The Experts below are selected from a list of 4521 Experts worldwide ranked by ideXlab platform
Yukiyasu Kamitani - One of the best experts on this subject based on the ideXlab platform.
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Corrigendum: Real-Time Control of a Neuroprosthetic Hand by Magnetoencephalographic Signals from Paralysed Patients.
Scientific reports, 2016Co-Authors: Ryohei Fukuma, Takufumi Yanagisawa, Youichi Saitoh, Koichi Hosomi, Haruhiko Kishima, Takeshi Shimizu, Hisato Sugata, Hiroshi Yokoi, Masayuki Hirata, Yukiyasu KamitaniAbstract:Corrigendum: Real-Time Control of a Neuroprosthetic Hand by Magnetoencephalographic Signals from Paralysed Patients
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Real-time control of a neuroprosthetic hand by magnetoencephalographic signals from paralysed patients
Scientific reports, 2016Co-Authors: Ryohei Fukuma, Takufumi Yanagisawa, Youichi Saitoh, Koichi Hosomi, Haruhiko Kishima, Takeshi Shimizu, Hisato Sugata, Hiroshi Yokoi, Masayuki Hirata, Yukiyasu KamitaniAbstract:Neuroprosthetic arms might potentially restore motor functions for severely paralysed patients. Invasive measurements of cortical currents using electrocorticography have been widely used for neuroprosthetic control. Moreover, magnetoencephalography (MEG) exhibits characteristic brain signals similar to those of invasively measured signals. However, it remains unclear whether non-invasively measured signals convey enough motor information to control a neuroprosthetic hand, especially for severely paralysed patients whose sensorimotor cortex might be reorganized. We tested an MEG-based neuroprosthetic system to evaluate the accuracy of using cortical currents in the sensorimotor cortex of severely paralysed patients to control a prosthetic hand. The patients attempted to grasp with or open their paralysed hand while the slow components of MEG signals (slow movement fields; SMFs) were recorded. Even without actual movements, the SMFs of all patients indicated characteristic spatiotemporal patterns similar to actual movements, and the SMFs were successfully used to control a neuroprosthetic hand in a closed-loop condition. These results demonstrate that the slow components of MEG signals carry sufficient information to classify movement types. Successful control by paralysed patients suggests the feasibility of using an MEG-based neuroprosthetic hand to predict a patient’s ability to control an invasive neuroprosthesis via the same signal sources as the non-invasive method.
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Closed-loop control of a neuroprosthetic hand by magnetoencephalographic signals
PloS one, 2015Co-Authors: Ryohei Fukuma, Takufumi Yanagisawa, Youichi Saitoh, Haruhiko Kishima, Hiroshi Yokoi, Masayuki Hirata, Yukiyasu Kamitani, Shiro Yorifuji, Ryu Kato, Toshiki YoshimineAbstract:Objective A neuroprosthesis using a brain–machine interface (BMI) is a promising therapeutic option for severely paralyzed patients, but the ability to control it may vary among individual patients and needs to be evaluated before any invasive procedure is undertaken. We have developed a neuroprosthetic hand that can be controlled by magnetoencephalographic (MEG) signals to noninvasively evaluate subjects’ ability to control a neuroprosthesis. Method Six nonparalyzed subjects performed grasping or opening movements of their right hand while the slow components of the MEG signals (SMFs) were recorded in an open-loop condition. The SMFs were used to train two decoders to infer the timing and types of movement by support vector machine and Gaussian process regression. The SMFs were also used to calculate estimated slow cortical potentials (eSCPs) to identify the origin of motor information. Finally, using the trained decoders, the subjects controlled a neuroprosthetic hand in a closed-loop condition. Results The SMFs in the open-loop condition revealed movement-related cortical field characteristics and successfully inferred the movement type with an accuracy of 75.0 ± 12.9% (mean ± SD). In particular, the eSCPs in the sensorimotor cortex contralateral to the moved hand varied significantly enough among the movement types to be decoded with an accuracy of 76.5 ± 10.6%, which was significantly higher than the accuracy associated with eSCPs in the ipsilateral sensorimotor cortex (58.1 ± 13.7%; p = 0.0072, paired two-tailed Student’s t-test). Moreover, another decoder using SMFs successfully inferred when the accuracy was the greatest. Combining these two decoders allowed the neuroprosthetic hand to be controlled in a closed-loop condition. Conclusions Use of real-time MEG signals was shown to successfully control the neuroprosthetic hand. The developed system may be useful for evaluating movement-related slow cortical potentials of severely paralyzed patients to predict the efficacy of invasive BMI.
