ArticleFrontiers in neuroscience2015
Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation.
Article in Frontiers in neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Closed-Loop Feedback Techniques in Sleep Studies-Related Issues and Future Directions.Sensors (Basel, Switzerland) · 2020Pooled it
- Adaptive Cognitive Intervention Architecture: An Exploratory Computational Framework for Precision Reading Comprehension in Higher Education.Journal of Intelligence · 2026Article
- A Precision Computational Framework for sLORETA Neurofeedback in Mild Cognitive Impairment: Integration of qEEG Biomarkers and Neuropsychological Metrics.International journal of environmental research and public health · 2026Article
- Modern views of machine learning for precision psychiatry.Patterns (New York, N.Y.) · 2022Review
- Neurosurgery and artificial intelligence.AIMS neuroscience · 2021Review
- The combination of brain-computer interfaces and artificial intelligence: applications and challenges.Annals of translational medicine · 2020Review
- Brain-Machine Neurofeedback: Robotics or Electrical Stimulation?Frontiers in bioengineering and biotechnology · 2020Article
- Recruitment of Additional Corticospinal Pathways in the Human Brain with State-Dependent Paired Associative Stimulation.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2018Article
- Reaction Time Predicts Brain-Computer Interface Aptitude.IEEE journal of translational engineering in health and medicine · 2018Article
- A New Frontier: The Convergence of Nanotechnology, Brain Machine Interfaces, and Artificial Intelligence.Frontiers in neuroscience · 2018Review
- Effect of Different Movement Speed Modes on Human Action Observation: An EEG Study.Frontiers in neuroscience · 2018Article
- Plasticity of premotor cortico-muscular coherence in severely impaired stroke patients with hand paralysis.NeuroImage. Clinical · 2017Article
- Proprioceptive Feedback Facilitates Motor Imagery-Related Operant Learning of Sensorimotor β-Band Modulation.Frontiers in neuroscience · 2017Article
- Constraints and Adaptation of Closed-Loop Neuroprosthetics for Functional Restoration.Frontiers in neuroscience · 2017Article
- Therapeutic Applications of BCI Technologies.Brain computer interfaces (Abingdon, England) · 2017Article
- Brain State-Dependent Closed-Loop Modulation of Paired Associative Stimulation Controlled by Sensorimotor Desynchronization.Frontiers in cellular neuroscience · 2016Article
- Closed-Loop Task Difficulty Adaptation during Virtual Reality Reach-to-Grasp Training Assisted with an Exoskeleton for Stroke Rehabilitation.Frontiers in neuroscience · 2016Article
- What Turns Assistive into Restorative Brain-Machine Interfaces?Frontiers in neuroscience · 2016Article
- Hybrid Neuroprosthesis for the Upper Limb: Combining Brain-Controlled Neuromuscular Stimulation with a Multi-Joint Arm Exoskeleton.Frontiers in neuroscience · 2016Article
- Closed-Loop Neuroprosthesis for Reach-to-Grasp Assistance: Combining Adaptive Multi-channel Neuromuscular Stimulation with a Multi-joint Arm Exoskeleton.Frontiers in neuroscience · 2016Article
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2 authors.
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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.
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