Mental Stress Detection from EEG Signals Using Comparative Analysis of Random Forest and Recurrent Neural Network

Mental stress is a common problem that affects people in numerous facts of their lives, and early discovery is critical for effective treatments. This study introduces a unique approach using sophisticated methods like Recurrent Neural Network (RNN), Random Forest, and Electroencephalogram (EEG) signal analysis. By analyzing EEG signals, the aim is to quickly and accurately identify signs of mental stress. The model is built using the GAMEEMO dataset and validated with the SEED dataset, focusing on recognizing patterns in EEG signals associated with varying stress levels. The model demonstrates high accuracy, precision, Fl score, and recall values, showcasing its effectiveness. With an 87 % accuracy for arousal and 83 % for valence, the RNN model outperformed Random Forest, which achieved 83% accuracy for arousal and 75% for valence. This study advances mental health monitoring and intervention systems, paving the way for practical application and better well-being.