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A user of Brain Computer Interface (BCI) system must be able to controlexternal computer devices with brain activity. Although the proof-of-conceptwas given decades ago, the reliable translation of user intent into devicecontrol commands is still a major challenge. There are problems associated withclassification of different BCI tasks. In this paper we propose the use ofchaotic indices of the BCI. We use largest Lyapunov exponent, mutualinformation, correlation dimension and minimum embedding dimension as thefeatures for the classification of EEG signals which have been released by BCICompetition IV. A multi-layer Perceptron classifier and a KM- SVM(supportvector machine classifier based on k-means clustering) is used forclassification process, which lead us to an accuracy of 95.5%, fordiscrimination between two motor imagery tasks.

A Technique Based on Chaos for Brain Computer Interfacing