Oscillatory entrainment of the motor cortical network during motor imagery is modulated by the feedback modality

Vukelić, Mathias; Gharabaghi, Alireza

doi:10.1016/j.neuroimage.2015.01.058

Cited by 88 publications

(114 citation statements)

References 77 publications

(97 reference statements)

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“…Moreover, physiological parameters, e.g., distributed cortical patterns in the α-range (Vukelić et al, 2014; Vukelić and Gharabaghi, 2015a,b) and the θ-range (Fels et al, 2015) which were linked to β-band self-regulation may also be used in the long term for this purpose.…”

Section: Resultsmentioning

confidence: 99%

Constraints and Adaptation of Closed-Loop Neuroprosthetics for Functional Restoration

Bauer

Gharabaghi

2017

Front. Neurosci.

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Closed-loop neuroprosthetics aim to compensate for lost function, e.g., by controlling external devices such as prostheses or wheelchairs. Such assistive approaches seek to maximize speed and classification accuracy for high-dimensional control. More recent approaches use similar technology, but aim to restore lost motor function in the long term. To achieve this goal, restorative neuroprosthetics attempt to facilitate motor re-learning and to strengthen damaged and/or alternative neural connections on the basis of neurofeedback training within rehabilitative environments. Such a restorative approach requires reinforcement learning of self-modulated brain activity which is considered to be beneficial for functional rehabilitation, e.g., improvement of β-power modulation over sensorimotor areas for post-stroke movement restoration. Patients with motor impairments, however, may also have a compromised ability for motor task-related regulation of the targeted brain activity. This would affect the estimation of feature weights and hence the classification accuracy of the feedback device. This, in turn, can frustrate the patients and compromise their motor learning. Furthermore, the feedback training may even become erroneous when unconstrained classifier adaptation—which is often used in assistive approaches—is also applied in this rehabilitation context. In conclusion, the conceptual switch from assistance toward restoration necessitates a methodological paradigm shift from classification accuracy toward instructional efficiency. Furthermore, a constrained feature space, a priori regularized feature weights, and difficulty adaptation present key elements of restorative brain interfaces. These factors need, therefore, to be addressed within a therapeutic framework to facilitate reinforcement learning of brain self-regulation for restorative purposes.

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Section: Resultsmentioning

confidence: 99%

Constraints and Adaptation of Closed-Loop Neuroprosthetics for Functional Restoration

Bauer

Gharabaghi

2017

Front. Neurosci.

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“…Exercises based on brain-robot feedback of motor-imagery related sensorimotor beta-band desynchronization may result in connectivity changes of cortico-cortical motor networks (Vukelić et al, 2014; Vukelić and Gharabaghi, 2015a,b), lead to a re-distribution of cortico-spinal connections (Kraus et al, 2016a) and to behavioral gains (Naros et al, 2016b). Combining these tools with an adaptive virtual environment similar to that applied in this study may thus maximize the impact of both approaches on sensorimotor function.…”

Section: Discussionmentioning

confidence: 99%

Closed-Loop Task Difficulty Adaptation during Virtual Reality Reach-to-Grasp Training Assisted with an Exoskeleton for Stroke Rehabilitation

2016

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Stroke patients with severe motor deficits of the upper extremity may practice rehabilitation exercises with the assistance of a multi-joint exoskeleton. Although this technology enables intensive task-oriented training, it may also lead to slacking when the assistance is too supportive. Preserving the engagement of the patients while providing “assistance-as-needed” during the exercises, therefore remains an ongoing challenge. We applied a commercially available seven degree-of-freedom arm exoskeleton to provide passive gravity compensation during task-oriented training in a virtual environment. During this 4-week pilot study, five severely affected chronic stroke patients performed reach-to-grasp exercises resembling activities of daily living. The subjects received virtual reality feedback from their three-dimensional movements. The level of difficulty for the exercise was adjusted by a performance-dependent real-time adaptation algorithm. The goal of this algorithm was the automated improvement of the range of motion. In the course of 20 training and feedback sessions, this unsupervised adaptive training concept led to a progressive increase of the virtual training space (p < 0.001) in accordance with the subjects' abilities. This learning curve was paralleled by a concurrent improvement of real world kinematic parameters, i.e., range of motion (p = 0.008), accuracy of movement (p = 0.01), and movement velocity (p < 0.001). Notably, these kinematic gains were paralleled by motor improvements such as increased elbow movement (p = 0.001), grip force (p < 0.001), and upper extremity Fugl-Meyer-Assessment score from 14.3 ± 5 to 16.9 ± 6.1 (p = 0.026). Combining gravity-compensating assistance with adaptive closed-loop feedback in virtual reality provides customized rehabilitation environments for severely affected stroke patients. This approach may facilitate motor learning by progressively challenging the subject in accordance with the individual capacity for functional restoration. It might be necessary to apply concurrent restorative interventions to translate these improvements into relevant functional gains of severely motor impaired patients in activities of daily living.

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“…Proprioceptive feedback, for example, might be more in tune with the demands of brain interfaces based on kinesthetic motor imagery (Stinear et al 2005;Proske and Gandevia 2009;Vukelić and Gharabaghi 2015;Brauchle et al 2015), which are applied for restoration of motor function in stroke. However, proprioceptive feedback also interacts with tactile (Kavounoudias et al 2008) and visual information (Hagura et al 2007).…”

Section: Multi-dimensional Feedback and Processing Capacitymentioning

confidence: 99%

“…Greater gains are currently being achieved by subacute (Pichiorri et al 2015) than by chronic patients (Ang et al 2014). On the basis of the neurophysiological correlates of motor imagery (Kaiser et al 2011) and motor cortex excitability (Takemi et al 2013;Kraus et al 2016a), such as modulation of β-power (15-30 Hz), these devices may provide an effective backdoor to the motor system (Sharma 2006;Bauer et al 2015), particularly when the subject receives contingent proprioceptive feedback with robotic rehabilitation technology (Gomez-Rodriguez et al 2011;Vukelić et al 2014;Vukelić and Gharabaghi 2015).…”

Section: Introductionmentioning

confidence: 99%

What is the optimal task difficulty for reinforcement learning of brain self-regulation?

Bauer

Vukelić

Gharabaghi

2016

Clinical Neurophysiology

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Oscillatory entrainment of the motor cortical network during motor imagery is modulated by the feedback modality

Cited by 88 publications

References 77 publications

Constraints and Adaptation of Closed-Loop Neuroprosthetics for Functional Restoration

Constraints and Adaptation of Closed-Loop Neuroprosthetics for Functional Restoration

Closed-Loop Task Difficulty Adaptation during Virtual Reality Reach-to-Grasp Training Assisted with an Exoskeleton for Stroke Rehabilitation

What is the optimal task difficulty for reinforcement learning of brain self-regulation?

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