Proceedings ArticleDOI
Detection of Stress in Human Brain
TLDR
A stress detection mechanism and a stress level indicator circuit for measuring the stress level of human brain using the Electro-encephalogram (EEG) Signal are developed and indicated in the ‘Stress Indicating’ circuit.Abstract:
The essence of the paper is to develop a stress detection mechanism and a stress level indicator circuit for measuring the stress level of human brain using the Electro-encephalogram (EEG) Signal. Signals coming from the frontal lobe of human brain have been used for the measurement of stress. The brain signals of the thirty subjects are recorded while they are solving five mathematical question sets with increasing complexity. We assume that the subjects undergo through five different stress levels i.e. ‘Relaxed’, ‘Less stressed’, ‘Moderately Stressed’, ‘High Stressed’ and ‘Alarmingly Stressed’ while solving these question sets. After that recorded EEG data is processed and features are extracted. We design a feed forward neural network for classifying the stress level in human brain. We prepare a new question set consisting of easy as well as complex numerical questions for testing purpose. We record the EEG data of a subject while solving this question set. We extract six feature values from the processed EEG data of the subject. These data is fed to the designed feed forward neural network. The neural network predicts the stress level and the predicted stress level is indicated in the ‘Stress Indicating’ circuit.read more
Citations
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Journal ArticleDOI
Extended ICA and M-CSP with BiLSTM towards improved classification of EEG signals
TL;DR: A Multiclass Common Spatial Pattern-based moving window technique is proposed here to obtain the most distinguishable time segment of EEG trials, and BiLSTM is used to improve classification results.
Proceedings ArticleDOI
A Deep Learning Approach for Human Stress Detection using Haar-Cascade Algorithm
TL;DR: In this paper , a hybrid system that combines a regression classifier with a Haar Cascade Algorithm (HCA) is described to detect and distinguish facial emotions (Indignation, disgust, neutrality, fear, sadness, joy, and surprise).
Proceedings ArticleDOI
A Deep Learning Approach for Human Stress Detection using Haar-Cascade Algorithm
TL;DR: In this article , a hybrid system that combines a regression classifier with a Haar Cascade Algorithm (HCA) is described to detect and distinguish facial emotions (Indignation, disgust, neutrality, fear, sadness, joy, and surprise).
Proceedings ArticleDOI
Stress Level Detection of IT Professionals Using Machine Learning
TL;DR: In this paper , a solution for organizations where they can know the levels of stress faced by the students and could calculate percentage of stress was proposed, where students can take up the survey through a google form which consist of the parameters which are helpful in collecting information about mental distress and many other psychological factors faced by students.
References
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EEG-Based Emotion Monitoring in Mental Task Performance
TL;DR: It is found that when the difficulty level of mental tasks increases under the stress, people tend to be more negative and more aroused.