A deep learning-based approach for distinguishing different stress levels of human brain using EEG and pulse rate.

In today's world, people suffer from many fatal maladies, and stress is one of them. Excessive stress can have deleterious effects on the health, brain, mind, and nervous system of humans. The goal of this paper is to design a deep learningbased human stress level measurement technique using electroencephalogram (EEG), and pulse rate. In this research, EEG signals and pulse rate of healthy subjects are recorded while they solve four different question sets of increasing complexity. It is assumed that the subjects undergo through four different stress levels, i.e., 'no stress', 'low stress', 'medium stress', and 'high stress', while solving these question sets. An attention mechanism-based CNN-TLSTM (convolutional neural network-tanh long short-term memory) model is proposed to detect the mental stress level of a person. An attention layer is incorporated into the designed TLSTM network to increase the classification accuracy of the CNN-TLSTM model. The CNN network is used for the automated extraction of intricate features from the EEG signals and pulse rate. Then TLSTM is used to classify the stress level of a person into four different categories using the CNNextracted features. The obtained average accuracy of the proposed CNN-TLSTM model is 97.86%. Experimentally, it is found that the designed stress level measurement technique is highly effective and outperforms most existing state-of-the-art techniques. In the future, functional Near-Infrared Spectroscopy (fNIRS), ECG, and Galvanic Skin Response (GSR) can be employed with EEG and pulse rate to increase the effectiveness of the designed stress level measurement technique.