Classifying brain-computer interface features based on statistics and density of power spectrum

The use of Electroencephalography (EEG) signals as a vector of communication between men and machines represents one of the current challenges in signal theory research. The principal element of such a communication system, more known as 'brain-computer interface (BCI)', is the interpretation of the EEG signals related to the characteristic parameters of brain electrical activity. In this paper, two feature extraction methods are applied: 'statistics method' and 'power spectral analysis (PSD)'. Then, two classification methods on a data set of BCI were compared: 'minimum distance classifier' and 'k-nearest neighbour classifier' to get the best results of discrimination between up and down movements. By applying the 'statistics method', it gives good results in both training data and test data. Also, the best classifier was the minimum distance for the training data and voting k-nearest neighbour for the test data.