Ushiba, Junichi

写真a

Affiliation

Faculty of Science and Technology, Department of Biosciences and Informatics (Yagami)

Position

Associate Professor

Related Websites

External Links

Career 【 Display / hide

  • 2002.11
    -
    2004.03

    文部省中核的研究拠点形成プログラム(COE), 研究員

  • 2003.03
    -
    2003.08

    デンマーク、オルボー大学感覚運動統合センター, 客員研究員

  • 2004.04
    -
    2007.03

    慶應義塾大学理工学部生命情報学科, 助手

  • 2004.04
    -
    2007.03

    慶應義塾大学医学部 月が瀬リハビリテーションセンター, 助手

  • 2007.04
    -
    2011.09

    慶應義塾大学医学部 月が瀬リハビリテーションセンター, 専任講師

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Academic Background 【 Display / hide

  • 1997.04
    -
    2001.03

    Keio University, Faculty of Science and Engineering, Applied Physics and Physico-Informatics

    University, Graduated

  • 2001.04
    -
    2002.09

    Keio University, Graduate School, Division of Science and Engineering, School of Fundamental Science and Technology

    Graduate School, Completed, Master's course

  • 2002.09
    -
    2004.03

    Keio University, Graduate School, Division of Science and Engineering, School of Fundamental Science and Technology

    Graduate School, Completed, Doctoral course

Academic Degrees 【 Display / hide

  • 博士(工学), Keio University, Coursework, 2004.03

  • 博士(工学), Keio University, Coursework, 2004.03

 

Books 【 Display / hide

  • オーグメンテッド・ヒューマン

    Junichi Ushiba、Junichi Rekimoto, 株式会社エヌ・ティー・エス, 2018.01

    Scope: 第1編第3章 神経機能を変容させるサイボーグ技術

  • 金融ジェロントロジー 「健康寿命」と「資産寿命」をいかに伸ばすか

    Junichi Ushiba, 東洋経済新報社, 2017.04

    Scope: 第4章 高齢社会を支えるテクノロジーはどうあるべきか

  • 脳神経外科医が知っておくべきニューロサイエンスの知識 【脳神経外科診療プラクティス 6】

    三國 信啓(編集), 深谷 親(編集), 牛場 潤一, 他, 文光堂, 2015.10

    Scope: V-6 ニューロリハビリテーション

  • リハビリテーションのためのニューロサイエンス -脳科学からみる機能回復-

    西条 寿夫(監修), 伊佐 正(監修), 浦川 将(編集),牛場 潤一, 春日 翔子, 他, メジカルビュー社, 2015.09

    Scope: 3章‐1 BMI(brain-machine interface)によるリハビリテーション

     View Summary

    ブレイン・マシン・インターフェース(BMI)を活用したニューロフィードバック治療の概略と研究動向を取り上げるとともに、BMIによる脳機能の再構築過程に関与していると考えられている、幾つかの可塑性原理について論じた。

  • 神経科学の最前線とリハビリテーション

    Meigen Liu, Junichi Ushiba, 医歯薬出版, 2015.06

    Scope: 9章 NEDO未来医療プロジェクトにおける革新的リハビリテーション機器開発

     View Summary

    「神経科学の基礎」、「病態 機能の評価」、「リハビリテーション治療の今と未来」で構成され、神経科学のリハに関連するキーワード65テーマを設定して解説した。

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Papers 【 Display / hide

  • Depth Sensor-Based Assessment of Reachable Work Space for Visualizing and Quantifying Paretic Upper Extremity Motor Function in People with Stroke

    Kohei Okuyama, Michiyuki Kawakami, Shohei Tsuchimoto, Miho Ogura, Kohsuke Okada, Katsuhiro Mizuno, Junichi Ushiba, Meigen Liu

    Physical Therapy (© 2020 American Physical Therapy Association)  pzaa025 2020.02

    Research paper (scientific journal), Joint Work, Accepted

  • Sensorimotor Connectivity after Motor Exercise with Neurofeedback in Post-Stroke Patients with Hemiplegia

    Tsuchimoto S., Shindo K., Hotta F., Hanakawa T., Liu M., Ushiba J.

