Driver Fatigue Detection using EEG Microstate Features and Support Vector Machines

EEG microstate analysis, in combination with SVM, proves to be an effective method for detecting driver fatigue. This approach can be utilized for real-time driver monitoring and fatigue alert systems, enhancing road safety.

Authors

Institutions

Publication Details

Journal
Journal of Biomedical Physics and Engineering
Published
2025-01-01
DOI
https://doi.org/10.31661/jbpe.v0i0.2501-1883
Primary Topic
Sleep and Work-Related Fatigue
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Driver Fatigue Detection using EEG Microstate Features and Support Vector Machines

Ardalan Aarabi, Habibollah Danyali, Zahra Yaddasht, Kamran Kazemi
Journal of Biomedical Physics and Engineering
Sleep and Work-Related Fatigue
article

Driver Fatigue Detection using EEG Microstate Features and Support Vector Machines

Ardalan Aarabi, Habibollah Danyali, Zahra Yaddasht, Kamran Kazemi
article en

Abstract

EEG microstate analysis, in combination with SVM, proves to be an effective method for detecting driver fatigue. This approach can be utilized for real-time driver monitoring and fatigue alert systems, enhancing road safety.

Journal of Biomedical Physics and EngineeringVol. online(6)
Shiraz University of Technology (IR), Centre Hospitalier Universitaire Amiens-Picardie (FR), Université de Picardie Jules Verne (FR)
Chongqing Science and Technology Commission
Openalex Percentile: Top 73%
Sleep and Work-Related Fatigue
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.