Classification of EEG signals recorded during in facial movements for human-machine interaction

In this paper, we presented a novel approach to classifying various facial movements in EEG signals. EEG signals were acquired in Karadeniz Technical University, Department of Electrical and Electronics Engineering EEG Laboratory. Data were acquired from three healthy human subjects in age group of between 28 and 30 years old and on different days. Extracted feature vectors based on root mean square, polynomial fitting and Hjorth descriptors were classified by k-nearest neighbor algorithm. The proposed method was successfully applied to our data sets and achieved 99%, 94% and 89% classification accuracy rate on the test data of three subjects.