Classification of EEG signals recorded during right/left hand movement imagery using Fourier Transform based features

Implementation of a fast and accurate brain computer interface system depends on using a very small number of training signals, better classification and feature extraction algorithms, fewer channels and features which are improved user-specific. In this paper, we propose a fast and accurate algorithm for classifying of right/left hand movement imagery electroencephalogram data. The algorithm is presented in three basic steps. In the first step, an unit variance normalization is implemented to electroencephalogram data as preprocessing. In the second step, features are extracted from signals by using Fourier Transform algorithm. In the last step, the features are classified by using the Support Vector Machines, the k-Nearest Neighbor and Linear Discriminant Analysis. The proposed algorithm was successfully applied to the BCI competition 2003 data set which is named as Data Set III, and achieved a classification accuracy of 91.4 % on test set. The performance of the proposed algorithm was compared in terms of accuracy and speed with other studies used the same data set.