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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 time course of action entropy as black contour lines (from 0.95 to 0.05 in steps of 0.05). The figures also show the threshold resulting in minimum entropy (blue trace) and maximum instructional efficiency (red trace) for each specific iteration. Training was performed with a fixed threshold (y-axis) and results are shown over iterations (x-axis in logarithmic scale). Subplots depict the illiterate (A), moderate (B) and expert (C) environment.
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Figure 4: Shows the time course of action entropy as black contour lines (from 0.95 to 0.05 in steps of 0.05). The figures also show the threshold resulting in minimum entropy (blue trace) and maximum instructional efficiency (red trace) for each specific iteration. Training was performed with a fixed threshold (y-axis) and results are shown over iterations (x-axis in logarithmic scale). Subplots depict the illiterate (A), moderate (B) and expert (C) environment.

Mentions: We observed a characteristic beam-like shape of progression toward minimal entropy originating from the threshold of maximum classification accuracy (see black trace in Figure 4). In all environments, reduction of entropy first commenced at the threshold of maximum classification accuracy, particularly in environments with higher classification accuracy. Interestingly enough, the range of thresholds that resulted in a reduction of action entropy was narrower for the expert than for the illiterate environment (see Figures 4A–C). Later, the transition between high and low entropy was at higher thresholds than at maximum classification accuracy (CA) thresholds. However, once learning commenced, transition to low entropy was more rapid. This was expressed by a highly asymmetric pattern of entropy reduction (see Figure 4).


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

Bauer R, Gharabaghi A - Front Neurosci (2015)

Shows the time course of action entropy as black contour lines (from 0.95 to 0.05 in steps of 0.05). The figures also show the threshold resulting in minimum entropy (blue trace) and maximum instructional efficiency (red trace) for each specific iteration. Training was performed with a fixed threshold (y-axis) and results are shown over iterations (x-axis in logarithmic scale). Subplots depict the illiterate (A), moderate (B) and expert (C) environment.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Shows the time course of action entropy as black contour lines (from 0.95 to 0.05 in steps of 0.05). The figures also show the threshold resulting in minimum entropy (blue trace) and maximum instructional efficiency (red trace) for each specific iteration. Training was performed with a fixed threshold (y-axis) and results are shown over iterations (x-axis in logarithmic scale). Subplots depict the illiterate (A), moderate (B) and expert (C) environment.
Mentions: We observed a characteristic beam-like shape of progression toward minimal entropy originating from the threshold of maximum classification accuracy (see black trace in Figure 4). In all environments, reduction of entropy first commenced at the threshold of maximum classification accuracy, particularly in environments with higher classification accuracy. Interestingly enough, the range of thresholds that resulted in a reduction of action entropy was narrower for the expert than for the illiterate environment (see Figures 4A–C). Later, the transition between high and low entropy was at higher thresholds than at maximum classification accuracy (CA) thresholds. However, once learning commenced, transition to low entropy was more rapid. This was expressed by a highly asymmetric pattern of entropy reduction (see Figure 4).

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.