With the development of brain activity detection and machine learning, as a new technology of man-machine-environment system engineering (MMESE), brain-computer interface (BCI) has been applied to human life. At present, the BCI using functional near-infrared spectroscopy (fNIRS) to obtain neural activity has developed rapidly. However, there is still a problem that the data dimension is too large for feature extraction. In this paper, we propose a feature extraction method based on neural synchronization, and verify our method based on the experimental data. Our results show that the neural synchronization between two brain regions (rTPJ and the rDLPFC) encodes the effective information of decision-making behavior. Based on the neural synchronization, decision-making behavior can be accurately decoded and predicted. This paper provides a reference for feature extraction of brain-computer interface.
Behaviour Prediction Based on Neural Synchronization
Lect. Notes Electrical Eng.
International Conference on Man-Machine-Environment System Engineering ; 2023 ; Beijing, China October 20, 2023 - October 23, 2023
05.09.2023
6 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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