Published papers

Introduction The complex nature of neurocognitive impairment in schizophrenia has been discussed in light of the mixed effects of antipsychotic drugs, psychotic symptoms, dopamine D 2 receptor blockade, and intelligence quotient (IQ). These factors have not been thoroughly examined before. Methods This study conducted a comprehensive re-analysis of the CATIE data using machine learning techniques, in particular Conditional Inference Tree (CTREE) analysis, to investigate associations between neurocognitive functions and moderating factors such as estimated trough dopamine D 2 receptor blockade with risperidone, olanzapine, or ziprasidone, Positive and Negative Syndrome Scale (PANSS), and baseline IQ in 573 patients with schizophrenia. Results The study reveals that IQ, age, and education consistently emerge as significant predictors across all neurocognitive domains. Furthermore, higher severity of PANSS-negative symptoms was associated with lower cognitive performance scores in several domains. CTREE analysis, in combination with a genetic algorithm approach, has been identified as particularly insightful for illustrating complex interactions between variables. Lower neurocognitive function was associated with factors such as age>52 years, IQ<94/95,<12/13 education years, and more pronounced negative symptoms (score<26). Conclusions These findings emphasize the multifaceted nature of neurocognitive functioning in patients with schizophrenia, with the PANSS-negative score being an important predictor. This gives rise to a role in addressing negative symptoms as a therapeutic objective for enhancing cognitive impairments in these patients. Further research must examine nonlinear relationships among various moderating factors identified in this work, especially the role of D 2 occupancy.

Cynomolgus monkeys (Macaca fascicularis) are vital in biomedical research, particularly for drug development and studying neurological diseases. However, accurately identifying individuals in group housing environments remains a significant challenge. This paper presents a near real-time facial recognition system tailored for cynomolgus monkeys, utilizing a fine-tuned Detectron2 model for face detection, followed by eigenface-based classification with Support Vector Machine (SVM) and radial basis function (RBF) kernel. The system achieved an accuracy of 97.65% in 10-fold cross-validation and identified individuals in under 1 minute under ideal conditions. This method eliminates the need for invasive identification techniques, potentially reducing stress and improving animal welfare, and has the potential to reduce the need for individualized housing or specialized enclosures. Additionally, as the system reduces the time and labor required for identifying monkeys, it might benefit research facilities with high turnover rates. This method could improve identification in non-human primate research while minimizing stress associated with traditional techniques. © 2025 Numata et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

© The Author(s) 2019. Getting enough quality sleep plays a vital role in protecting our mental health, physical health, and quality of life. Sleep deprivation can make it difficult to concentrate on daily activities, and lower sleep quality is associated with hypertension, hyperglycemia, and hyperlipidemia. The amount of sleep we get is important, but in recent years, quality sleep has also been deemed significant. Polysomnography, which has been the gold standard in assessing sleep quality based on stages, requires that the subject be attached to electrodes, which can disrupt sleep. An easier method to objectively measure sleep is therefore needed. The aim of this study was to construct an easy and objective sleep stage monitoring method. A cross-sectional study for healthy subjects has been done in our research. A new easy model for monitoring the sleep stages is built on only heart rate calculated by the electrocardiogram. This enabled us to easily assess the sleep quality based on five stages. This experiment included a total of 50 subjects. The overall accuracy in determining the five sleep stages was 66.0 percent. Four stages for sleep are identified accurately compared with other conventional methods. Despite there are no five sleep stage separation method using only heart rate, our method achieved the five separation for sleep with a relatively good accuracy. This study represents a great contribution to the field of sleep science. Because sleep stages can be recognized by the heart rate alone, sleep can be noninvasively assessed with any heart rate meter. This method will make it easier to determine sleep stages and diagnose sleep disorders.

Recently, flying car (urban air mobility) technologies have been actively studied. However, the social acceptability of such vehicles is yet to be fully investigated. This study discusses the social acceptability of novel air mobilities. This may be evaluated in real time by electroencephalography (EEG) measurements using the Kansei analyzer. Some authors experimentally evaluated the applicability of the Kansei analyzer for various sounds such as stationary rotor sound and nonstationary metallic sound in the previous study. Different from those experiments, experiments were conducted here based on the industrial drone sound with four levels of volume. Subsequently, we investigated the difference between the stress levels using the Kansei analyzer. As indicated by the result, once a loud noise is heard, it is difficult to lower the stress even if the volume is subsequently lowered.

Journal of Serviceology, Vol.2, No.1, pp.1-11, 2017.e-ラーニングは教育サービスの効率化に有用であるが，言語化し難い身体動作を対象とし，暗黙知が介在する技能教育の場合にはうまく機能していない．その理由の一つは，サービス提供者である指導者と受容者である学習者の双方の心理変化に強く関係している「経験価値」を考慮していないためである．そこで本論文では「経験価値共創プラットフォーム」の概念を紹介し，同プラットフォームに則って，技能教育サービスを向上させるツールとして開発した心理・動作計測e-ラーニングアプリを紹介する．本アプリには，学習者の短期的な経験価値の変化点と考えられる指導タイミングを脳波推定心理状態から判定する指導支援法，並びに満足感推定アルゴリズムが実装されている．本アプリを用いて介護技能指導を模擬したe-ラーニング実験を行い，a)学習者の主観満足度，b)脳波心理推定による客観的心理量，c)技能熟達者による習得技能の評価，の3点を相互比較して，提案する手法・概念を検証した．その結果，本指導支援法を用いた場合でも学習者の技能は通常指導と同様に向上 (Welch's t test : t(38.28)=0.04, p=0.03)でき，さらに本指導法を用いた方が有意に学習者の主観ならびに客観的な満足感が向上(t(20)=3.86, p<.001, d=0.66)した．以上のことから介護技能指導の一例ではあるが，経験価値共創プラットフォームの有用性が示された．和文概要：鈴木 聡(東京電機大学)

© 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.

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.

In this research, we aim to extract features of menstrual cycle and mood using features of electroencephalogram (EEG). The menstrual cycle is classified into four categories: luteal phase, menstrual phase, follicular phase, and ovulation phase. We extract characteristic EEG of these periods. In addition, this study examines the extent of the difference in emotional state at the four stages of the menstrual cycle and quantitatively shows the period during which mental disorders are likely to occur.

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.