Classification of EEG Signals for Prosthetic Limb Movements with ARMA Features Using C4.5 Decision Tree Algorithm

Abstract

Objectives: This paper presented a novel approach with a set of Auto Regressive Moving Average (ARMA) features for the best classification of different hand moments in Electroencephalogram (EEG) signals using C4.5 Decision tree algorithm. Methods/Analysis: The characteristics of EEG signals can be represented through the best features is the most prominent and significant role in the classification systems. The classification is more flawless when the specimen is streamlined through the feature extraction and feature selection process. Findings: In this study, there are four kinds of EEG signals recorded from strong volunteers with finger open, finger close, wrist clockwise and wrist counterclockwise. The well performing statistical features are acquired from the EEG signals. C4.5 Decision tree classifier is used to identify the changes in the EEG signals. The yield of the classifier confirmed that the proposed C4.5 Decision tree classifier has potential to classify the EEG signals of the specific hand movements. Improvement: The proposed work is contributed to manage the right hand movements through the EEG signals. The efficient techniques are required to process the complex EEG signals to achieve the better classification result. To improve the classification accuracy, an efficient feature extraction technique may be applied.