Key result
An EEG-based detection model using regularization linear discriminant analysis detected driver emergency braking intentions 420 ms after emergency onset with over 94% accuracy.
An EEG-based detection model using regularization linear discriminant analysis can accurately and rapidly detect driver emergency braking intention for brain-controlled vehicles.
May support EEG-based driver monitoring systems; leaves open real-world validation before clinical or automotive adoption.
In this paper, we propose a new approach of detecting emergency braking intention for brain-controlled vehicles by interpreting electroencephalography (EEG) signals of drivers. Regularization linear discriminant analysis with spatial-frequency features is applied to build the detection model. These spatial-frequency features are selected from the powers of frequency points across sixteen channels by using the sequential forward floating search. Experimental results from twelve subjects show that on average, the proposed method can detect emergency braking intentions 420 ms after the onset of emergency situations with the system accuracy of over 94%, showing the feasibility of developing a practical system of detecting driver emergency braking intention with the power spectra of EEG signals for brain-controlled vehicles.
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Teng et al. (2017) studied Driver emergency braking intention (n=12). EEG-based detection model using regularization linear discriminant analysis was evaluated on Detection of emergency braking intentions (accuracy and time). An EEG-based detection model using regularization linear discriminant analysis detected driver emergency braking intentions 420 ms after emergency onset with over 94% accuracy.
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