|Yoshinori Katayama||Last modified date：2021.06.11|
Assistant Professor / Intelligence Science / Department of Informatics / Faculty of Information Science and Electrical Engineering
|Yoshinori Katayama||Last modified date：2021.06.11|
|1.||Kazuki Onikura, Yoshinori Katayama, Keiji Iramina, Evaluation of a Method of Removing Head Movement Artifact from EEG by Independent Component Analysis and Filtering, Advanced Biomedical Engineering, DOI:10.14326 / abe.4.67, 4, 67-72, 2015.03, Artifacts that contaminate electroencephalography (EEG) signals make it difcult to analyze EEG. The aim of this study was to removal artifacts on EEG, especially those caused by motion, to measure EEG in unconstrained situations. In a previous study, head movements were detected by an accelerometer, and motion artifact components were separated from the recorded EEG by independent component analysis (ICA). This method is effective for reducing the effect of artifacts, but has a risk that EEG components are also removed.
In this paper, we introduce an improved artifact removal method based on ICA and filtering. EEG were decomposed by ICA, and a Pearson’s correlation coefficient was calculated between each independent component and each hybrid accelerometer value to distinguish artifact components. Artifact components were then high-pass filtered. In this study, subjects were instructed to move their heads randomly, while keeping their eyes closed. The previous method was adapted using 1, 2, 3, 5 and 10 s to find the most suitable epoch to minimize the mean absolute amplitude of the cleaned EEG. Then, using this epoch, the proposed method was compared with the previous method by frequency analysis.
Low frequency power (0.1–3 Hz) was normalized to unity because most power caused by motion artifacts exists in the low power band. If the normalized theta (4–8Hz), alpha (8–13Hz) and beta (13–40Hz) powers of cleaned EEG are higher than that of raw EEG, this indicates that the effect of motion artifacts is small and EEG components are retained.
The results obtained from theta and alpha power comparison showed that the proposed method performed better than the previous method. This result suggests that the proposed artifact removal method is more effective to reduce the effect of artifacts while retaining the EEG components..
|2.||Latest Report about Biomagnetism, including Transcranial Magnetic Stimulation.|
|3.||Kazuhisa Nojima, Yoshinori Katayama, Keiji Iramina, rTMS and tDCS effects on the power and ERD of mu wave, IEEJ Transactions on Fundamentals and Materials, 133, 9, 478-483, 2013.09, We investigated the effects of repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS) on the mu wave. The power of the mu wave decreases with the imagination of movement or actual movement. This phenomenon is described as the desynchronization (ERD) of the mu wave. The aim of our study was to investigate the effects of different rTMS and tDCS stimulation parameters on the mu wave. We used rTMS and tDCS to either facilitate or inhibit cortical excitability. EEG was measured over the sensorimotor cortex before and after stimulation. We performed four stimulation trials with different stimulus conditions: (1) 1Hz rTMS stimulation at 90% of the resting motor threshold (RMT), (2) 1Hz rTMS stimulation at 110% RMT,; (3) anodal tDCS,; and (4) cathodal tDCS over the motor area of the thumb in the left hemisphere. During EEG recording, participants were asked to (1) maintain a resting state, (2) imagine moving their right hand, or (3) actually move their right hand. We assessed the power difference and the amount of relative ERD before and after stimulation. We found no significant change in the power of the mu wave before or after rTMS or tDCS. Anodal tDCS, which produces facilitation, produced a significant increase in ERD. Both 1Hz rTMS at 110% of the RMT and cathodal tDCS, which produce inhibition, produced a significant decrease in ERD. We found no significant difference in ERD resulting from 1Hz rTMS at 90% of the RMT..|
|4.||Kazuhisa Nojima, Yoshinori Katayama, Keiji Iramina, Predicting rTMS effect for deciding stimulation parameters, Proceedings of The 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’13), 10.1109/EMBC.2013.6611011, 6369 -6372, 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..|
