A novel classification approaches For eeg signals in brain computer interface

Abstract

Brain Computer signal processing is more complex compare to other signal processing. To reduce the complexity and increase the computational efficiency, we are using classification approaches. In this paper, we classify the brain EEG Signals using Hoeffding Tree, Decision Stump, Bayes Net classifier. The above algorithms to classify the BCI dataset more accurate compare to the existing methodologies and also the classification results are visualized.