Published papers

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。

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.

Brain machine/computer interface (BMI/BCI) technologies are based on analyzing brain activity to control machines and support the communication of commands and messages. To sense brain activities, a functional NIRS and electroencephalogram (EEG) that has been developed for that purpose is often employed. Analysis techniques and algorithms for the NIRS and EEG signals have also been created, and human support systems in the form of BMI/BCI applications have been developed. In the field of rehabilitation, BMI/BCI is used to control environment control systems and electric wheelchairs. In medicine, BMI/BCI is used to assist in communications for patient support. In industry, BMI/BCI is used to analyze sensibility and develop novel games. This special issue on Brain Machine/Computer Interface and its Application includes six interesting papers that cover the following topics: an EEG analysis method for human-wants detection, cognitive function using EEG analysis, auditory P300 detection, a wheelchair control BCI using SSVEP, a drone control BMI based on SSVEP that uses deep learning, and an improved CMAC model. We thank all authors and reviewers of the papers and the Editorial Board of Journal of Robotics and Mechatronics for its help with this special issue.

Sensors and actuators are key technologies to develop industrial products and to achieve social innovation. In particular, intelligent sensor applications are important to improve system reliability and safety. To achieve the sophisticated and advanced system, because the compromise among the information processing and the regulating system of each node through the network of sensors and actuators becomes so important, it is necessary to develop a new and emerging research area of sensors and actuators technology. The key issues are summarized as: advanced vision-based control application, advanced tactile sensing, advanced electroencephalography, advanced human motion sensing, and advanced approaches for human motion detection. The chapter describes the techniques for tactile sensors. It describes the classification of tactile sensing techniques followed by descriptions of techniques in each classification. The chapter illustrates human preference detection by simple electroencephalography.

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.

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 on the advent of the so-called Aerial Industrial Revolution because of the industrial use of drones and commercialization of flying cars. To overcome these issues, herein, the social acceptability of this revolution was evaluated by two different approaches: sociopsychological evaluation using a questionnaire and real-time evaluation by electroencephalograph (EEG) measurement using a Kansei analyzer. The authors experimentally evaluated the applicability of the Kansei analyzer for various sounds including stationary rotor sound and nonstationary metallic sound in the previous study. Different from such an experiment, this study conducted experiments based on the industrial drone sound with four levels of volume. Subsequently, the difference between the stress levels was investigated using the questionnaire and the Kansei analyzer. The result may impact the formulation of the social acceptability survey methods in future.

近年，空飛ぶクルマに関する技術が国内外で盛んに研究されている。しかし，空飛ぶクルマやドローンなどの社会受容性については，まだ十分に研究されていない。社会受容性に関する十分な検討を行っていないと，今後ドローンの産業利用や空飛ぶクルマの普及により到来する「空の産業革命」が健全に浸透せず，新たな社会問題が発生することも予想される。そこで，社会受容性評価の方法として二つのアプローチを組み合わせた方法を提案する。一つはアンケートによる社会心理学的評価である。もう一つは感性アナライザを用いた脳波計測によるリアルタイム評価である。著者らは前報において騒音に対するストレスの有無を判定するために感性アナライザが適用可能であることを，ロータが発生する定常音や金属がキンキンと発生する非定常音など，複数の種類の音源を用いた実験により明らかにした。本研究では，前報と異なり音源を頭上通過する産業用ドローンの飛行音に限定し，その音量を変えながら何度も聞くことにより発生する，アンケート評価と脳波計測によるストレス評価の違いについて明らかにして，将来の社会受容性調査方法の策定に有用な知見を与える。