Shekh Naziullah
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Shekh Naziullah has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Jahid Hassan
Electrical and Electronic Engineering, Pabna University of Science and Technology, Kismotprotap Pur, Pabna, Pabna, 6600, BANGLADESH
1 shared publication - Mamunur Rashid
Department of Electrical and Computer Engineering, Tennessee Tech University, Cookeville, USA
1 shared publication - Nahidul Islam
Faculty of Electrical and Electronic Engineering, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Pahang, Malaysia
1 shared publication - Shofiqul Islam
Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Warunponds, Australia
1 shared publication - Shoyeb Mahmud
Department of Electrical and Electronic Engineering, Pabna University of Science and Technology, Pabna, Bangladesh
1 shared publication - Thamina Islam
Department of Information & Communication Engineering (ICE), University of Rajshahi, Rajshahi, Bangladesh
1 shared publication
Latest Publications
- 1 September 2025
- Published by Springer Nature in Cognitive Neurodynamics
- Vol. 19 (1) , 1-38
Current status and challenges in electroencephalography (EEG)-based driver fatigue detection: a comprehensive survey
Driver fatigue is a major contributor to traffic accidents, leading to increased fatality rates and severe damage compared to incidents involving alert drivers. Electroencephalography (EEG) has emerged as a widely used method for detecting driver fatigue due to its ability to capture brain activity patterns. This survey provides a thorough analysis of devices that detect driver fatigue using EEG, analyzing existing methodologies, challenges, and future research directions. This study was carried out according to PRISMA criteria. Relevant studies were retrieved from SpringerLink, Web of Science, IEEE Xplore, Scopus, and ScienceDirect, covering research published until February 16, 2025. After 267 publications were identified, 87 scientific papers were fully analyzed based on their relevance and contribution to the identification of driver fatigue using EEG. The review explores the article selection process, followed by an in-depth discussion of driver fatigue detection systems across various domains. Applications of Machine Learning (ML) in EEG-based fatigue evaluation are carefully reviewed, covering data collection, preliminary processing, feature extraction, categorization techniques, and performance assessment. Additionally, a comparative evaluation of cutting-edge research provides a comprehensive visualization of current research trends. This survey highlights the advantages, limitations, and future prospects of EEG-based driver fatigue detection, offering valuable insights for improving road safety. The findings contribute to the development of more reliable and real-time fatigue detection systems by addressing existing challenges and recommending potential solutions.
- 1 September 2025
- Published by Springer Nature in Cognitive Neurodynamics
- Vol. 19 (1) , 1-38
Current status and challenges in electroencephalography (EEG)-based driver fatigue detection: a comprehensive survey
Driver fatigue is a major contributor to traffic accidents, leading to increased fatality rates and severe damage compared to incidents involving alert drivers. Electroencephalography (EEG) has emerged as a widely used method for detecting driver fatigue due to its ability to capture brain activity patterns. This survey provides a thorough analysis of devices that detect driver fatigue using EEG, analyzing existing methodologies, challenges, and future research directions. This study was carried out according to PRISMA criteria. Relevant studies were retrieved from SpringerLink, Web of Science, IEEE Xplore, Scopus, and ScienceDirect, covering research published until February 16, 2025. After 267 publications were identified, 87 scientific papers were fully analyzed based on their relevance and contribution to the identification of driver fatigue using EEG. The review explores the article selection process, followed by an in-depth discussion of driver fatigue detection systems across various domains. Applications of Machine Learning (ML) in EEG-based fatigue evaluation are carefully reviewed, covering data collection, preliminary processing, feature extraction, categorization techniques, and performance assessment. Additionally, a comparative evaluation of cutting-edge research provides a comprehensive visualization of current research trends. This survey highlights the advantages, limitations, and future prospects of EEG-based driver fatigue detection, offering valuable insights for improving road safety. The findings contribute to the development of more reliable and real-time fatigue detection systems by addressing existing challenges and recommending potential solutions.