During the ongoing COVID-19 pandemic, remote learning and conferencing have become increasingly common. However, these modalities are often facilitated through digital devices, resulting in a lack of immersion and difficulties in sustaining focus. This study explores how to enhance the sense of presence in remote lectures through the use of the Metaverse, a concept that has gained worldwide recognition. This report outlines the basic design and conceptual framework of the system.

The purpose of this study is to develop a novel Brain Machine Interface (BMI) algorithm using near-infrared spectroscopy (NIRS) to supplement data on readiness potential (RP) obtained from electroencephalography (EEG) in order to recognize human motions. Congenital and acquired disabilities make it impossible to live comfortably as an able-bodied person. BMI assists people with disabilities in moving prosthetics, computer operations, etc. Our recent research has shown that the hybrid 1DCNN-BiLSTM algorithm, which combines two deep learning algorithms, can more correctly identify between left- and right-handed motions than EEG when total hemoglobin is monitored by NIRS. This is due to the fact that brain waves come in a variety of unique waveforms, such as RP. EEG has a weakness of external and internal noise. NIRS, on the other hand, is more noise-resistant than EEG, despite it lacking the precise indicators of activity readiness that EEG does. In this study, NIRS signals were combined into EEG signals to support EEG in distinguishing left-right hand movements. The EEG-NIRS and EEG dataset were passed hybrid 1DCNN-BiLSTM to discriminate accuracy separately to compare the accuracy and the SD. As a result, EEG-NIRS can more distinguish between left- and right-hand movements than using only-EEG signals. Considering the result, the combination of EEG and NIRS signal is possible to support BMI based on EEG to distinguish the left- and right-hand movement. It may be possible to determine the non-movement and movement signals using RP that appears on EEG.

Due to the recent influence of the new coronavirus (SARS-CoV-2), opportunities to improve knowledge of new medical technologies are lacking. To provide safe medical technology, we examined the medical device operation support system using smart glasses, which is a wearable device. This system enables actual operation while watching operation videos of various devices when necessary and can also provide remote technical support. By comparing the performance of multiple smart glasses, we arrived at an appropriate system that can be used in clinical practice.

Various kinds of analysis including fast Fourier transform (FFT) were widely used for the classification of electroencephalogram (EEG) based human interfaces. However, the morphological characteristics of EEG waveform were rarely used, since the EEG waveform is thought to show no significant meaning due to its stochastic features. The authors have studied on SSVEP-based BCI for disabled patients. The objective of this study is to verify feasibility of amplitude probability density distribution (APD) used as a feature contributing classification EEG. In this study, the amplitude probability density distribution, which indicated as the index of morphological characteristics of EEG, was applied for state classification using deep learning. CNN was introduced to construct the model of deep learning, classify the obtained data calculated by introduced novel APD method and FFT in order to compare the feasibility. The data were obtained from EEG recorded when subjects were presented flashing light with low stimulus luminosity reversed at 20 and 60 Hz. EEG measurement was conducted in shield room and 9 healthy adulthood male subjects participated in this study. As a result, the case of EEG spectrum by FFT as the control data, the classification accuracy was 85.81

The purpose of this study is to investigate the spectral changes of electroencephalogram (EEG) toward development of a new Brain Computer Interface (BCI) for disabled people with verbal communication disorders such as Amyotrophic Lateral Sclerosis (ALS). In this study, an experiment using EEG recordings was carried out in nine healthy adult volunteers. Periodically reversing checker-board stimuli with two kinds of frequencies (5, 15 Hz) were used to observe users' selective attention from EEG spectral changes. The stimuli were displayed in two different ways, independently displayed and simultaneously displayed, on the LCD of a personal computer. Volunteers were instructed to attend either 5, 15 Hz or neither of the reversing stimulus during EEG recordings. Obtained EEG data were analyzed by FFT and those power spectra were calculated. As a result, two different frequencies reversal stimuli generated peak of EEG spectrum with attended stimulus frequency. However, the peak generated by 5 Hz stimulus was somehow bigger than that of 15 Hz stimulus due to individual differences. To obtain the comparable height of EEG spectral peaks, the compensate procedure to reduce the sensitivity difference between the two frequencies for each person is required. From a comparison of the EEG power spectral structures, subjective binary decision (5 or 15 Hz reversal stimuli) could be discriminated objectively. Utilizing this phenomenon, EEG based BCI for subjective selection extraction can be constructed. Some problems of feasibility of this method as a BCI were also discussed.

For animal studies with psychotropic drugs, such as sleep-inducer, fundamental data for establishing the characteristics of a standard state of EEG is essential. In this study, statistical analysis using rat biometric system MUPREMS was utilized and polygraphic measurements were carried out over a 24 hour period. Since visually operated classification of recorded data takes a long time, an automated computer analysis system for rat EEG was developed using FFT. EEG, ECG, EMG and EOG were measured from three Wistar rats. Infrared video recordings were also carried out. Four stages from the video recording, six stages from biomedical recordings were defined to determine the sleep stage and wakefulness. Outputs were compared with the classification results of the visual inspection. As a result, this method was comparable to visual classification. The future problem is also found for the state integration such contextual characteristics on the time axis with the behavior data.