    Neuroscience (Neuroscience)  416   109 - 125 2019.09

    Research paper (scientific journal), Joint Work, Accepted,  ISSN  03064522

     View Summary

    © 2019 Elsevier Ltd Impaired finger motor function in post-stroke hemiplegia is a debilitating condition with no evidence-based or accessible treatments. Here, we evaluated the neurophysiological effectiveness of direct brain control of robotic exoskeleton that provides movement support contingent with brain activity. To elucidate the mechanisms underlying the neurofeedback intervention, we assessed resting-state functional connectivity with functional magnetic resonance imaging (rsfcMRI) between the ipsilesional sensory and motor cortices before and after a single 1-h intervention. Eighteen stroke patients were randomly assigned to crossover interventions in a double-blind and sham-controlled design. One patient dropped out midway through the study, and 17 patients were included in this analysis. Interventions involved motor imagery, robotic assistance, and neuromuscular electrical stimulation administered to a paretic finger. The neurofeedback intervention delivered stimulations contingent on desynchronized ipsilesional electroencephalographic (EEG) oscillations during imagined movement, and the control intervention delivered sensorimotor stimulations that were independent of EEG oscillations. There was a significant time × intervention interaction in rsfcMRI in the ipsilesional sensorimotor cortex. Post-hoc analysis showed a larger gain in increased functional connectivity during the neurofeedback intervention. Although the neurofeedback intervention delivered fewer total sensorimotor stimulations compared to the sham-control, rsfcMRI in the ipsilesional sensorimotor cortices was increased during the neurofeedback intervention compared to the sham-control. Higher coactivation of the sensory and motor cortices during neurofeedback intervention enhanced rsfcMRI in the ipsilesional sensorimotor cortices. This study showed neurophysiological evidence that EEG-contingent neurofeedback is a promising strategy to induce intrinsic ipsilesional sensorimotor reorganization, supporting the importance of integrating closed-loop sensorimotor processing at a neurophysiological level.

  • Fast Electrophysiological Mapping of Rat Cortical Motor Representation on a Time Scale of Minutes during Skin Stimulation

    Kosugi A., Castagnola E., Carli S., Ricci D., Fadiga L., Taoka M., Iriki A., Ushiba J.

    Neuroscience (Neuroscience)  414   245 - 254 2019.08

    Research paper (scientific journal), Joint Work, Accepted,  ISSN  03064522

     View Summary

    © 2019 Elsevier Ltd The topographic map of motor cortical representation, called the motor map, is not invariant, but can be altered by motor learning, neurological injury, and functional recovery from injury. Although much attention has been paid to short-term changes of the motor map, robust measures have not been established. The existing mapping methods are time-consuming, and the obtained maps are confounded by time preference. The purpose of this study was to examine the dynamics of the motor map on a timescale of minutes during transient somatosensory input by a fast motor mapping technique. We applied 32-channel micro-electrocorticographic electrode arrays to the rat sensorimotor cortex for cortical stimulation, and the topographic profile of motor thresholds in forelimb muscle was identified by fast motor mapping. Sequential motor maps were obtained every few minutes before, during, and just after skin stimulation to the dorsal forearm using a wool buff. During skin stimulation, the motor map expanded and the center of gravity of the map was shifted caudally. The expansion of the map persisted for at least a few minutes after the end of skin stimulation. Although the motor threshold of the hotspot was not changed, the area in which it was decreased appeared caudally to the hotspot, which may be in the somatosensory cortex. The present study demonstrated rapid enlargement of the forelimb motor map in the order of a few minutes induced by skin stimulation. This helps to understand the spatial dynamism of motor cortical representation that is modulated rapidly by somatosensory input.

  • Two-stage regression of high-density scalp electroencephalograms visualizes force regulation signaling during muscle contraction

    Hayashi M., Tsuchimoto S., Mizuguchi N., Miyatake M., Kasuga S., Ushiba J.

