Key result
EEG signals classified using convolutional neural networks and support vector machines detected emergency braking intention with an average accuracy of 71.8% and 71.1%, respectively, across different driving conditions.
Population
7 right-handed male students with a valid driver's license, no medical history of neurological and/or…
Comparison
EEG-based detection of emergency braking… vs Normal driving (non-braking epochs).
Design
Other
Authors
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Supports EEG-based braking detection for driver-assistance systems; leaves open real-world validation before safety-critical adoption.
Absolute Event Rate: 71.8% vs 53.6%
p-value: p=<0.05
EEG signals can detect emergency braking intention with approximately 71% average accuracy using SVM or CNN classifiers, demonstrating feasibility for integration into advanced driver-assistance systems.
Hernández et al. (2018) studied Healthy drivers (n=7). EEG-based detection using CNN and SVM vs. Normal driving (chance level) was evaluated on Classification accuracy of emergency braking intention vs normal driving (p=<0.05). EEG signals classified using convolutional neural networks and support vector machines detected emergency braking intention with an average accuracy of 71.8% and 71.1%, respectively, across different driving conditions.
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