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Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation.

Bauer R, Gharabaghi A - Front Neurosci (2015)

Bottom Line: For each feedback iteration, we first determined the thresholds that result in minimal action entropy and maximal instructional efficiency.We then used the resulting vector for the simulation of continuous threshold adaptation.Finally, on the basis of information-theory, we provided an explanation for the achieved benefits of adaptive threshold setting.

View Article: PubMed Central - PubMed

Affiliation: Division of Functional and Restorative Neurosurgery and Division of Translational Neurosurgery, Department of Neurosurgery, Eberhard Karls University Tuebingen Tuebingen, Germany ; Neuroprosthetics Research Group, Werner Reichardt Centre for Integrative Neuroscience, Eberhard Karls University Tuebingen Tuebingen, Germany.

ABSTRACT
Restorative brain-computer interfaces (BCI) are increasingly used to provide feedback of neuronal states in a bid to normalize pathological brain activity and achieve behavioral gains. However, patients and healthy subjects alike often show a large variability, or even inability, of brain self-regulation for BCI control, known as BCI illiteracy. Although current co-adaptive algorithms are powerful for assistive BCIs, their inherent class switching clashes with the operant conditioning goal of restorative BCIs. Moreover, due to the treatment rationale, the classifier of restorative BCIs usually has a constrained feature space, thus limiting the possibility of classifier adaptation. In this context, we applied a Bayesian model of neurofeedback and reinforcement learning for different threshold selection strategies to study the impact of threshold adaptation of a linear classifier on optimizing restorative BCIs. For each feedback iteration, we first determined the thresholds that result in minimal action entropy and maximal instructional efficiency. We then used the resulting vector for the simulation of continuous threshold adaptation. We could thus show that threshold adaptation can improve reinforcement learning, particularly in cases of BCI illiteracy. Finally, on the basis of information-theory, we provided an explanation for the achieved benefits of adaptive threshold setting.

No MeSH data available.


Shows in pseudo code the computations performed for the reinforcement learning simulation, with the first study exploring the effect of different fixed thresholds, and the second the effect of threshold adaption on the basis of the findings from the first study.
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Figure 3: Shows in pseudo code the computations performed for the reinforcement learning simulation, with the first study exploring the effect of different fixed thresholds, and the second the effect of threshold adaption on the basis of the findings from the first study.

Mentions: All simulations were performed for each research question and environment using 10,000 iterations (i), for thresholds (θ) ranging from −10 to 10 and a step size (δ) of 0.1. The prior belief of the subject was initialized by setting αF, αT, βF, and βT to 1. The computations were realized with a custom written code in Matlab R 2014A on a Windows 7 machine. The pseudo-code example (Figure 3) provides a clearer description of this algorithm.


Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation.

Bauer R, Gharabaghi A - Front Neurosci (2015)

Shows in pseudo code the computations performed for the reinforcement learning simulation, with the first study exploring the effect of different fixed thresholds, and the second the effect of threshold adaption on the basis of the findings from the first study.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4325901&req=5

Figure 3: Shows in pseudo code the computations performed for the reinforcement learning simulation, with the first study exploring the effect of different fixed thresholds, and the second the effect of threshold adaption on the basis of the findings from the first study.
Mentions: All simulations were performed for each research question and environment using 10,000 iterations (i), for thresholds (θ) ranging from −10 to 10 and a step size (δ) of 0.1. The prior belief of the subject was initialized by setting αF, αT, βF, and βT to 1. The computations were realized with a custom written code in Matlab R 2014A on a Windows 7 machine. The pseudo-code example (Figure 3) provides a clearer description of this algorithm.

Bottom Line: For each feedback iteration, we first determined the thresholds that result in minimal action entropy and maximal instructional efficiency.We then used the resulting vector for the simulation of continuous threshold adaptation.Finally, on the basis of information-theory, we provided an explanation for the achieved benefits of adaptive threshold setting.

View Article: PubMed Central - PubMed

Affiliation: Division of Functional and Restorative Neurosurgery and Division of Translational Neurosurgery, Department of Neurosurgery, Eberhard Karls University Tuebingen Tuebingen, Germany ; Neuroprosthetics Research Group, Werner Reichardt Centre for Integrative Neuroscience, Eberhard Karls University Tuebingen Tuebingen, Germany.

ABSTRACT
Restorative brain-computer interfaces (BCI) are increasingly used to provide feedback of neuronal states in a bid to normalize pathological brain activity and achieve behavioral gains. However, patients and healthy subjects alike often show a large variability, or even inability, of brain self-regulation for BCI control, known as BCI illiteracy. Although current co-adaptive algorithms are powerful for assistive BCIs, their inherent class switching clashes with the operant conditioning goal of restorative BCIs. Moreover, due to the treatment rationale, the classifier of restorative BCIs usually has a constrained feature space, thus limiting the possibility of classifier adaptation. In this context, we applied a Bayesian model of neurofeedback and reinforcement learning for different threshold selection strategies to study the impact of threshold adaptation of a linear classifier on optimizing restorative BCIs. For each feedback iteration, we first determined the thresholds that result in minimal action entropy and maximal instructional efficiency. We then used the resulting vector for the simulation of continuous threshold adaptation. We could thus show that threshold adaptation can improve reinforcement learning, particularly in cases of BCI illiteracy. Finally, on the basis of information-theory, we provided an explanation for the achieved benefits of adaptive threshold setting.

No MeSH data available.