Automatic Recognition of Human Psychological State Based on EEG Data

The paper deals with the problem of binary classification of Electroencephalography (EEG) data using ordinary personal computers at making computation in real time. Eleven different criteria of similarity of two Multivariate Time Series (MTS) were used for this purpose. Basis on computation results of 32 dimensional EEG signals was established the advantages of the considered methods over each other. Methods “ascending eigenvalue-weighted difference between eigenvector matrices”, “getting into the confidence regions of the linear trends of MTS” and of the method which is obtained by the union of previous two methods gave better results by classification accuracy than others.