It is generally admitted that alcohol drinking elicits the decline of cognitive function. There was little literature mentioned about the relationship between cognitive level and alcohol intake in a quantitative way. The P300 is one of the event related potentials (ERP) and can be used for the indicator of cognitive function. The P300 evoked by auditory Odd-ball task was measured in 10 volunteers. After baseline measurement, the subjects drank alcohol, equivalent to 350 mL of beer (17.5mL of ethanol). Five minutes later, the breath alcohol concentration was checked, and P300 was recorded. If needed, the additional 350mL of beer or equivalent ethanol containing beverages was taken, and P300 was measured repeatedly until the breath concentration reached 1mg⁄L or the participants or⁄and researchers felt that volunteers got drunk enough for the safety. After alcohol loading, the latencies of P300 were significantly prolonged (p<0.001). This phenomenon means a delay of the recognition. Also, the amplitudes of P300 were significantly reduced (p<0.005). This indicates a fall of the cognitive level. Moreover, the positive relationship between the breath alcohol concentrations and delays of P300 latency was clearly observed (p<0.05). This relation was also admitted in the declines of P300 amplitude and breath alcohol levels (p<0.05). It was confirmed that cognitive function decreased in proportion to the amount of the alcohol drinking.

As a basic experiment for the development of neuromotor prostheses (NMPs), movement related neural signals were measured using rats. The electrodes were surgically implanted in the motor cortex, spinal cord, sciatic, and femoral nerves. The measurements were carried out under two conditions, which were voluntary movement and involuntary motion evoked by startle auditory stimuli. Obtained signal waveforms under two separate conditions were compared and analyzed. In both conditions, a descending transmitting signal was observed. However, remarkable readiness activity of the motor cortex was not seen in both cases with a 5 times averaging. Using our experimental model, neurophysiological characteristics of motor generation were revealed. Based upon these findings, the possibility of new NMPs for the disabled was discussed.

The rat is one of the most popular experimental animals in medical fields. However, the rat is not so often used in complicated neurological studies because of its size of the brain. To investigate the localization of waveform of auditory brainstem responses (ABRs), two rats were examined. Twelve or 13 platinum-rhodium coated electrodes were implanted on the dura mater through the cranial bone. The waveform of ABRs with 6 peaks was clearly recorded. These peaks were comparable with those of the human. However, the latency of these peaks was slightly shorter compared with that of the human. These 6 peaks were clearly identified in the waveform measured on the occipital area. On the other hand, these peaks recorded in frontal region were fused together. From these results, electrodes should be placed on the occipital area of the cranial bone to identify these peaks originated from brainstem by the auditory stimulation. Therefore, it is expected that latency changes can be evaluated clearly. This measurement system has a new possibility to analyze the details of various kinds of evoked potentials waveforms.

In order to develop a brief assessment system of sleep stage for untrained users utilizing water mat pressure sensors placed in a bed, polysomnographic(PSG) recodings were compared to data obtained by present sensor. Coincident ratio were calcurated using estimated sleep stage by the sensor and polysomnographic judgement. Results showed that sleep diagram by present sensor was consistent with that by PSG.

The authors have been studying psychophysiological workload of human interface (HI) with physiological measurements and analysis. In this study, we investigated a kind of mental workload produced by user's unexpected waiting period from the request input to the termination of data processing during personal computer (PC) operation. As the experimental setting of HI, we used interactive software containing easy questions with unexpected time interval between each question. The effects of progress indicator (PI) indicating during waiting period on psychophysiological status of users were analyzed by using respiration, finger plethysmogram (PTG), heart rate (HR) and electroencephalogram (EEG) measurements. Results showed that the theta wave component of the EEG increased in the non-PI condition, even though autonomic nervous system parameters showed no significant change. Negative correlation between preference score for HI and integrated theta component percentage was observed only in non-PI condition. It is supposed that the PI was controlling theta activity coused by waiting stress in experimental condition. Utilizing physiological indices for HI assessment, this experimental method could be available to waiting stress estimation.

In order to estimate the mental workload, electroencephalogram (EEG) was measured during the Figure Stroop Task performance from ten healthy adult voluntary participants. In the experiment, two kind of visual stimuli were prepared: congruent and incongruent stimuli (figure and number of the figure was congruent or incongruent). Participants were asked to answer the number of displayed figures by pressing designated key. In the task, two different instructions to the participants were used: (1) respond as soon as possible, and (2) respond accurately. Integrated theta component of the EEG were compared between under condition of (1) and that of (2). Relative power percentage of theta component in condition (2) was larger than that in (1). The result suggests that the theta component was related with subjective difficulty of the task.

We used a stabilometer to examine development of postural control in 174 school-age boys aged 6-14. Subjects stood upright on a stabilometer for 30 s under both eyes-open and eyes-closed conditions. At that time, we determined the mean location of the center of pressure (CP), the migration area of CP, the migration distance of CP, and the mean migration velocity of CP (VCP) using the measuring instrument. Along with age, the sway in all measuring items decreased under both eyes-open and eyes-closed conditions so that it shows that posture control developed as age advanced. Each measuring item was evaluated according to the ability of discretion by Mahalanobis distance under both eyes-open and eyes-closed conditions. The greatest discretion through all grades was shown for VCP. Nevertheless, VCP decreases more with eyes open than with eyes closed, the difference grows larger between the ages of 9 and 11, indicating that the degree of dependence on visual information in postural control increases during that period. Differences between eyes-open and eyes-closed decrease after age 11; they become constant after age 12, indicating that the ability to maintain a stable posture is obtainable without much use of visual information.