Single Trial Reaction Time Prediction Using Optimal Synchrony Window Detection

Abstract

The Optimal Synchrony Window Detection (OSWD) method addresses the challenge of capturing dynamic temporal patterns in non-stationary EEG data for reaction time (RT) prediction. Traditional fixed-window approaches often fail to capture critical neural dynamics due to inter trial variability, leading to suboptimal performance in time-series modeling. OSWD introduces an adaptive window selection mechanism based on neural synchrony, dynamically optimizing time windows to capture task-relevant features that vary across trials and individuals. By integrating OSWD with Cov-CNN-LSTM architectures, the model achieves state-of-the-art accuracy while reducing computational complexity. Our experiments on the Lane Keeping Task dataset demonstrate that OSWD significantly enhances RT prediction by improving feature extraction and reducing prediction errors. This adaptability makes OSWD an effective solution for real-time EEG-based applications, with broader implications for time-series forecasting and signal processing.