The Experts below are selected from a list of 43905 Experts worldwide ranked by ideXlab platform
Junichi Ushiba - One of the best experts on this subject based on the ideXlab platform.
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Functional recovery from chronic writer’s cramp by Brain-Computer Interface rehabilitation: a case report
BMC Neuroscience, 2014Co-Authors: Yasunari Hashimoto, Masahiko Mukaino, Junichi UshibaAbstract:Background Dystonia is often currently treated with botulinum toxin injections to spastic muscles, or deep Brain stimulation to the basal ganglia. In addition to these pharmacological or neurosurgical measures, a new noninvasive treatment concept, functional modulation using a Brain-Computer Interface, was tested for feasibility. We recorded electroencephalograms (EEGs) over the bilateral sensorimotor cortex from a patient suffering from chronic writer’s cramp. The patient was asked to suppress an exaggerated beta frequency component in the EEG during hand extension. Results The patient completed biweekly one-hour training for 5 months without any adverse effects. Significant decrease of the beta frequency component during handwriting was confirmed, and was associated with clear functional improvement. Conclusion The current pilot study suggests that a Brain-Computer Interface can give explicit feedback of ongoing cortical excitability to patients with dystonia and allow them to suppress exaggerated neural activity, resulting in functional recovery.
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functional recovery from chronic writer s cramp by Brain Computer Interface rehabilitation a case report
BMC Neuroscience, 2014Co-Authors: Yasunari Hashimoto, Masahiko Mukaino, Tetsuo Ota, Meigen Liu, Junichi UshibaAbstract:Background: Dystonia is often currently treated with botulinum toxin injections to spastic muscles, or deep Brain stimulation to the basal ganglia. In addition to these pharmacological or neurosurgical measures, a new noninvasive treatment concept, functional modulation using a Brain-Computer Interface, was tested for feasibility. We recorded electroencephalograms (EEGs) over the bilateral sensorimotor cortex from a patient suffering from chronic writer’s cramp. The patient was asked to suppress an exaggerated beta frequency component in the EEG during hand extension. Results: The patient completed biweekly one-hour training for 5 months without any adverse effects. Significant decrease of the beta frequency component during handwriting was confirmed, and was associated with clear functional improvement. Conclusion: The current pilot study suggests that a Brain-Computer Interface can give explicit feedback of ongoing cortical excitability to patients with dystonia and allow them to suppress exaggerated neural activity, resulting in functional recovery.
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efficacy of Brain Computer Interface driven neuromuscular electrical stimulation for chronic paresis after stroke
Journal of Rehabilitation Medicine, 2014Co-Authors: Masahiko Mukaino, Keiichiro Shindo, Toshiyuki Fujiwara, Akio Kimura, Junichi UshibaAbstract:OBJECTIVE: Brain Computer Interface technology is of great interest to researchers as a potential therapeutic measure for people with severe neurological disorders. The aim of this study was to examine the efficacy of Brain Computer Interface, by comparing conventional neuromuscular electrical stimulation and Brain Computer Interface-driven neuromuscular electrical stimulation, using an A-B-A-B withdrawal single-subject design. METHODS: A 38-year-old male with severe hemiplegia due to a putaminal haemorrhage participated in this study. The design involved 2 epochs. In epoch A, the patient attempted to open his fingers during the application of neuromuscular electrical stimulation, irrespective of his actual Brain activity. In epoch B, neuromuscular electrical stimulation was applied only when a significant motor-related cortical potential was observed in the electroencephalogram. RESULTS: The subject initially showed diffuse functional magnetic resonance imaging activation and small electro-encephalogram responses while attempting finger movement. Epoch A was associated with few neurological or clinical signs of improvement. Epoch B, with a Brain Computer Interface, was associated with marked lateralization of electroencephalogram (EEG) and blood oxygenation level dependent responses. Voluntary electromyogram (EMG) activity, with significant EEG-EMG coherence, was also prompted. Clinical improvement in upper-extremity function and muscle tone was observed. CONCLUSION: These results indicate that self-directed training with a Brain Computer Interface may induce activity- dependent cortical plasticity and promote functional recovery. This preliminary clinical investigation encourages further research using a controlled design.
