論文

Objective. Auditory brain-computer interfaces (BCIs) enable users to select commands based on the brain activity elicited by auditory stimuli. However, existing auditory BCI paradigms cannot increase the number of available commands without decreasing the selection speed, because each stimulus needs to be presented independently and sequentially under the standard oddball paradigm. To solve this problem, we propose a double-stimulus paradigm that simultaneously presents multiple auditory stimuli. Approach. For addition to an existing auditory BCI paradigm, the best discriminable sound was chosen following a subjective assessment. The new sound was located on the right-hand side and presented simultaneously with an existing sound from the left-hand side. A total of six sounds were used for implementing the auditory BCI with a 6 × 6 letter matrix. We employ semi-supervised learning (SSL) and prior probability distribution tuning to improve the accuracy of the paradigm. The SSL method involved updating of the classifier weights, and their prior probability distributions were adjusted using the following three types of distributions: uniform, empirical, and extended empirical (e-empirical). The performance was evaluated based on the BCI accuracy and information transfer rate (ITR). Main results. The double-stimulus paradigm resulted in a BCI accuracy of 67.89 ± 11.46% and an ITR of 2.67 ± 1.09 bits min−1, in the absence of SSL and with uniform distribution. The proposed combination of SSL with e-empirical distribution improved the BCI accuracy and ITR to 74.59 ± 12.12% and 3.37 ± 1.27 bits min−1, respectively. The event-related potential analysis revealed that contralateral and right-hemispheric dominances contributed to the BCI performance improvement. Significance. Our study demonstrated that a BCI based on multiple simultaneous auditory stimuli, incorporating SSL and e-empirical prior distribution, can increase the number of commands without sacrificing typing speed beyond the acceptable level of accuracy.

Decommissioning nuclear reactor sites presents challenges due to the presence of various radionuclides, including alpha emitters (e.g., Pu, Am, Cm) and beta emitters (e.g., 137Cs, 90Sr–90Y), which pose significant internal exposure risks to workers. Traditional measurement methods require multiple instruments and are time-consuming, particularly in high gamma-ray environments. To address these issues, we developed a remote alpha and beta discrimination measurement system that integrates a stilbene scintillator detector with a silicon photomultiplier, enabling simultaneous detection of both alpha and beta particles. This study further incorporates machine learning techniques, specifically Support Vector Machines (SVM), for automatic discrimination, eliminating the need for user-defined thresholds and ensuring consistent operational conditions. The system was tested with known radiation sources, demonstrating over 96 % classification accuracy for alpha and beta particles. Measurements conducted in motion effectively identified contamination sources, confirming the system's capability for real-time analysis. This innovative approach enhances radiation safety and efficiency in nuclear decommissioning operations, making it particularly beneficial in environments where human access is limited.

Dorsal raphe (DR) 5-HT neurons regulate sleep–wake transitions. Previous studies demonstrated that single-unit activity of DR 5-HT neurons is high during wakefulness, decreases during non-rapid eye movement (NREM) sleep, and ceases during rapid eye movement (REM) sleep. However, characteristics of the population-level activity of DR 5-HT neurons, which influence the entire brain, are largely unknown. Here, we measured population activities of 5-HT neurons in the male and female mouse DR across the sleep–wake cycle by ratiometric fiber photometry. We found a slow oscillatory activity of compound intracellular Ca21 signals during NREM sleep. The trough of the concave 5-HT activity increased across sleep progression, but 5-HT activity always returned to that seen during the wake period. When the trough reached a minimum and remained there, REM sleep was initiated. We also found a unique coupling of the oscillatory 5-HT activity and wideband EEG power fluctuation. Furthermore, optogenetic activation of 5-HT neurons during NREM sleep triggered a high EMG power and induced wakefulness, demonstrating a causal role of 5-HT neuron activation. Optogenetic inhibition induced REM sleep or sustained NREM, with an EEG power increase and EEG fluctuation, and pharmacological silencing of 5-HT activity using a selective serotonin reuptake inhibitor led to sustained NREM, with an EEG power decrease and EEG fluctuation. These inhibitory manipulations supported the association between oscillatory 5-HT activity and EEG fluctuation. We propose that NREM sleep is not a monotonous state, but rather it contains dynamic changes that coincide with the oscillatory population-level activity of DR 5-HT neurons.

Pain is an undesirable sensory experience that can induce depression and limit individuals’ activities of daily living, in turn negatively impacting the labor force. Affected people frequently feel pain during activity; however, pain is subjective and difficult to judge objectively, particularly during activity. Here, we developed a system to objectively judge pain levels in walking subjects by recording their quantitative electroencephalography (qEEG) and analyzing data by machine learning. To do so, we enrolled 23 patients who had undergone total hip replacement for pain, and recorded their qEEG during a five-minute walk via a wearable device with a single electrode placed over the Fp1 region, based on the 10–20 Electrode Placement System, before and three months after surgery. We also assessed subject hip pain using a numerical rating scale. Brain wave amplitude differed significantly among subjects with different levels of hip pain at frequencies ranging from 1 to 35 Hz. qEEG data were also analyzed by a support vector machine using the Radial Basis Functional Kernel, a function used in machine learning. That approach showed that an individual’s hip pain during walking can be recognized and subdivided into pain quartiles with 79.6% recognition Accuracy. Overall, we have devised an objective and non-invasive tool to monitor an individual’s pain during walking.

