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Yang H, Yanagisawa T. Is Phantom Limb Awareness Necessary for the Treatment of Phantom Limb Pain? Neurol Med Chir (Tokyo) 2024; 64:101-107. [PMID: 38267056 PMCID: PMC10992984 DOI: 10.2176/jns-nmc.2023-0206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 10/31/2023] [Indexed: 01/26/2024] Open
Abstract
Phantom limb pain is attributed to abnormal sensorimotor cortical representations. Various feedback treatments have been applied to induce the reorganization of the sensorimotor cortical representations to reduce pain. We developed a training protocol using a brain-computer interface (BCI) to induce plastic changes in the sensorimotor cortical representation of phantom hand movements and demonstrated that BCI training effectively reduces phantom limb pain. By comparing the induced cortical representation and pain, the mechanisms worsening the pain have been attributed to the residual phantom hand representation. Based on our data obtained using neurofeedback training without explicit phantom hand movements and hand-like visual feedback, we suggest a direct relationship between cortical representation and pain. In this review, we summarize the results of our BCI training protocol and discuss the relationship between cortical representation and phantom limb pain. We propose a treatment for phantom limb pain based on real-time neuroimaging to induce appropriate cortical reorganization by monitoring cortical activities.
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Affiliation(s)
- Huixiang Yang
- Institute for Advanced Co-creation Studies, Osaka University
| | - Takufumi Yanagisawa
- Institute for Advanced Co-creation Studies, Osaka University
- Department of Neurosurgery, Graduate School of Medicine, Osaka University
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2
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Yanagisawa T, Fukuma R, Seymour B, Tanaka M, Yamashita O, Hosomi K, Kishima H, Kamitani Y, Saitoh Y. Neurofeedback Training without Explicit Phantom Hand Movements and Hand-Like Visual Feedback to Modulate Pain: A Randomized Crossover Feasibility Trial. THE JOURNAL OF PAIN 2022; 23:2080-2091. [PMID: 35932992 DOI: 10.1016/j.jpain.2022.07.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 06/25/2022] [Accepted: 07/20/2022] [Indexed: 01/04/2023]
Abstract
Phantom limb pain is attributed to abnormal sensorimotor cortical representations, although the causal relationship between phantom limb pain and sensorimotor cortical representations suffers from the potentially confounding effects of phantom hand movements. We developed neurofeedback training to change sensorimotor cortical representations without explicit phantom hand movements or hand-like visual feedback. We tested the feasibility of neurofeedback training in fourteen patients with phantom limb pain. Neurofeedback training was performed in a single-blind, randomized, crossover trial using two decoders constructed using motor cortical currents measured during phantom hand movements; the motor cortical currents contralateral or ipsilateral to the phantom hand (contralateral and ipsilateral training) were estimated from magnetoencephalograms. Patients were instructed to control the size of a disk, which was proportional to the decoding results, but to not move their phantom hands or other body parts. The pain assessed by the visual analogue scale was significantly greater after contralateral training than after ipsilateral training. Classification accuracy of phantom hand movements significantly increased only after contralateral training. These results suggested that the proposed neurofeedback training changed phantom hand representation and modulated pain without explicit phantom hand movements or hand-like visual feedback, thus showing the relation between the phantom hand representations and pain. PERSPECTIVE: Our work demonstrates the feasibility of using neurofeedback training to change phantom hand representation and modulate pain perception without explicit phantom hand movements and hand-like visual feedback. The results enhance the mechanistic understanding of certain treatments, such as mirror therapy, that change the sensorimotor cortical representation.
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Affiliation(s)
- Takufumi Yanagisawa
- Osaka University, Institute for Advanced Co-Creation Studies, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan; Osaka University Graduate School of Medicine, Department of Neurosurgery, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan; ATR Computational Neuroscience Laboratories, Department of Neuroinformatics, 2-2-2 Hikaridai, Seika-cho, Kyoto 619-0288, Japan.
| | - Ryohei Fukuma
- Osaka University, Institute for Advanced Co-Creation Studies, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan; Osaka University Graduate School of Medicine, Department of Neurosurgery, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan; ATR Computational Neuroscience Laboratories, Department of Neuroinformatics, 2-2-2 Hikaridai, Seika-cho, Kyoto 619-0288, Japan
| | - Ben Seymour
- University of Oxford, Institute of Biomedical Engineering, Department of Engineering Science, Old Road Campus Research Building, Oxford OX3 7DQ, UK; National Institute for Information and Communications Technology, Center for Information and Neural Networks, 1-3 Suita, Osaka 565-0871, Japan
| | - Masataka Tanaka
- Osaka University Graduate School of Medicine, Department of Neurosurgery, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Okito Yamashita
- RIKEN Center for Advanced Intelligence Project, Nihonbashi 1-chome Mitsui Building, 15th floor, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; ATR Neural Information Analysis Laboratories, Department of Computational Brain Imaging, 2-2-2 Hikaridai, Seika-cho, Kyoto 619-0288, Japan
| | - Koichi Hosomi
- Osaka University Graduate School of Medicine, Department of Neurosurgery, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan; Osaka University Graduate School of Medicine, Department of Neuromodulation and Neurosurgery, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Haruhiko Kishima
- Osaka University Graduate School of Medicine, Department of Neurosurgery, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Yukiyasu Kamitani
- ATR Computational Neuroscience Laboratories, Department of Neuroinformatics, 2-2-2 Hikaridai, Seika-cho, Kyoto 619-0288, Japan; Kyoto University, Graduate School of Informatics, Yoshidahonmachi, Sakyoku, Kyoto, Kyoto 606-8501, Japan
| | - Youichi Saitoh
- Osaka University Graduate School of Medicine, Department of Neurosurgery, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan; Osaka University Graduate School of Medicine, Department of Neuromodulation and Neurosurgery, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan
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Moly A, Costecalde T, Martel F, Martin M, Larzabal C, Karakas S, Verney A, Charvet G, Chabardès S, Benabid AL, Aksenova T. An adaptive closed-loop ECoG decoder for long-term and stable bimanual control of an exoskeleton by a tetraplegic. J Neural Eng 2022; 19. [PMID: 35234665 DOI: 10.1088/1741-2552/ac59a0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 03/01/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE The article aims at addressing 2 challenges to step motor BCI out of laboratories: asynchronous control of complex bimanual effectors with large numbers of degrees of freedom, using chronic and safe recorders, and the decoding performance stability over time without frequent decoder recalibration. APPROACH Closed-loop adaptive/incremental decoder training is one strategy to create a model stable over time. Adaptive decoders update their parameters with new incoming data, optimizing the model parameters in real time. It allows cross-session training with multiple recording conditions during closed loop BCI experiments. In the article, an adaptive tensor-based Recursive Exponentially Weighted Markov-Switching multi-Linear Model (REW-MSLM) decoder is proposed. REW-MSLM uses a Mixture of Expert (ME) architecture, mixing or switching independent decoders (experts) according to the probability estimated by a "gating" model. A Hidden Markov model approach is employed as gating model to improve the decoding robustness and to provide strong idle state support. The ME architecture fits the multi-limb paradigm associating an expert to a particular limb or action. MAIN RESULTS Asynchronous control of an exoskeleton by a tetraplegic patient using a chronically implanted epidural electrocorticography (EpiCoG) recorder is reported. The stable over a period of 6 months (without decoder recalibration) 8-dimensional alternative bimanual control of the exoskeleton and its virtual avatar is demonstrated. SIGNIFICANCE Based on the long-term (>36 months) chronic bilateral epidural ECoG recordings in a tetraplegic (ClinicalTrials.gov, NCT02550522), we addressed the poorly explored field of asynchronous bimanual BCI. The new decoder was designed to meet to several challenges: the high-dimensional control of a complex effector in experiments closer to real-world behaviour (point-to-point pursuit versus conventional center-out tasks), with the ability of the BCI system to act as a stand-alone device switching between idle and control states, and a stable performance over a long period of time without decoder recalibration.
