Deep learning model-based identification of stress resilience using electroencephalograph (eeg)

Abstract

A device, method, and non-transitory computer readable medium for identification of stress resilience. The method for identification of stress resilience includes stimulating a human subject by at least one of a plurality of stressful events in a virtual reality environment, acquiring multichannel real-time electroencephalograph (EEG) signals by an EEG monitor worn by a human subject, recording the real-time EEG signals received during the stressful event, transmitting the real-time EEG signals to a computing device. The computing device generates a plurality of filtered brain wave frequencies related to the stressful event by filtering the multichannel real-time EEG signals, classifies the brain wave frequencies by frequency level by applying the filtered brain wave frequencies to the deep learning model, applies each frequency level associated with the stressful event to the convolutional neural network, and identifies a level of stress resilience of the human subject associated with the stressful event.