Optimized EEG-Based Stress Detection: A Novel Approach

Mental stress from tight deadlines and financial worries often causes both mental and physical health issues, affecting productivity and decision-making. This study aims to improve stress detection by analyzing EEG signals, which provide a cost-effective, non-invasive method for tracking brain activity. Recent stress detection systems face challenges such as computational complexity, noisy data, and high dimensionality. This study introduces optimal feature selection in an EEG-based stress detection system using the Archimedes Optimization Algorithm (AOA) and Analytical Hierarchical Process (AHP). AOA balances exploration and exploitation, while AHP prioritizes EEG criteria. The system processes EEG data from the DEAP dataset, which includes recordings from 32 participants who watch 40 music clips. It operates in four main stages: enhancing EEG signals with Wavelet Packet Transform (WPT), extracting features, selecting relevant features with the AOA-AHP algorithm, and detecting stress using deep convolutional neural networks and long short-term memory networks (DCNN-LSTM). After evaluating various features with 244 EEG samples, the system optimizes to 350 key features, achieving 95.25% accuracy, 0.97 recall, 0.98 precision, and 0.98 F1 score. This setup enhances accuracy, reduces training time, and minimizes parameters, making it highly reliable for real-time mental stress detection.