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Brain computer interface (BCI) is a fast evolving field of research enabling computers and machines to be directly controlled by the human neural system. This enables people with muscular disability to directly control machines using their thought process. The brain signals are recorded using Electroencephalography (EEG) and patterns extracted so that the BCI system should be able to classify various patterns of brain signal accurately to perform different tasks. The raw EEG signal contains different kinds of interference waveforms (artifacts) and noise. Thus raw signals cannot be directly used for classification, the EEG signals has to undergo preprocessing, to remove artifacts and to extract the right attributes for classification. In this paper it is proposed to extract the energies in the EEG signal and classify the signal using Naive Bayes and Instance based learners. The proposed method performs well for the two class problem in the multiple datasets used..

Energy Computation for Bci Using Dct and Moving Average Window for Noise Smoothening