Kyushu University Academic Staff Educational and Research Activities Database
List of Presentations
Yoshinori Katayama Last modified date:2021.06.11

Assistant Professor / Intelligence Science / Department of Informatics / Faculty of Information Science and Electrical Engineering


Presentations
1. Yuki Noguchi, Miki Kaneko, Keita Higashi, Yasushi Miyagi, R. Murayama, Keiji Iramina, Yoshinori Katayama, Evaluation for Treatment of Deep Brain Stimulation by Pronation and Supination of Forearms using Wireless Sensors
, 7th Biomedical Engineering International Conference (BMEiCON 2014), 2014.11.
2. Pronation and supination of forearms at full speed performance.
3. Miki Kaneko, Hiroshi Okui, Keita Higashi, Yuki Noguchi, Yoshinori Katayama, Takashi Ohya, Yushiro Yamashita, Keiji Iramina, The Comparison of Pronation and Supination between Typically Developing Children and Children with ADHD, The International MultiConference of Engineers and Computer Scientists 2014, 2014.03, Diagnostic methods of attention deficit hyperactivity disorder (ADHD) have soft neurological signs (SNS). Motion of pronation and supination is one of SNS tests. When diagnosing ADHD children, Medical doctors observe the regularity of children’s pronation and supination. It is hoped that a quantitative and simple evaluation method is established. This study’s aim is to compare differences of pronation and supination between typically developing children (TDC) and ADHD children in order to establish a more quantitative evaluation system. The subjects are 85 TDC and 29 ADHD (children aged 7-11). As results, we could obtain different motion between TDC and ADHD children by our system. These differences have the potential to become diagnostic criteria for ADHD..
4. Miki Kaneko, Hiroshi Okui, Keita Higashi, Yuki Noguchi, Yoshinori Katayama, Keiji Iramina, The Comparison of Neuromotor Function between Dominant Hand and Non-Dominant Hand in Pronation and Supination of Forearms, The 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’13), 2013.07, Pronation and supination of forearms is a
movement to bend elbows to 90 degrees, and to rotate the palm
and the back of the hand. This movement is used as a diagnostic
method for soft neurological signs. We have developed a
quantitative evaluation system for pronation and supination of
forearms. In this study, we measured 400 subjects and compared
the neuromotor function between the dominant hand and the
non-dominant hand by our system. From the result, we could
obtain the difference of aging curves between the dominant hand
and the non-dominant hand..
5. Kazuhisa Nojima, Yoshinori Katayama, Keiji Iramina, Predicting rTMS Effect for Deciding Stimulation Parameters, Proceedings of 35nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’13), 2013.07, Repetitive transcranial magnetic stimulation (rTMS) is used in the medical field to modulate cortical excitability. However, when applied in this setting, rTMS stimulation parameters are not usually decided objectively. The aim of this study is to make a model that predicts the rTMS effect, allowing stimulation parameters (intensity and pulse number) to be easily determined before use. First, we investigated the relationship between stimulation condition and rTMS outcome. rTMS delivered at 1 Hz was applied with stimulation intensities of 85%, 100%, or 115% resting motor threshold (RMT) over the primary motor cortex in the left hemisphere. Motor-evoked potentials (MEPs) were measured before rTMS and after every 200 rTMS pulses. Eighteen hundred pulses were applied for each stimulation condition. Results showed that more pulses and stronger intensities lead to a larger decrease in MEP amplitude. An initial prediction model was then made by applying multiple regression analysis over the experimental data. We then adjusted the model depending on the size of the initial MEP amplitude before rTMS, and confirmed the improvement. .
6. Prediction of rTMS effects on primary motor cortex for deciding the stimulus condition.
7. Miki Kaneko, Hiroshi Okui, Keita Higashi, Yuki Noguchi, Takashi Ohya, Yushiro Yamashita, Yoshinori Katayama, Keiji Iramina, The comparison with the function of children's pronation and supination using acceleration and angular velocity sensors, The 5th Biomedical Engineering International Conference (BMEiCON), 2012 , 2012.12.
8. Fumiyoshi Matsusaki, Yoshinori Katayama, Keiji Iramina, Simulation of neuronal excitement by transcranial magnetic stimulation using a cerebral cortex modelk, 生体医工学シンポジウム2012, 2012.09.
9. Miki Kaneko, Hiroshi Okui, Go Hirakawa, Hiroshi Ishinishi, Yoshinori Katayama, Keiji Iramina, Aging Curve of Neuromotor Function by Pronation and Supination of Forearms using Three-dimensional Wireless Acceleration and Angular Velocity Sensors, 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'12), 2012.08.
10. Kazuhisa Nojima, Dilok Puanhvan, Yoshinori Katayama, Yodchanan Wongsawat, Keiji Iramina, rTMS and tDCS to the motor cortex modulate event related desynchronization of mu wave, 18th International Conference on Biomagnetism, 2012.08.
