Can SVM be used for automatic EEG detection of drowsiness during car driving

By Mervyn Yeo, Xiansheng Li, Kai Quan Shen, E. Wilder-Smith. 2009

384 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

EEG-Based Spatio–Temporal Convolutional Neural Network for Driver Fatigue Evaluation (2019, positive): “In [15], four frequency features were extracted across four principal frequency bands from EEG signals and then fed into the support vector machine (SVM), which achieved an excellent classification effect on fatigue detection.”
Bestial boredom: a biological perspective on animal boredom and suggestions for its scientific investigation (2017, background): “(Yeo et al., 2009) Beta activity increases in cats, dogs and”
Driver Inattention Monitoring System for Intelligent Vehicles: A Review (2009, background): “Performing a cognitively demanding task while driving influences both the driver's visual behavior and driving performance (as indicated by braking behavior).”
Driver Behavior Analysis for Safe Driving: A Survey (2015, methodology): “7 [69].”
Driver Fatigue Detection Systems: A Review (2019, background): “Various parameters such as EEG [54], ECG [55], PERCLOS [56] and EoG [57]”
Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm (2011, background): “The authors also reported that the achieved classification accuracy would be compromised, if less EEG channels were used [22].”