Feature Extraction and Classification for Electro-Encephalography Based Bci: A Review

A Brain Computer Interface (BCI) system provides a method for controlling a peripheral device. A BCI may use the magnetic, electrical, or metabolic activity of the brain. Electro-encephalography (EEG) is a non-invasive technique. It is popular for BCI research and is preferred due to its high temporal resolution, low cost of devices, convenience and movability. BCI based applications have massive potential in assistive devices, health care, and amusement industry. A regular BCI system comprises of these steps: signal acquisition, pre-processing, feature extraction and classification. An EEG contains the impulsive electrical activity of the brain taken from electrodes placed on the scalp of the subject. The EEG signal is then processed to remove noise and enhance the signal for analysing further. Features are mined from the amplitude and frequency of the recorded analog signals which can be transformed into feature vectors, and given as input to a classifier. Since EEG is non-stationary in nature, vulnerable to artifacts and has high variability, we need algorithms that efficiently extract relevant features and classify the signals accurately. This study reviews some recent applications of BCI and the feature extraction techniques used by them. Machine learning algorithms typically used in EEG-based BCI applications are also studied.