11. Kazuhisa Nojima, Yoshinori Katayama, Keiji Iramina, Modulation of Mu Wave by Repetitive Transcranial Magnetic Stimulation and Transcranial Direct Current Stimulation to the Motor Cortex, World Congress 2012 Medical Physics and Biomedical Engineering, 2012.05.
12. Fumiyoshi Matsusaki, Takahiro Ikuno, Yoshinori Katayama, Keiji Iramina, Online Artifact Removal in EEG Signals, World Congress 2012 Medical Physics and Biomedical Engineering, 2012.05.
13. Miki Kaneko, Yuichiro Kamei, Hiroshi Okui, Yoshinori Katayama, Go Hirakawa, Hiroshi Ishinishi, Yushiro Yamashita, Keiji Iramina, A Measurement Of A Motion Of Pronation And Supination Of A Forearm For Healthy Subjects Using Wireless Acceleration And Angular Velocity Sensors, World Congress 2012 Medical Physics and Biomedical Engineering, 2012.05.
14. Extended Equivalent Circuit Model to Eliminate the Artifact Induced by Transcranial Magnetic Stimulation on the Measured EEG
.
15. Conditions of the neuronal excitement by transcranial magnetic stimulation using an axon model within a brain sulcus model describing cerebral cortex
.
16. Yoshinori Katayama, Fumiyoshi Matsusaki, Keiji Iramina, Characteristics of neuronal excitement by
transcranial magnetic stimulation using a cerebral
cortex model, IEEE International Magnetics Conference, INTERMAG 2012, 2012.05, In this study, a cerebral cortex model which includes
gyri and sulci is proposed in order to investigate the
characteristics of neuronal excitement by transcranial magnetic
stimulation (TMS). The model, which consists of white matter,
gray matter, cerebrospinal fluid (CSF) and neuronal axons, shows
the anatomical structure of cerebral cortex. Although TMS is
used for diagnostic and therapeutic purposes with respect to
neurological disorders, the characteristics of neuronal excitement
caused by TMS have not been explained quantitatively. Because
the conductivity of the brain involves a complicated structure
with many kinds of tissues that have their own properties, it
is difficult to determine the stimulus location in the brain by
applying TMS using the conventional head model with simple
structure. Once the TMS target area is obtained, the setting of
TMS parameters such as the location where TMS applies and
the TMS coil parameters (diameter,and initial current gradient,
etc.) depends almost entirely on the implicit expertise of the
TMS operator. We investigated the characteristics of neuronal
excitement by computer simulation using our proposed model.
In this model the neuronal axon was located in the cerebral
cortex and the membrane potential of the axon and excitement
of the nerve are simulated at the time when TMS is applied. The
results show that the neuronal excitement characteristics vary by
changing TMS parameters such as the coil radius and the TMS
coil current. The excitement of the neuronal axon begins at the
boundary between gray matter and white matter. It also shows
that the neuronal excitement characteristics vary by changing
the direction of the neuronal axon. These obtained results give
useful information about appropriate TMS parameters for the
effective stimulation of the target area in the brain..
17. Analysis of induced gamma band response in EEG induced by different visual stimulus using modified band power method (MBPM).
18. Evaluation of mental status using EEG・ECG・NIRS.
19. The effects of rTMS and tDCS on the motor area to the mu rhythm.
20. Selection and removal of artifacts in EEG based on independent
components..
21. Analysis of Induced Gamma Band Response in EEG Using the Method Based on Band Power Method.
22. The Effect of TMS to Supramarginal Gyrus on Event-Related Potential P300.
23. The Long-Lasting Effect Caused by Direct Current for Somatosensory Evoked Potentials and High Frequency Oscillations.
24. Change of EEG Activity by Repetitive Transcranial Magnetic Stimulation.
25. rTMS effects of the pulses number on the inter-reversal time of perceptual reversal.
26. The change of electroencephalogram by repetitive transcranial magnetic stimulation.
27. Approximated Circuit Model of the TMS Artifact and Its Elimination.
28. LONG TERM CHANGES OF HIGH FREQUENCY COMPONENTS CAUSED BY DIRECT CURRENT STIMULATIONS OVER SOMATOSENSORY CORTEX.
29. The effect of the brain activity by the repetitive transcranial magnetic stimulation (rTMS).
30. The change of brain activity before and after the repetitive transcranial magnetic stimulation (rTMS) .
31. The effect of repetitive transcranial magnetic stimulation to supramarginal gyrus on P300 .
32. Optimum stimulus conditions of Transcranial Magnetic Stimulation by computer simulation.
33. The effect of repetitive transcranial magnetic stimulation (rTMS) to supramarginal gyrus on P300 .
34. The effect in the brain activity before and after the repetitive transcranial magnetic stimulation (rTMS) .
35. TMS artifact model and EEG signal processing.
36. Performance Improvement about the Simulation of the TMS Artifact on the EEG .
37. Simulation of the induced TMS artifact in the measured Electroencefalogram(EEG) .