Ryohei Fukuma - One of the best experts on this subject based on the ideXlab platform.
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Corrigendum: Real-Time Control of a Neuroprosthetic Hand by Magnetoencephalographic Signals from Paralysed Patients.
Scientific reports, 2016Co-Authors: Ryohei Fukuma, Takufumi Yanagisawa, Youichi Saitoh, Koichi Hosomi, Haruhiko Kishima, Takeshi Shimizu, Hisato Sugata, Hiroshi Yokoi, Masayuki Hirata, Yukiyasu KamitaniAbstract:Corrigendum: Real-Time Control of a Neuroprosthetic Hand by Magnetoencephalographic Signals from Paralysed Patients
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Real-time control of a neuroprosthetic hand by magnetoencephalographic signals from paralysed patients
Scientific reports, 2016Co-Authors: Ryohei Fukuma, Takufumi Yanagisawa, Youichi Saitoh, Koichi Hosomi, Haruhiko Kishima, Takeshi Shimizu, Hisato Sugata, Hiroshi Yokoi, Masayuki Hirata, Yukiyasu KamitaniAbstract:Neuroprosthetic arms might potentially restore motor functions for severely paralysed patients. Invasive measurements of cortical currents using electrocorticography have been widely used for neuroprosthetic control. Moreover, magnetoencephalography (MEG) exhibits characteristic brain signals similar to those of invasively measured signals. However, it remains unclear whether non-invasively measured signals convey enough motor information to control a neuroprosthetic hand, especially for severely paralysed patients whose sensorimotor cortex might be reorganized. We tested an MEG-based neuroprosthetic system to evaluate the accuracy of using cortical currents in the sensorimotor cortex of severely paralysed patients to control a prosthetic hand. The patients attempted to grasp with or open their paralysed hand while the slow components of MEG signals (slow movement fields; SMFs) were recorded. Even without actual movements, the SMFs of all patients indicated characteristic spatiotemporal patterns similar to actual movements, and the SMFs were successfully used to control a neuroprosthetic hand in a closed-loop condition. These results demonstrate that the slow components of MEG signals carry sufficient information to classify movement types. Successful control by paralysed patients suggests the feasibility of using an MEG-based neuroprosthetic hand to predict a patient’s ability to control an invasive neuroprosthesis via the same signal sources as the non-invasive method.
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Closed-loop control of a neuroprosthetic hand by magnetoencephalographic signals
PloS one, 2015Co-Authors: Ryohei Fukuma, Takufumi Yanagisawa, Youichi Saitoh, Haruhiko Kishima, Hiroshi Yokoi, Masayuki Hirata, Yukiyasu Kamitani, Shiro Yorifuji, Ryu Kato, Toshiki YoshimineAbstract:Objective A neuroprosthesis using a brain–machine interface (BMI) is a promising therapeutic option for severely paralyzed patients, but the ability to control it may vary among individual patients and needs to be evaluated before any invasive procedure is undertaken. We have developed a neuroprosthetic hand that can be controlled by magnetoencephalographic (MEG) signals to noninvasively evaluate subjects’ ability to control a neuroprosthesis. Method Six nonparalyzed subjects performed grasping or opening movements of their right hand while the slow components of the MEG signals (SMFs) were recorded in an open-loop condition. The SMFs were used to train two decoders to infer the timing and types of movement by support vector machine and Gaussian process regression. The SMFs were also used to calculate estimated slow cortical potentials (eSCPs) to identify the origin of motor information. Finally, using the trained decoders, the subjects controlled a neuroprosthetic hand in a closed-loop condition. Results The SMFs in the open-loop condition revealed movement-related cortical field characteristics and successfully inferred the movement type with an accuracy of 75.0 ± 12.9% (mean ± SD). In particular, the eSCPs in the sensorimotor cortex contralateral to the moved hand varied significantly enough among the movement types to be decoded with an accuracy of 76.5 ± 10.6%, which was significantly higher than the accuracy associated with eSCPs in the ipsilateral sensorimotor cortex (58.1 ± 13.7%; p = 0.0072, paired two-tailed Student’s t-test). Moreover, another decoder using SMFs successfully inferred when the accuracy was the greatest. Combining these two decoders allowed the neuroprosthetic hand to be controlled in a closed-loop condition. Conclusions Use of real-time MEG signals was shown to successfully control the neuroprosthetic hand. The developed system may be useful for evaluating movement-related slow cortical potentials of severely paralyzed patients to predict the efficacy of invasive BMI.
