An efficient algoritm for classification of EEG eye state data

A recent technology which makes possible for us to interact with automated systems without using any body part is called Brain Computer Interface (BCI). In its concrete applications, electroencephalogram (EEG) is benefited by a BCI environment for being capable of obtaining brain waves. In our study, evaluation of success rates of the predictions made by C x k - Nearest Neighborhood (Cxk-NN) Algorithm for EEG Eye State Data whose states are called “Opened Eye“ and “Closed Eye“ is applied. This EEG Eye State dataset is obtained from UCI Machine Learning Repository on the web and it is a highly-used benchmark data on this field. As there are only two classes of the signals, we test binary classification performance of our classification algorithm (Cxk –NN). Comparison of those values with the ones obtained by the other successful classification algorithms in the literature applied on the same data set also take place in our study. Cxk-NN is an instance-based classification method advanced from simple k – Nearest Neighborhood Algorithm, and improved success results are observed when it is compared with k-NN. Keywords: brain computer interface (bci), classificaiton, eeg, c x k - nearest neighborhood algorihtm.