Drowsiness Detection by Bayesian-Copula Discriminant Classifier Based on EEG Signals During Daytime Short Nap
@article{Qian2017DrowsinessDB, title={Drowsiness Detection by Bayesian-Copula Discriminant Classifier Based on EEG Signals During Daytime Short Nap}, author={Dong Qian and Bei Wang and Xiangyun Qing and Tao Zhang and Yu Zhang and Xingyu Wang and Masatoshi Nakamura}, journal={IEEE Transactions on Biomedical Engineering}, year={2017}, volume={64}, pages={743-754} }
Objective: Daytime short nap involves individual physiological states including alertness and drowsiness. In order to have a better understanding of the periodical rhymes of physiological states and then promote a good interpretability of alertness, the aim of this study is to detect drowsiness during daytime short nap. Methods: A method of Bayesian-copula discriminant classifier (BCDC) was introduced to detect individual drowsiness based on the physiological features extracted from…
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References
SHOWING 1-10 OF 45 REFERENCES
Automatic detection of drowsiness in EEG records based on multimodal analysis.
- Computer ScienceMedical engineering & physics
- 2014
Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm
- Computer ScienceIEEE Transactions on Biomedical Engineering
- 2011
The experimental results proved the significance of FMIWPT in extracting features that highly correlate with the different drowsiness levels achieving a classification accuracy of 95%-97% on an average across all subjects.
Can SVM be used for automatic EEG detection of drowsiness during car driving
- Computer Science
- 2009
Uncorrelated fuzzy neighborhood preserving analysis based feature projection for driver drowsiness recognition
- Computer ScienceFuzzy Sets Syst.
- 2013
On-Line Detection of Drowsiness Using Brain and Visual Information
- Computer ScienceIEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans
- 2012
A drowsiness detection system using both brain and visual activity is presented and it is shown that EEG and EOG detectors are redundant: EEG-based detections are used to confirm EOG-based detection and thus enable the false alarm rate to be reduced to 5% while the true positive rate is not decreased, compared with a single Eog-based detector.
Generalized EEG-Based Drowsiness Prediction System by Using a Self-Organizing Neural Fuzzy System
- Computer ScienceIEEE Transactions on Circuits and Systems I: Regular Papers
- 2012
A generalized EEG-based Self-organizing Neural Fuzzy system to predict driver's drowsiness was proposed and interpreted the performances of the proposed system significantly better than using other traditional Neural Networks ( p-value <;0.038).
EEG-based drowsiness estimation for safety driving using independent component analysis
- Computer ScienceIEEE Transactions on Circuits and Systems I: Regular Papers
- 2005
A drowsiness-estimation system based on electroencephalogram (EEG) by combining independent component analysis (ICA), power-spectrum analysis, correlation evaluations, and linear regression model to estimate a driver's cognitive state when he/she drives a car in a virtual reality (VR)-based dynamic simulator is developed.
Construction and validation of the EEG analogues of the Karolinska sleepiness scale based on the Karolinska drowsiness test
- MedicineClinical Neurophysiology
- 2013
Wireless and Wearable EEG System for Evaluating Driver Vigilance
- Computer ScienceIEEE Transactions on Biomedical Circuits and Systems
- 2014
This work presents a novel dry EEG sensor based mobile wireless EEG system (referred to herein as Mindo) to monitor in real time a driver's vigilance status in order to link the fluctuation of driving performance with changes in brain activities.
Multimodal information improves the rapid detection of mental fatigue
- Computer ScienceBiomed. Signal Process. Control.
- 2013