EEG-Based Emergency Braking Intensity Prediction Using Blind Source Separation

TL;DR

This paper introduces a novel EEG signal processing framework using blind source separation and clustering to predict emergency braking intensity more accurately, outperforming existing methods.

Contribution

The study presents a new EEG analysis approach combining independent component analysis, clustering, and power features for improved braking intensity prediction.

Findings

Outperforms state-of-the-art methods with RMSE reductions of 8.0% and 23.8%.

Identifies stable neural signatures associated with emergency braking.

Demonstrates the effectiveness of blind source separation in EEG-based prediction.

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.).