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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 the three learning environments with different maximum classification accuracies, achieved by selecting an appropriate discriminatory steepness of the model. (A) shows the illiterate environment with low classification accuracy, (B) shows the moderate environment with middle classification accuracy, and (C) shows the expert environment with high classification accuracy.
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Figure 2: Shows the three learning environments with different maximum classification accuracies, achieved by selecting an appropriate discriminatory steepness of the model. (A) shows the illiterate environment with low classification accuracy, (B) shows the moderate environment with middle classification accuracy, and (C) shows the expert environment with high classification accuracy.

Mentions: The first research goal was to clarify whether instructional efficiency is optimal at this threshold, or whether alternative thresholds might result in a lower action entropy H or in a better instructional efficiency IE. Furthermore, even if the classification accuracy were maximal for a certain threshold, its magnitude could still vary. A classification accuracy of below 70%, for example, has been proposed as an indicator of BCI-illiteracy (Vidaurre and Blankertz, 2010). Furthermore, accuracies close to chance level and close to perfect classification are of particular interest when seeking to improve restorative BCIs. We therefore simulated different classification accuracies, i.e., 55, 70, and 95%, by using a fixed distance Δ of 1 and setting the discriminatory steepness value D to 0.4, 1.7, or 5.9, respectively. We termed these the illiterate, moderate and expert environments accordingly (see Figure 2).


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

Bauer R, Gharabaghi A - Front Neurosci (2015)

Shows the three learning environments with different maximum classification accuracies, achieved by selecting an appropriate discriminatory steepness of the model. (A) shows the illiterate environment with low classification accuracy, (B) shows the moderate environment with middle classification accuracy, and (C) shows the expert environment with high classification accuracy.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Shows the three learning environments with different maximum classification accuracies, achieved by selecting an appropriate discriminatory steepness of the model. (A) shows the illiterate environment with low classification accuracy, (B) shows the moderate environment with middle classification accuracy, and (C) shows the expert environment with high classification accuracy.
Mentions: The first research goal was to clarify whether instructional efficiency is optimal at this threshold, or whether alternative thresholds might result in a lower action entropy H or in a better instructional efficiency IE. Furthermore, even if the classification accuracy were maximal for a certain threshold, its magnitude could still vary. A classification accuracy of below 70%, for example, has been proposed as an indicator of BCI-illiteracy (Vidaurre and Blankertz, 2010). Furthermore, accuracies close to chance level and close to perfect classification are of particular interest when seeking to improve restorative BCIs. We therefore simulated different classification accuracies, i.e., 55, 70, and 95%, by using a fixed distance Δ of 1 and setting the discriminatory steepness value D to 0.4, 1.7, or 5.9, respectively. We termed these the illiterate, moderate and expert environments accordingly (see Figure 2).

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.