Emotion Recognition and Stress Reduction Based on Electroencephalograph (EEG) Signals validated by Machine Learning Algorithms

Research shows that stress is a typical reaction to external or internal irritants, but prolonged stress may impair health and brain function. Stress causes brain damage and sadness. Emotional awareness may reduce stress. Emotions complicate life. Assess emotions. Suicide note sentiment analysis has not used NLP and ML. Three parts follow. SVM algorithms categorise individuals as “under stress” or “not under stress” using EEG-based psychological states and signals. Merged LSTM reveals yoga and calming music reduce stress. Finally, emotions explain stress. DEAP synchronised brainwave dataset comprises patient data and EEG signal levels. Valence/arousal and EEG detect emotions. The discrete wavelet method (DWT) recovers alpha, gamma, theta, and beta EEG data spectral characteristics. Same-dimensional transforms isolate ICA features. ANN sorts emotions. SVM's 92.86 percent accuracy and LSTM's 75.23 percent show yoga and relaxing music soothe 75% of stressed people. A cross-validated RBF kernel-based SVM classifies beta emotions with 90.3% arousal and 91.11% valence. This research analyses mental stress EEG signals. It underlines research inconsistencies and suggests data analysis may cause them. Standardised approach, brain region of interest, stressor type, experiment duration, EEG processing, feature extraction, and classifier may alter results. Use appropriate traits to detect stress-related emotions. Yoga and music are also used to identify and treat stress in the study.