Classification procedure for motor imagery EEG data

Abstract

Brain computer interface establishes a new model of communication, whereby it is possible to communicate using only cerebral signals, that can be obtained from different kind of cerebral stimuli. By the way, one of the most common stimulus is the motor imagery of the arms. However, since a set of variables leads to different levels of classification accuracy, it is necessary to search for procedures that can enhance the recognition accuracy of brain signals in order to create more precise systems. This paper proposes a classification procedure for discrimination of two motor imagery classes obtained using the Emotiv EPOC+ EEG signal acquisition device. The Emotiv EPOC+ has 14 input channels, but only four were used – the ones directly related with the capture of motor imagery signals. The presented procedure was created considering the MI common spatial pattern package from the OpenVibe software and the support vector machine (SVM) classification approach. As well, the procedure runs under the OpenVibe scenarios. A database with motor imagery signals from five subjects was built in order to perform the classification tests. In order to select the best features, several aspects from the signal acquisition until the classification process were analysed, such as selection of the best Kernel to SVM classifier, frequency band, filter output channels, and a grid-search to estimate the classifier parameters. At the end, an increase of 28,96% in the mean accuracy was achieved, regarding to the OpenVibe MI standard scenario.