Details of a Researcher - Mitsukura, Yasue

Papers - Mitsukura, Yasue

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Evaluation of stress accumulation using the EEG and NIRS

Asano T., Mitsukura Y.

2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017 (2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017) 348 - 352 2018.06

Accepted, ISSN 9781538646625

© 2017 IEEE. In the recent research, accumulation of stress has been shown to induce lifestyle diseases and mental/nervous diseases. In this research, visual search task was chosen to stress subjects, and the brain activity was measured by using near infrared spectroscopy (NIRS) and an electroencephalograph (EEG) was measured. Advantages of using these in combination are that it is relatively simple and less burdensome to the subject and it can be measured from the two viewpoints of neural activity and hemodynamics for the same brain region. As prior research, there are studies investigating the presence of stress by using brain activity, but there are no researches measuring the change in brain activity under continuous stress. The aim of this research is to extract features of active sites of brain function and electroencephalograms in stress accumulation. This research conducted experiments and analysis focusing on the two poles of Fp1 and Fp2 in international 10-20 method. In NIRS, when every time stress was accumulated, the difference was confirmed between right and left in brain activity. In EEG, there was no common feature between data in frequency and phase. From the above, it is possible to objectively and quantitatively evaluate the degree of stress accumulation by combining near-infrared spectroscopy and electroencephalograph by extracting a feature amount that changes every time stress accumulates in the EEG signal. As future tasks, the number of subjects should be increased. Also, different types of EEG analysis and the number of measurement points should be considered.

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Influence on electroencephalogram at the prefrontal cortex due to tinnitus and sounds

Matsuoka M., Mitsukura Y., Kanzaki S.

Accepted, ISSN 9781538646625

© 2017 IEEE. The purpose of this paper is to investigate the influence on electroencephalogram (EEG) at the prefrontal cortex due to tinnitus and sounds. Tinnitus is a phenomenon that a person hears sounds even though there is no sound source. Conventional methods of investigating tinnitus are subjective and need long time. In this paper, EEG at the prefrontal cortex was used because the relationship between tinnitus and prefrontal cortex has been revealed. Tinnitus patients and healthy controls were classified and the distress level was estimated using EEG in researches so far, and identified the necessary features for these analyses. However, there is a problem that it is not certain whether features used in these analyses arise similarly in tinnitus and other sounds. The purpose of this paper is to verify which EEG features identified in previous researches arise from tinnitus or sounds. EEG data of tinnitus patients and healthy controls were measured and a questionnaire was conducted on tinnitus distress to the tinnitus patients. EEG measurements of healthy controls at resting with no stimulation and at listening to sounds which simulated tinnitus were performed and a questionnaire was conducted to see which sound they felt distress. Differences of EEG between tinnitus patients and healthy controls and differences of EEG between controls at resting with no stimulation and at listening to sound were different. It is thought that EEG changes of tinnitus patients at the prefrontal cortex arise from only tinnitus.

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A Practical BCG Measuring System with Bed Sensors and Algorithm for Heartbeat Detection

Masaki Nagura, Yasue Mitsukura, Koichi Fukunaga, Masato Yasui, Masaru Mimura

Proc. of The 19th International Workshop on Advanced Motion Control (AMC 2018) (Proceedings - 2018 IEEE 15th International Workshop on Advanced Motion Control, AMC 2018) 317 - 321 2018.06

Accepted, ISSN 9781538619469

© 2018 IEEE. Ballistocardiogram (BCG) is useful and informative signal result from the cardiac activity. In this paper, we present a practical BCG measurement system with bed sensors. Moreover, the simple algorithm to detect all individual heartbeats and estimate beat-to-beat interval lengths is presented. These are effective for all night continuous monitoring of cardiovascular system and enable us to detect sleep stage. Heart beats is detected by cross-correlation method. This method has three steps, preprocessing, typical wave form creation and cross-correlation calculation. Output of this process were validated with 10 subjects. As the results, the average 71.5% of the beat-to-beat intervals were detected. Moreover this study revealed that the individual difference is significant factor to detect heart beats precisely.

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Quantification of pain degree by frequency features of single-chanelled EEG

Kagita J., Mitsukura Y.

Proceedings - 2018 IEEE 15th International Workshop on Advanced Motion Control, AMC 2018 (Proceedings - 2018 IEEE 15th International Workshop on Advanced Motion Control, AMC 2018) 359 - 363 2018.06

Accepted, ISSN 9781538619469

© 2018 IEEE. The final aim of this paper is to classify pain degree by only using Electroencephalogram (EEG) measured with single-channel. In clinical care, pain degree is needed for choosing and evaluating treatments, and it is important for clinicians to quantify pain degree as objectively as possible. Pain rating scales such as the Visual Analogue Scales (VAS) are usually used to quantify pain degree, which is only capable of subjective values due to self-report. From that, a method to quantify pain degree objectively has great importance. In this paper, we propose the possibility of quantifying pain degree by only using EEG measured with single-channel. 28 Subjects' EEG is measured while in 2 states; pain-free (VAS score of 0) and pain (VAS score of 3-4). By extracting frequency features from the measured EEG, and classifying using Support Vector Machine (SVM), the subjects in 2 states were classified with the accuracy of 100%. The results show reliability and validity of classifying pain degree using EEG measured with single-channel.