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Affiliation(s)
- Alexandre Moly
- Univ. Grenoble Alpes, CEA, LETI, Clinatec,, 17 av. des martyrs, Grenoble, Auvergne-Rhône-Alpes, 38000, FRANCE
| | - Thomas Costecalde
- Univ. Grenoble Alpes, CEA, LETI, Clinatec,, 17 av. des martyrs, Grenoble, Auvergne-Rhône-Alpes, 38000, FRANCE
| | - Félix Martel
- Univ. Grenoble Alpes, CEA, LETI, Clinatec,, 17 av. des martyrs, Grenoble, Auvergne-Rhône-Alpes, 38000, FRANCE
| | - Matthieu Martin
- Univ. Grenoble Alpes, CEA, LETI, Clinatec,, 17 av. des Martyrs, Grenoble, 38000, FRANCE
| | - Christelle Larzabal
- Univ. Grenoble Alpes, CEA, LETI, Clinatec,, 17 av. des martyrs, Grenoble, Auvergne-Rhône-Alpes, 38000, FRANCE
| | - Serpil Karakas
- Univ. Grenoble Alpes, CEA, LETI, Clinatec,, 17 av. des martyrs, Grenoble, Auvergne-Rhône-Alpes, 38000, FRANCE
| | - Alexandre Verney
- Université Paris-Saclay, Palaiseau, Palaiseau, Île-de-France, 91120, FRANCE
| | - Guillaume Charvet
- Univ. Grenoble Alpes, CEA, LETI, Clinatec,, 17 av. des martyrs, Grenoble, Auvergne-Rhône-Alpes, 38000, FRANCE
| | - Stephan Chabardès
- CHU Grenoble Alpes, Boulevard de la Chantourne, La Tronche, Auvergne-Rhône-Alpes, 38700, FRANCE
| | - Alim-Louis Benabid
- Univ. Grenoble Alpes, CEA, LETI, Clinatec, 17, avenue des Martyrs, Grenoble, 38000, FRANCE
| | - Tatiana Aksenova
- Univ. Grenoble Alpes, CEA, LETI, Clinatec,, 17 av. des martyrs, Grenoble, Auvergne-Rhône-Alpes, 38000, FRANCE
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Dutta D, Upreti SR. Artificial intelligence‐based process control in chemical, biochemical, and biomedical engineering. CAN J CHEM ENG 2021. [DOI: 10.1002/cjce.24246] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Debaprasad Dutta
- Department of Chemical Engineering Ryerson University Toronto Ontario Canada
| | - Simant R. Upreti
- Department of Chemical Engineering Ryerson University Toronto Ontario Canada
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Yanagisawa T, Fukuma R, Seymour B, Tanaka M, Hosomi K, Yamashita O, Kishima H, Kamitani Y, Saitoh Y. BCI training to move a virtual hand reduces phantom limb pain: A randomized crossover trial. Neurology 2020; 95:e417-e426. [PMID: 32675074 PMCID: PMC7455320 DOI: 10.1212/wnl.0000000000009858] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 02/12/2020] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To determine whether training with a brain-computer interface (BCI) to control an image of a phantom hand, which moves based on cortical currents estimated from magnetoencephalographic signals, reduces phantom limb pain. METHODS Twelve patients with chronic phantom limb pain of the upper limb due to amputation or brachial plexus root avulsion participated in a randomized single-blinded crossover trial. Patients were trained to move the virtual hand image controlled by the BCI with a real decoder, which was constructed to classify intact hand movements from motor cortical currents, by moving their phantom hands for 3 days ("real training"). Pain was evaluated using a visual analogue scale (VAS) before and after training, and at follow-up for an additional 16 days. As a control, patients engaged in the training with the same hand image controlled by randomly changing values ("random training"). The 2 trainings were randomly assigned to the patients. This trial is registered at UMIN-CTR (UMIN000013608). RESULTS VAS at day 4 was significantly reduced from the baseline after real training (mean [SD], 45.3 [24.2]-30.9 [20.6], 1/100 mm; p = 0.009 < 0.025), but not after random training (p = 0.047 > 0.025). Compared to VAS at day 1, VAS at days 4 and 8 was significantly reduced by 32% and 36%, respectively, after real training and was significantly lower than VAS after random training (p < 0.01). CONCLUSION Three-day training to move the hand images controlled by BCI significantly reduced pain for 1 week. CLASSIFICATION OF EVIDENCE This study provides Class III evidence that BCI reduces phantom limb pain.
