Closed-loop adaptation of neurofeedback based on mental effort facilitates reinforcement learning of brain self-regulation

Bauer, Robert; Fels, Meike; Royter, Vladislav; Raco, Valerio; Gharabaghi, Alireza

doi:10.1016/j.clinph.2016.06.020

Cited by 32 publications

(31 citation statements)

References 53 publications

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“…A detailed description of the neurofeedback environment applied in this study has already been provided in previous work of our group with healthy subjects and stroke patients and is cited here accordingly: The effector of the device applied in this study is a commercially available electromechanical hand orthosis (Amadeo, Tyromotion GmbH, Graz, Austria) which enables mass finger extension and flexion, while the wrist remains fixed without any movement (Bauer et al, 2016a, Bauer et al, 2016b). This robotic orthosis is regularly used in standard rehabilitation exercises independent of brain-interfacing.…”

Section: Methodsmentioning

confidence: 99%

Plasticity of premotor cortico-muscular coherence in severely impaired stroke patients with hand paralysis

Belardinelli

Laer

Ortiz

et al. 2017

NeuroImage: Clinical

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Motor recovery in severely impaired stroke patients is often very limited. To refine therapeutic interventions for regaining motor control in this patient group, the functionally relevant mechanisms of neuronal plasticity need to be detected. Cortico-muscular coherence (CMC) may provide physiological and topographic insights to achieve this goal. Synchronizing limb movements to motor-related brain activation is hypothesized to reestablish cortico-motor control indexed by CMC.In the present study, right-handed, chronic stroke patients with right-hemispheric lesions and left hand paralysis participated in a four-week training for their left upper extremity. A brain-robot interface turned event-related beta-band desynchronization of the lesioned sensorimotor cortex during kinesthetic motor-imagery into the opening of the paralyzed hand by a robotic orthosis. Simultaneous MEG/EMG recordings and individual models from MRIs were used for CMC detection and source reconstruction of cortico-muscular connectivity to the affected finger extensors before and after the training program. The upper extremity-FMA of the patients improved significantly from 16.23 ± 6.79 to 19.52 ± 7.91 (p = 0.0015). All patients showed significantly increased CMC in the beta frequency-band, with a distributed, bi-hemispheric pattern and considerable inter-individual variability. The location of CMC changes was not correlated to the severity of the motor impairment, the motor improvement or the lesion volume. Group analysis of the cortical overlap revealed a common feature in all patients following the intervention: a significantly increased level of ipsilesional premotor CMC that extended from the superior to the middle and inferior frontal gyrus, along with a confined area of increased CMC in the contralesional premotor cortex.In conclusion, functionally relevant modulations of CMC can be detected in patients with long-term, severe motor deficits after a brain-robot assisted rehabilitation training. Premotor beta-band CMC may serve as a biomarker and therapeutic target for novel treatment approaches in this patient group.

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Section: Methodsmentioning

confidence: 99%

Plasticity of premotor cortico-muscular coherence in severely impaired stroke patients with hand paralysis

Belardinelli

Laer

Ortiz

et al. 2017

NeuroImage: Clinical

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“…Accordingly, several methods have been proposed for locating the threshold for maximum learning (Ivanova et al, 2005; Cegarra and Chevalier, 2008; Naros et al, 2016; Bauer et al, 2016a,b). 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%

“…This may cause frustration, which, in turn, may be exacerbated due to the low classification accuracy caused by the constrained and regularized feature space (Nijboer et al, 2008; Fels et al, 2015). In this context, we propose that difficulty adaptation be applied to overcome cognitive load issues (Bauer and Gharabaghi, 2015a; Bauer et al, 2016a,b). Such an approach may also improve the instructional efficiency of feedback (Bauer and Gharabaghi, 2015b) and maintain motivation (Bauer et al, 2016a,b).…”

Section: Methodological Adjustmentsmentioning

confidence: 99%

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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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“…More specifically, both robot-assisted rehabilitation of proprioceptive hand function (Metzger et al, 2014) and inertial sensor-based virtual reality feedback of the arm (Wittmann et al, 2015) benefit from assessment-driven adjustments of exercise difficulty. Furthermore, a direct comparison between adaptive BRI training and non-adaptive training (Naros et al, 2016b) or sham adaptation (Bauer et al, 2016a) in healthy patients revealed the impact of reinforcement-based adaptation for the improvement of performance. Moreover, the exercise difficulty has been shown to influence the learning incentive during the training; more specifically, the optimal difficulty level could be determined empirically while disentangling the relative contribution of neurofeedback specificity and sensitivity (Bauer et al, 2016b).…”

Section: Introductionmentioning

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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Closed-loop adaptation of neurofeedback based on mental effort facilitates reinforcement learning of brain self-regulation

Cited by 32 publications

References 53 publications

Plasticity of premotor cortico-muscular coherence in severely impaired stroke patients with hand paralysis

Plasticity of premotor cortico-muscular coherence in severely impaired stroke patients with hand paralysis

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

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