    Journal of Neural Engineering (Journal of Neural Engineering)  16 ( 5 )  2019.08

    Research paper (scientific journal), Joint Work, Accepted,  ISSN  17412560

     View Summary

    © 2019 IOP Publishing Ltd. Objective. A critical feature for the maintenance of precise skeletal muscle force production by the human brain is its ability to configure motor function activity dynamically and adaptively in response to visual and somatosensory information. Existing studies have concluded that not only the sensorimotor area but also distributed cortical areas act cooperatively in the generation of motor commands for voluntary force production to the desired level. However, less attention has been paid to such physiological mechanisms in conventional brain-computer interface (BCI) design and implementation. We proposed a new, physiologically inspired two-stage decoding method to see its contribution on accuracy improvement of BCI. Approach. We performed whole-head high-density scalp electroencephalographic (EEG) recording during a right finger force-matching task at three strength levels (20%, 40%, and 60% maximal voluntary contraction following a resting state). A two-stage regression approach was employed that decodes muscle contraction level from EEG signals in the multi-level force-matching task and translates them into: (1) presence/absence of muscle contraction as a first stage; and (2) muscle contraction level as a second stage. Dimensionality reduction of the EEG signals, using principal component analysis, avoided multicollinearity during multiple regression, and data-driven stepwise multiple regression identified EEG components that were involved in the multi-level force-matching task. Main results. An alternatively tuned two-stage regressor accurately decoded muscle contraction level with online processing rather than the conventional decoders, and identified EEG components that were related to voluntary force production. Relaxation/contraction state-dependent EEG components were localized dominantly in the contralateral parieto-temporal regions, whereas multi-level force regulation-dependent EEG components came from the fronto-parietal regions. Significance. Our findings identify respective cortical signalings during relaxation/contraction and multi-level force regulation using a sensor-based approach with EEG. Simulation-based assessment of the current physiologically inspired decoding technique proved improved accuracy in online BCI control.

  • Development of Rehabilitation System with Brain-Computer Interface for Subacute Stroke Patients

    Hashimoto Y., Kakui T., Ushiba J., Liu M., Kamada K., Ota T.

    Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018)     51 - 56 2019.01

    Research paper (international conference proceedings), Joint Work,  ISSN  9781538666500

     View Summary

    © 2018 IEEE. There have been recent advances in brain-computer interfaces for post-stroke rehabilitation. In particular, compact and embedded brain-computer interface systems with neuromuscular electrical stimulation have been developed by industry and academia, and some of them can potentially be used at the bedside. However, limited studies have demonstrated their safety and feasibility for treatment in subacute stroke patients. The aim of this pilot study was to first develop a brain-computer interface system for subacute stroke inpatients that is usable at the bedside and to show the safety and feasibility using a small cohort of inpatients. Four hemiplegic stroke inpatients in the very early phase (7-24 days from stroke onset) participated in this study. The portable brain-computer interface system shows the amplitude of sensorimotor rhythms extracted from scalp electroencephalograms in real time. Patients attempted to extend the wrist on their affected side, and neuromuscular electrical stimulation was applied only when the brain-computer interface system detected significant movement intention-related electroencephalogram changes. Each brain-computer interface training lasted 40 minutes. On average, 120-200 training trials of the wrist extension task were successfully and safely conducted over 3.3 days (range 2-4 days) with the bedside brain-computer interface system. Furthermore, electroencephalogram results showed a new significant event-related desynchronization in the damaged hemisphere after training. These results clearly show the proposed bedside brain-computer interface system's safety and feasibility and also demonstrated electrophysiological plasticity in the damaged hemisphere in subacute patients with post-stroke hemiplegia. Larger clinical studies are needed to identify the brain-computer interface system's clinical efficacy and its effect size in the subacute post-stroke patient population.

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Papers, etc., Registered in KOARA 【 Display / hide

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Reviews, Commentaries, etc. 【 Display / hide

  • Neurofeedback

    Hampson M., Ruiz S., Ushiba J.

    NeuroImage (NeuroImage)  116473 2019.12

    Research paper, Joint Work,  ISSN  10538119

  • ブレイン・マシン・インターフェースによる神経リハビリテーションと人工知能

    牛場 潤一

    Brain and NERVE (医学書院)  71 ( 7 ) 793 - 804 2019.07

    Introduction and explanation (scientific journal), Single Work

     View Summary

    ブレイン・マシン・インターフェース研究における人工知能技術について解説した。

  • Neurorehabilitation: Neural Plasticity and Functional Recovery 2018

    Fujiwara T., Ushiba J., Soekadar S.

    Neural plasticity (Neural plasticity)  2019 2019.01

    Introduction and explanation (scientific journal), Joint Work,  ISSN  20905904

  • Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis

    Maria A. Cervera, Surjo R. Soekadar, Junichi Ushiba, José del R. Millán, Meigen Liu, Niels Bibaumer, Gangadhar Garipelli

    Annals of Clinical and Translational Neurology 5 ( 5 ) 651 - 663 2018.03

    Introduction and explanation (scientific journal), Joint Work

     View Summary

    脳卒中片麻痺上肢に対するブレイン・マシン・インターフェース訓練に関するランダム比較試験(9件)をメタアナリシスした結果、比較対象群に対して有意に治療効果が高いことを明らかにした。

