EEG-Based Emergency Braking Intensity Prediction Using Blind Source Separation
For brain-computer interface applications in driving safety, this method improves braking intensity prediction accuracy by identifying stable neural signatures.
The paper proposes a novel framework using blind source separation to predict emergency braking intensity from EEG signals, achieving RMSE reductions of 8.0% on an open dataset and 23.8% in human-in-the-loop simulations compared to state-of-the-art methods.
Electroencephalography (EEG) signals have been promising for long-term braking intensity prediction but are prone to various artifacts that limit their reliability. Here, we propose a novel framework that models EEG signals as mixtures of independent blind sources and identifies those strongly correlated with braking action. Our method employs independent component analysis to decompose EEG into different components and combines time-frequency analysis with Pearson correlations to select braking-related components. Furthermore, we utilize hierarchical clustering to group braking-related components into two clusters, each characterized by a distinct spatial pattern. Additionally, these components exhibit trial-invariant temporal patterns and demonstrate stable and common neural signatures of the emergency braking process. Using power features from these components and historical braking data, we predict braking intensity at a 200 ms horizon. Evaluations on the open source dataset (O.D.) and human-in-the-loop simulation (H.S.) show that our method outperforms state-of-the-art approaches, achieving RMSE reductions of 8.0% (O.D.) and 23.8% (H.S.).