Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation
Identifieur interne : 000629 ( Main/Merge ); précédent : 000628; suivant : 000630Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation
Auteurs : Robert Bauer [Allemagne] ; Alireza Gharabaghi [Allemagne]Source :
- Frontiers in Neuroscience [ 1662-4548 ] ; 2015.
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
Url:
DOI: 10.3389/fnins.2015.00036
PubMed: 25729347
PubMed Central: 4325901
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<author><name sortKey="Gharabaghi, Alireza" sort="Gharabaghi, Alireza" uniqKey="Gharabaghi A" first="Alireza" last="Gharabaghi">Alireza Gharabaghi</name>
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation</title>
<author><name sortKey="Bauer, Robert" sort="Bauer, Robert" uniqKey="Bauer R" first="Robert" last="Bauer">Robert Bauer</name>
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<author><name sortKey="Gharabaghi, Alireza" sort="Gharabaghi, Alireza" uniqKey="Gharabaghi A" first="Alireza" last="Gharabaghi">Alireza Gharabaghi</name>
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<series><title level="j">Frontiers in Neuroscience</title>
<idno type="ISSN">1662-4548</idno>
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<front><div type="abstract" xml:lang="en"><p>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 <italic>assistive</italic>
BCIs, their inherent class switching clashes with the operant conditioning goal of <italic>restorative</italic>
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.</p>
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<author><name sortKey="Poon, C S" uniqKey="Poon C">C.-S. Poon</name>
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<author><name sortKey="Blankertz, B" uniqKey="Blankertz B">B. Blankertz</name>
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<author><name sortKey="Birbaumer, N" uniqKey="Birbaumer N">N. Birbaumer</name>
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