In this paper we review the classification algorithm for the Brain Computer Interface. The Characteristics of the EEG signals are changes over time, updating the classifiers of the Brain Computer Interface, (BCI). It will eventually improve the performance of the system. The development of the new adaptive classifiers are not easy because of we cannot predict the intension of the user, in some cases it may be possible to predict the labels of the EEG segments using some information of the state. We briefly present commonly used algorithm and their description of properties. By literature review we presented them in terms of performance. The main point come into Scenario that there is no comprehensive review of signal processing algorithm for EEG Signals. The aim of this review paper is to obtain the limitations and importance of the algorithm is to provide a guideline to the researchers in these fields. Techniques employed for the signal preprocessing, feature extraction and feature classification are discussed, but review focused on signals classification.

BCI, EEG, SSVEP-NIRS Hybrid BCI, EEG-EMG Hybrid BCI , EEG-EOG Hybrid BCI

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