A review on EEG based brain computer interface systems feature extraction methods

The brain – computer interface (BCI) provides a communicational channel between human and machine. Most of these systems are based on brain activities. Brain Computer-Interfacing is a methodology that provides a way for communication with the outside environment using the brain thoughts. The success of this methodology depends on the selection of methods to process the brain signals in each phase Feature extraction is one of the most important stages in distinguishing of brain activities from EEG. New features are produced by primary features. Today, in the field of EEG signal processing methods for the best feature extraction are so important. In this article we mentioned EEG based brain computer interface ( BCI) systems feature extraction such as Principle Component Analysis (PCA), Linear Discriminant Analysis(LDA), Independent Component Analysis (ICA), Mutual information theory (MI), Empirical Mode Decomposition(EMD), High–order frequency component, Wavelet Transform, Common Spatial Pattern ( CSP), Complex Band Power (CBP). © 2016 Published by CASRP publishing company Ltd. UK. Selection and/or peer-review under responsibility of Center of Advanced Scientific Research and Publications Ltd. UK.

کلیدواژه ها:

Feature extraction ، EEG ، Brain Computer Interface (BCI) ، Principle Component Analysis (PCA) ، Linear Discriminant Analysis (LDA) ، Independent Component Analysis (ICA) ، Mutual Information theory (MI) ، Empirical Mode Decomposition (EMD) ، Common Spatial Pattern ( CSP) ، Complex Band Power (CBP)