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Affiliation(s)
- Takufumi Yanagisawa
- From the Institute for Advanced Co-Creation Studies (T.Y.), Osaka University; Departments of Neurosurgery (T.Y., R.F., M.T., K.H., H.K., Y.S.) and Neuromodulation and Neurosurgery (K.H., Y.S.), Osaka University Graduate School of Medicine; Department of Neuroinformatics (T.Y., R.F., Y.K.), ATR Computational Neuroscience Laboratories, Kyoto, Japan; Computational and Biological Learning Laboratory, Department of Engineering (B.S.), University of Cambridge, UK; Center for Information and Neural Networks (B.S.), National Institute for Information and Communications Technology, Osaka; RIKEN Center for Advanced Intelligence Project (O.Y.), Tokyo; Department of Computational Brain Imaging (O.Y.), ATR Neural Information Analysis Laboratories, Kyoto; and Graduate School of Informatics (Y.K.), Kyoto University, Japan.
| | - Ryohei Fukuma
- From the Institute for Advanced Co-Creation Studies (T.Y.), Osaka University; Departments of Neurosurgery (T.Y., R.F., M.T., K.H., H.K., Y.S.) and Neuromodulation and Neurosurgery (K.H., Y.S.), Osaka University Graduate School of Medicine; Department of Neuroinformatics (T.Y., R.F., Y.K.), ATR Computational Neuroscience Laboratories, Kyoto, Japan; Computational and Biological Learning Laboratory, Department of Engineering (B.S.), University of Cambridge, UK; Center for Information and Neural Networks (B.S.), National Institute for Information and Communications Technology, Osaka; RIKEN Center for Advanced Intelligence Project (O.Y.), Tokyo; Department of Computational Brain Imaging (O.Y.), ATR Neural Information Analysis Laboratories, Kyoto; and Graduate School of Informatics (Y.K.), Kyoto University, Japan
| | - Ben Seymour
- From the Institute for Advanced Co-Creation Studies (T.Y.), Osaka University; Departments of Neurosurgery (T.Y., R.F., M.T., K.H., H.K., Y.S.) and Neuromodulation and Neurosurgery (K.H., Y.S.), Osaka University Graduate School of Medicine; Department of Neuroinformatics (T.Y., R.F., Y.K.), ATR Computational Neuroscience Laboratories, Kyoto, Japan; Computational and Biological Learning Laboratory, Department of Engineering (B.S.), University of Cambridge, UK; Center for Information and Neural Networks (B.S.), National Institute for Information and Communications Technology, Osaka; RIKEN Center for Advanced Intelligence Project (O.Y.), Tokyo; Department of Computational Brain Imaging (O.Y.), ATR Neural Information Analysis Laboratories, Kyoto; and Graduate School of Informatics (Y.K.), Kyoto University, Japan
| | - Masataka Tanaka
- From the Institute for Advanced Co-Creation Studies (T.Y.), Osaka University; Departments of Neurosurgery (T.Y., R.F., M.T., K.H., H.K., Y.S.) and Neuromodulation and Neurosurgery (K.H., Y.S.), Osaka University Graduate School of Medicine; Department of Neuroinformatics (T.Y., R.F., Y.K.), ATR Computational Neuroscience Laboratories, Kyoto, Japan; Computational and Biological Learning Laboratory, Department of Engineering (B.S.), University of Cambridge, UK; Center for Information and Neural Networks (B.S.), National Institute for Information and Communications Technology, Osaka; RIKEN Center for Advanced Intelligence Project (O.Y.), Tokyo; Department of Computational Brain Imaging (O.Y.), ATR Neural Information Analysis Laboratories, Kyoto; and Graduate School of Informatics (Y.K.), Kyoto University, Japan
| | - Koichi Hosomi
- From the Institute for Advanced Co-Creation Studies (T.Y.), Osaka University; Departments of Neurosurgery (T.Y., R.F., M.T., K.H., H.K., Y.S.) and Neuromodulation and Neurosurgery (K.H., Y.S.), Osaka University Graduate School of Medicine; Department of Neuroinformatics (T.Y., R.F., Y.K.), ATR Computational Neuroscience Laboratories, Kyoto, Japan; Computational and Biological Learning Laboratory, Department of Engineering (B.S.), University of Cambridge, UK; Center for Information and Neural Networks (B.S.), National Institute for Information and Communications Technology, Osaka; RIKEN Center for Advanced Intelligence Project (O.Y.), Tokyo; Department of Computational Brain Imaging (O.Y.), ATR Neural Information Analysis Laboratories, Kyoto; and Graduate School of Informatics (Y.K.), Kyoto University, Japan
| | - Okito Yamashita
- From the Institute for Advanced Co-Creation Studies (T.Y.), Osaka University; Departments of Neurosurgery (T.Y., R.F., M.T., K.H., H.K., Y.S.) and Neuromodulation and Neurosurgery (K.H., Y.S.), Osaka University Graduate School of Medicine; Department of Neuroinformatics (T.Y., R.F., Y.K.), ATR Computational Neuroscience Laboratories, Kyoto, Japan; Computational and Biological Learning Laboratory, Department of Engineering (B.S.), University of Cambridge, UK; Center for Information and Neural Networks (B.S.), National Institute for Information and Communications Technology, Osaka; RIKEN Center for Advanced Intelligence Project (O.Y.), Tokyo; Department of Computational Brain Imaging (O.Y.), ATR Neural Information Analysis Laboratories, Kyoto; and Graduate School of Informatics (Y.K.), Kyoto University, Japan
| | - Haruhiko Kishima
- From the Institute for Advanced Co-Creation Studies (T.Y.), Osaka University; Departments of Neurosurgery (T.Y., R.F., M.T., K.H., H.K., Y.S.) and Neuromodulation and Neurosurgery (K.H., Y.S.), Osaka University Graduate School of Medicine; Department of Neuroinformatics (T.Y., R.F., Y.K.), ATR Computational Neuroscience Laboratories, Kyoto, Japan; Computational and Biological Learning Laboratory, Department of Engineering (B.S.), University of Cambridge, UK; Center for Information and Neural Networks (B.S.), National Institute for Information and Communications Technology, Osaka; RIKEN Center for Advanced Intelligence Project (O.Y.), Tokyo; Department of Computational Brain Imaging (O.Y.), ATR Neural Information Analysis Laboratories, Kyoto; and Graduate School of Informatics (Y.K.), Kyoto University, Japan
| | - Yukiyasu Kamitani
- From the Institute for Advanced Co-Creation Studies (T.Y.), Osaka University; Departments of Neurosurgery (T.Y., R.F., M.T., K.H., H.K., Y.S.) and Neuromodulation and Neurosurgery (K.H., Y.S.), Osaka University Graduate School of Medicine; Department of Neuroinformatics (T.Y., R.F., Y.K.), ATR Computational Neuroscience Laboratories, Kyoto, Japan; Computational and Biological Learning Laboratory, Department of Engineering (B.S.), University of Cambridge, UK; Center for Information and Neural Networks (B.S.), National Institute for Information and Communications Technology, Osaka; RIKEN Center for Advanced Intelligence Project (O.Y.), Tokyo; Department of Computational Brain Imaging (O.Y.), ATR Neural Information Analysis Laboratories, Kyoto; and Graduate School of Informatics (Y.K.), Kyoto University, Japan
| | - Youichi Saitoh
- From the Institute for Advanced Co-Creation Studies (T.Y.), Osaka University; Departments of Neurosurgery (T.Y., R.F., M.T., K.H., H.K., Y.S.) and Neuromodulation and Neurosurgery (K.H., Y.S.), Osaka University Graduate School of Medicine; Department of Neuroinformatics (T.Y., R.F., Y.K.), ATR Computational Neuroscience Laboratories, Kyoto, Japan; Computational and Biological Learning Laboratory, Department of Engineering (B.S.), University of Cambridge, UK; Center for Information and Neural Networks (B.S.), National Institute for Information and Communications Technology, Osaka; RIKEN Center for Advanced Intelligence Project (O.Y.), Tokyo; Department of Computational Brain Imaging (O.Y.), ATR Neural Information Analysis Laboratories, Kyoto; and Graduate School of Informatics (Y.K.), Kyoto University, Japan