Robin Mange - One of the best experts on this subject based on the ideXlab platform.
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multisensory bionic limb to achieve prosthesis embodiment and reduce distorted phantom limb perceptions
Journal of Neurology Neurosurgery and Psychiatry, 2019Co-Authors: Giulio Rognini, Francesco Maria Petrini, Stanisa Raspopovic, Giacomo Valle, Giuseppe Granata, Ivo Strauss, Marco Solcà, Bruno Herbelin, Javier Belloruiz, Robin MangeAbstract:A major goal of Neuroprosthetics is to design artificial limbs that are experienced (‘embodied’) like real limbs. However, despite important technological advances, this goal has not been reached and prosthesis embodiment is still very limited. Differently from our physical body, current bionic limbs do not provide the continuous multisensory feedback required for a limb to be experienced as one’s own. Here, we present a novel neuroprosthetic approach that combines peripheral neurotactile stimulation—inducing tactile sensation on the missing limb—and immersive digital technology—providing visual illumination of the prosthetic hand. We tested whether coherent multisensory visuo-tactile neural stimulation (VTNS)1 induced higher prosthesis embodiment and reduced the distorted perception of the phantom limb (telescoping, ie, the phantom limb is perceived as shorter than the intact limb). Patient 1 and patient 2 are transradial left forearm chronic amputees, who suffered upper limb telescoping. Patients were implanted with transverse intrafascicular multichannel electrodes (TIMEs), which induced the sensation of a vibration in a circumscribed skin region of the finger 2 via medial nerve stimulation in patient 1 (online supplementary figure 1A) and in a skin region of finger 5 via ulnar nerve stimulation in patient 2 (online supplementary figure 1B and material 1). Neurotactile stimulation2 was coupled with automatised visual illumination of a skin region on the patient’s prosthetic hand that corresponded to the somatotopic location of touch sensations experienced on the phantom hand (VTNS; online supplementary video 1, online supplementary figure 1, online supplementary material 1). VTNS was administered in two conditions, either with synchronous visual and neurotactile stimulation or in a control condition of asynchronous stimulation (1.5–2.5s delay). ### Supplementary data [jnnp-2018-318570-supp1.pdf] ### Supplementary video [jnnp-2018-318570-supp1.mp4] Prosthesis embodiment was measured via a questionnaire, whereas changes in phantom limb perception were tested via a body landmark task where patients indicated the perceived position of different parts of the phantom limb …
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Multisensory bionic limb to achieve prosthesis embodiment and reduce distorted phantom limb perceptions
Journal of Neurology Neurosurgery and Psychiatry, 2018Co-Authors: Giulio Rognini, Francesco Maria Petrini, Stanisa Raspopovic, Giacomo Valle, Giuseppe Granata, Ivo Strauss, Marco Solcà, Javier Bello-ruiz, Bruno Herbelin, Robin MangeAbstract:Introduction A major goal of Neuroprosthetics is to design artificial limbs that are experienced (‘embodied’) like real limbs. However, despite important technological advances, this goal has not been reached and prosthesis embodiment is still very limited. Differently from our physical body, current bionic limbs do not provide the continuous multisensory feedback required for a limb to be experienced as one’s own. Here, we present a novel neuroprosthetic approach that combines peripheral neurotactile stimulation—inducing tactile sensation on the missing limb—and immersive digital technology—providing visual illumination of the prosthetic hand. We tested whether coherent multisensory visuo-tactile neural stimulation (VTNS)1 induced higher prosthesis embodiment and reduced the distorted perception of the phantom limb (telescoping, ie, the phantom limb is perceived as shorter than the intact limb). Methods Patient 1 and patient 2 are transradial left forearm chronic amputees, who suffered upper limb telescoping. Patients were implanted with transverse intrafascicular multichannel electrodes (TIMEs), which induced the sensation of a vibration in a circumscribed skin region of the finger 2 via medial nerve stimulation in patient 1 (online supplementary figure 1A) and in a skin region of finger 5 via ulnar nerve stimulation in patient 2 (online supplementary figure 1B and material 1). Neurotactile stimulation2 was coupled with automatised visual illumination of a skin region on the patient’s prosthetic hand that corresponded to the somatotopic location of touch sensations experienced on the phantom hand (VTNS; online supplementary video 1, online supplementary figure 1, online supplementary material 1). VTNS was administered in two conditions, either with synchronous visual and neurotactile stimulation or in a control condition of asynchronous stimulation (1.5–2.5s delay).
