Classification of imagined beats for use in a brain computer interface

Abstract

The power spectrum of an EEG signal shows differences with respect to its baseline the moment a subject is hearing, or expecting, a tone. As this difference also occurs when one is not actually hearing it, a Brain Computer Interface can be developed in which imagined rhythms are used to transfer information. Four healthy subjects participated in this study in which they had to imagine a simple rhythm. A metronome was kept ticking so that the subjects would not drift in their tempo. Solely based on the EEG signals, the classifier had to distinguish between imagined accented and non-accented tones. The features for the classification were automatically selected out of a set of possible features that focussed on phase and power differences of independent components. The classification rate found is about 0.6 for two of the four subjects, and several classifications can be combined to increase this classification rate to values larger than 0.7 with 2 s worth of data for the best performing subject. Chance level for our classification task is 0.5.