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Korik A, Sosnik R, Siddique N, Coyle D. Decoding Imagined 3D Arm Movement Trajectories From EEG to Control Two Virtual Arms-A Pilot Study. Front Neurorobot 2019; 13:94. [PMID: 31798438 PMCID: PMC6868122 DOI: 10.3389/fnbot.2019.00094] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Accepted: 10/28/2019] [Indexed: 11/26/2022] Open
Abstract
Background: Realization of online control of an artificial or virtual arm using information decoded from EEG normally occurs by classifying different activation states or voluntary modulation of the sensorimotor activity linked to different overt actions of the subject. However, using a more natural control scheme, such as decoding the trajectory of imagined 3D arm movements to move a prosthetic, robotic, or virtual arm has been reported in a limited amount of studies, all using offline feed-forward control schemes. Objective: In this study, we report the first attempt to realize online control of two virtual arms generating movements toward three targets/arm in 3D space. The 3D trajectory of imagined arm movements was decoded from power spectral density of mu, low beta, high beta, and low gamma EEG oscillations using multiple linear regression. The analysis was performed on a dataset recorded from three subjects in seven sessions wherein each session comprised three experimental blocks: an offline calibration block and two online feedback blocks. Target classification accuracy using predicted trajectories of the virtual arms was computed and compared with results of a filter-bank common spatial patterns (FBCSP) based multi-class classification method involving mutual information (MI) selection and linear discriminant analysis (LDA) modules. Main Results: Target classification accuracy from predicted trajectory of imagined 3D arm movements in the offline runs for two subjects (mean 45%, std 5%) was significantly higher (p < 0.05) than chance level (33.3%). Nevertheless, the accuracy during real-time control of the virtual arms using the trajectory decoded directly from EEG was in the range of chance level (33.3%). However, the results of two subjects show that false-positive feedback may increase the accuracy in closed-loop. The FBCSP based multi-class classification method distinguished imagined movements of left and right arm with reasonable accuracy for two of the three subjects (mean 70%, std 5% compared to 50% chance level). However, classification of the imagined arm movement toward three targets was not successful with the FBCSP classifier as the achieved accuracy (mean 33%, std 5%) was similar to the chance level (33.3%). Sub-optimal components of the multi-session experimental paradigm were identified, and an improved paradigm proposed.
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Affiliation(s)
- Attila Korik
- Intelligent Systems Research Centre, Ulster University, Derry, United Kingdom
| | - Ronen Sosnik
- Hybrid BCI Lab, Holon Institute of Technology, Holon, Israel
| | - Nazmul Siddique
- Intelligent Systems Research Centre, Ulster University, Derry, United Kingdom
| | - Damien Coyle
- Intelligent Systems Research Centre, Ulster University, Derry, United Kingdom
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Belkacem AN, Nishio S, Suzuki T, Ishiguro H, Hirata M. Neuromagnetic Decoding of Simultaneous Bilateral Hand Movements for Multidimensional Brain-Machine Interfaces. IEEE Trans Neural Syst Rehabil Eng 2019; 26:1301-1310. [PMID: 29877855 DOI: 10.1109/tnsre.2018.2837003] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
To provide multidimensional control, we describe the first reported decoding of bilateral hand movements by using single-trial magnetoencephalography signals as a new approach to enhance a user's ability to interact with a complex environment through a multidimensional brain-machine interface. Ten healthy participants performed or imagined four types of bilateral hand movements during neuromagnetic measurements. By applying a support vector machine (SVM) method to classify the four movements regarding the sensor data obtained from the sensorimotor area, we found the mean accuracy of a two-class classification using the amplitudes of neuromagnetic fields to be particularly suitable for real-time applications, with accuracies comparable to those obtained in previous studies involving unilateral movement. The sensor data from over the sensorimotor cortex showed discriminative time-series waveforms and time-frequency maps in the bilateral hemispheres according to the four tasks. Furthermore, we used four-class classification algorithms based on the SVM method to decode all types of bilateral movements. Our results provided further proof that the slow components of neuromagnetic fields carry sufficient neural information to classify even bilateral hand movements and demonstrated the potential utility of decoding bilateral movements for engineering purposes such as multidimensional motor control.
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8
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Yanagisawa T, Fukuma R, Seymour B, Hosomi K, Kishima H, Shimizu T, Yokoi H, Hirata M, Yoshimine T, Kamitani Y, Saitoh Y. MEG-BMI to Control Phantom Limb Pain. Neurol Med Chir (Tokyo) 2018; 58:327-333. [PMID: 29998936 PMCID: PMC6092605 DOI: 10.2176/nmc.st.2018-0099] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
A brachial plexus root avulsion (BPRA) causes intractable pain in the insensible affected hands. Such pain is partly due to phantom limb pain, which is neuropathic pain occurring after the amputation of a limb and partial or complete deafferentation. Previous studies suggested that the pain was attributable to maladaptive plasticity of the sensorimotor cortex. However, there is little evidence to demonstrate the causal links between the pain and the cortical representation, and how much cortical factors affect the pain. Here, we applied lesioning of the dorsal root entry zone (DREZotomy) and training with a brain–machine interface (BMI) based on real-time magnetoencephalography signals to reconstruct affected hand movements with a robotic hand. The DREZotomy successfully reduced the shooting pain after BPRA, but a part of the pain remained. The BMI training successfully induced some plastic changes in the sensorimotor representation of the phantom hand movements and helped control the remaining pain. When the patient tried to control the robotic hand by moving their phantom hand through association with the representation of the intact hand, this especially decreased the pain while decreasing the classification accuracy of the phantom hand movements. These results strongly suggested that pain after the BPRA was partly attributable to cortical representation of phantom hand movements and that the BMI training controlled the pain by inducing appropriate cortical reorganization. For the treatment of chronic pain, we need to know how to modulate the cortical representation by novel methods.