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effects of neurofeedback training with an electroencephalogram based Brain Computer Interface for hand paralysis in patients with chronic stroke a preliminary case series study
Journal of Rehabilitation Medicine, 2011Co-Authors: Keiichiro Shindo, Junichi Ushiba, Akio Kimura, Kimiko Kawashima, Naoki Ota, Mari Ito, Tetsuo Ota, Meige LiuAbstract:Objective To explore the effectiveness of neurorehabilitative training using an electroencephalogram-based Brain- Computer Interface for hand paralysis following stroke. Design A case series study. Subjects Eight outpatients with chronic stroke demonstrating moderate to severe hemiparesis. Methods Based on analysis of volitionally decreased amplitudes of sensory motor rhythm during motor imagery involving extending the affected fingers, real-time visual feedback was provided. After successful motor imagery, a mechanical orthosis partially extended the fingers. Brain-Computer Interface interventions were carried out once or twice a week for a period of 4-7 months, and clinical and neurophysiological examinations pre- and post-intervention were compared. Results New voluntary electromyographic activity was measured in the affected finger extensors in 4 cases who had little or no muscle activity before the training, and the other participants exhibited improvement in finger function. Significantly greater suppression of the sensory motor rhythm over both hemispheres was observed during motor imagery. Transcranial magnetic stimulation showed increased cortical excitability in the damaged hemisphere. Success rates of Brain-Computer Interface training tended to increase as the session progressed in 4 cases. Conclusion Brain-Computer Interface training appears to have yielded some improvement in motor function and Brain plasticity. Further controlled research is needed to clarify the role of the Brain-Computer Interface system.
Moritz Grossewentrup - One of the best experts on this subject based on the ideXlab platform.
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personalized Brain Computer Interface models for motor rehabilitation
Systems Man and Cybernetics, 2017Co-Authors: Anastasiaatalanti Mastakouri, Sebastian Weichwald, Ozan Ozdenizci, Timm Meyer, Bernhard Scholkopf, Moritz GrossewentrupAbstract:We propose to fuse two currently separate research lines on novel therapies for stroke rehabilitation: Brain-Computer Interface (BCI) training and transcranial electrical stimulation (TES). Specifically, we show that BCI technology can be used to learn personalized decoding models that relate the global configuration of Brain rhythms in individual subjects (as measured by EEG) to their motor performance during 3D reaching movements. We demonstrate that our models capture substantial across-subject heterogeneity, and argue that this heterogeneity is a likely cause of limited effect sizes observed in TES for enhancing motor performance. We conclude by discussing how our personalized models can be used to derive optimal TES parameters, e.g., stimulation site and frequency, for individual patients.
Girijesh Prasad - One of the best experts on this subject based on the ideXlab platform.
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tangent space features based transfer learning classification model for two class motor imagery Brain Computer Interface
International Journal of Neural Systems, 2019Co-Authors: Pramod Gaur, Hui Wang, Karl Mccreadie, Ram Bilas Pachori, Girijesh PrasadAbstract:The performance of a Brain–Computer Interface (BCI) will generally improve by increasing the volume of training data on which it is trained. However, a classifier’s generalization ability is often ...
Chin-teng Lin - One of the best experts on this subject based on the ideXlab platform.
