Comprehensive Analysis on Feature Selection, Machine Learning and Deep Learning Algorithms to Detect Driver Drowsiness-An EEG Study-Reference-Cited by-同舟云学术

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Comprehensive Analysis on Feature Selection, Machine Learning and Deep Learning Algorithms to Detect Driver Drowsiness-An EEG Study

Published:2023-11-09 Issue: Volume: Page:147-153
ISSN:
Container-title:Proceedings of the 2023 10th International Conference on Biomedical and Bioinformatics Engineering
language:
Short-container-title:

Author:

Ferdous Talukdar Raian¹^ORCID,Tushar Md Abdul Kader¹^ORCID,Rafi Rifath Hasan²^ORCID,Asif Nazmus Salehin³^ORCID,Palash Torikul Islam²^ORCID,Arman Md Adil⁴^ORCID,Saha Debasish Kumar⁵^ORCID,Islam Muhammad Muinul⁵^ORCID

Affiliation:

1. Department of Biomedical Engineering, University of Houston, United States

2. Department of Biomedical Engineering, New Jersey Institute of Technology, United States

3. Department of Transportation Studies, Texas Southern University, United States

4. Department of Biomedical Engineering, Florida International University, United States

5. Department of Biomedical Engineering, Khulna University of Engineering & Technology, Bangladesh

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637732.3637791

Reference9 articles.

1. A compact and interpretable convolutional neural network for cross-subject driver drowsiness detection from single-channel EEG

2. Car drivers coping with hazardous events in real versus simulated situations: Declarative, behavioral and physiological data used to assess drivers’ feeling of presence

3. A Public Health Perspective of Road Traffic Accidents

4. Driving Drowsiness Detection with EEG Using a Modified Hierarchical Extreme Learning Machine Algorithm with Particle Swarm Optimization: A Pilot Study

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