The study of fear-induced power modulations for Cognitive Man-Machine Communication

Abstract

The efficient development of a Brain Computer Interface is based on rapid and effective discrimination of brain signals based on Electroencephalography (EEG) patterns. Any specific mental state or thinking activity results in specific pattern of brain signals. In this paper, a method has been proposed that combines recent advances in brain imaging and machine learning techniques to predict the cognitive state of the subjects whether they are feeling themselves in a safe or dangerous environment. This method is based on the mechanism of neuroception. The changes in this state are correlated with power modulations of oscillatory rhythms in the human EEG called ERD / ERS (Event-related De-synchronization / Synchronization). In order to predict these changes, it is of high significance to find the spatio-temporal distribution of EEG band-power modulations induced by the feeling of fear or danger.