Using brain–computer interfaces to induce neural plasticity and restore function

Grosse-Wentrup, Moritz; Mattia, Donatella; Oweiss, Karim G.

doi:10.1088/1741-2560/8/2/025004

Cited by 193 publications

(174 citation statements)

References 15 publications

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“…6 Accordingly, NF should help to induce adaptive neural plasticity and thereby contribute to restoring lost motor function. 10 Several studies indicate that MI training in combination with NF can indeed induce positive changes at behavioral, functional, and structural levels. [11][12][13][14][15] At least 1 study provides clear evidence that functional changes are more pronounced when MI is combined with NF as compared with MI without NF.…”

Section: Introductionmentioning

confidence: 99%

High-Intensity Chronic Stroke Motor Imagery Neurofeedback Training at Home: Three Case Reports

et al. 2017

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Motor imagery (MI) with neurofeedback has been suggested as promising for motor recovery after stroke. Evidence suggests that regular training facilitates compensatory plasticity, but frequent training is difficult to integrate into everyday life. Using a wireless electroencephalogram (EEG) system, we implemented a frequent and efficient neurofeedback training at the patients' home. Aiming to overcome maladaptive changes in cortical lateralization patterns we presented a visual feedback, representing the degree of contralateral sensorimotor cortical activity and the degree of sensorimotor cortex lateralization. Three stroke patients practiced every other day, over a period of 4 weeks. Training-related changes were evaluated on behavioral, functional, and structural levels. All 3 patients indicated that they enjoyed the training and were highly motivated throughout the entire training regime. EEG activity induced by MI of the affected hand became more lateralized over the course of training in all three patients. The patient with a significant functional change also showed increased white matter integrity as revealed by diffusion tensor imaging, and a substantial clinical improvement of upper limb motor functions. Our study provides evidence that regular, home-based practice of MI neurofeedback has the potential to facilitate cortical reorganization and may also increase associated improvements of upper limb motor function in chronic stroke patients.

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Section: Introductionmentioning

confidence: 99%

High-Intensity Chronic Stroke Motor Imagery Neurofeedback Training at Home: Three Case Reports

et al. 2017

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“…Over the recent years, brain-computer interface (BCI) technology has been proposed for neurorehabilitation by inducing cortical plasticity which is the proposed mechanism for motor learning/recovery [5,7,11,34].…”

Section: Introductionmentioning

confidence: 99%

Detecting and classifying three different hand movement types through electroencephalography recordings for neurorehabilitation

Jochumsen

Niazi

Dremstrup

et al. 2015

Med Biol Eng Comput

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Brain-computer interfaces can be used for motor substitution and recovery; therefore, detection and classification of movement intention is crucial for optimal control. In this study, palmar, lateral and pinch grasps were differentiated from the idle state and classified from single-trial EEG using only information prior the movement onset. Fourteen healthy subjects performed the three grasps 100 times while EEG was recorded from 25 electrodes. Temporal and spectral features were extracted from each electrode, and feature reduction was performed using sequential forward selection (SFS) and principal component analysis (PCA).The detection problem was investigated as the ability to discriminate between movement preparation and the idle state. Furthermore, all task pairs and the three movements together were classified. The best detection performance across movements (79±8%) was obtained by combining temporal and spectral features. The best movement-movement discrimination was obtained using spectral features; 76±9% (2-class) and 63±10% (3-class). For movement detection and discrimination, the performance was similar across grasp types and task pairs; SFS outperformed PCA. The results show it is feasible to detect different grasps and classify the distinct movements using only information prior to the movement onset; which may enable brain-computer interface-based neurorehabilitation of upper limb function through Hebbian learning mechanisms.

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“…In severely impaired patients, this approach may be used to sychronize movement intent with execution by having the robot carry out the movement inferred by the BCI. Preliminary evidence suggests that this may support processes of brain plasticity involved in motor-recovery [18,35,36].…”

Section: Discussion and Outlookmentioning

confidence: 90%

“…Identifying such correlates may yield helpful insight into processes of brain plasticity during therapy, which could be used to evaluate a therapy's success and adapt current rehabilitation strategies. In particular, the combination of rehabilitation robotics with brain-computer interfaces (BCIs) may facilitate a real-time adaptation of the robotic system to the patient's needs, e.g., by optimizing factors such as task-difficulty [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

A brain-robot interface for studying motor learning after stroke

Meyer

Peters

Brtz

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Despite intensive efforts, no significant benefit of rehabilitation robotics in post-stroke motor-recovery has yet been demonstrated in large-scale clinical trials. The present work is based on the premise that future advances in rehabilitation robotics require an enhanced understanding of the neural processes involved in motor learning after stroke. We present a system that combines a Barret WAM TM seven degreeof-freedom robot arm with neurophysiological recordings for the purpose of studying post-stroke motor learning. We used this system to conduct a pilot study on motor learning during reaching movements with two stroke patients. Preliminary results indicate that pre-trial brain activity in ipsilesional sensorimotor areas may be a neural correlate of the current state of motor learning. These results are discussed in terms of their relevance for future rehabilitation strategies that combine rehabilitation robotics with real-time analyses of neurophysiological recordings.

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Using brain–computer interfaces to induce neural plasticity and restore function

Cited by 193 publications

References 15 publications

High-Intensity Chronic Stroke Motor Imagery Neurofeedback Training at Home: Three Case Reports

High-Intensity Chronic Stroke Motor Imagery Neurofeedback Training at Home: Three Case Reports

Detecting and classifying three different hand movement types through electroencephalography recordings for neurorehabilitation

A brain-robot interface for studying motor learning after stroke

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