Improving Performance Of Motor Imagery Systems Using Ensemble Classifier
Abstract
The algorithms and methods used in the field of recognizing brain signals have varied, especially the motor imagery systems based on the electroencephalogram, whether in the pre-processing stage or in the classification stage, but these systems still lack sufficient accuracy to implement them practically, so it is necessary to improve the recognition rate and on several standard data sets to ensure the effectiveness of these systems.
This research proposes a hybrid recognition system to recognize brain signals based on the electroencephalogram (EEG), using the Ensemble classifier, Where two ensemble classifiers were designed, the first consisting of four sub-classifiers, SVM, Fandom Forest, Logistic Regression, and KNN, and the second classifier consisting of two sub-classifiers, Random Forest and Logistic Regression, and using FBCSP in the pre-processing stage. The proposed system was tested on three datasets, IV2a. , IV2b, and AlexMI, reaching recognition rates of 88.69%, 82.71%, and 87.18%, respectively.
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