Comparison of EEG Signal Features and Ensemble Learning Methods for Motor Imagery Classification

Classifying electroencephalogram (EEG) signal inBrain Computer Interface (BCI) is a useful methods to analysisdifferent organs of human body and it can be used for communicatewith the outside world and controlling external device.Accuracy classification of extracted features from EEG signals isa problem which many researcher try to improve it. Althoughmany methods for extracting feature and classifying EEG signalhave been proposed and developed, many of them suffer fromextracting less accurate data from EEG signals. In this work,four signal feature extraction and three ensemble learning methodhave been reviewed and performances of classification techniquesare compared for motor imagery task.

کلیدواژه ها:

EEG Signal ، Motor Imagery ، Feature Extraction ، Classification ، Ensemble Learning