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Neurofeedback Difficulty and Mental Effort

In sports training, a good way to set the intensity of a training session is according to your ability. If you train too hard, go softer. If you train too soft, go harder. This approach seems intuitive. Difficulty adaptation based on actual challenge seems sensible. But how can challenge be measured objectively? When is the… Read more »

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On being challenged adequately by a restorative BCI

The utility of a restorative BCI depends not only on machine learning measures like classification accuracy, but also on how the subject experiences the intervention. Roughly a quarter of subjects appear to suffer from BCI-illiteracy, i.e. they have difficulty when trying to control a BCI. An interesting question is whether they differ in their experience… Read more »

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Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces

Restorative brain-computer interfaces (BCI) provide feedback of neuronal states to normalize pathological brain activity and achieve behavioral gains. Adaptive algorithms have proven to be powerful for assistive BCIs, but their inherent class switching clashes with the operant conditioning goal of restorative BCIs. Due to the treatment rationale of restorative BCIs, the classifier should be limited… Read more »

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Restorative or assistive BCI? The Argument for a constrained feature space

There is a major aspect which i believe is especially prevalent in the area of BCI for Stroke Rehab. It is the apparent gap between how Brain-computer-Interface (BCI) people, mostly recruited from mathematics and engineering departments and the Neurofeedback (NFB) people, mostly recruited from practitioners and psychologists understand the treatment. In my opinion this distinction… Read more »