用脑电图预测紧急刹车强度，提升驾驶安全
EEG-Based Emergency Braking Intensity Prediction Using Blind Source Separation
- 将脑电信号分解为独立源，筛选与刹车相关的成分
- 200毫秒前瞻预测刹车力度，准确率显著优于现有方法
- 适合智能驾驶、人机交互领域研究者参考
脑电图（EEG）信号在长期刹车强度预测中具有潜力，但易受多种伪影干扰。本文提出一种新框架，将EEG视为独立盲源的混合信号，识别与刹车动作强相关的成分。通过独立成分分析（ICA）分解EEG，并结合时频分析与皮尔逊相关性筛选刹车相关成分。进一步采用分层聚类将相关成分分为两个空间模式不同的簇。这些成分表现出试验无关的时间模式，且揭示了紧急刹车过程的稳定、共有的神经特征。利用这些成分的功率特征与历史刹车数据，实现200毫秒前瞻的刹车强度预测。在公开数据集（O.D.）和人机闭环模拟（H.S.）上的评估显示，本方法优于当前最优方法，分别实现8.0%（O.D.）和23.8%（H.S.）的均方根误差（RMSE）降低。
原文摘要 · Abstract (English)
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.).
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。