Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Monitoring Head/Eye Motion for Driver Alertness with One Camera
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Wearable Alertness Monitoring for Industrial Applications
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
Identification of driver state for lane-keeping tasks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
EEG-based subject- and session-independent drowsiness detection: an unsupervised approach
EURASIP Journal on Advances in Signal Processing
Tonic Changes in EEG Power Spectra during Simulated Driving
FAC '09 Proceedings of the 5th International Conference on Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience: Held as Part of HCI International 2009
Wearable and Wireless Brain-Computer Interface and Its Applications
FAC '09 Proceedings of the 5th International Conference on Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience: Held as Part of HCI International 2009
Computational intelligent brain computer interaction and its applications on driving cognition
IEEE Computational Intelligence Magazine
EEG activities of dynamic stimulation in VR driving motion simulator
EPCE'07 Proceedings of the 7th international conference on Engineering psychology and cognitive ergonomics
EPCE'07 Proceedings of the 7th international conference on Engineering psychology and cognitive ergonomics
Combination of EEG complexity and spectral analysis for epilepsy diagnosis and seizure detection
EURASIP Journal on Advances in Signal Processing
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The growing number of traffic accidents in recent years has become a serious concern to society. Accidents caused by driver's drowsiness behind the steering wheel have a high fatality rate because of the marked decline in the driver's abilities of perception, recognition, and vehicle control abilities while sleepy. Preventing such accidents caused by drowsiness is highly desirable but requires techniques for continuously detecting, estimating, and predicting the level of alertness of drivers and delivering effective feedbacks to maintain their maximum performance. This paper proposes an EEG-based drowsiness estimation system that combines electroencephalogram (EEG) log subband power spectrum, correlation analysis, principal component analysis, and linear regression models to indirectly estimate driver's drowsiness level in a virtual-reality-based driving simulator. Our results demonstrated that it is feasible to accurately estimate quantitatively driving performance, expressed as deviation between the center of the vehicle and the center of the cruising lane, in a realistic driving simulator.