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An Empirical Analysis of Neurofeedback Using PID Control Systems.

Zeyda, F., Aranyi, G., Charles, F. and Cavazza, M., 2016. An Empirical Analysis of Neurofeedback Using PID Control Systems. In: IEEE International Conference on Systems, Man, and Cybernetics, 9-12 October 2015, Hong Kong, 3197 - 3202.

Full text available as:

smc2015.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


DOI: 10.1109/SMC.2015.555


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.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:neurofeedback , BCI , model fitting; PID; linear control systems; statistical analysis; optimization
Group:Faculty of Science & Technology
ID Code:35469
Deposited By: Symplectic RT2
Deposited On:10 May 2021 14:28
Last Modified:14 Mar 2022 14:27


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