Published papers

© 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.

© 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.

© 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.

© 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.

© 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.

© 2019 IEEE. In this paper, the robust table detection method for distortion is proposed in image acquired by camera. Images acquired by the camera contain distortion due to the curvature of the paper and it makes difficult to detect the table in images. In order to address this issue, a method of dividing the frame line of the table in the vertical direction and the horizontal direction and detecting the frame lines by curve approximation in each direction is proposed. Dividing the frame lines in each direction, it is possible to simplify the multiple curve detection. As a result, the lines detection accuracy in the curved surface image was 99.4%. In addition, similar results were obtained for curved images rotated by 90°. This method can cope with distortion in both the vertical and horizontal directions. From these results, it was confirmed the effectiveness of the proposed method.

© 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.

© 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.

© Copyright © 2019 Ogino, Kanoga, Muto and Mitsukura. An electroencephalogram (EEG)-based brain-computer interface (BCI) is a tool to non-invasively control computers by translating the electrical activity of the brain. This technology has the potential to provide patients who have severe generalized myopathy, such as those suffering from amyotrophic lateral sclerosis (ALS), with the ability to communicate. Recently, auditory oddball paradigms have been developed to implement more practical event-related potential (ERP)-based BCIs because they can operate without ocular activities. These paradigms generally make use of clinical (over 16-channel) EEG devices and natural sound stimuli to maintain the user's motivation during the BCI operation; however, most ALS patients who have taken part in auditory ERP-based BCIs tend to complain about the following factors: (i) total device cost and (ii) setup time. The development of a portable auditory ERP-based BCI could overcome considerable obstacles that prevent the use of this technology in communication in everyday life. To address this issue, we analyzed prefrontal single-channel EEG data acquired from a consumer-grade single-channel EEG device using a natural sound-based auditory oddball paradigm. In our experiments, EEG data was gathered from nine healthy subjects and one ALS patient. The performance of auditory ERP-based BCI was quantified under an offline condition and two online conditions. The offline analysis indicated that our paradigm maintained a high level of detection accuracy (%) and ITR (bits/min) across all subjects through a cross-validation procedure (for five commands: 70.0 ± 16.1 and 1.29 ± 0.93, for four commands: 73.8 ± 14.2 and 1.16 ± 0.78, for three commands: 78.7 ± 11.8 and 0.95 ± 0.61, and for two commands: 85.7 ± 8.6 and 0.63 ± 0.38). Furthermore, the first online analysis demonstrated that our paradigm also achieved high performance for new data in an online data acquisition stream (for three commands: 80.0 ± 19.4 and 1.16 ± 0.83). The second online analysis measured online performances on the different day of offline and first online analyses on a different day (for three commands: 62.5 ± 14.3 and 0.43 ± 0.36). These results indicate that prefrontal single-channel EEGs have the potential to contribute to the development of a user-friendly portable auditory ERP-based BCI.

© 2019 IEEE. In this paper, we used Brain Healthcare Quotient (BHQ) to analyze the influence of lung function on brain function. A large-scale survey was conducted for the purpose. In the measurement survey, magnetic resonance images were acquired, and BHQ was calculated and used as brain function. As lung function, we measured forced vital capacity (FVC) and calculated forced expiratory volume 1.0 sec (FEV1.0%). As a result of analysis, it became clear that the decrease in FEV1.0% affects GM BHQ and that the decrease in lung function also affects brain function by using BHQ.

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.

© 2018 The Society of Instrument and Control Enginners - SICE. A product assessment is the important process to develop a new product. After a new product has been developed, the product developers hire ordinary people and give an interview to them. In recent years, a new method called 'neuromarketing' is used for product evaluation. However, it is difficult to use the conventional measurement devices and they are mainly used in an experimental environment. In this paper, we developed the model to estimate human emotions, especially valence by using single-channel EEG device. We used the fast Fourier transform, the robust scaling and the support vector regression to predict the valence score. The parameters of the methods were selected by using the grid search and the genetic algorithm. The designed model was evaluated by the correlation coefficient and the classification accuracy of two classes between predicted valence data and labeled valence data. The scores were 0.36 and 72.40%.

【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/

The central nervous system has been considered as an organ devoid of lymphatic vessels. Meningeal lymphatic vessels were discovered and it is thought that elucidating mechanism of these vessels contributes to the studies on neurodegenerative diseases such as Alzheimer's disease and multiple sclerosis. Previous researches focused on material flow in the brain and excretion of proteins and immune cells. There are few researches directly investigating the relationship between the brain and meningeal lymphatic vessels. Our research focused on structure of brain cells and neural activities. Meningeal lymphatic vessels connect to the deep cervical lymph nodes (dcLNs). Ligating efferent lymphatic vessels of dcLNs lead to the reduction of cerebrospinal fluid (CSF) drainage. Electrocorticography (ECoG) was recorded for 8 weeks and glial cells and synapses were immunostained in mice 1, 4 and 8 weeks after ligation. Results showed that reducing lymphatic flow concerned neural activities.