Driver Reaction Time Prediction Through Adaptive Evolutionary Synchrony Window and Convolutional-LSTM

Drowsy driving is a leading cause of traffic accidents, often linked to delayed reaction times (RT). While EEG-based methods are effective in detecting drowsiness, they struggle with the lack of adaptive mechanisms for selecting optimal time windows that capture neural synchrony, and the limited generalizability due to trial-to-trial variability. This study proposes Adaptive Windowed Evolutionary Synchrony Analysis (AWESA), which uses evolutionary algorithms to dynamically optimize EEG time windows. AWESA combined with Convolutional-LSTM model achieves state-of-the-art performance in RT prediction on the Lane Keeping Task dataset, reducing the time window by a factor of 100. Despite the shorter window, AWESA delivers comparable prediction accuracy (MAE and RMSE), reducing complexity and offering a robust approach for enhancing drowsiness detection and for capturing relevant synchrony patterns for other neuro-cognitive tasks while drastically reducing complexity.