research, the performance of CSCA for prosthetic limb movements of EEG signals has been reported. Findings: In this paper, the EEG signals are acquired for four different limb movements like finger open (fopen), finger close (fclose), wrist clockwise (wcw) and wrist counterclock wise (wccw). These EEG signals can be used to build a model to control the prosthetic limb movements using CSCA algorithm. The statistical parameters were extracted from the EEG signals. The best feature set was identified using J48 decision tree classifier. The well performing features were then classified using CSCA algorithm. The classification performance of CSCA has been reported. Novelty/Improvement: Our work is useful for controlling artificial limb with movements using EEG signals. The signal processing of EEG signals is a complex task and requires sophisticated techniques to yield a better classification accuracy.