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Affiliation(s)
- Takufumi Yanagisawa
- Department of Neurosurgery, Osaka University Graduate School of Medicine.,Osaka University Institute for Advanced Co-Creation Studies.,Department of Neuroinformatics, ATR Computational Neuroscience Laboratories.,Division of Clinical Neuroengineering, Global Center for Medical Engineering and Informatics, Osaka University
| | - Ryohei Fukuma
- Department of Neurosurgery, Osaka University Graduate School of Medicine.,Department of Neuroinformatics, ATR Computational Neuroscience Laboratories
| | - Ben Seymour
- Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge.,Center for Information and Neural Networks, National Institute for Information and Communications Technology
| | - Koichi Hosomi
- Department of Neurosurgery, Osaka University Graduate School of Medicine.,Department of Neuromodulation and Neurosurgery, Osaka University Graduate School of Medicine
| | - Haruhiko Kishima
- Department of Neurosurgery, Osaka University Graduate School of Medicine
| | - Takeshi Shimizu
- Department of Neurosurgery, Osaka University Graduate School of Medicine.,Department of Neuromodulation and Neurosurgery, Osaka University Graduate School of Medicine
| | - Hiroshi Yokoi
- Department of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications
| | - Masayuki Hirata
- Department of Neurosurgery, Osaka University Graduate School of Medicine.,Division of Clinical Neuroengineering, Global Center for Medical Engineering and Informatics, Osaka University
| | - Toshiki Yoshimine
- Department of Neurosurgery, Osaka University Graduate School of Medicine.,Division of Clinical Neuroengineering, Global Center for Medical Engineering and Informatics, Osaka University
| | - Yukiyasu Kamitani
- Department of Neuroinformatics, ATR Computational Neuroscience Laboratories.,Graduate School of Informatics, Kyoto University
| | - Youichi Saitoh
- Department of Neurosurgery, Osaka University Graduate School of Medicine.,Department of Neuromodulation and Neurosurgery, Osaka University Graduate School of Medicine
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9
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Fukuma R, Yanagisawa T, Yokoi H, Hirata M, Yoshimine T, Saitoh Y, Kamitani Y, Kishima H. Training in Use of Brain-Machine Interface-Controlled Robotic Hand Improves Accuracy Decoding Two Types of Hand Movements. Front Neurosci 2018; 12:478. [PMID: 30050405 PMCID: PMC6050372 DOI: 10.3389/fnins.2018.00478] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Accepted: 06/25/2018] [Indexed: 11/21/2022] Open
Abstract
Objective: Brain-machine interfaces (BMIs) are useful for inducing plastic changes in cortical representation. A BMI first decodes hand movements using cortical signals and then converts the decoded information into movements of a robotic hand. By using the BMI robotic hand, the cortical representation decoded by the BMI is modulated to improve decoding accuracy. We developed a BMI based on real-time magnetoencephalography (MEG) signals to control a robotic hand using decoded hand movements. Subjects were trained to use the BMI robotic hand freely for 10 min to evaluate plastic changes in the cortical representation due to the training. Method: We trained nine young healthy subjects with normal motor function. In open-loop conditions, they were instructed to grasp or open their right hands during MEG recording. Time-averaged MEG signals were then used to train a real decoder to control the robotic arm in real time. Then, subjects were instructed to control the BMI-controlled robotic hand by moving their right hands for 10 min while watching the robot's movement. During this closed-loop session, subjects tried to improve their ability to control the robot. Finally, subjects performed the same offline task to compare cortical activities related to the hand movements. As a control, we used a random decoder trained by the MEG signals with shuffled movement labels. We performed the same experiments with the random decoder as a crossover trial. To evaluate the cortical representation, cortical currents were estimated using a source localization technique. Hand movements were also decoded by a support vector machine using the MEG signals during the offline task. The classification accuracy of the movements was compared among offline tasks. Results: During the BMI training with the real decoder, the subjects succeeded in improving their accuracy in controlling the BMI robotic hand with correct rates of 0.28 ± 0.13 to 0.50 ± 0.11 (p = 0.017, n = 8, paired Student's t-test). Moreover, the classification accuracy of hand movements during the offline task was significantly increased after BMI training with the real decoder from 62.7 ± 6.5 to 70.0 ± 11.1% (p = 0.022, n = 8, t(7) = 2.93, paired Student's t-test), whereas accuracy did not significantly change after BMI training with the random decoder from 63.0 ± 8.8 to 66.4 ± 9.0% (p = 0.225, n = 8, t(7) = 1.33). Conclusion: BMI training is a useful tool to train the cortical activity necessary for BMI control and to induce some plastic changes in the activity.
