EEG Feature Extraction Using Wavelet Techniques For Brain Computer Interface

The aim of this study was to compare methods for feature extraction and classification of EEG signals for a brain–computer interface (BCI) according to different mental task conditions. EEG data was obtained either from BCI data base or from EEG experimental recording. There were different methods for feature Extraction like temporal methods, frequential methods, and Time-frequency representations. Among these methods wavelet which was type of Time frequency representation method most popularly used for feature extraction.