Search Results for author:
Found 11 papers, 6 papers with code
Multiagent Copilot Approach for Shared Autonomy between Human EEG and TD3 Deep Reinforcement Learning
We also proposed a disparity d-index to evaluate the effect of contradicting agent decisions on the control accuracy and authority of the copilot model.
SHARM: Segmented Head Anatomical Reference Models
Reliable segmentation of anatomical tissues of human head is a major step in several clinical applications such as brain mapping, surgery planning and associated computational simulation studies.
COVID-19 forecasting using new viral variants and vaccination effectiveness models
Background: Recently, a high number of daily positive COVID-19 cases have been reported in regions with relatively high vaccination rates; hence, booster vaccination has become necessary.
Mobility-Dependent and Mobility-Compensated Effective Reproduction Number of COVID-19 Viral Variants: New Metric for Infectivity Evaluation
The mobility-adjusted ERN of the alpha variant was 15%-30%, and was 20%-40% higher than the standard type in Osaka, Aichi, and London.
Knowledge discovery from emergency ambulance dispatch during COVID-19: A case study of Nagoya City, Japan
One significant problem is the management of ambulance dispatch and control during a pandemic.
Influence of segmentation accuracy in structural MR head scans on electric field computation for TMS and tES
In brain, sensitivity to segmentation accuracy is relatively high in cerebrospinal fluid (CSF), moderate in gray matter (GM) and low in white matter for transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES).
Model-based approach for analyzing prevalence of nuclear cataracts in elderly residents
However, the prevalence of nuclear cataracts is higher in elderly people in tropical areas than in temperate areas.
Development of accurate human head models for personalized electromagnetic dosimetry using deep learning
However, most studies have focused on the segmentation of brain tissue only and little attention has been paid to other tissues, which are considerably important for electromagnetic dosimetry.
End-to-end semantic segmentation of personalized deep brain structures for non-invasive brain stimulation
However, it is difficult to determine the amount and distribution of the electric field (EF) in the different brain regions due to anatomical complexity and high inter-subject variability.
Learning-based estimation of dielectric properties and tissue density in head models for personalized radio-frequency dosimetry
The smooth distribution of the dielectric properties in head models, which is realized using a process without tissue segmentation, improves the smoothness of the specific absorption rate (SAR) distribution compared with that in the commonly used procedure.
Deep learning-based development of personalized human head model with non-uniform conductivity for brain stimulation
This paper proposes a novel approach to the automatic estimation of electric conductivity in the human head for volume conductor models without anatomical segmentation.
