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Brain Computer Interface (BCI) systems are the devices which are proposed to help the disabled, people who are incapable of making motor response to communicate with computer using brain signal. The aim of BCI is to interpret brain activity into digital form which acts as a command for a computer. One key challenge in current BCI research is how to extract features of random time-varying EEG signals and its classification as accurately as possible. Feature extraction techniques are used to extract the features which represent a unique property obtained from pattern of brain signal. Earlier EEG analysis was restricted to visual inspection only. The visual inspection of the signal is very subjective and hardly allows any standardization or statistical analysis. Hence, several different techniques were intended in order to quantify the information of the brain signal. Many linear and non-linear methods for feature extraction exist. The purpose of this paper is to give a brief introduction to the EEG signals and BCI system. The paper also includes a review on the conventional methods that are used for feature extraction of the signal.
Vaid et al. (Sun,) studied this question.