Details of a Researcher - Mitsukura, Yasue

Papers - Mitsukura, Yasue

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Semi-Supervised Learning for Auditory Event-Related Potential-Based Brain-Computer Interface

Ogino M., Kanoga S., Ito S.I., Mitsukura Y.

IEEE Access (IEEE Access) 9 47008 - 47023 2021

Accepted, ISSN 2169-3536

A brain-computer interface (BCI) is a communication tool that analyzes neural activity and relays the translated commands to carry out actions. In recent years, semi-supervised learning (SSL) has attracted attention for visual event-related potential (ERP)-based BCIs and motor-imagery BCIs as an effective technique that can adapt to the variations in patterns among subjects and trials. The applications of the SSL techniques are expected to improve the performance of auditory ERP-based BCIs as well. However, there is no conclusive evidence supporting the positive effect of SSL techniques on auditory ERP-based BCIs. If the positive effect could be verified, it will be helpful for the BCI community. In this study, we assessed the effects of SSL techniques on two public auditory BCI datasets - AMUSE and PASS2D - using the following machine learning algorithms: step-wise linear discriminant analysis, shrinkage linear discriminant analysis, spatial temporal discriminant analysis, and least-squares support vector machine. These backbone classifiers were firstly trained by labeled data and incrementally updated by unlabeled data in every trial of testing data based on SSL approach. Although a few data of the datasets were negatively affected, most data were apparently improved by SSL in all cases. The overall accuracy was logarithmically increased with every additional unlabeled data. This study supports the positive effect of SSL techniques and encourages future researchers to apply them to auditory ERP-based BCIs.

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Applicability of Kansei Analyzer for Noise-Induced Stress Assessment ─Toward Social Acceptability Survey of Flying Cars─

Hara Susumu, Hayashi Yusuke, Mitsukura Yasue, Kamide Hiroko

Technical Journal of Advanced Mobility (Japan UAS Industrial Development Association) 2 ( 3 ) 31 - 41 2021

Research paper (other academic), Joint Work, Accepted

In recent years, flying car (Urban Air Mobility, UAM) technologies have been actively studied in Japan and other countries. However, the social acceptability of flying vehicles such as UAM and drones (Unmanned Aircraft Systems, UAS) has not been extensively investigated. Social issues often arise when social acceptance is neglected. Thus, novel issues are expected to arise upon the advent of the so-called Aerial Industrial Revolution because of the industrial use of drones and the commercialization of flying cars. To overcome these issues, herein, the social acceptability of this revolution may be evaluated by two different approaches: real-time evaluation by electroencephalograph (EEG) measurement using a Kansei analyzer and social-psychological evaluation by using a questionnaire. However, at this stage, the applicability of the Kansei analyzer for the evaluation has not yet been fully addressed. Thus, in this study, an experiment was conducted to verify the applicability of the Kansei analyzer. In this experiment, subjects answered a questionnaire after hearing some noises. The effects of four types of sounds and three levels of volume on the noise-induced stress levels were investigated. This study described the results and consideration about the experiment to evaluate noise-induced stress among participants and confirmed that the Kansei analyzer is applicable for such stress detection. Moreover, the differences of results in both approaches were revealed.

Predicting COVID-19 Severe Patients and Evaluation Method of 3 Stages Severe Level by Machine Learning

Qu J., Sumali B., Mitsukura Y.

2021 IEEE 4th International Conference on Electronics and Communication Engineering, ICECE 2021 (2021 IEEE 4th International Conference on Electronics and Communication Engineering, ICECE 2021) 277 - 281 2021

Since the outbreak COVID-19 in Wuhan, China in December 2019, a large number of patients have been seen worldwide, and the number of infections continues to show an increasing trend. The vast majority of COVID-19 patients will have fever, headache, and mild respiratory symptoms, but a small number of severely ill patients will experience respiratory distress and related complications, which seriously endanger their lives. The large number of patients also puts the healthcare system to the test. To maximize the protection of patients' lives and the effective use of medical resources, this study collected blood data from 313 patients by machine learning, used 7 blood test items as the feature quantity, established an effective linear SVM prediction model for severe/non-severe disease (recall: 93.55%, specificity: 93.22%), and for 3 stages evaluation of the degree of severe level in severe patients was developed for patients with critical illness. The abnormal increase in Ferritin values was also found to be closely related to the development of severity.

