Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm

By Rami N. Khushaba, Sarath Kodagoda, Sara Lal, Gamini Dissanayake. IEEE Transactions on Biomedical Engineering, 2011

514 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/d1bcd047a8688917b543bb93f9e719aa31c7008c

How researchers see this paper

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

In Cognitive Neuroscience

1. Wei-Long Zheng
2. Bao-Liang Lu
3. Wei Liu
4. Zhen Cui
5. Wenming Zheng
6. Jinpeng Li
7. Huiguang He
8. Dongrui Wu

In Experimental and Cognitive Psychology

1. Wei-Long Zheng
2. Bao-Liang Lu
3. Kenneth Sundaraj
4. Murugappan Murugappan
5. Arun Sahayadhas
6. Wei Liu
7. Zhen Cui
8. Wenming Zheng

In Human-Computer Interaction

1. Wei-Long Zheng
2. Bao-Liang Lu
3. Kenneth Sundaraj
4. Murugappan Murugappan
5. Arun Sahayadhas

Citations in the corpus

• Detecting Driver Drowsiness Based on Sensors: A Review (2012, methodology): “The extracted features are then classified using Artificial Neural Networks (ANN), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), or other similar methods [12,28,61].”
• A multimodal approach to estimating vigilance using EEG and forehead EOG (2016, methodology): “For the single EEG modality, the posterior EEG contains the most critical information for vigilance estimation, which is consistent with previous findings (Khushaba et al. 2011, Shi & Lu 2013).”