  A user of Brain Computer Interface (BCI) system must be able to control
external computer devices with brain activity. Although the proof-of-concept
was given decades ago, the reliable translation of user intent into device
control commands is still a major challenge. There are problems associated with
classification of different BCI tasks. In this paper we propose the use of
chaotic indices of the BCI. We use largest Lyapunov exponent, mutual
information, correlation dimension and minimum embedding dimension as the
features for the classification of EEG signals which have been released by BCI
Competition IV. A multi-layer Perceptron classifier and a KM- SVM(support
vector machine classifier based on k-means clustering) is used for
classification process, which lead us to an accuracy of 95.5%, for
discrimination between two motor imagery tasks.
