Selection of Efficient Features for Discrimination of Hand Movements from MEG Using a BCI Competition IV Data Set

The aim of a brain–computer interface (BCI) system is to establish a new communication system that translates human intentions, reflected by measures of brain signals such as magnetoencephalogram (MEG), into a control signal for an output device. In this paper, an algorithm is proposed for discriminating MEG signals, which were recorded during hand movements in four directions. These signals were presented as data set 3 of BCI competition IV. The proposed algorithm has four main stages: pre-processing, primary feature extraction, the selection of efficient features, and classification. The classification stage was a combination of linear SVM and linear discriminant analysis classifiers. The proposed method was validated in the BCI competition IV, where it obtained the best result among BCI competitors: a classification accuracy of 59.5 and 34.3% for subject 1 and subject 2 on the test data respectively.