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.).

翻译：脑电图（EEG）信号在长期制动强度预测方面具有良好前景，但容易受到各种伪迹的影响，从而限制了其可靠性。为此，我们提出了一种新型框架，将EEG信号建模为独立盲源信号的混合，并识别出与制动动作强相关的成分。该方法采用独立分量分析将EEG分解为不同分量，并结合时频分析与皮尔逊相关系数来选择制动相关分量。此外，我们利用层次聚类将制动相关分量分为两个簇，每个簇具有独特的空间分布特征。同时，这些分量呈现试验不变的时间模式，并在紧急制动过程中表现出稳定且共有的神经特征。利用这些分量的功率特征及历史制动数据，我们实现了200毫秒预测视界的制动强度预测。在开源数据集（O.D.）和人在环仿真（H.S.）上的评估表明，本方法优于现有最优方法，分别在O.D.和H.S.上实现了8.0%和23.8%的均方根误差降低。