Harrison W. Lin - One of the best experts on this subject based on the ideXlab platform.
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Interfacing with the nervous system: a review of current bioelectric technologies
Neurosurgical Review, 2019Co-Authors: Ronald Sahyouni, Omid Moshtaghi, Hamid R. Djalilian, Jefferson W. Chen, Amin Mahmoodi, David T Chang, Harrison W. LinAbstract:The aim of this study is to discuss the state of the art with regard to established or promising bioelectric therapies meant to alter or control neurologic function. We present recent reports on bioelectric technologies that interface with the nervous system at three potential sites—(1) the end organ, (2) the peripheral nervous system, and (3) the central nervous system—while exploring practical and clinical considerations. A literature search was executed on PubMed, IEEE, and Web of Science databases. A review of the current literature was conducted to examine functional and histomorphological effects of neuroprosthetic interfaces with a focus on end-organ, peripheral, and central nervous system interfaces. Innovations in bioelectric technologies are providing increasing selectivity in stimulating distinct nerve fiber populations in order to activate discrete muscles. Significant advances in electrode array design focus on increasing selectivity, stability, and functionality of implantable Neuroprosthetics. The application of Neuroprosthetics to paretic nerves or even directly stimulating or recording from the central nervous system holds great potential in advancing the field of nerve and tissue bioelectric engineering and contributing to clinical care. Although current physiotherapeutic and surgical treatments seek to restore function, structure, or comfort, they bear significant limitations in enabling cosmetic or functional recovery. Instead, the introduction of bioelectric technology may play a role in the restoration of function in patients with neurologic deficits.
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Interfacing with the nervous system: a review of current bioelectric technologies
Neurosurgical Review, 2019Co-Authors: Ronald Sahyouni, Omid Moshtaghi, Hamid R. Djalilian, Jefferson W. Chen, Amin Mahmoodi, David T Chang, Harrison W. LinAbstract:The aim of this study is to discuss the state of the art with regard to established or promising bioelectric therapies meant to alter or control neurologic function. We present recent reports on bioelectric technologies that interface with the nervous system at three potential sites—(1) the end organ, (2) the peripheral nervous system, and (3) the central nervous system—while exploring practical and clinical considerations. A literature search was executed on PubMed, IEEE, and Web of Science databases. A review of the current literature was conducted to examine functional and histomorphological effects of neuroprosthetic interfaces with a focus on end-organ, peripheral, and central nervous system interfaces. Innovations in bioelectric technologies are providing increasing selectivity in stimulating distinct nerve fiber populations in order to activate discrete muscles. Significant advances in electrode array design focus on increasing selectivity, stability, and functionality of implantable Neuroprosthetics. The application of Neuroprosthetics to paretic nerves or even directly stimulating or recording from the central nervous system holds great potential in advancing the field of nerve and tissue bioelectric engineering and contributing to clinical care. Although current physiotherapeutic and surgical treatments seek to restore function, structure, or comfort, they bear significant limitations in enabling cosmetic or functional recovery. Instead, the introduction of bioelectric technology may play a role in the restoration of function in patients with neurologic deficits.
Takufumi Yanagisawa - One of the best experts on this subject based on the ideXlab platform.
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Corrigendum: Real-Time Control of a Neuroprosthetic Hand by Magnetoencephalographic Signals from Paralysed Patients.
