Bridging the gap between motor imagery and motor execution with a brain–robot interface

Bauer, Robert; Fels, Meike; Vukelić, Mathias; Ziemann, Ulf; Gharabaghi, Alireza

doi:10.1016/j.neuroimage.2014.12.026

Cited by 84 publications

(86 citation statements)

References 69 publications

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“…Proprioceptive feedback of MI-associated β-oscillations with a BRI facilitated decoding of MI induced brain states (Gomez-Rodriguez et al, 2011), activated a distributed cortical network (Vukelić et al, 2014) and bridged the gap between the abilities and cortical networks of motor imagery and motor execution (Bauer et al, 2015). A direct comparison of these two feedback modalities and their neural oscillatory signatures, particularly with regard to the skill for regional selfregulation of β-oscillations and the engagement of distributed functional cortical networks, is however still missing.…”

Section: Introductionmentioning

confidence: 99%

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

2015

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Section: Introductionmentioning

confidence: 99%

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

2015

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“…Along the same lines, recent neurofeedback interventions have explored the plasticity of the nondominant, right hemisphere in the healthy [39] and lesioned brain [37, 68]. These findings indicate that combining motor imagery-related β -band event-related desynchronization with proprioceptive feedback in a brain-robot interface environment [69, 70] might be sufficient to unmask latent corticospinal connectivity [37], redistribute sensorimotor connectivity patterns, and enhance corticospinal pathways of both the S1 and PM cortex [39, 71]. Moreover, pilot data applying this concept demonstrated operant conditioning of the targeted brain state and provided a direct brain-behavior relationship [72] with functional gains after stroke, which were specific for the trained task [68].…”

Section: Discussionmentioning

confidence: 99%

Neuromuscular Plasticity: Disentangling Stable and Variable Motor Maps in the Human Sensorimotor Cortex

Kraus

Gharabaghi

2016

Neural Plasticity

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Motor maps acquired with transcranial magnetic stimulation (TMS) are evolving as a biomarker for monitoring disease progression or the effects of therapeutic interventions. High test-retest reliability of this technique for long observation periods is therefore required to differentiate daily or weekly fluctuations from stable plastic reorganization of corticospinal connectivity. In this study, a novel projection, interpolation, and coregistration technique, which considers the individual gyral anatomy, was applied in healthy subjects for biweekly acquired TMS motor maps over a period of twelve weeks. The intraclass correlation coefficient revealed long-term reliability of motor maps with relevant interhemispheric differences. The sensorimotor cortex and nonprimary motor areas of the dominant hemisphere showed more extended and more stable corticospinal connectivity. Long-term correlations of the MEP amplitudes at each stimulation site revealed mosaic-like clusters of consistent corticospinal excitability. The resting motor threshold, centre of gravity, and mean MEPs across all TMS sites, as highly reliable cortical map parameters, could be disentangled from more variable parameters such as MEP area and volume. Cortical TMS motor maps provide high test-retest reliability for long-term monitoring when analyzed with refined techniques. They may guide restorative interventions which target dormant corticospinal connectivity for neurorehabilitation.

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“…The scope for recovery may also be improved when using advanced assistive rehabilitation technology based on brain-robot interfaces, since these devices were found to constitute a back-door to the motor system (Gomez-Rodriguez et al, 2011; Bauer et al, 2015). 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).…”

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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Bridging the gap between motor imagery and motor execution with a brain–robot interface

Cited by 84 publications

References 69 publications

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

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

Neuromuscular Plasticity: Disentangling Stable and Variable Motor Maps in the Human Sensorimotor Cortex

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

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