Automated real-time classification of psychological functional state based on discrete wavelet transform of EEG data
A method for the automated real-time classification of psychological functional state is proposed. The classification is based on discrete wavelet transform of electroencephalographic data. The method consists of two preliminary stages — global feature selection and individual tuning, and the main stage — real-time classification. All stages are fully automated. The software implementation of this method revealed high reliability of classification and good potential of the method for applications dealing with virtual caves, including stress resistance evaluation, training, phobia therapy, etc.