Scientific reports, 2016Co-Authors: Ryohei Fukuma, Takufumi Yanagisawa, Youichi Saitoh, Koichi Hosomi, Haruhiko Kishima, Takeshi Shimizu, Hisato Sugata, Hiroshi Yokoi, Masayuki Hirata, Yukiyasu KamitaniAbstract:Corrigendum: Real-Time Control of a Neuroprosthetic Hand by Magnetoencephalographic Signals from Paralysed Patients
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Real-time control of a neuroprosthetic hand by magnetoencephalographic signals from paralysed patients
Scientific reports, 2016Co-Authors: Ryohei Fukuma, Takufumi Yanagisawa, Youichi Saitoh, Koichi Hosomi, Haruhiko Kishima, Takeshi Shimizu, Hisato Sugata, Hiroshi Yokoi, Masayuki Hirata, Yukiyasu KamitaniAbstract:Neuroprosthetic arms might potentially restore motor functions for severely paralysed patients. Invasive measurements of cortical currents using electrocorticography have been widely used for neuroprosthetic control. Moreover, magnetoencephalography (MEG) exhibits characteristic brain signals similar to those of invasively measured signals. However, it remains unclear whether non-invasively measured signals convey enough motor information to control a neuroprosthetic hand, especially for severely paralysed patients whose sensorimotor cortex might be reorganized. We tested an MEG-based neuroprosthetic system to evaluate the accuracy of using cortical currents in the sensorimotor cortex of severely paralysed patients to control a prosthetic hand. The patients attempted to grasp with or open their paralysed hand while the slow components of MEG signals (slow movement fields; SMFs) were recorded. Even without actual movements, the SMFs of all patients indicated characteristic spatiotemporal patterns similar to actual movements, and the SMFs were successfully used to control a neuroprosthetic hand in a closed-loop condition. These results demonstrate that the slow components of MEG signals carry sufficient information to classify movement types. Successful control by paralysed patients suggests the feasibility of using an MEG-based neuroprosthetic hand to predict a patient’s ability to control an invasive neuroprosthesis via the same signal sources as the non-invasive method.
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Closed-loop control of a neuroprosthetic hand by magnetoencephalographic signals
PloS one, 2015Co-Authors: Ryohei Fukuma, Takufumi Yanagisawa, Youichi Saitoh, Haruhiko Kishima, Hiroshi Yokoi, Masayuki Hirata, Yukiyasu Kamitani, Shiro Yorifuji, Ryu Kato, Toshiki YoshimineAbstract:Objective A neuroprosthesis using a brain–machine interface (BMI) is a promising therapeutic option for severely paralyzed patients, but the ability to control it may vary among individual patients and needs to be evaluated before any invasive procedure is undertaken. We have developed a neuroprosthetic hand that can be controlled by magnetoencephalographic (MEG) signals to noninvasively evaluate subjects’ ability to control a neuroprosthesis. Method Six nonparalyzed subjects performed grasping or opening movements of their right hand while the slow components of the MEG signals (SMFs) were recorded in an open-loop condition. The SMFs were used to train two decoders to infer the timing and types of movement by support vector machine and Gaussian process regression. The SMFs were also used to calculate estimated slow cortical potentials (eSCPs) to identify the origin of motor information. Finally, using the trained decoders, the subjects controlled a neuroprosthetic hand in a closed-loop condition. Results The SMFs in the open-loop condition revealed movement-related cortical field characteristics and successfully inferred the movement type with an accuracy of 75.0 ± 12.9% (mean ± SD). In particular, the eSCPs in the sensorimotor cortex contralateral to the moved hand varied significantly enough among the movement types to be decoded with an accuracy of 76.5 ± 10.6%, which was significantly higher than the accuracy associated with eSCPs in the ipsilateral sensorimotor cortex (58.1 ± 13.7%; p = 0.0072, paired two-tailed Student’s t-test). Moreover, another decoder using SMFs successfully inferred when the accuracy was the greatest. Combining these two decoders allowed the neuroprosthetic hand to be controlled in a closed-loop condition. Conclusions Use of real-time MEG signals was shown to successfully control the neuroprosthetic hand. The developed system may be useful for evaluating movement-related slow cortical potentials of severely paralyzed patients to predict the efficacy of invasive BMI.