Classification I-EEG Signals Using Ensemble Algorithms

Abstract

The study was research in the bioinformatics field. The imagined signals are classified as the Support Vector Machine, K-Nearest Neighbor, and Ensemble Classifiers. A 5-channel device recorded the data, including four labels (thinking backward, thinking forward, thinking turn the left, and thinking turn the right). The data is optimized using z-score and maxmin normalization techniques and compared with data without normalization. The Stratified-Repeated cross-validation method was applied to split into training and testing data instead of the traditional data division technique. A key factor determining classifier performance is feature extraction. The time-frequency domain characteristics recommended by the Discrete Wavelet Transform method are five. The research examined 17 models (6 sub-model of Support Vector Machine and K-Nearest Neighbor classifiers, five Ensemble Classifiers). A model in the proposed Stratified-Repeated cross-validation Subspace Ensemble classifier with a classification result of 89.25%.