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An EEG-Based Robot Arm Control to Express Human Emotions

Mikito Ogino, Yasue Mitsukura

Proc. of Advanced Motion Control (AMC 2018) (Proceedings - 2018 IEEE 15th International Workshop on Advanced Motion Control, AMC 2018) 322 - 327 2018.06

Accepted, ISSN 9781538619469

© 2018 IEEE. Prosthetic limbs are designed to move naturally like human limbs. To reduce the feeling of abnormality when moving prosthetic limbs, previous studies have altered the appearance of robotic limbs along with the speed of the motors inside these limbs. The use of the wearer's emotions to control prosthetic limbs would allow robotic arms and legs to move more naturally. Recently, electroencephalograms (EEGs) have been used to estimate human emotions. In this study, we developed an emotion analyzer and applied it in a robotic arm system that changes its movement based on the user's emotions. In the proposed system, EEG data are measured by a simplified EEG headset and transferred to an iPad. An iPad application then transforms the signal to a value of 'wakuwaku' feeling using the band-pass filtering, fast Fourier transform, and support vector machine methods. The accuracy of the algorithm estimating the wakuwaku feeling was 75.10%, and the robot arm moved accurately based on the user emotion estimated from the EEG data. Our findings are expected to lead to a new field of study focused on controlling robot arms using human emotions.

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Development of an affordable and easy-to-install fall detection system

Güttler J., Bittner A., Langosch K., Bock T., Mitsukura Y.

IEEJ Transactions on Electrical and Electronic Engineering (IEEJ Transactions on Electrical and Electronic Engineering) 13 ( 5 ) 664 - 670 2018.05

Accepted, ISSN 19314973

© 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. Falling, especially in old age, is a serious health threat. Depending on the severity of such accidents, risk of injury, and the helpless state of the elderly and infirm persons, it is difficult to impossible for the concerned persons to call for help. This kind of situation and its associated perceptions already cause anxiety. There is, therefore, much interest in fall detection for persons in need of care and caregivers as these systems promise security and help when needed, and they also provide a feeling of safety. However, modern fall detection systems are mostly wearables or are expensive in their implementation. As such, this paper focuses on a novel approach to fall detection, developed in two iterative main cycles and finally implemented in a real apartment for testing purposes. The proposed fall detection aims to be installed into the baseboard of a room, which allows an easy and cost-effective integration of the proposed system. Especially in rooms like the bathroom, where discretion is very important, the proposed system benefits from its use of light barriers instead of image recognition to identify a fallen person. Additionally, the collected data can be wirelessly transmitted to a server via a gateway using XBee modules and Wi-Fi. The latest developments of the fall detection prototype, including the hardware and software implementation, will be presented in this paper, and its strength and weak points will also be discussed. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Development of an affordable and easy‐to‐install fall detection system

J Güttler, A Bittner, K Langosch, T Bock, Y Mitsukura

IEEJ Transactions on Electrical and Electronic Engineering 13 ( 5 ) 664 - 670 2018.05

An Estimation of Heart Rate Variability from Ballistocardiogram Measured with Bed Leg Sensors

Masaki Nagura, Yasue Mitsukura, Koichi Fukunaga, Masato Yasui, Masaru Mimura

Proc. of The 19th IEEE International Conference on Industrial Technology (ICIT 2018) (Lyon (France)) 2018-February 2005 - 2009 2018.04

Accepted, ISSN 9781509059492

© 2018 IEEE. Ballistocardiogram (BCG) is the repetitive body movements related to cardiac cycle. All night continuous measuring of BCG by using bed sensors enable us to detect sleep stage. However conventional methods are seemed to have unreliability caused by position of lying down. In this paper, we propose a reliable method of all night continuous measuring of BCG. Heart rate variability (HRV) is estimated from BCG measured with new bed sensors by template-matching approach. This approach has three steps, pre-processing, template-creation and template matching. Output of this process were validated with 4 subjects. The mean beat-to-beat interval error was 9.8[ms] and on an average 87.1% of the beat-to-beat intervals were detected.

