Drivers cognitive and physiological states affect their ability to control their vehicles. Thus, these driver states are important to the safety of automobiles. The design of advanced driver assistance systems (ADAS) or autonomous vehicles will depend on their ability to interact effectively with the driver. A deeper understanding of the driver state is, therefore, paramount. EEG is proven to be one of the most effective methods for driver state monitoring and human error detection. This paper discusses EEG-based driver state detection systems and their corresponding analysis algorithms over the last three decades. First, the commonly used EEG system setup for driver state studies is introduced. Then, the EEG signal preprocessing, feature extraction, and classification algorithms for driver state detection are reviewed. Finally, EEG-based driver state monitoring research is reviewed in-depth, and its future development is discussed. It is concluded that the current EEG-based driver state monitoring algorithms are promising for safety applications. However, many improvements are still required in EEG artifact reduction, real-time processing, and between-subject classification accuracy.
EEG based Emotion Recognition: A Tutorial and Review
Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））；Zhengzhou University of Light Industry（郑州轻工业大学）；Nanjing University（南京大学）；Aalto University（阿尔托大学）；Beijing Institute of Technology（北京理工大学）
LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring
College of Computer and Big Data, Fuzhou University（福州大学计算机与大数据学院）；Polytechnic University of Catalonia（加泰罗尼亚理工大学）；New Engineering Industry College, Putian University（莆田大学新工程产业学院）；School of Computer and Artificial Intelligence, Zhengzhou University（郑州大学计算机与人工智能学院）；College of Computer and Control Engineering, Minjiang University（闽江学院计算机与控制工程学院）；Sichuan Conservatory of Music（四川音乐学院）；Fuzhou Technology Innovation Center of Intelligent Manufacturing information System, Minjiang University（闽江学院福州智能制造信息系统技术创新中心）；Fujian Polytechnic Normal University（福建技术师范学院）
An EEG Channel Selection Framework for Driver Drowsiness Detection via Interpretability Guidance
Nanyang Technological University（南洋理工大学）；Continental-NTU Corporate Lab（大陆集团-南洋理工大学联合实验室）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）
A Comprehensive Survey on EEG-Based Emotion Recognition: A Graph-Based Perspective
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；School of Computer Science, Central China Normal University（华中师范大学计算机学院）；University of Science and Technology of China（中国科学技术大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Adaptive Subspace Sampling for Class Imbalance Processing-Some clarifications, algorithm, and further investigation including applications to Brain Computer Interface
National Chiao Tung University（国立交通大学）；Indian Statistical Institute（印度统计研究所）；University of Technology Sydney（悉尼科技大学）；University of Pennsylvania（宾夕法尼亚大学）
