Design of Attention Prediction System Based on EEG Signal Analysis

Electroencephalogram (EEG) signals are crucial in understanding brain activity, particularly in assessing attention levels, and are vital for various cognitive functions and daily activities. This study presents the design of an attention-level prediction system utilizing EEG signal analysis, employing Long Short-Term Memory (LSTM) networks enhanced by Particle Swarm Optimization (PSO). The system collects EEG data through non-invasive single-channel devices and processes these signals to predict attention levels using an LSTM model. To address the challenge of optimal parameter selection in LSTM, the PSO algorithm is integrated, significantly improving prediction accuracy. Experimental results demonstrate that the PSO_LSTM model outperforms traditional LSTM networks in terms of accuracy, convergence speed, and robustness. The proposed system not only enhances the predictive performance of attention levels but also provides a scalable approach for real-time applications in fields such as education, healthcare, and human-computer interaction. This study highlights the potential of combining EEG signal analysis with advanced machine learning techniques for more effective attention monitoring.