AI RESEARCH
EEG-Based Emergency Braking Intensity Prediction Using Blind Source Separation
arXiv CS.LG
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ArXi:2604.18220v1 Announce Type: cross 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.