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Affiliation(s)
- Ryohei Fukuma
- Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita, Japan.,Department of Neuroinformatics, ATR Computational Neuroscience Laboratories, Seika-cho, Japan
| | - Takufumi Yanagisawa
- Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita, Japan.,Department of Neuroinformatics, ATR Computational Neuroscience Laboratories, Seika-cho, Japan.,Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita, Japan.,Institute for Advanced Co-Creation Studies, Osaka University, Suita, Japan.,Endowed Research Department of Clinical Neuroengineering, Global Center for Medical Engineering and Informatics, Osaka University, Suita, Japan
| | - Hiroshi Yokoi
- Department of Mechanical Engineering and Intelligent Systems, University of Electro-Communications, Chofu, Japan
| | - Masayuki Hirata
- Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita, Japan.,Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita, Japan.,Endowed Research Department of Clinical Neuroengineering, Global Center for Medical Engineering and Informatics, Osaka University, Suita, Japan
| | - Toshiki Yoshimine
- Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita, Japan.,Endowed Research Department of Clinical Neuroengineering, Global Center for Medical Engineering and Informatics, Osaka University, Suita, Japan
| | - Youichi Saitoh
- Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita, Japan.,Department of Neuromodulation and Neurosurgery, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Yukiyasu Kamitani
- Department of Neuroinformatics, ATR Computational Neuroscience Laboratories, Seika-cho, Japan.,Graduate School of Informatics, Kyoto University, Kyoto, Japan
| | - Haruhiko Kishima
- Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita, Japan
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10
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Korik A, Sosnik R, Siddique N, Coyle D. Decoding Imagined 3D Hand Movement Trajectories From EEG: Evidence to Support the Use of Mu, Beta, and Low Gamma Oscillations. Front Neurosci 2018; 12:130. [PMID: 29615848 PMCID: PMC5869206 DOI: 10.3389/fnins.2018.00130] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2017] [Accepted: 02/19/2018] [Indexed: 12/03/2022] Open
Abstract
Objective: To date, motion trajectory prediction (MTP) of a limb from non-invasive electroencephalography (EEG) has relied, primarily, on band-pass filtered samples of EEG potentials i.e., the potential time-series model. Most MTP studies involve decoding 2D and 3D arm movements i.e., executed arm movements. Decoding of observed or imagined 3D movements has been demonstrated with limited success and only reported in a few studies. MTP studies normally use EEG potentials filtered in the low delta (~1 Hz) band for reconstructing the trajectory of an executed or an imagined/observed movement. In contrast to MTP, multiclass classification based sensorimotor rhythm brain-computer interfaces aim to classify movements using the power spectral density of mu (8–12 Hz) and beta (12–28 Hz) bands. Approach: We investigated if replacing the standard potentials time-series input with a power spectral density based bandpower time-series improves trajectory decoding accuracy of kinesthetically imagined 3D hand movement tasks (i.e., imagined 3D trajectory of the hand joint) and whether imagined 3D hand movements kinematics are encoded also in mu and beta bands. Twelve naïve subjects were asked to generate or imagine generating pointing movements with their right dominant arm to four targets distributed in 3D space in synchrony with an auditory cue (beep). Main results: Using the bandpower time-series based model, the highest decoding accuracy for motor execution was observed in mu and beta bands whilst for imagined movements the low gamma (28–40 Hz) band was also observed to improve decoding accuracy for some subjects. Moreover, for both (executed and imagined) movements, the bandpower time-series model with mu, beta, and low gamma bands produced significantly higher reconstruction accuracy than the commonly used potential time-series model and delta oscillations. Significance: Contrary to many studies that investigated only executed hand movements and recommend using delta oscillations for decoding directional information of a single limb joint, our findings suggest that motor kinematics for imagined movements are reflected mostly in power spectral density of mu, beta and low gamma bands, and that these bands may be most informative for decoding 3D trajectories of imagined limb movements.
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Affiliation(s)
- Attila Korik
- Intelligent Systems Research Centre, Ulster University, Derry, United Kingdom
| | - Ronen Sosnik
- Hybrid BCI Lab, Holon Institute of Technology, Holon, Israel
| | - Nazmul Siddique
- Intelligent Systems Research Centre, Ulster University, Derry, United Kingdom
| | - Damien Coyle
- Intelligent Systems Research Centre, Ulster University, Derry, United Kingdom
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11
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Dong E, Li C, Li L, Du S, Belkacem AN, Chen C. Classification of multi-class motor imagery with a novel hierarchical SVM algorithm for brain-computer interfaces. Med Biol Eng Comput 2017; 55:1809-1818. [PMID: 28238175 DOI: 10.1007/s11517-017-1611-4] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2016] [Accepted: 01/20/2017] [Indexed: 10/20/2022]
Abstract
Pattern classification algorithm is the crucial step in developing brain-computer interface (BCI) applications. In this paper, a hierarchical support vector machine (HSVM) algorithm is proposed to address an EEG-based four-class motor imagery classification task. Wavelet packet transform is employed to decompose raw EEG signals. Thereafter, EEG signals with effective frequency sub-bands are grouped and reconstructed. EEG feature vectors are extracted from the reconstructed EEG signals with one versus the rest common spatial patterns (OVR-CSP) and one versus one common spatial patterns (OVO-CSP). Then, a two-layer HSVM algorithm is designed for the classification of these EEG feature vectors, where "OVO" classifiers are used in the first layer and "OVR" in the second layer. A public dataset (BCI Competition IV-II-a)is employed to validate the proposed method. Fivefold cross-validation results demonstrate that the average accuracy of classification in the first layer and the second layer is 67.5 ± 17.7% and 60.3 ± 14.7%, respectively. The average accuracy of the classification is 64.4 ± 16.7% overall. These results show that the proposed method is effective for four-class motor imagery classification.
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Affiliation(s)
- Enzeng Dong
- Key Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin, 300384, China
| | - Changhai Li
- Key Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin, 300384, China
| | - Liting Li
- Key Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin, 300384, China
| | - Shengzhi Du
- Department of Mechanical Engineering, Tshwane University of Technology, Pretoria, 0001, South Africa
| | - Abdelkader Nasreddine Belkacem
- Endowed Research Department of Clinical Neuroengineering, Global Center for Medical Engineering and Informatics, Osaka University, Suita, 565-0871, Japan
| | - Chao Chen
- Key Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin, 300384, China.
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12
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Yanagisawa T, Fukuma R, Seymour B, Hosomi K, Kishima H, Shimizu T, Yokoi H, Hirata M, Yoshimine T, Kamitani Y, Saitoh Y. Induced sensorimotor brain plasticity controls pain in phantom limb patients. Nat Commun 2016; 7:13209. [PMID: 27807349 PMCID: PMC5095287 DOI: 10.1038/ncomms13209] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Accepted: 09/12/2016] [Indexed: 12/02/2022] Open
Abstract
The cause of pain in a phantom limb after partial or complete deafferentation is an important problem. A popular but increasingly controversial theory is that it results from maladaptive reorganization of the sensorimotor cortex, suggesting that experimental induction of further reorganization should affect the pain, especially if it results in functional restoration. Here we use a brain–machine interface (BMI) based on real-time magnetoencephalography signals to reconstruct affected hand movements with a robotic hand. BMI training induces significant plasticity in the sensorimotor cortex, manifested as improved discriminability of movement information and enhanced prosthetic control. Contrary to our expectation that functional restoration would reduce pain, the BMI training with the phantom hand intensifies the pain. In contrast, BMI training designed to dissociate the prosthetic and phantom hands actually reduces pain. These results reveal a functional relevance between sensorimotor cortical plasticity and pain, and may provide a novel treatment with BMI neurofeedback. Pain in a phantom limb after limb deafferentation may be due to maladaptive sensorimotor representation. Here the authors find that sensorimotor plasticity induced by BMI training with the phantom hand, contrary to expectation, increased pain while dissociating prosthetic movements from the phantom arm relieved the pain.
