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Fu, R., Chen, Y., Huang, Y., Chen, S., Duan, F., Li, J., et al. (2022) Symmetric Convolutional and Adversarial Neural Network Enables Improved Mental Stress Classification from EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30, 1384-1400.
https://doi.org/10.1109/tnsre.2022.3174821

has been cited by the following article:

• TITLE: A State of the Art: EEG-Based Classification and Recognition Models of Mental Stress

  AUTHORS: Azleena Kamarul Bahrain, Zulkifli Mahmoodin, Muhammad Noor Nordin, Mohd Zubir Suboh

  KEYWORDS: Electroencephalograph (EEG), Mental Stress, Feature Extraction

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.12, December 30, 2024

  ABSTRACT: Over the past several decades, numerous methods have been applied by research and professionals to detect and measure mental stress, varying from subjective methods such questionnaires and face-to-face interviews up to the objective methods using physiological signals and neuroimaging equipment such as salivary cortisol and functional magnetic resonance imaging (fMRI), respectively. Among those methods, an Electroencephalograph (EEG) is one of the utmost chosen non-invasive methods by professionals and researchers in recording real time brain signals. This paper highlights the state of art for each of the studies, by comparing and analyzing the method and protocol of EEG data collection, including the selection of electrodes and brain regions involving two major categories of mental stress: acute and chronic. Selection of EEG features, with the necessary signal pre-processing and processing techniques, and the classification models used in these studies have been summarized and discussed.