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Multimodal Fuzzy Fusion for Enhancing the Motor-Imagery-Based Brain Computer Interface
IEEE Computational Intelligence Magazine, 2019Co-Authors: Humberto Bustince, Yu-cheng Chang, Yang Chang, Javier Ferandez, Yu-kai Wang, José Antonio Sanz, Graçaliz Pereira Dimuro, Chin-teng LinAbstract:Brain-Computer Interface technologies, such as steady-state visually evoked potential, P300, and motor imagery are methods of communication between the human Brain and the external devices. Motor imagery-based Brain-Computer Interfaces are popular because they avoid unnecessary external stimuli. Although feature extraction methods have been illustrated in several machine intelligent systems in motor imagery-based Brain-Computer Interface studies, the performance remains unsatisfactory. There is increasing interest in the use of the fuzzy integrals, the Choquet and Sugeno integrals, that are appropriate for use in applications in which fusion of data must consider possible data interactions. To enhance the classification accuracy of Brain-Computer Interfaces, we adopted fuzzy integrals, after employing the classification method of traditional Brain-Computer Interfaces, to consider possible links between the data. Subsequently, we proposed a novel classification framework called the multimodal fuzzy fusion-based Brain-Computer Interface system. Ten volunteers performed a motor imagery-based Brain-Computer Interface experiment, and we acquired electroencephalography signals simultaneously. The multimodal fuzzy fusion-based Brain-Computer Interface system enhanced performance compared with traditional Brain-Computer Interface systems. Furthermore, when using the motor imagery-relevant electroencephalography frequency alpha and beta bands for the input features, the system achieved the highest accuracy, up to 78.81% and 78.45% with the Choquet and Sugeno integrals, respectively. Herein, we present a novel concept for enhancing Brain-Computer Interface systems that adopts fuzzy integrals, especially in the fusion for classifying Brain-Computer Interface commands.
Yasunari Hashimoto - One of the best experts on this subject based on the ideXlab platform.
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Functional recovery from chronic writer’s cramp by Brain-Computer Interface rehabilitation: a case report
BMC Neuroscience, 2014Co-Authors: Yasunari Hashimoto, Masahiko Mukaino, Junichi UshibaAbstract:Background Dystonia is often currently treated with botulinum toxin injections to spastic muscles, or deep Brain stimulation to the basal ganglia. In addition to these pharmacological or neurosurgical measures, a new noninvasive treatment concept, functional modulation using a Brain-Computer Interface, was tested for feasibility. We recorded electroencephalograms (EEGs) over the bilateral sensorimotor cortex from a patient suffering from chronic writer’s cramp. The patient was asked to suppress an exaggerated beta frequency component in the EEG during hand extension. Results The patient completed biweekly one-hour training for 5 months without any adverse effects. Significant decrease of the beta frequency component during handwriting was confirmed, and was associated with clear functional improvement. Conclusion The current pilot study suggests that a Brain-Computer Interface can give explicit feedback of ongoing cortical excitability to patients with dystonia and allow them to suppress exaggerated neural activity, resulting in functional recovery.
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functional recovery from chronic writer s cramp by Brain Computer Interface rehabilitation a case report
BMC Neuroscience, 2014Co-Authors: Yasunari Hashimoto, Masahiko Mukaino, Tetsuo Ota, Meigen Liu, Junichi UshibaAbstract:Background: Dystonia is often currently treated with botulinum toxin injections to spastic muscles, or deep Brain stimulation to the basal ganglia. In addition to these pharmacological or neurosurgical measures, a new noninvasive treatment concept, functional modulation using a Brain-Computer Interface, was tested for feasibility. We recorded electroencephalograms (EEGs) over the bilateral sensorimotor cortex from a patient suffering from chronic writer’s cramp. The patient was asked to suppress an exaggerated beta frequency component in the EEG during hand extension. Results: The patient completed biweekly one-hour training for 5 months without any adverse effects. Significant decrease of the beta frequency component during handwriting was confirmed, and was associated with clear functional improvement. Conclusion: The current pilot study suggests that a Brain-Computer Interface can give explicit feedback of ongoing cortical excitability to patients with dystonia and allow them to suppress exaggerated neural activity, resulting in functional recovery.