Abstract: Brain Computer Interfacing (BCI) is a methodology which provides a way for communication from outside world using brain signals. It detects the specific patterns in a personís ongoing brain activity which relates to the personís intention to initiate control. The BCI system translates these patterns into meaningful control command. To develop BCI system, various signal processing algorithms are proposed. Electroencephalogram (EEG) signals are used to extract the features and further it is classified. A survey of different Classification algorithms is used in EEG-based BCI research and to identify their critical properties. This paper is organized with a recent methodology of feature extraction and feature Classification algorithms. It also aims at addressing the methods and technology adapted in each phase of the EEG signal processing. It also highlights the pros and cons by reviewing literatures, books and other related documents. This survey helps in designing a suitable algorithm for the development and implementation of further classification of signals.

Keywords: Brain Computer Interface (BCI), Electroencephalogram (EEG), Feature Extraction, Wavelet Transform, Feature Classification.