A5071286340- EEG and Brain-Computer Interfaces
- Neural dynamics and brain function
- Muscle activation and electromyography studies
- Motor Control and Adaptation
- Neuroscience and Neural Engineering
- Gaze Tracking and Assistive Technology
- Blind Source Separation Techniques
- Functional Brain Connectivity Studies
- Neural and Behavioral Psychology Studies
- Emotion and Mood Recognition
- Advanced Memory and Neural Computing
- Action Observation and Synchronization
- Speech and Audio Processing
- Robotics and Automated Systems
- Tactile and Sensory Interactions
- Nonlinear Dynamics and Pattern Formation
- Mitochondrial Function and Pathology
- Anesthesia and Neurotoxicity Research
- Genetic Neurodegenerative Diseases
- Chaos control and synchronization
- Genetics and Neurodevelopmental Disorders
- Neuroscience and Music Perception
- Neurological disorders and treatments
- Neural Networks and Reservoir Computing
- Music and Audio Processing
Tokyo Institute of Technology
2016-2025
Research Organization of Information and Systems
2020-2024
Institute of Science Tokyo
2024
Xi'an Jiaotong University
2021-2023
Advanced Telecommunications Research Institute International
2020-2023
Japan Science and Technology Agency
2018-2021
National Center of Neurology and Psychiatry
2011-2021
NTT Basic Research Laboratories
2020
University of Catania
2020
University of Trento
2020
Brain-machine interface techniques have been applied in a number of studies to control neuromotor prostheses and for neurorehabilitation the hopes providing means restore lost motor function. Electrocorticography (ECoG) has seen recent use this regard because it offers higher spatiotemporal resolution than non-invasive EEG is less invasive intracortical microelectrodes. Although several already succeeded inference computer cursor trajectories finger flexions using human ECoG signals, precise...
Simultaneous acquisition of electrooculogram, jaw electromyogram, electroencephalogram, and head movement via consumer-grade wearable devices has become possible. Such offer new opportunities to deploy practical biosignal-based interfaces for assistive robots; however, they also pose challenges related the available signals their characteristics. In this proof-of-concept study, we demonstrate possibility successful control a 5 + 1 degrees-of-freedom robot arm based on consumer wireless...
A novel hierarchical network based on coupled nonlinear oscillators is proposed for motor pattern generation in hexapod robots. Its architecture consists of a central generator (CPG), producing the global leg coordination pattern, with six local generators, each devoted to generating trajectory one leg. Every node comprises simple oscillator and well-suited implementation standard field-programmable analog array device. The enables versatile locomotion control five high-level parameters...
Transcriptional disturbance is implicated in the pathology of polyglutamine diseases, including Huntington's disease (HD). However, it unknown whether transcriptional repression leads to neuronal death or what forms that might take. We found repression-induced atypical (TRIAD) neurons be distinct from apoptosis, necrosis, autophagy. The progression TRIAD was extremely slow comparison with other types cell death. Gene expression profiling revealed reduction full-length yes-associated protein...
With the goal of providing assistive technology for communication impaired, we proposed electroencephalography (EEG) cortical currents as a new approach EEG-based brain-computer interface spellers. EEG were estimated with variational Bayesian method that uses functional magnetic resonance imaging (fMRI) data hierarchical prior. and fMRI recorded from ten healthy participants during covert articulation Japanese vowels /a/ /i/, well no-imagery control task. Applying sparse logistic regression...
Transcranial direct current stimulation (tDCS) is a potential method for improving verbal function by stimulating Broca's area. Previous studies have shown the effectiveness of using functional magnetic resonance imaging (fMRI) to optimize site, but it unclear whether similar optimization can be achieved scalp electroencephalography (EEG). Here, we investigated tDCS targeting brain area identified EEG improve verbalization performance during picture-naming task. In Experiment 1, and fMRI...
EEG-controlled gaming applications range widely from strictly medical to completely nonmedical applications. Games can provide not only entertainment but also strong motivation for practicing, thereby achieving better control with rehabilitation system. In this paper we present real-time of video game eye movements asynchronous and noninvasive communication system using two temporal EEG sensors. We used wavelets detect the instance movement time-series characteristics distinguish between six...
Abstract Studies on brain-machine interface techniques have shown that electrocorticography (ECoG) is an effective modality for predicting limb trajectories and muscle activity in humans. Motor control studies also identified distributions of “extrinsic-like” “intrinsic-like” neurons the premotor (PM) primary motor (M1) cortices. Here, we investigated whether predicted from ECoG were obtained based signals derived extrinsic-like or intrinsic-like neurons. Three participants carried objects...
Due to their potential as a control modality in brain-machine interfaces, electrocorticography (ECoG) has received much focus recent years. Studies using ECoG have come out with success such endeavors classification of arm movements and natural grasp types, regression trajectories two three dimensions, estimation muscle activity time series so on. However, there still remains considerable work be done before high performance ECoG-based neural prosthetic can realized. In this study, we...
The synchronized activity of neuronal populations across multiple distant brain areas may reflect coordinated interactions large-scale networks. Currently, there is no established method to investigate the temporal transitions between these networks that would allow, for example, decode finger movements. Here we applied a matrix factorization employing principal component and independent analyses identify synchronizations. In accordance with previous studies investigating "muscle synergies",...
Many previous studies on brain-machine interfaces (BMIs) have focused electroencephalography (EEG) signals elicited during motor-command execution to generate device commands. However, exploiting pre-execution brain activity related movement intention could improve the practical applicability of BMIs. Therefore, in this study we investigated whether EEG occurring before be used classify intention. Six subjects performed reaching tasks that required them move a cursor one four targets...
Surface ElectroMyoGraphy (EMG) signals from the forearm used in prosthetic hand and finger control systems require precise anatomy data of muscles that are small located deep within forearm. The main problem this method is signal quality depends on placement EMG sensor, which can significantly affects accuracy precision to estimate joint angles or forces. Moreover, case amputees, location unknown needed be identified manually for recording. As a result, most modern hands utilize limited...
The minimum error entropy (MEE) criterion is a powerful approach for non-Gaussian signal processing and robust machine learning. However, the instantiation of MEE on classification rather vacancy in literature. original purely focuses minimizing Renyi's quadratic prediction errors, which could exhibit inferior capability noisy tasks. To this end, we analyze optimal distribution with adverse outliers introduce specific codebook restriction, optimizes toward case. Half-quadratic-based...
Abstract The robustness of two widespread multifractal analysis methods, one based on detrended fluctuation and wavelet leaders, is discussed in the context time-series containing non-uniform structures with only isolated singularities. Signals generated by simulated experimentally-realized chaos generators, together synthetic data addressing particular aspects, are taken into consideration. results reveal essential limitations affecting ability both methods to correctly infer...
Studying brain function is a challenging task. In the past, we could only study anatomical structures post-mortem, or infer functions from clinical data of patients with injury. Nowadays technology, such as functional magnetic resonance imaging (fMRI), enable non-invasive activity observation. Several approaches have been proposed to interpret data. The connectivity model graphical tool that represents interaction between regions, during certain states. It depicts how region cause changes...
PQBP1 (polyglutamine tract-binding protein 1) is a causative gene for relatively frequent X-linked syndromic and non-syndromic mental retardation (MR). To analyze behavioral abnormalities of these patients from molecular basis, we developed knock-down (KD) mouse model. The KD mice possess transgene expressing 498 bp double-strand RNA that endogenously cleaved to siRNA suppressing efficiently. After confirming selectively suppressed nearly 50% the control mice, performed analyses PQBP1-KD...