EEG-Based Emergency Braking Intensity Prediction Using Blind Source Separation

2026-04-20

AI Summary

This study addresses the challenge of artifact contamination in electroencephalography (EEG) signals, which undermines the reliability of models for predicting emergency braking intensity. The authors propose a novel approach that integrates blind source separation with time–frequency and spatial clustering. Specifically, independent component analysis decomposes mixed EEG signals, and braking-related components are identified through time–frequency analysis combined with Pearson correlation. Hierarchical clustering is then employed to extract neural features exhibiting stable spatiotemporal patterns, enabling 200-millisecond-ahead prediction. This method achieves, for the first time, trial-invariant and spatially interpretable neural component extraction, substantially enhancing model robustness. Evaluated on both a public dataset and a human-in-the-loop simulation, the approach reduces prediction root mean square error (RMSE) by 8.0% and 23.8%, respectively, outperforming current state-of-the-art methods.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Intelligent Robots: State Estimation
• Machine Learning: Hardware-aware ML

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.).

Problem

• EEG
• braking intensity prediction
• artifacts
• reliability
• emergency braking

Innovation

• blind source separation
• independent component analysis
• braking intensity prediction
• time-frequency analysis
• hierarchical clustering