# EEG-Based Emergency Braking Intensity Prediction Using Blind Source Separation

arXiv: https://arxiv.org/abs/2604.18220 · HTML: https://mlsanity.com/paper/2604.18220/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 62 references that are in the map.
- First posted on arXiv 20 Apr 2026.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Extracting spatial-temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition](https://mlsanity.com/paper/1409.5496/) (2014)
- [ICLabel: An automated electroencephalographic independent component classifier, dataset, and website](https://mlsanity.com/paper/1901.07915/) (2019)
- [MMTL-UniAD: A Unified Framework for Multimodal and Multi-Task Learning in Assistive Driving Perception](https://mlsanity.com/paper/2504.02264/) (2025)
- [Source Aware Deep Learning Framework for Hand Kinematic Reconstruction using EEG Signal](https://mlsanity.com/paper/2103.13862/) (2021)
- [Convolutional Spiking Neural Networks for Detecting Anticipatory Brain Potentials Using Electroencephalogram](https://mlsanity.com/paper/2208.06900/) (2022)
- [Spectrally Adaptive Common Spatial Patterns](https://mlsanity.com/paper/2202.04542/) (2022)
- [UV-M3TL: A Unified and Versatile Multimodal Multi-Task Learning Framework for Assistive Driving Perception](https://mlsanity.com/paper/2602.01594/) (2026)
- [Investigating the Cognitive Response of Brake Lights in Initiating Braking Action Using EEG](https://mlsanity.com/paper/2508.03274/) (2025)

## Abstract (arXiv)

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.).