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Your search keyword '"Koike Yasuharu"' showing total 5 results
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5 results on '"Koike Yasuharu"'

2. Prediction of three-dimensional arm trajectories based on ECoG signals recorded from human sensorimotor cortex

Author

Nakanishi, Yasuhiko, Yanagisawa, T., shin, duk, Fukuma, R., Chin, Chiyou, Kambara, Hiroyuki, Yoshimura, Natsue, Hirata, M., Yoshimine, T., and Koike, Yasuharu

Subjects
Male, Wrist Joint, Anatomy and Physiology, lcsh:Medicine, Electroencephalography, Brain mapping, Behavioral Neuroscience, Engineering, Elbow, lcsh:Science, Electrocorticography, Physics, Cerebral Cortex, Brain Mapping, Multidisciplinary, medicine.diagnostic_test, Linear model, Motor Cortex, Middle Aged, medicine.anatomical_structure, Neurology, Muscle Spasticity, Trajectory, Arm, Medicine, Algorithms, Motor cortex, Research Article, Biotechnology, Shoulder, Neuroprosthetics, Adolescent, Movement, Biomedical Engineering, Neurophysiology, Bioengineering, Neurological System, Hypesthesia, Motion, Developmental Neuroscience, Neurorehabilitation and Trauma, medicine, Humans, neoplasms, Biology, Neurorehabilitation, Aged, Motor Systems, Computational Neuroscience, business.industry, lcsh:R, Pattern recognition, Signal Processing, Linear Models, lcsh:Q, Artificial intelligence, Physiotherapy and Rehabilitation, business, Neuroscience
Abstract

Brain-machine interface techniques have been applied in a number of studies to control neuromotor prostheses and for neurorehabilitation in the hopes of providing a means to restore lost motor function. Electrocorticography (ECoG) has seen recent use in this regard because it offers a higher spatiotemporal resolution than non-invasive EEG and is less invasive than intracortical microelectrodes. Although several studies have already succeeded in the inference of computer cursor trajectories and finger flexions using human ECoG signals, precise three-dimensional (3D) trajectory reconstruction for a human limb from ECoG has not yet been achieved. In this study, we predicted 3D arm trajectories in time series from ECoG signals in humans using a novel preprocessing method and a sparse linear regression. Average Pearson’s correlation coefficients and normalized root-mean-square errors between predicted and actual trajectories were 0.44∼0.73 and 0.18∼0.42, respectively, confirming the feasibility of predicting 3D arm trajectories from ECoG. We foresee this method contributing to future advancements in neuroprosthesis and neurorehabilitation technology.

Published
2013

5. Prediction of Muscle Activities from Electrocorticograms in Primary Motor Cortex of Primates.

Author

Duk Shin, Watanabe, Hidenori, Kambara, Hiroyuki, Nambu, Atsushi, Isa, Tadashi, Nishimura, Yukio, and Koike, Yasuharu

Subjects
ELECTROCOCHLEOGRAPHY, BRAIN-computer interfaces, ELECTRODIAGNOSIS, SENSORIMOTOR integration, GAUSSIAN beams, REGRESSION analysis
Abstract

Electrocorticography (ECoG) has drawn attention as an effective recording approach for brain-machine interfaces (BMI). Previous studies have succeeded in classifying movement intention and predicting hand trajectories from ECoG. Despite such successes, however, there still remains considerable work for the realization of ECoG-based BMIs as neuroprosthetics. We developed a method to predict multiple muscle activities from ECoG measurements. We also verified that ECoG signals are effective for predicting muscle activities in time varying series when performing sequential movements. ECoG signals were band-pass filtered into separate sensorimotor rhythm bands, z-score normalized, and smoothed with a Gaussian filter. We used sparse linear regression to find the best fit between frequency bands of ECoG and electromyographic activity. The best average correlation coefficient and the normalized root-mean-square error were 0.92±0.06 and 0.06±0.10, respectively, in the flexor digitorum profundus finger muscle. The δ (1.5,4Hz) and ϒ2 (50,90Hz) bands contributed significantly more strongly than other frequency bands (P,0.001). These results demonstrate the feasibility of predicting muscle activity from ECoG signals in an online fashion. [ABSTRACT FROM AUTHOR]

Published
2012
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