Classification of various facial movement artifacts in EEG signals

In this paper, a novel approach to classify various facial movement artifacts in EEG signals is presented. EEG signals were obtained in EEG Laboratory from three healthy human subjects in age groups 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 the data sets and achieved an average classification rate of 94% on the test data.