Advanced EEG Signal Processing and Deep Q-Learning for Accurate Student Attention Monitoring

Measuring student attention during learning remains a challenging task. Traditional methods, such as surveys and observations, are subjective and often inaccurate. While EEG can detect attentional changes, its interpretation is complicated by noise and artifacts. This study introduces an enhanced approach that leverages advanced data preprocessing techniques and a Double Deep Q-Network (DDQN) model, a specialized deep reinforcement learning (DRL) algorithm adept at handling complex, multidimensional EEG data. We employ wavelet transformations to generate robust frequency-based representations of raw multi-channel EEG signals, followed by Butterworth bandpass and notch filters to eliminate noise and artifacts. Dimensionality reduction and feature scaling are achieved employing Principal Component Analysis (PCA) alongside Independent Component Analysis (ICA), leading in cleaner and more representative EEG data. Our method significantly improves the accuracy of attention state classification, achieving a test accuracy of 98.4%. This advancement sets a new standard for utilizing EEG data to monitor attention in educational settings, showcasing the effectiveness of our preprocessing techniques in enhancing the DDQN’s neural decoding capabilities.