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Quantification of Pain Degree Using EEG with Simple Device

Junichiro Kagita, Yasue Mitsukura

Proc. of The 19th IEEE International Conference on Industrial Technology (ICIT 2018) (Proceedings of the IEEE International Conference on Industrial Technology) 2018-February 2014 - 2017 2018.04

Accepted, ISSN 9781509059492

© 2018 IEEE. The final aim of this paper is to quantify pain degree by only using Electroencephalogram (EEG) measured with simple device. Pain degree dominates the choice and assessment of a treatment in clinical care. It is conventionally quantified using pain rating scales, although they are not capable of attaining objective values. Therefore, a method which can quantify pain degree objectively has great importance. In this paper, the possibility of quantifying pain degree by only using EEG measured with simple device is proposed. The proposed method is in an easy manner considering its time consummation and procedures. Significant difference in EEG features between pain-free, painful, and psychologically painful state were confirmed as the results of the experiment, showing the possibility of attaining objective pain degree by using EEG measured with simple device.

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脳波解析を用いた前向き感の定量評価

太田英作, 小杉亘, 逸見将, 岡田賢次, 岡本正文, 篠塚真仲, 児玉晴佳, 満倉靖恵

電気学会研究会資料. CT/制御研究会 [編] (電気学会) 2018 ( 67-74・76 ) 21 - 24 2018.03

香り・においを脳波で測る

荻野幹人, 満倉靖恵, 中辻晴彦, 青木恵

AROMA RESEARCH 19 ( 1 ) 2018.02

Research paper (scientific journal), Joint Work, Accepted

様々な感性を定量化し、心と暮らしにフィードバック

満倉靖恵

日経サイエンス 48 ( 2 ) 2018.02

Research paper (scientific journal), Single Work, Accepted

簡易型脳波計を用いた眠気評価による香りの選定

荻野幹人, 満倉靖恵, 中辻晴彦, 青木恵

AROMA RESEARCH (フレグランスジャーナル社) 76 ( 19 ) 10 - 15 2018

Research paper (scientific journal), Joint Work, ISSN 1345-4722

Portable Drowsiness Detection through Use of a Prefrontal Single-Channel Electroencephalogram

Mikito Ogino and Yasue Mitsukura

Sensors (Sensors (Switzerland)) 18 ( 12 ) 4477 - 4477 2018

Research paper (scientific journal), Joint Work, ISSN 14248220

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Drowsiness detection has been studied in the context of evaluating products, assessing driver alertness, and managing office environments. Drowsiness level can be readily detected through measurement of human brain activity. The electroencephalogram (EEG), a device whose application relies on adhering electrodes to the scalp, is the primary method used to monitor brain activity. The many electrodes and wires required to perform an EEG place considerable constraints on the movement of users, and the cost of the device limits its availability. For these reasons, conventional EEG devices are not used in practical studies and businesses. Many potential practical applications could benefit from the development of a wire-free, low-priced device; however, it remains to be elucidated whether portable EEG devices can be used to estimate human drowsiness levels and applied within practical research settings and businesses. In this study, we outline the development of a drowsiness detection system that makes use of a low-priced, prefrontal single-channel EEG device and evaluate its performance in an offline analysis and a practical experiment. Firstly, for the development of the system, we compared three feature extraction methods: power spectral density (PSD), autoregressive (AR) modeling, and multiscale entropy (MSE) for detecting characteristics of an EEG. In order to efficiently select a meaningful PSD, we utilized step-wise linear discriminant analysis (SWLDA). Time-averaging and robust-scaling were used to fit the data for pattern recognition. Pattern recognition was performed by a support vector machine (SVM) with a radial basis function (RBF) kernel. The optimal hyperparameters for the SVM were selected by the grind search method so as to increase drowsiness detection accuracy. To evaluate the performance of the detections, we calculated classification accuracy using the SVM through 10-fold cross-validation. Our model achieved a classification accuracy of 72.7% using the PSD with SWLDA and the SVM. Secondly, we conducted a practical study using the system and evaluated its performance in a practical situation. There was a significant difference (* p < 0.05) between the drowsiness-evoked task and concentration-needed task. Our results demonstrate the efficacy of our low-priced portable drowsiness detection system in quantifying drowsy states. We anticipate that our system will be useful to practical studies with aims as diverse as measurement of classroom mental engagement, evaluation of movies, and office environment evaluation.

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A development of new method to measure a human sensitivity related to the feeling "desire to eat" by using EEG analyses.