  • 麻痺側上肢機能とニューロモジュレーション ブレイン・マシン・インターフェースによる脳卒中片麻痺上肢の運動機能回復

    牛場 潤一

    理学療法ジャーナル (医学書院)  51 ( 11 ) 1026 - 1034 2017.11

    Introduction and explanation (scientific journal), Single Work

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Presentations 【 Display / hide

  • EEG-based brain-computer interface using deep neural network for point by point decoding: A pilot study

    Yosuke Fujiwara, Satoshi Honda, Junichi Ushiba

    The 48th Annual Meeting of the Society for Neuroscience (San Diego CA, U.S.A) , 2018.11, Poster (general)

  • Transgenic marmoset as a novel non-human primate model of Parkinson's disease

    R. KOBAYASHI, S. SHIOZAWA, J. OKAHARA, C. YOKOYAMA, T. KONDO, A. KOSUGI, J. USHIBA, D. KUMAR, M. SAKAGUCHI, J. TAKAHASHI-FUJIGASAKI, T. INOUE, C. HARA-MIYAUCHI, H. J. OKANO, E. SASAKI, H. OKANO

    The 48th Annual Meeting of the Society for Neuroscience (San Diego CA, U.S.A) , 2018.11, Poster (general)

  • Short-term structural change in fractional anisotropy correlates rapid performance improvement

    Midori Kodama, Takashi Ono, F. YAMASHITA , Hiroki Ebata, Meigen Liu, Shoko Kasuga, Junichi Ushiba

    The 48th Annual Meeting of the Society for Neuroscience (San Diego CA, U.S.A) , 2018.11, Poster (general)

  • Acquisition of body schema to control a virtual tail via EEG-based brain-computer interface

    Shohei Kimura, Shoko Kasuga, Nobuaki Mizuguchi, Junichi Ushiba

    The 48th Annual Meeting of the Society for Neuroscience (San Diego CA, U.S.A) , 2018.11, Poster (general)

  • Dynamic muscle representations in marmoset motor cortex: Static lower limb position modulates upper limb motor somatotopy

    A. IRIKI, M. TAKEMI, B. TIA, A. KOSUGI, E. CASTAGNOLA, D. RICCI, L. FADIGA, J. USHIBA

    The 48th Annual Meeting of the Society for Neuroscience (San Diego CA, U.S.A) , 2018.11, Poster (general)

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Research Projects of Competitive Funds, etc. 【 Display / hide

  • A study of biological plasticity behind brain-machine interface learning

    2019.04
    -
    2022.03

    MEXT,JSPS, Grant-in-Aid for Scientific Research, 牛場 潤一, Grant-in-Aid for Scientific Research (B), Principal Investigator

Works 【 Display / hide

  • 注目の大学研究室 vol.12 慶應義塾大学 理工学部 リハビリテーション神経科学研究室

    牛場 潤一

    Technologist's magazine, February 2017 (vol.6), 

    2017.02

    Other

  • AXN これがALMOST HUMANの世界だ!

    2014.06

    Other

  • BS朝日「いま世界は」朝日新聞GLOBE連動特集「脳のふしぎ」

    BS朝日

    2014.03

    Other

  • TBS「未来の起源」#1

    USHIBA JIYUN'ICHI

    2013.04

    Other

  • 先端人 研究の垣根超え医工連携

    日経産業新聞 (11ページ)

    2013.03

    Other

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Intellectual Property Rights, etc. 【 Display / hide