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Affiliation(s)
- Takufumi Yanagisawa
- Department of Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan.,Division of Functional Diagnostic Science, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan.,Department of Neuroinformatics, ATR Computational Neuroscience Laboratories, 2-2-2 Hikaridai, Seika-cho, Kyoto 619-0288, Japan.,Department of Neuroinformatics, CiNet Computational Neuroscience Laboratories, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan.,JST PRESTO, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan.,Division of Clinical Neuroengineering, Osaka University, Global Center for Medical Engineering and Informactics, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Ryohei Fukuma
- Department of Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan.,Department of Neuroinformatics, ATR Computational Neuroscience Laboratories, 2-2-2 Hikaridai, Seika-cho, Kyoto 619-0288, Japan.,Department of Neuroinformatics, CiNet Computational Neuroscience Laboratories, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan.,Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayamacho, Ikoma, Nara 630-0192, Japan
| | - Ben Seymour
- Department of Engineering, University of Cambridge, Computational and Biological Learning Laboratory, Trumpington Street, Cambridge CB2 1PZ, UK.,National Institute for Information and Communications Technology, Center for Information and Neural Networks, 1-3 Suita, Osaka 565-0871, Japan
| | - Koichi Hosomi
- Department of Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan.,Department of Neuromodulation and Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Haruhiko Kishima
- Department of Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Takeshi Shimizu
- Department of Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan.,Department of Neuromodulation and Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Hiroshi Yokoi
- Department of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan
| | - Masayuki Hirata
- Department of Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan.,Department of Neuroinformatics, CiNet Computational Neuroscience Laboratories, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan.,Division of Clinical Neuroengineering, Osaka University, Global Center for Medical Engineering and Informactics, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Toshiki Yoshimine
- Department of Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan.,Department of Neuroinformatics, CiNet Computational Neuroscience Laboratories, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan.,Division of Clinical Neuroengineering, Osaka University, Global Center for Medical Engineering and Informactics, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Yukiyasu Kamitani
- Department of Neuroinformatics, ATR Computational Neuroscience Laboratories, 2-2-2 Hikaridai, Seika-cho, Kyoto 619-0288, Japan.,Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayamacho, Ikoma, Nara 630-0192, Japan.,Graduate School of Informatics, Kyoto University, Yoshidahonmachi, Sakyoku, Kyoto 606-8501, Japan
| | - Youichi Saitoh
- Department of Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan.,Department of Neuromodulation and Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan
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13
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Torregrosa T, Koppes RA. Bioelectric Medicine and Devices for the Treatment of Spinal Cord Injury. Cells Tissues Organs 2016; 202:6-22. [PMID: 27701161 DOI: 10.1159/000446698] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/09/2016] [Indexed: 11/19/2022] Open
Abstract
Recovery of motor control is paramount for patients living with paralysis following spinal cord injury (SCI). While a cure or regenerative intervention remains on the horizon for the treatment of SCI, a number of neuroprosthetic devices have been employed to treat and mitigate the symptoms of paralysis associated with injuries to the spinal column and associated comorbidities. The recent success of epidural stimulation to restore voluntary motor function in the lower limbs of a small cohort of patients has breathed new life into the promise of electric-based medicine. Recently, a number of new organic and inorganic electronic devices have been developed for brain-computer interfaces to bypass the injury, for neurorehabilitation, bladder and bowel control, and the restoration of motor or sensory control. Herein, we discuss the recent advances in neuroprosthetic devices for treating SCI and highlight future design needs for closed-loop device systems.
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14
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Common neural correlates of real and imagined movements contributing to the performance of brain-machine interfaces. Sci Rep 2016; 6:24663. [PMID: 27090735 PMCID: PMC4835797 DOI: 10.1038/srep24663] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2015] [Accepted: 04/04/2016] [Indexed: 02/06/2023] Open
Abstract
The relationship between M1 activity representing motor information in real and imagined movements have not been investigated with high spatiotemporal resolution using non-invasive measurements. We examined the similarities and differences in M1 activity during real and imagined movements. Ten subjects performed or imagined three types of right upper limb movements. To infer the movement type, we used 40 virtual channels in the M1 contralateral to the movement side (cM1) using a beamforming approach. For both real and imagined movements, cM1 activities increased around response onset, after which their intensities were significantly different. Similarly, although decoding accuracies surpassed the chance level in both real and imagined movements, these were significantly different after the onset. Single virtual channel-based analysis showed that decoding accuracy significantly increased around the hand and arm areas during real and imagined movements and that these are spatially correlated. The temporal correlation of decoding accuracy significantly increased around the hand and arm areas, except for the period immediately after response onset. Our results suggest that cM1 is involved in similar neural activities related to the representation of motor information during real and imagined movements, except for presence or absence of sensory-motor integration induced by sensory feedback.