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騒音に対するストレス評価のための感性アナライザ適用可能性の検討

原進, 林裕介, 満倉靖恵, 上出寛子

ロボティクス・メカトロニクス講演会講演概要集 2021

In recent years, flying cars have been actively researched both in Japan and abroad. However, little research has been carried out on the social acceptance of flying vehicles. Social problems often arise when neglecting the social acceptance. It is expected that problems will arise in the future when “Aerial Industrial Revolution” occurs due to industrial use of drones and commercialization of flying cars. In this study, the social acceptance is evaluated by two approaches. One is real-time evaluation by Kansei-analyzer and the other is social psychological evaluation by questionnaire. But the applicability of Kansei-analyzer to the evaluation has not been fully addressed yet. Then this study conducts two kinds of experiments to clarify the applicability of Kansei-analyzer. This paper describes the results and consideration about the experiments to evaluate stress on noise.

Applicability of Kansei analyzer for noise-induced stress assessment─ toward social acceptability survey of flying cars─

Susumu Hara, Yusuke Hayashi, Yasue Mitsukura, Hiroko Kamide

Technical Journal of Advanced Mobility (Japan UAS Industrial Development Association) Vol. 2 ( Issue 3 ) 31 - 41 2021

【感覚の可視化】「脳」を見える化する　感情の可視化と脳を用いた研究　脳機能および心電計測を用いた定量評価によるストレスに対する脂質摂取の影響評価

満倉靖恵

オレオサイエンス ((公社)日本油化学会) 20 ( 11 ) 499 - 505 2020.11

Accepted, ISSN 1345-8949

A mobile auditory brain-computer interface system with sequential auditory feedback

Ogino M., Mitsukura Y.

2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020 (2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020) 590 - 593 2020.10

Accepted, ISSN 9781728198026

Auditory brain-computer interface (BCI) is a necessary communication tool for disabled people who can hardly communicate. However, general EEG systems are difficult to be handled. Moreover, the accuracy of the auditory BCI is less than a visual BCI because auditory stimuli cannot be presented rapidly, so the users can hardly feel to control the BCI. In this paper, we propose a comfortable BCI using a headband type device and an auditory feedback. The mobile system is more comfortable, and the auditory feedback improved the accuracy. Moreover, it enables users understand how to control the auditory BCI more easily.

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Sleep stage estimation from bed leg ballistocardiogram sensors

Mitsukura Y., Sumali B., Nagura M., Fukunaga K., Yasui M.

Sensors (Switzerland) (Sensors (Switzerland)) 20 ( 19 ) 1 - 21 2020.10

Accepted, ISSN 14248220

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Ballistocardiogram (BCG) is a graphical representation of the subtle oscillations in body movements caused by cardiovascular activity. Although BCGs cause less burden to the user, electrocardiograms (ECGs) are still commonly used in the clinical scene due to BCG sensors’ noise sensitivity. In this paper, a robust method for sleep time BCG measurement and a mathematical model for predicting sleep stages using BCG are described. The novel BCG measurement algorithm can be described in three steps: preprocessing, creation of heartbeat signal template, and template matching for heart rate variability detection. The effectiveness of this algorithm was validated with 99 datasets from 36 subjects, with photoplethysmography (PPG) to compute ground truth heart rate variability (HRV). On average, 86.9% of the inter-beat intervals were detected and the mean error was 8.5ms. This shows that our method successfully extracted beat-to-beat intervals from BCG during sleep, making its usability comparable to those of clinical ECGs. Consequently, compared to other conventional BCG systems, even more accurate sleep heart rate monitoring with a smaller burden to the patient is available. Moreover, the accuracy of the sleep stages mathematical model, validated with 100 datasets from 25 subjects, is 80%, which is higher than conventional five-stage sleep classification algorithms (max: 69%). Although, in this paper, we applied the mathematical model to heart rate interval features from BCG, theoretically, this sleep stage prediction algorithm can also be applied to ECG-extracted heart rate intervals.

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The project for objective measures using computational psychiatry technology (PROMPT): Rationale, design, and methodology

Kishimoto T., Takamiya A., Liang K.c., Funaki K., Fujita T., Kitazawa M., Yoshimura M., Tazawa Y., Horigome T., Eguchi Y., Kikuchi T., Tomita M., Bun S., Murakami J., Sumali B., Warnita T., Kishi A., Yotsui M., Toyoshiba H., Mitsukura Y., Shinoda K., Sakakibara Y., Mimura M.

