The University of Osaka · Neuroscience
Professor Takufumi Yanagisawa's research lab specializes in brain-machine interface (BMI) systems and neuroprosthetics, focusing on decoding motor intentions from neural signals—particularly electrocorticographic (ECoG) and magnetoencephalographic (MEG) signals—for real-time control of prosthetic limbs. The lab investigates neural mechanisms underlying motor control and cortical plasticity, including phase-amplitude coupling in high-gamma activity and synaptic reorganization in the sensorimotor cortex following injury or deafferentation. Their work bridges fundamental neuroscience with clinical applications, aiming to restore motor function in poststroke and severely paralyzed patients through BMI-driven neurorehabilitation.
Figures are computed from collected data and may differ slightly.
The present integrated BMI system successfully decoded the hand movements of a poststroke patient and controlled a prosthetic hand in real time. This success paves the way for the restoration of the patient's motor function using a prosthetic arm controlled by a BMI using ECoG signals.
High-γ amplitude (80-150 Hz) represents motor information, such as movement types, on the sensorimotor cortex. In several cortical areas, high-γ amplitudes are coupled with low-frequency phases, e.g., α and θ (phase-amplitude coupling, PAC). However, such coupling has not been studied in the sensorimotor cortex; thus, its potential functional role has yet to be explored. We investigated PAC of high-γ amplitude in the sensorimotor cortex during waiting for and the execution of movements using ele
The RC active filter, which has been established by J. G. Linvill, has a circuit configuration of a negative impedance converter sandwiched between two RC networks. This paper deals with a new circuit also using two RC networks and a converter, but its configuration is based on the parallel or series connection and the converter must be a current-inversion type. The main results obtained here are as follows: 1) The filter characteristic function which has attenuation poles at finite frequency ca
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 moveme
Thalamocortical connections undergo remarkable plasticity during the critical period and mounting evidence serves to demonstrate that the activation of silent synapses at postsynaptic sites is an important underlying mechanism in this process. However, relatively little is known about the nature of the presynaptic properties. In the present study, we examined the release probability (Pr) of thalamocortical synaptic terminals on a layer IV neuron in the developing mouse barrel cortex. Using the c
Use of real-time MEG signals was shown to successfully control the neuroprosthetic hand. The developed system may be useful for evaluating movement-related slow cortical potentials of severely paralyzed patients to predict the efficacy of invasive BMI.
This study provides Class III evidence that BCI reduces phantom limb pain.
DMD successfully captured the spatiotemporal patterns characterizing the movement types and contributed to improving the decoding accuracy. This method can be applied to improve BCIs to help severely paralyzed patients communicate.
Dementia and mild cognitive impairment (MCI) represent significant health challenges in an aging population. As the search for noninvasive, precise and accessible diagnostic methods continues, the efficacy of electroencephalography (EEG) combined with deep convolutional neural networks (DCNNs) in varied clinical settings remains unverified, particularly for pathologies underlying MCI such as Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and idiopathic normal-pressure hydrocephalus (i
These results suggest that the low gamma ERDs measured by MEG strongly relate to the language function of picture naming in the left IFG. Finally, we propose a novel method to identify language-related cortical functions by combining TMS and MEG.
<i>Objective.</i>Diagnosing epilepsy still requires visual interpretation of electroencephalography (EEG) and magnetoencephalography (MEG) by specialists, which prevents quantification and standardization of diagnosis. Previous studies proposed automated diagnosis by combining various features from EEG and MEG, such as relative power (Power) and functional connectivity (FC). However, the usefulness of interictal phase-amplitude coupling (PAC) in diagnosing epilepsy is still unknown. We hypothesi
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