Send your doctor after this one to see if this could help you.
http://journal.frontiersin.org/article/10.3389/fnhum.2015.00391/full?
- 1Division of Functional and Restorative
Neurosurgery and Division of Translational Neurosurgery, Department of
Neurosurgery, Eberhard Karls University Tuebingen, Tuebingen, Germany
- 2Neuroprosthetics Research Group, Werner
Reichardt Centre for Integrative Neuroscience, Eberhard Karls University
Tuebingen, Tuebingen, Germany
Neurofeedback training of Motor imagery (MI)-related brain-states
with brain-computer/brain-machine interfaces (BCI/BMI) is currently
being explored as an experimental intervention prior to standard
physiotherapy to improve the motor outcome of stroke rehabilitation. The
use of BCI/BMI technology increases the adherence to MI training more
efficiently than interventions with sham or no feedback. Moreover, pilot
studies suggest that such a
priming intervention before
physiotherapy might—like some brain stimulation techniques—increase the
responsiveness of the brain to the subsequent physiotherapy, thereby
improving the general clinical outcome. However, there is little
evidence up to now that these BCI/BMI-based interventions have achieved
operate conditioning of specific brain states that facilitate task-specific functional gains beyond the practice of
primed
physiotherapy. In this context, we argue that BCI/BMI technology
provides a valuable neurofeedback tool for rehabilitation but needs to
aim at physiological features relevant for the targeted behavioral gain.
Moreover, this therapeutic intervention has to be informed by concepts
of reinforcement learning to develop its full potential. Such a refined
neurofeedback approach would need to address the following issues: (1)
Defining a physiological feedback target specific to the intended
behavioral gain, e.g., β-band oscillations for cortico-muscular
communication. This targeted brain state could well be different from
the brain state optimal for the neurofeedback task, e.g., α-band
oscillations for differentiating MI from rest; (2) Selecting a BCI/BMI
classification and thresholding approach on the basis of learning
principles, i.e., balancing challenge and reward of the neurofeedback
task instead of maximizing the classification accuracy of the difficulty
level device; and (3) Adjusting the difficulty level in the course of
the training period to account for the cognitive load and the learning
experience of the participant. Here, we propose a comprehensive
neurofeedback strategy for motor restoration after stroke that addresses
these aspects, and provide evidence for the feasibility of the
suggested approach by demonstrating that dynamic threshold adaptation
based on reinforcement learning may lead to frequency-specific operant
conditioning of β-band oscillations paralleled by task-specific motor
improvement; a proposal that requires investigation in a larger cohort
of stroke patients.
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