|5.||Fumiyoshi Matsusaki, Yoshinori Katayama, Keiji Iramina, Influence of TMS Coil Orientation in the Simulation of Neuronal Excitation by TMS Using an Axon Model and Cerebral Cortex Model, Advanced Biomedical Engineering, DN/JST.JSTAGE/abe/1.55, 1, 1, 55-59, 2013.06, Transcranial magnetic stimulation (TMS) allows non-invasive and painless stimulation of local
cerebral nerves using eddy current generated by electromagnetic induction with a TMS coil. Although TMS is
used in various fields, which area of the brain is stimulated is not known because of the complicated structure of
the organ. In this study, we simulated neuronal excitement by TMS using the finite element method. First, we
designed a brain sulcus model consisting of cerebrospinal fluid, gray matter and white matter, using 0.5 mm cube
elements. To improve calculation accuracy, cube element size was set to 0.5/3 mm only in regions near the
boundary surface. Second, we applied TMS stimulation to the model in different conditions. We used coil radii of
10, 20, 30 and 40 mm, and coil orientation at 0°, 30°, 45°, 60° and 90°, which is defined as the angle between the
orientation of the electric field and the axon. Finally, we calculated the membrane potential and compared the
results obtained under different conditions. We found that membrane potential changed rapidly at the white
matter and gray matter interface when the coil radius was over 20 mm and coil orientation was within 60°
between the orientation of the electric field and the axon. These results provide useful information on appropriate
TMS parameters for effective stimulation of target area in the brain..
|6.||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, IEEE Conference Publication, Biomedical Engineering International Conference (BMEiCON), 2012 , 10.1109/BMEiCon.2012.6465423, 1 - 5, 2012.12,
A pronation and supination of forearms is used as one of diagnostic indices for developmental disorders. However, this index has a demerit in which evaluation results between doctors are not consistent. The present study aims to establish more quantitative and evaluating method. We have developed a simple and portable evaluation system to measure pronation and supination of forearms. In this study, we have measured the pronation and supination of the forearms of 261 subjects aged 7 to 12 years (healthy children: 201, ADHD children: 60) and compared the function of healthy children's pronation and supination with the function of ADHD children's pronation and supination by new indices. From the results, we could obtain a difference between healthy children and ADHD children. The results showed that our developed system has the potential to become diagnostic criteria for developmental disorders.
|7.||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, IEEE Conference Publication, Biomedical Engineering International Conference (BMEiCON), 2012 , 10.1109/EMBC.2012.6347010, 4376-4379, 2012.08, We have developed an evaluation system for pronation and supination of forearms. The motion of pronation and supination of the forearm is used as a diagnosis method of developmental disability, etc. However, this diagnosis method has a demerit in which diagnosis results between doctors are not consistent. It is hoped that a more quantitative and simple evaluation method is established. Moreover it is hoped a diagnostic criteria obtained from healthy subjects can be established to diagnose developmental disorder patients. We developed a simple and portable evaluation system for pronation and supination of forearms. Three-dimensional wireless acceleration and angular velocity sensors are used for this system. In this study, pronation and supination of forearms of 570 subjects (subjects aged 6-12, 21-100) were examined. We could obtain aging curves in the neuromotor function of pronation and supination. These aging curves obtained by our developed system, has the potential to become diagnostic criteria for a developmental disability, etc.
|8.||Kaneko, M. Iramina, K. Ohya, T. Yamashita, Y. Kamei, Y. Katayama, Y. Takashima, S. , A measurement of soft neurological signs by pronosupination using wireless acceleration and angular velocity sensors , Biomedical Engineering International Conference (BMEiCON), 2011, 10.1109/BMEiCon.2012.6172050, 194 - 197 , 2012.03.|
|9.||Ikuno, T. Katayama, Y. Iramina, K. , Selection and removal of artifacts in EEG based on independent components , Biomedical Engineering International Conference (BMEiCON), 2011, 10.1109/BMEiCon.2012.6172067, 266 - 268 , 2012.03.|
|10.||Optimum Stimulus Conditions of Transcranial Magnetic Stimulation
by Computer Simulation.