Contemporary Clinical Trials Communications (Contemporary Clinical Trials Communications) 19 100649 2020.09

Accepted, ISSN 24518654

© 2020 The Authors Introduction: Depressive and neurocognitive disorders are debilitating conditions that account for the leading causes of years lived with disability worldwide. However, there are no biomarkers that are objective or easy-to-obtain in daily clinical practice, which leads to difficulties in assessing treatment response and developing new drugs. New technology allows quantification of features that clinicians perceive as reflective of disorder severity, such as facial expressions, phonic/speech information, body motion, daily activity, and sleep. Methods: Major depressive disorder, bipolar disorder, and major and minor neurocognitive disorders as well as healthy controls are recruited for the study. A psychiatrist/psychologist conducts conversational 10-min interviews with participants ≤10 times within up to five years of follow-up. Interviews are recorded using RGB and infrared cameras, and an array microphone. As an option, participants are asked to wear wrist-band type devices during the observational period. Various software is used to process the raw video, voice, infrared, and wearable device data. A machine learning approach is used to predict the presence of symptoms, severity, and the improvement/deterioration of symptoms. Discussion: The overall goal of this proposed study, the Project for Objective Measures Using Computational Psychiatry Technology (PROMPT), is to develop objective, noninvasive, and easy-to-use biomarkers for assessing the severity of depressive and neurocognitive disorders in the hopes of guiding decision-making in clinical settings as well as reducing the risk of clinical trial failure. Challenges may include the large variability of samples, which makes it difficult to extract the features that commonly reflect disorder severity. Trial Registration: UMIN000021396, University Hospital Medical Information Network (UMIN).

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環境条件が心的要因と作業効率に及ぼす影響の評価その 3 冬季換気条件における実験結果

折戸真理，満倉靖恵，古橋拓也，伏江遼

2020 61 - 62 2020.09

環境条件が心的要因と作業効率に及ぼす影響の評価その 1 夏季冷房条件における実験結果

古橋拓也，満倉靖恵，折戸真理，伏江遼

環境工学 I 57 - 58 2020.09

住環境が脳機能に及ぼす影響に関する実測調査その11: 住宅内温熱環境が脳機能・呼吸機能に与える影響に関する縦断調査

渡邉秀斗，満倉靖恵，伊香賀俊治，山川義徳，内田泰史，安藤真太朗，福永興壱

環境工学 I 2020 149 - 150 2020.09

Effect of Environmental Conditions for Mental Factor and Work Efficiency, Part 2. Thermal Environment Evaluation using Electroencephalogram and Air Flow Analysis

満倉靖恵，古橋拓也，折戸真理，伏江遼

環境工学 I 2020 59 - 60 2020.07

Speech quality feature analysis for classification of depression and dementia patients

Sumali B., Mitsukura Y., Liang K.C., Yoshimura M., Kitazawa M., Takamiya A., Fujita T., Mimura M., Kishimoto T.

Sensors (Switzerland) (Sensors (Switzerland)) 20 ( 12 ) 1 - 17 2020.06

Accepted, ISSN 14248220

© MDPI AG. All rights reserved. Loss of cognitive ability is commonly associated with dementia, a broad category of progressive brain diseases. However, major depressive disorder may also cause temporary deterioration of one’s cognition known as pseudodementia. Differentiating a true dementia and pseudodementia is still difficult even for an experienced clinician and extensive and careful examinations must be performed. Although mental disorders such as depression and dementia have been studied, there is still no solution for shorter and undemanding pseudodementia screening. This study inspects the distribution and statistical characteristics from both dementia patient and depression patient, and compared them. It is found that some acoustic features were shared in both dementia and depression, albeit their correlation was reversed. Statistical significance was also found when comparing the features. Additionally, the possibility of utilizing machine learning for automatic pseudodementia screening was explored. The machine learning part includes feature selection using LASSO algorithm and support vector machine (SVM) with linear kernel as the predictive model with age-matched symptomatic depression patient and dementia patient as the database. High accuracy, sensitivity, and specificity was obtained in both training session and testing session. The resulting model was also tested against other datasets that were not included and still performs considerably well. These results imply that dementia and depression might be both detected and differentiated based on acoustic features alone. Automated screening is also possible based on the high accuracy of machine learning results.

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Loss of concentration may occur by blink inhibition in DED simulation models

Mitsukura Y., Negishi K., Ayaki M., Santo M., Kawashima M., Tsubota K.

