Graph based eeg decoding predicts passenger danger in self driving cars
GraTED: Graph--Tensor EEG Decoding for Passenger Hazard Perception in Autonomous Vehicles
Machine LearningHuman-Computer Interaction
Summary
Passengers' brain signals can show when they sense danger on the road, even before anything happens. The authors created a new way to study these signals that keeps the natural layout of the brain sensors and the timing of the signals. Their method, called GraTED, uses a graph to represent sensors and a special technique to keep track of time and features. They tested this on real data and found it better at spotting risks than other methods while using fewer settings to learn.
What this means in practice
- •For autonomous vehicle engineers: Improve passenger safety systems by detecting potential hazards through real-time decoding of EEG signals using spatial and temporal brain data.
- •For brain computer interface developers: Design EEG-based systems that preserve electrode geometry and multi-dimensional data to better interpret user states and reactions.
Authors
Yuxuan Gu, Yingkai Yang, Qiyu Rao, Tania Stathaki, Danilo Mandic
Abstract
Passenger electroencephalography (EEG) can reveal road risk through anticipatory responses before hazardous events and neural reactions around their onset. However, existing EEG decoding methods often map physiologically meaningful arrangement of electrodes onto artificial 2D grids and thus flatten the higher-order time-feature representations into matrices, obscuring meaningful structure in the data. To preserve and exploit the spatial and time-feature structure of EEG recordings, we propose the Graph-Tensor EEG Decoding Model (GraTED) for passenger risk prediction (RP) and danger identification (DI) in autonomous-vehicle scenarios. GraTED models the electrode arrangement as an undirected graph based on their scalp locations, using graph convolution to capture spatial dependencies and low-rank tensor classification to preserve the multidimensional time-feature structure, while controlling model complexity. Evaluated on the PassengerEEG dataset, comprising 11 participants and 45 hours of recordings, GraTED achieves the highest balanced accuracy, compared to the state-of-the-art, across all three evaluation settings, while requiring the fewest learnable parameters among seven baseline methods.