研究者情報 | Keio Research Showcase

教授

牛場潤一

Ushiba, Junichi

理工学部生命情報学科

Researchmap Member ID: B000003280

Keio Researchers Information System (K-RIS): Researcher Details - Ushiba, Junichi

Introduction

How do humans integrate sensation and movement to express precise actions? In the process of acquiring uniquely human physical motor abilities, how does the brain embody the mind? Fascinated by the various mysteries of the nervous system, I am advancing research based on neuroscientific methods. I am also interested in the academic reorganization activities that expand research outcomes into medicine and art.

Achievements

■Medical Application of Brain-Machine Interface (BCI)
・Brain-Computer Interface (BCI) technology, which connects the brain and AI, is being applied to induce functional recovery of post-stroke hand palsy. BCI can be used to “visualize” activity in the brain, facilitating self-regulation of activity in the brain region targeted for treatment (somatosensory-motor cortex) and eliciting recovery from treatment-resistant hand palsy. This paper constitutes an international research team and is a medico-demographic analysis of the results of several clinical trials conducted around the world.

Cervera MA, Soekadar SR, Ushiba J, Millán JDR, Liu M, Birbaumer N, Garipelli G. Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis. Ann Clin Transl Neurol. 2018 Mar 25;5(5):651-663. doi:10.1002/acn3.544.

■Brain-Machine Interface (BCI) and plasticity induction
・Brain-Computer Interface (BCI) technology, which connects the brain and AI, is applied to train self-regulation of the activity of the brain region targeted for treatment (somatosensory-motor cortex). This technology has been shown to immediately strengthen the functional connectivity of the targeted brain regions by self-regulating the activity of the targeted brain regions (somatosensory-motor cortex). This is a clinical study using functional brain imaging (fMRI) in hemiplegic stroke patients.

Tsuchimoto S, Shindo K, Hotta F, Hanakawa T, Liu M, Ushiba J. Sensorimotor Connectivity after Motor Exercise with Neurofeedback in Post-Stroke Patients with Hemiplegia. Neuroscience. 2019 Sep 15;416:109-125. doi:10.1016/j.neuroscience.2019.07.037.

・By applying the “Brain-Computer Interface (BCI)” technology, which connects the brain and AI, we have revealed that self-regulation training of activity in the target brain region (somatosensory-motor cortex) immediately changes the cortical and fiber bundle structure of the target brain region. The results showed that self-regulation of activity in the target brain region (somatosensory-motor cortex) immediately changes cortical structure and fiber bundle structure in the targeted brain region. This is a basic study using structural MRI in healthy adult subjects.

Kodama M, Iwama S, Morishige M, Ushiba J. Thirty-minute motor imagery
exercise aided by EEG sensorimotor rhythm neurofeedback enhances morphing of
sensorimotor cortices: a double-blind sham-controlled study. Cereb Cortex. 2023
May 24;33(11):6573-6584.

・The resting state of the brain changes and fluctuates from moment to moment. This is a physiological phenomenon of which subjects themselves are not aware. We identified the state of such “fluctuation” from scalp EEG, and revealed that when a sound cue is given at the timing when a specific brain state is reached and keyboard operation is started, the finger tangling tendency is reduced. This is an example of how the Brain-Computer Interface (BCI) technology, which connects the brain and AI, can be applied to reveal the fundamental properties of our brain.

Iwama S, Yanagisawa T, Hirose R, Ushiba J. Beta rhythmicity in human motor cortex reflects neural population coupling that modulates subsequent finger coordination stability. Commun Biol. 2022 Dec 15;5(1):1375. doi:10.1038/s42003-022-04326-4.

By applying the “Brain-Computer Interface (BCI)” technology, which connects the brain and AI, we have revealed that self-regulation training of activity in a targeted brain region (somatosensory-motor cortex) increases the excitability of the somatosensory cortex. This is a basic study using an electrophysiological test called somatosensory evoked potentials in healthy adult subjects. The study also shows an accompanying improvement in performance of finger dexterity exercises.

Iwama S, Ueno T, Fujimaki T, Ushiba J. Enhanced human sensorimotor integration via self-modulation of the somatosensory activity. iScience. 2025 Mar 3;28(4):112145. doi: 10.1016/j.isci.2025.112145.

This basic research shows that it is possible to self-regulate the somatosensory motor cortex of the right hemisphere and the somatosensory motor cortex of the left hemisphere to different excitation levels by applying the Brain-Computer Interface (BCI) technology, which links the brain and AI. It was surprising that even non-surgical wearable electroencephalography could modulate the state of the target brain region with such a high degree of freedom.

Hayashi M, Mizuguchi N, Tsuchimoto S, Ushiba J. Neurofeedback of scalp bi-hemispheric EEG sensorimotor rhythm guides hemispheric activation of sensorimotor cortex in the targeted hemisphere. Neuroimage. 2020 Dec;223:117298. doi: 10.1016/j.neuroimage.2020.117298.

This basic research revealed that self-regulation of left and right somatosensory-motor cortex activity by applying “Brain-Computer Interface (BCI)” technology, which connects the brain and AI, modulates inhibitory connections between the corpus callosum.

Hayashi M, Okuyama K, Mizuguchi N, Hirose R, Okamoto T, Kawakami M, Ushiba J. Spatially bivariate EEG-neurofeedback can manipulate interhemispheric inhibition. Elife. 2022 Jul 7;11:e76411. doi: 10.7554/eLife.76411.

■ Exploring the Hidden Functions of Brain Networks with AI
・AI analysis of multi-point EEG measurements from the whole head while selecting and performing various types of one-handed movements revealed that the somatosensory motor cortex on the “same side” as the hand being exercised contributes to the selection of the movement type. This is a new finding that extends the classic idea that “one hand's movement is dominated by the brain on the opposite side.”

Iwama S, Tsuchimoto S, Hayashi M, Mizuguchi N, Ushiba J. Scalp electroencephalograms over ipsilateral sensorimotor cortex reflect contraction patterns of unilateral finger muscles. Neuroimage. 2020 Nov 15;222:117249. doi:10.1016/j.neuroimage.2020.117249.

■Development of highly sensitive readout of brain information using AI
・We developed and evaluated a sequential Bayesian estimation algorithm to quickly identify an individual's alpha frequency (IAF) from a short period of resting EEG data. This enabled highly sensitive readout of brain information, which is highly practical because it can identify individual differences in EEG characteristics in less than 30 seconds.

Iwama S, Ushiba J. Rapid-IAF: Rapid Identification of Individual Alpha
Frequency in EEG Data Using Sequential Bayesian Estimation. IEEE Trans Neural
Syst Rehabil Eng. 2024;32:915-922. doi: 10.1109/TNSRE.2024.3365197.

Areas of Research

・Brain-Machine Interface, Brain-Computer Interface
・Neural plasticity and motor learning
・Neurorehabilitation

Social Contributions

・Creation of Neurological Rehabilitation
・Development of Human Neuroscience and Neurotechnology
・Social implementation of academic knowledges through University-Based Startup

Introduction

Achievements

Areas of Research

Social Contributions

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