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15
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Fukuma R, Yanagisawa T, Saitoh Y, Hosomi K, Kishima H, Shimizu T, Sugata H, Yokoi H, Hirata M, Kamitani Y, Yoshimine T. Real-Time Control of a Neuroprosthetic Hand by Magnetoencephalographic Signals from Paralysed Patients. Sci Rep 2016; 6:21781. [PMID: 26904967 PMCID: PMC4764841 DOI: 10.1038/srep21781] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2015] [Accepted: 02/01/2016] [Indexed: 11/18/2022] Open
Abstract
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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Affiliation(s)
- Ryohei Fukuma
- Osaka University Graduate School of Medicine, Department of Neurosurgery, Suita 565-0871, Japan
- ATR Computational Neuroscience Laboratories, Department of Neuroinformatics, Seika-cho 619-0288, Japan
- Nara Institute of Science and Technology, Graduate School of Information Science, Ikoma 630-0192, Japan
| | - Takufumi Yanagisawa
- Osaka University Graduate School of Medicine, Department of Neurosurgery, Suita 565-0871, Japan
- ATR Computational Neuroscience Laboratories, Department of Neuroinformatics, Seika-cho 619-0288, Japan
- Osaka University Graduate School of Medicine, Division of Functional Diagnostic Science, Suita 565-0871, Japan
| | - Youichi Saitoh
- Osaka University Graduate School of Medicine, Department of Neurosurgery, Suita 565-0871, Japan
- Osaka University Graduate School of Medicine, Department of Neuromodulation and Neurosurgery, Suita 565-0871, Japan
| | - Koichi Hosomi
- Osaka University Graduate School of Medicine, Department of Neurosurgery, Suita 565-0871, Japan
- Osaka University Graduate School of Medicine, Department of Neuromodulation and Neurosurgery, Suita 565-0871, Japan
| | - Haruhiko Kishima
- Osaka University Graduate School of Medicine, Department of Neurosurgery, Suita 565-0871, Japan
| | - Takeshi Shimizu
- Osaka University Graduate School of Medicine, Department of Neurosurgery, Suita 565-0871, Japan
- Osaka University Graduate School of Medicine, Department of Neuromodulation and Neurosurgery, Suita 565-0871, Japan
| | - Hisato Sugata
- Osaka University Graduate School of Medicine, Department of Neurosurgery, Suita 565-0871, Japan
| | - Hiroshi Yokoi
- The University of Electro-Communications, Department of Mechanical Engineering and Intelligent Systems, Chofu 182-8585, Japan
| | - Masayuki Hirata
- Osaka University Graduate School of Medicine, Department of Neurosurgery, Suita 565-0871, Japan
| | - Yukiyasu Kamitani
- ATR Computational Neuroscience Laboratories, Department of Neuroinformatics, Seika-cho 619-0288, Japan
- Nara Institute of Science and Technology, Graduate School of Information Science, Ikoma 630-0192, Japan
- Kyoto University, Graduate School of Informatics, Kyoto 606-8501, Japan
| | - Toshiki Yoshimine
- Osaka University Graduate School of Medicine, Department of Neurosurgery, Suita 565-0871, Japan
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16
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Adewole DO, Serruya MD, Harris JP, Burrell JC, Petrov D, Chen HI, Wolf JA, Cullen DK. The Evolution of Neuroprosthetic Interfaces. Crit Rev Biomed Eng 2016; 44:123-52. [PMID: 27652455 PMCID: PMC5541680 DOI: 10.1615/critrevbiomedeng.2016017198] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The ideal neuroprosthetic interface permits high-quality neural recording and stimulation of the nervous system while reliably providing clinical benefits over chronic periods. Although current technologies have made notable strides in this direction, significant improvements must be made to better achieve these design goals and satisfy clinical needs. This article provides an overview of the state of neuroprosthetic interfaces, starting with the design and placement of these interfaces before exploring the stimulation and recording platforms yielded from contemporary research. Finally, we outline emerging research trends in an effort to explore the potential next generation of neuroprosthetic interfaces.
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Affiliation(s)
- Dayo O. Adewole
- Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Mijail D. Serruya
- Department of Neurology, Jefferson University, Philadelphia, PA, USA
| | - James P. Harris
- Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
| | - Justin C. Burrell
- Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
| | - Dmitriy Petrov
- Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
| | - H. Isaac Chen
- Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - John A. Wolf
- Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
| | - D. Kacy Cullen
- Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
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17
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Nakamura M, Yanagisawa T, Okamura Y, Fukuma R, Hirata M, Araki T, Kamitani Y, Yorifuji S. Categorical discrimination of human body parts by magnetoencephalography. Front Hum Neurosci 2015; 9:609. [PMID: 26582986 PMCID: PMC4631816 DOI: 10.3389/fnhum.2015.00609] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2015] [Accepted: 10/23/2015] [Indexed: 11/13/2022] Open
Abstract
Humans recognize body parts in categories. Previous studies have shown that responses in the fusiform body area (FBA) and extrastriate body area (EBA) are evoked by the perception of the human body, when presented either as whole or as isolated parts. These responses occur approximately 190 ms after body images are visualized. The extent to which body-sensitive responses show specificity for different body part categories remains to be largely clarified. We used a decoding method to quantify neural responses associated with the perception of different categories of body parts. Nine subjects underwent measurements of their brain activities by magnetoencephalography (MEG) while viewing 14 images of feet, hands, mouths, and objects. We decoded categories of the presented images from the MEG signals using a support vector machine (SVM) and calculated their accuracy by 10-fold cross-validation. For each subject, a response that appeared to be a body-sensitive response was observed and the MEG signals corresponding to the three types of body categories were classified based on the signals in the occipitotemporal cortex. The accuracy in decoding body-part categories (with a peak at approximately 48%) was above chance (33.3%) and significantly higher than that for random categories. According to the time course and location, the responses are suggested to be body-sensitive and to include information regarding the body-part category. Finally, this non-invasive method can decode category information of a visual object with high temporal and spatial resolution and this result may have a significant impact in the field of brain-machine interface research.
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Affiliation(s)
- Misaki Nakamura
- Department of Functional Diagnostic Science, Osaka University Graduate School of Medicine Suita, Japan
| | - Takufumi Yanagisawa
- Department of Functional Diagnostic Science, Osaka University Graduate School of Medicine Suita, Japan ; Department of Neurosurgery, Osaka University Graduate School of Medicine Suita, Japan ; Department of Neuroinformatics, ATR Computational Neuroscience Laboratories Kyoto, Japan ; Japan Science and Technology Agency, Precursory Research for Embryonic Science and Technology Osaka, Japan
| | - Yumiko Okamura
- Department of Functional Diagnostic Science, Osaka University Graduate School of Medicine Suita, Japan
| | - Ryohei Fukuma
- Department of Neurosurgery, Osaka University Graduate School of Medicine Suita, Japan ; Department of Neuroinformatics, ATR Computational Neuroscience Laboratories Kyoto, Japan ; Graduate School of Information Science, Nara Institute of Science and Technology Ikoma, Japan
| | - Masayuki Hirata
- Department of Functional Diagnostic Science, Osaka University Graduate School of Medicine Suita, Japan ; Department of Neurosurgery, Osaka University Graduate School of Medicine Suita, Japan
| | - Toshihiko Araki
- Department of Functional Diagnostic Science, Osaka University Graduate School of Medicine Suita, Japan
| | - Yukiyasu Kamitani
- Department of Neuroinformatics, ATR Computational Neuroscience Laboratories Kyoto, Japan ; Graduate School of Information Science, Nara Institute of Science and Technology Ikoma, Japan ; Graduate School of Informatics, Kyoto University Kyoto, Japan
| | - Shiro Yorifuji
- Department of Functional Diagnostic Science, Osaka University Graduate School of Medicine Suita, Japan
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