EEG-based Eye Tracking with Combination of Self-Attention and Long-Short-Term Memory Model

Eye tracking is a physiological technique that quantifies ocular motor behavior to analyze human visual engagement. Traditional methods rely on eye trackers to measure eye movements and spatial coordinates, typically based on video analysis. Recent advancements have explored integrating AI-driven EEG analysis to enhance eye-tracking capabilities. However, most existing approaches focus solely on real-time gaze estimation, overlooking past gaze information and eye movement kinematics. In this study, we propose an AI-based framework for real-time gaze position prediction using EEG signals that incorporate previous gaze patterns. The model architecture combines a self-attention mechanism for extracting salient features from multi-channel EEG time-series data with an LSTM network to predict current gaze positions based on past information. The training process consists of two phases: pre-training for gaze movement classification and subsequent fine-tuning for gaze position regression. Our proposed framework demonstrated an average improvement of 44% over the previous state-of-the-art model. While the earlier study indicated a gaze tracking error of 55.4 mm, the proposed model achieved a significantly lower average gaze tracking error of 24.4 mm compared to the ground truth.