Jahid Hassan

Objective. Electroencephalography (EEG) has evolved into an indispensable instrument for estimating cognitive workload in various domains. Machine Learning (ML) and deep learning (DL) techniques have been increasingly employed to develop accurate workload estimation and classification models based on EEG data. The goal of this systematic review is to compile the body of research on EEG workload estimation and classification using ML and DL approaches. Methods. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses procedures were followed in conducting the review, searches were conducted through databases at SpringerLink, ACM Digital Library, IEEE Explore, PubMed, and Science Direct from the beginning to the end of 16 February 2024. Studies were selected based on predefined inclusion criteria. Data were extracted to capture study design, participant demographics, EEG features, ML/DL algorithms, and reported performance metrics. Results. Out of the 125 items that emerged, 33 scientific papers were fully evaluated. The study designs, participant demographics, and EEG workload measurement and categorization techniques used in the investigations differed. Support vector machine (SVM), convolutional neural network (CNN), and hybrid networks are examples of ML and DL approaches that were often used. Analyzing the accuracy scores achieved by different ML/DL models. Furthermore, a relationship was noted between sample frequency and model accuracy, with higher sample frequencies generally leading to improved performance. The percentage distribution of ML/DL methods revealed that SVMs, CNNs, and recurrent neural networks were the most commonly utilized techniques, reflecting their robustness in handling EEG data. Significance. The comprehensive review emphasizes how ML may be used to identify mental workload across a variety of disciplines using EEG data. Optimizing practical applications requires multimodal data integration, standardization efforts, and real-world validation studies. These systems will also be further improved by addressing ethical issues and investigating new EEG properties, which will improve human–computer interaction and performance assessment.

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.