  • リハビリテーションシステムおよびリハビリテーションシステムの制御方法

    Application No.: 2014-135355  2014.06 

    Announcement No.: WO2016/002207  2016.01 

    Registration No.: 特許第6536870号  2019.06

    Patent, Joint, PCT international application

  • リハビリテーション装置

    Application No.: 2014-087470  2014.04 

    Announcement No.: 特開2015-205042  2015.11 

    Registration No.: 特許第6304626号  2018.03

    Patent, Joint, National application

  • ブレイン・マシン・インターフェース

    Application No.: 2012-219542  2012.10 

    Announcement No.: 2014-071825  2014.04 

    Registration No.: 特許第6046439号  2016.11

    Patent, Joint, National application

  • リハビリテーション用脳波信号処理装置、及びリハビリテーションシステム

    Application No.: 2011-088356  2011.04 

    Announcement No.: 2012-217721  2012.11 

    Registration No.: 特許第5813981号  2015.10

    Patent, Single, National application

  • 制御装置

    Application No.: 2010-277253  2010.12 

    Announcement No.: 2012-128512  2012.07 

    Registration No.: 特許第5576257号  2014.07

    Patent, Joint, National application

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Awards 【 Display / hide

  • The BCI Research Award 2017 Top 12 Nominees

    Kenichi Takasaki, Fumio Liu, Miho Hiramoto, Kohei Okuyama, Michiyuki Kawakami, Katsuhiko Mizuno, Shoko Kasuga, Tomoyuki Noda, Jun Morimoto, Toshiyuki Fujiwara, Junichi Ushiba, Meigen Liu, 2017.07, Guger Technologies OG (g.tec), Targeted up-conditioning of contralesional corticospinal pathways promotes motor recovery in poststroke patients with severe chronic hemiplegia

  • フロンティアサロン 第六回永瀬賞特別賞

    2016.09, 一般財団法人 フロンティアサロン財団, ブレイン・マシン・インターフェースによる神経医療研究

    Type of Award: Awards of Publisher, Newspaper Company and Foundation

  • Nakatani Prize (Incentive Prize)

    Junichi Ushiba, 2016.02, Nakatani Foundation for Advancement of Measuring Technologies in Biomedical Engineering, Development of Brain-Machine Interface towards assisting recovery of brain's motor function

    Type of Award: Awards of Publisher, Newspaper Company and Foundation.  Country: Japan

  • The Young Scientists’ Prize

    2015.04, Ministry of Education, Culture, Sports, Science and Technology JAPAN, ブレインマシンインターフェースによる神経医療研究

    Country: Japan

  • The BCI Research Award 2013 Top 10 Nominees

    Hashimoto Y, Ota T, Mukaino M, Ushiba J, 2013.07, Guger Technologies OG (g.tec), Motor recovery of chronic writer’s cramp by brain-computer interface rehabilitation

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Courses Taught 【 Display / hide

  • TOPICS IN BIOSCIENCES AND INFORMATICS 1

    2020

  • SEMINAR IN BIOSCIENCES AND INFORMATICS

    2020

  • OFF-CAMPUS STUDY PROGRAM 3

    2020

  • OFF-CAMPUS STUDY PROGRAM 2

    2020

  • OFF-CAMPUS STUDY PROGRAM 1

    2020

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Courses Previously Taught 【 Display / hide

  • バイオサイバネティクス

    Keio University, 2018, Spring Semester, Major subject, Lecture, Within own faculty, 43people

  • ニューロインフォマティクス

    Keio University, 2018, Spring Semester, Major subject, Lecture, Within own faculty, 40people

 

Social Activities 【 Display / hide

  • The Annual BCI Research Award 2015 選考委員長

    2015.02
    -
    Present
  • 公益財団法人山田科学振興財団 海外研究留学費助成 選考委員

    2014.12
    -
    2015.02
  • 文部科学省 脳科学研究戦略推進プログラム 広報小委員会

    2014.04
    -
    Present
  • 科学研究費委員会専門委員

    2013.12
    -
    2014.11
  • 文部科学省科学技術・学術政策研究所 科学技術専門家ネットワーク 専門調査員

    2013.07
    -
    Present

Memberships in Academic Societies 【 Display / hide

  • Japan Society for Marmoset Research, 

    2016.11
    -
    Present
  • 日本神経科学学会, 

    2009.04
    -
    Present
  • Society for Neuroscience, 

    2005.04
    -
    Present
  • 日本リハビリテーション医学会, 

    2003.04
    -
    Present
  • 日本臨床神経生理学会, 

    2002.06
    -
    Present

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Committee Experiences 【 Display / hide

  • 2017.11
    -
    Present

    日本臨床神経生理学会 代議員, 日本臨床神経生理学会

  • 2015.06
    -
    2017.12

    Executive Board, Real-time Functional Imaging and Neurofeedback 2017 (rtFIN2017) , Real-time Functional Imaging and Neurofeedback community

  • 2015.06
    -
    2016.09

    第10回Motor Control研究会 大会長, Motor Control研究会

  • 2015.03

    Clinical Brain Neural-Machine Interface Systems (CBMI2015) 大会長, 国際ワークショップClinical Brain Neural-Machine Interface Systems (CBMI2015)

  • 2013.08
    -
    Present

    第94春季年会ATPバイオセッション 「脳科学の新展開」サブセッションオーガナイザー, 日本化学学会

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