Human Identification Based on Electroencephalogram Analysis When Entering a Password Phrase on a Keyboard - Alexey Sulavko; Alexander Samotuga (Applied System Innovation)
The paper proposes a method for identifying a person based on EEG parameters recorded during the process of entering user password phrases on the keyboard. The method is presented in two versions: for a two-channel EEG (frontal leads only) and a six-channel EEG. A database of EEGs of 95 subjects was formed, who entered a password phrase on the keyboard, including states in an altered psychophysiological state (sleepy and tired). During the experiment, the subjects’ EEG data were recorded. The experiment on collecting data in each state was conducted on different days. The signals were segmented in such a way that the time of entering the password phrase corresponded to the time used during the EEG to identify the subject. The EEG signals are processed using two autoencoders trained on EEG data (on spectrograms of the original signals and their autocorrelation functions). The encoder is used to extract signal features. After identifying the features, identification is performed using the Bayesian classifier. The achieved error level was 0.8% for six-channel EEGs and 1.3% for two-channel EEGs. The advantages of the proposed identification method are that the subject does not need to be put into a state of rest, and no additional stimulation is required.