Background: Previous studies using EEG (electroencephalography) as biomarker for dementia have attempted to research, but results have been inconsistent. Most of the studies have extremely small number of samples (average N = 15) and studies with large number of data do not have control group. We identified EEG features that may be biomarkers for dementia with 120 subjects (dementia 10, MCI 33, against control 77). Methods: We recorded EEG from 120 patients with dementia as they stayed in relaxed state using a single-channel EEG device while conducting real-time noise reduction and compared them to healthy subjects. Differences in EEG between patients and controls, as well as differences in patients’ severity, were examined using the ratio of power spectrum at each frequency. Results: In comparing healthy controls and dementia patients, significant power spectrum differences were observed at 3 Hz, 4 Hz, and 10 Hz and higher frequencies. In patient group, differences in the power spectrum were observed between asymptomatic patients and healthy individuals, and between patients of each respective severity level and healthy individuals. Conclusions: A study with a larger sample size should be conducted to gauge reproducibility, but the results implied the effectiveness of EEG in clinical practice as a biomarker of MCI (mild cognitive impairment) and/or dementia.

Background: Since the outbreak of COVID-19 in Wuhan in December 2019, lifestyle has been changing to an online-based learning and working environment rather than on-site, and improvisation training is no exception. However, no research compares the efficacy of online versus on-site training. Although we believed that the most effective way to learn improvisation is an on-site format, it is important to explore how format differences can affect learners. Method: We offer three types of training such as on-site training (n = 6) (Consisting of 1 female with age ≥40 and <50, and 5 males with ages ≥20 and <50), hybrid training (Instructor participates from online and learners participate on-site) (n = 120) (Consisting of 55 female with age ≥15 and <20, and 65 males with ages ≥15 and <50), and online training (n = 20) (Consisting of 4 female with age ≥20 and <30, and 16 males with ages ≥20 and <50) We collected pretest, test, and posttest data by using the Kansei Analyzer, a simplified electroencephalograph (EEG) and Profile of Mood States (POMS) questionnaire. Results: All formats of training displayed an increase in vigor and a decrease in depression, confusion, tension, anger, and fatigue. The online training displayed better results than the on-site training. Regardless of the format, all training displayed an increase in stress during the activities and a decrease in stress after the activity without changes in other indexes. Additionally, on-site training displayed an increase in sleepiness and stress during the activities. Some participants were tested twice but no significant differences were found between the initial results and the secondary results. Conclusion: In this study, we found evidence that online improvisation can lead to the prevention of depressive symptoms and can function as a method for the reduction of stress in conjunction with the increase of individual vigor. However, a future study is required due to the low number of participants and the absence of POMS data for the on-site training. Any future studies should account for these factors while examining other data such as blood pressure, blood sugar, and pulse.

Purpose : Smartphone is easy tool for patients and ophthalmologists to communicate the data access. We propose “Hikaru ring”, a portable, low-cost, and easily measurable keratoconus detection device which is based on smartphone selfie camera images. Methods : We designed the Hikaru ring as a gadget comprising of a 3D-printed placido’s rings of LED light combined with the smartphone. Two circle LEDs (12-cm and 6-cm) were attached with cone-shaped gadget and both corneas were irradiated at a distance of 15 cm and 19 cm, respectively. Selfie photographs were taken using the ring attached smartphone in normal and keratoconus subjects. Captured mire images of the reflection of the placido’s rings on the cornea were analyzed by an intelligent Windows software based on the corneal parameters. Anterior segment optical coherence tomography was also performed in each subject. Results : In a clinical study, we successfully identified the diagnosis of keratoconus based on eccentricity and steepest keratometry in smartphone selfie style. We found significant correlation of corneal astigmatism and steepest keratometry between Hikaru ring and standard tomography (r = 0.533, p < 0.001 and r = 0.656, p < 0.001). Conclusions : Preliminary experimental results show that the proposed method could detect keratoconus. Our novel placido’s rings attached with smartphone is useful for the screening under remote medicine.

It is important to prevent hypertension, which is a risk factor for cardiovascular disease and severe COVID-19. In addition, as more people work from home, it is desirable to improve intellectual productivity at home. Therefore, in this study, we clarify the effects of the thermal insulation performance of houses on physiological responses and productivity in remote workers. We obtained the following findings. (1) When insulation performance was higher, systolic blood pressure was lower, and the skin temperature and the blood flow rate of the feet were higher; (2) As insulation performance increased, productivity increased.