Life (Life) 10 ( 5 ) 2020.05

Accepted

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Purpose: Patients with dry eye disease (DED) often suffer productivity loss and distress due to bothersome symptoms. The aim of this study was to objectively quantify and compare productivity-related emotional states obtained from brain waveforms in natural and simulated DED conditions. Method: 25 healthy adults (6 females and 19 males; mean age ± standard deviation, 22.6 ± 8.3 years) were recruited for the study, which included an electroencephalogram (EEG), measurements of interblinking time, and questionnaires. DED was simulated by suppressing blinking, while spontaneous blinking served as a control. Elements of concentration, stress, and alertness were extracted from the raw EEG waveforms and the values were compared during spontaneous and suppressed blinking. The relation with DED-related parameters was then explored. Written informed consent was obtained from all participants. Results: All participants successfully completed the experimental protocol. Concentration significantly decreased during suppressed blinking in 20 participants (80%), when compared with spontaneous blinking, whereas there were no or small differences in stress or alertness between spontaneous and suppressed blinking. The change in concentration was correlated with interblinking time (β = −0.515, p = 0.011). Conclusion: Loss of concentration was successfully captured in an objective manner using the EEG. The present study may enable us to understand how concentration is affected during blink suppression, which may happen in office work, particularly during computer tasks. Further study using detailed ocular evaluation is warranted to explore the effect of different interventions.

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教養講座脳波に見る女性の生理周期とバイオリズム

満倉靖恵

翼: 航空自衛隊連合幹部会機関誌 44 ( 東風 ) 103 - 105 2020.03

Evaluating depression with multimodal wristband-type wearable device: screening and assessing patient severity utilizing machine-learning

Tazawa Y., Liang K.c., Yoshimura M., Kitazawa M., Kaise Y., Takamiya A., Kishi A., Horigome T., Mitsukura Y., Mimura M., Kishimoto T.

Heliyon (Heliyon) 6 ( 2 ) e03274 2020.02

Accepted, ISSN 24058440

© 2020 Objective: We aimed to develop a machine learning algorithm to screen for depression and assess severity based on data from wearable devices. Methods: We used a wearable device that calculates steps, energy expenditure, body movement, sleep time, heart rate, skin temperature, and ultraviolet light exposure. Depressed patients and healthy volunteers wore the device continuously for the study period. The modalities were compared hourly between patients and healthy volunteers. XGBoost was used to build machine learning models and 10-fold cross-validation was applied for the validation. Results: Forty-five depressed patients and 41 healthy controls participated, creating a combined 5,250 days' worth of data. Heart rate, steps, and sleep were significantly different between patients and healthy volunteers in some comparisons. Similar differences were also observed longitudinally when patients' symptoms improved. Based on seven days' data, the model identified symptomatic patients with 0.76 accuracy and predicted Hamilton Depression Rating Scale-17 scores with a 0.61 correlation coefficient. Skin temperature, sleep time-related features, and the correlation of those modalities were the most significant features in machine learning. Limitations: The small number of subjects who participated in this study may have weakened the statistical significance of the study. There are differences in the demographic data among groups although we performed a correction for multiple comparisons. Validation in independent datasets was not performed, although 10-fold cross validation with the internal data was conducted. Conclusion: The results indicated that utilizing wearable devices and machine learning may be useful in identifying depression as well as assessing severity.

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Brain Visualization: Evaluate the Effect of Lipid Intake on Stress by Quantitative Evaluation Using Brain Function and Electrocardiographic Measurement

MITSUKURA Yasue

Oleoscience (Japan Oil Chemists' Society) 20 ( 11 ) 499 - 505 2020

Accepted, ISSN 1345-8949

KANSEI Detection and Its Application Using the EEG: Widespread of KANSEI Research in Society

MITSUKURA Yasue

IEICE ESS Fundamentals Review (The Institute of Electronics, Information and Communication Engineers) 13 ( 3 ) 180 - 186 2020

Accepted

<p>In recent years, attempts have been made to quantify human emotions and sensibilities. We have proposed a technique for acquiring emotions and sensibilities from EEG in real time, and have made various applications. In particular, a wide variety of measurement methods and evaluation methods that is important in handling the brain or its activity. We are studying emotional analysis using mice from studies such as differences due to gender and national differences. In particular, emotions and sensibilities are one of the higher-order functions of human beings, and sensibility is a research area that should continue to be important in the future because research has begun in Japan. This paper describes the background and method of doing this, and mentions future prospects.</p>

炭酸水の飲用による作業時の感性変動の検証

太田英作, 満倉靖恵, 小杉亘, 水野征一

Annual convention of the Japanese Association of Health Psychology : proceedings (The Japanese Association of Health Psychology) 33 ( 0 ) P10 2020

Accepted

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