|11.||Y. Katayama K. Iramina, Fitting and Eliminating to the TMS Induced Artifact on the Measured EEG
by the Equivalent Circuit Simulation Improved Performance
, 5th Kuala Lumpur International Conference on Biomedical Engineering 2011: Biomed 2011, 20-23 June 2011, Kuala Lumpur, Malaysia (Ifmbe Proceedings), 10.1007/ 978-3-642-21729-6, 35, 519-522, 2011.06, Transcranial magnetic stimulation (TMS) is the non-invasive stimulus method to the brain by inducing the eddy current within the brain from the TMS coil placed outside the scalp. The induced eddy current stimulates the nerve circuit causes to suppress the brain activity partially in time and space, and it is applied to detect the brain functions etc. When TMS is applied to the brain while measuring the electroencephalography (EEG), the induced artifact caused by TMS covers on the EEG, it is called the TMS artifact. The amplitude of the TMS artifact is generally too large to omit diagnosing the EEG. Therefore some methods have been proposed to negate the TMS artifact from the EEG including the TMS artifact using the EEG only. We proposed a new method for describing the shape of the induced artifact in the EEG applied TMS using two equivalent circuit models, the TMS equipment model and the equivalent circuit model of bioelectric measurement system, under some simplified approximations.
This paper shows that some attempts are applied to the proposed method to improve the performance of fitting and eliminating the TMS artifact. One is to derive the strict solution of the TMS artifact by omitting some simplified approximations. Other is the countermeasure against errors calculating inverse matrix while estimat-ing parameters of fitting shape of the TMS artifact. One attempt is found that the strict solution improves the shape fitting but not so far from the approximated solu-tion. Other attempt improves stability of the simulation. These attempts are found that some time parameters which determine the time-transition such as “Tc” should separately treat against other time constant parameters which are derived from circuit parameters. Moreover, considering the residual component this is not described on the circuit model.
|12.||Yoshinori Katayama, Keiji Iramina, Equivalent Circuit Simulation of the Induced Artifacts Resulted from Transcranial Magnetic Stimulation on Human Electroencephalography, IEEE Trans. on Magnetics, Vol. 45, Issue 10, pp.4833 - 4836, 2009.10.|
|13.||Tsuyama, S.; Katayama, Y.; Hyodo, A.; Hayami, T.; Ueno, S.; Iramina, K.;, Effects of Coil Parameters on the Stimulated Area by Transcranial Magnetic Stimulation, IEEE Trans. on Magnetics, Vol. 45, Issue 10, pp.4845 - 4848 , 2009.10.|
|14.||Masakuni Iwahashi, Yoshinori Katayama, Shoogo ueno, Keiji Iramina, Effect of Transcranial Magnetic Stimulation on P300 of Event-Related Potential, Proceedings of 3１th Annual International IEEE EMBS Conference, MInneapolis, Minnesota,USA, 1359-1362, September 2-6, 2009, 2009.09.|
|15.||Takahiro Matsunaga, Tetsuya Fukuta, Yoshinori Katayama, Keiji Iramina, Analysis of evoked response and induced response in alpha wave and gamma wave during visual attention, Brain Topography and Multimodal Imaging, Proceedings of the 18th International Congress on Brain Electromagnetic Topography , 23-26, 2009.09.|
|16.||Y.Katayama, S.Uchida, H.Sakoe , Stochastic Model of Stroke Order Variation, ICDAR 2009, pp.803-807, 2009.07.|
|17.||Y.Katayama, S.Uchida, H.Sakoe, A New HMM for On-Line Character Recognition Using Pen-Direction and Pen-Coordinate Features, The 19th ICPR2008, CDROM, 2008.12.|
|18.||H.Sakoe M.M.Ali Y.Katayama , One Dimensional-Two Dimensional Dynamic Programming Matching Algorithm for Character Recognition, IEICE transactions, E77D, 9, 1047-1054, Vol.E77-D, No.9, pp. 1047-1054, 1994.09.|