Nishiwaki Mika, Goto Mayu, Asakura Anna, Ishida Ken, Yamaguchi Takashi, Watanabe Kenichi, Mitsukura Yasue

Abstracts of the Annual Meeting of the Japan Society of Cookery Science (The Japan Society of Cookery Science) 30 ( 0 ) 2018

Accepted

【目的】 脳波分析を活用した、ヒトの「食べたい気持ち」「やみつき感」に関連した感性の数値化、可視化 【方法】 被験者に感性アナライザ1)（簡易脳波計）を装着して、連続的に脳波を測定しながら以下の実験を実施した。１）同一食品群で数種の商品（メニュー）を提示し、アンケートにて「食べたい」「食べたくない」を回答。２）全食品（メニュー）に対するアンケートを回答後、それらの中から1品を決定して実際に試食を実施。３）試食したメニューについて再度アンケートで「食べたい」「食べたくない」を回答。 【結果】 得られた脳波については、ノイズ除去などの前処理をした後、周波数変換によってスペクトルを算出、４－２２Ｈｚにおける特徴量を抽出した。アンケートで得られた評価をもとに、「食べたい」と答えたときの脳波の特徴と「食べたくない」と答えたときの脳波の特徴を比較し、周波数４－２２Ｈｚのすべての組合せ（19変数、19!=1.2165×10^17通り）から最大の分類率を示す周波数の組合せ５種を選び出した（分類率；食べたい時に「食べたい度合い」が取れている＝認識しているか否か）。周波数４－２２Ｈｚの特徴量における分類率が59.8％だったのに対し、選ばれた周波数５種の特徴量の分類率は85.5％を示した（約25%分類率が向上した）。以上の一連の実験結果から、「食べたい」に関連する脳波特徴の抽出が可能となり、食前・食後の脳波を測定することで、その食品の「食べたい」気持ちが測定できる可能性が示された。 1) 感性アナライザc 電通サイエンスジャム https://kansei-analyzer.com/

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AN AIR ENVIRONMENT EVALUATION USING ELECTROENCEPHALOGRAM(EEG)

木幡容子, 篠塚真仲, 山本聡彦, 船守宏和, 満倉靖恵

Techinical Papers of Annual Meeting the Society of Heating,Air-conditioning and Sanitary Engineers of Japan (The Society of Heating,Air-Conditioning&Sanitary Engineers of Japan) 2018 ( 0 ) 141 - 144 2018

Accepted, ISSN 1880-3806

本論文の目的は、空気環境が脳波に与える影響を明らかにした上で、更に日常生活にどのような影響を及ぼすかを明らかにすることである。我々は空気環境の違いが脳に与える影響、感情や感性に与える影響、そさらには日常生活に与える影響を明らかにすべく、一般に空気の浄化を行うことができると言われるイオン発生機に着目し、イオンのある空気と脳波の関係を明らかにする。更に屋外の自然環境と室内環境間での脳波の比較も行いその変化を確認することで、より広範囲における空気環境の脳波に与える影響を明らかにする。

笑顔によるストレスコーピングの検証

太田英作, 満倉靖恵

Annual convention of the Japanese Association of Health Psychology : proceedings (The Japanese Association of Health Psychology) 31 ( 0 ) P79 2018

Accepted

Relationship between the Indoor Thermal Environment and Grey Matter Volume or Fractional Anisotropy

松本実紗, 伊香賀俊治, 山川義徳, 内田泰史, 村上周三, 安藤真太朗, 満倉靖恵, 中島侑江

Accepted, ISSN 1880-3806

寒冷暴露が脳機能に悪影響を及ぼすと仮定し、高知県梼原町在住の90名について、2016年度及び2017年度の冬季に住宅の温熱環境測定、対象者の血圧及び活動量測定、MRI 装置による脳機能検査、アンケート調査を実施した。多変量解析の結果、個人属性、生活習慣、血圧及び活動量を考慮した上でも、居間の床上1.1mの冬季室温が寒冷な居住者は全脳領域神経線維拡散度の得点が有意に低いことを確認した。

Relationship between indoor temperature in winter and Brain Healthcare Quotient

Matsumoto M., Ikaga T., Yamakawa Y., Uchida Y., Murakami S., Ando S., Mitsukura Y., Hayashi Y.

15th Conference of the International Society of Indoor Air Quality and Climate, INDOOR AIR 2018 (15th Conference of the International Society of Indoor Air Quality and Climate, INDOOR AIR 2018) 2018

Accepted, ISSN 9781713826514

In Japan, where the population is aging, it is necessary to maintain mental and physical health over the long term in order to solve social problems such as increasing medical expenses. It has been suggested that exposure to cold adversely affects brain health. Thus, we hypothesized that the brain health of residents in cold houses would be worse than that of residents in warm houses. In the winter season from 2016 to 2017, we measured indoor temperature, conducted brain health tests using MRI examination, and administered questionnaire surveys for 59 residents of a town in northern Japan. The results of multiple regression analysis show that residents who had a cold living room had lower indexes representing the quality of cranial nerve fibers than residents who had a warm living room in winter.

Feature analysis of electroencephalography in patients with depression

Risa Nakamura, Yasue Mitsukura

2018 IEEE Life Sciences Conference (LSC) (IEEE) 53 - 56 2018

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