@inproceedings{5f96a6d29da145fbad47c7cc6008f4cb,
title = "Modelling mental states via computational psychophysiology: Benefits and challenges",
abstract = "The human psychophysiological processes are complex phenomenon built upon the physical scaffolding of the body. Machine learning approaches facilitate the understanding of numerous physiological processes underlying complex human mental states and behavior, leading to a new research direction named Computational Psychophysiology. Computational Psychophysiology aims to reveal the psychophysiological processes underlying complex human emotion and mental states from a computational perspective, and can be used to predict affective and psychological outcomes based on different physiological features or experimental manipulations. In this paper, we discuss the benefits and challenges in the future of bringing computing technologies into decoding human mental states.",
keywords = "Benefits, Challenges, Computational Psychophysiology, Machine learning, Psychophysiological processes",
author = "Weihao Zheng and Hanshu Cai and Zhijun Yao and Xiaowei Zhang and Xiaowei Li and Bin Hu",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 5th International Conference on Human Centered Computing, HCC 2019 ; Conference date: 05-08-2019 Through 07-08-2019",
year = "2019",
doi = "10.1007/978-3-030-37429-7_67",
language = "English",
isbn = "9783030374280",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "659--670",
editor = "Danijela Milo{\v s}evic and Yong Tang and Qiaohong Zu",
booktitle = "Human Centered Computing - 5th International Conference, HCC 2019, Revised Selected Papers",
address = "Germany",
}