An Empirical Analysis of Neurofeedback Using PID Control Systems

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Authors: Zeyda, F., Aranyi, G., Charles, F. and Cavazza, M.

Journal: Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015

Pages: 3197-3202

ISBN: 9781479986965

DOI: 10.1109/SMC.2015.555

© 2015 IEEE. Neurofeedback systems can be modeled as closed loop control systems with negative feedback. However, little work to date has investigated the potential of this representation in gaining a better understanding of the actual dynamics of neurofeedback towards explaining subjects' performance. In this paper, we analyze neurofeedback training data through a PID control model. We first show that PID model fitting can produce curves that are qualitatively aligned to the measured BCI signal. Secondly, we examine how brain activity during neurofeedback can be related to common characteristics of control systems. For this, we formalized a pre-existing neurofeedback EEG experiment using a SimulinkR model that captures both the neural activity and the external algorithm that was utilized to generate the feedback signal. We then used a regression model to fit individual trial data to PID coefficients for the control model. Our results suggest that successful trials tend to be associated to higher average values of Ki, which represents the error-reducing component of the PID controller. It hints that convergence in successful neurofeedback is progressive but complete in approaching the target.

This data was imported from Web of Science (Lite):

Authors: Zeyda, F., Aranyi, G., Charles, F., Cavazza, M. and IEEE

Journal: 2015 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2015): BIG DATA ANALYTICS FOR HUMAN-CENTRIC SYSTEMS

Pages: 3197-3202

ISSN: 1062-922X

DOI: 10.1109/SMC.2015.555

The data on this page was last updated at 04:58 on May 27, 2019.