Analysis of Stress Using Electroencephalogram Data for Feature Extraction

Abstract

Patient denial, insensitivity, subjective biases, and inaccuracy are only some of the issues that arise from relying solely on doctor-patient interaction and scale analysis when diagnosing Stress. The creation of an objective, computerized approach for predicting therapeutic outcomes is crucial for enhancing the precision of Stress diagnosis and treatment. In an effort to better detect Stress, this research modifies EEG data and use machine learning algorithms. Ten participants' EEGs were recorded using a Narosky system while they were exposed to various emotional face cues. Psychologists relied on the EEG signal as a diagnostic tool for Stress. Machine learning and deep learning were the methods that handled the feature processing. Using PCA, ICA, and EMD for BCI applications yields significant results. Using SVM, a programmer can reap many benefits: The stress and pressure can be detected by employing EEG signals, and PCA has great generalization properties. The effect of overtraining is particularly vulnerable to the curse-of-dimensionality when the signals are negative. The use of EEG signals for stress detection allowed for these benefits to be realized. The experimental study provides a somewhat comprehensive summary of the various methods, all of which rely on frequency domain analysis of 14 EEG data.