Individuals exhibit a physiological and psychological response known as the stress coping response when they are exposed to external stimuli. This response represents a patterned reaction to stimuli and can be identified through fluctuations in hemodynamic parameters such as heart rate. The stress coping response is widely studied in physiological psychology, including evaluations of task-related concentration, and is expected to have practical applications in daily life. However, existing methods for measuring hemodynamic parameters typically require attaching devices to the fingers, which poses challenges for routine use due to physical constraints. Therefore, we focused on facial thermal images (FTIs) and facial near-infrared (FNI) images, which can be measured remotely using infrared thermography and near-infrared cameras, respectively. Fluctuations in skin blood flow due to changes in hemodynamic parameters are reflected in FTIs and FNIs. However, it remains unclear what specific characteristics appear in these multispectral facial images during such fluctuations. The purpose of this study was to investigate the characteristics of multispectral facial images and to evaluate response delays during fluctuations in hemodynamic parameters. We conducted stress coping response elicitation experiments and performed cross-correlation analysis between the acquired multispectral facial images and hemodynamic parameters. In typical subjects under passive coping responses, hemodynamic parameters and FTI showed a 17-second delay, with a negative correlation of approximately -0.5 in the nasal region and a positive correlation of approximately 0.64 in the cheek region. However, when averaging the correlations and time delays across all subjects under active coping responses, both FTI and FNI showed correlation values ranging from -0.10 to 0.09, indicating no significant correlation. Under passive coping responses, the correlation was weak, ranging from -0.20 to 0.14. This suggests that individual differences caused the values to offset one another when averaged. Regarding the delay, differences in wavelength were confirmed. For FNI, a delay of approximately 14 to 20 seconds was observed across the entire face under passive coping responses, while a delay of approximately 17 to 23 seconds was observed across the entire face under active coping responses. For FTI, delays in specific areas such as the nose and eye sockets were relatively short, at 1.75-4.81 seconds, while the rest of the face showed delays of approximately 15.38-37.06 seconds, indicating regional differences in delay. For FNI, no significant differences were observed in specific areas; a delay of approximately 14-23 seconds was found across the entire face. Differences in correlation and delay were dependent on the response and wavelength. Individual differences were confirmed. These results indicate that future remote measurements using facial images should select data considering wavelength properties and individual variability in correlation and delay.
This study aimed to develop a high-precision and high-speed registration method to support clinicians during minimally invasive therapies such as high-intensity focused ultrasound (HIFU) therapy. In these procedures, accurate localization and treatment of pathological regions within a restricted surgical field require a high level of expertise. Therefore, real-time and highly accurate intraoperative navigation is essential. To address this need, we proposed a method of extracting blood vessel cross-sections from intraoperative ultrasound images, quantifying their planar dispersion and structural complexity, and investigating geometric parameters that contribute to improved registration accuracy. Additionally, we examined suitable point cloud generation methods for point-based registration to enhance real-time performance. The feasibility of the proposed method was further validated under simulated clinical condition such as natural respiration. Our method achieved the HIFU-required accuracy of less than 5 mm and demonstrated processing within 0.2 seconds, which is significantly faster than conventional approaches. These findings suggest the potential applicability of our method as a surgeon assistant system in ultrasound-guided therapy.
The nurse scheduling problem (NSP) is a complex operational challenge in healthcare, with direct implications for hospital costs, staff well-being, and quality of patient care. Although many methods have been proposed, generating feasible schedules remains challenging due to the diverse and complex constraints. This study addresses a real-world NSP by formulating it as a single quadratic unconstrained binary optimization (QUBO) model, incorporating more than 15 intricate constraints derived from an active hospital ward. To rigorously enforce numerical constraints & horbar;such as total working hours & horbar;we introduce a novel slack variable method using logarithmic encoding. In addition, preprocessing nurse availability and skill data reduces problem size by eliminating infeasible assignments. A key contribution of this study is the development of a hierarchical hyperparameter optimization framework using Bayesian optimization (Optuna), which addresses the manual and critical task of parameter tuning in metaheuristics. This methodology was applied to a 32-nurse ward, generating an error-free schedule that satisfied all the hard constraints. The schedule was validated by a head nurse, confirming its practical utility. This research demonstrates the feasibility and potential of applying simulated annealing to a real-world NSP. It also establishes a practical QUBO formulation and an automated tuning pipeline, paving the way for future deployment on next-generation annealing hardware.
This study aimed to develop a method for identifying tactile sensations based on the interactions between fingertips and material surfaces using fingertip acceleration and angular velocity measurement data, and to relate the obtained data to subjective sensory ratings of tactile sensations, using machine learning. A 6-axis Inertial Measurement Unit (IMU) was attached to the fingertip of each participant, and the acceleration and angular velocity were measured while the participant traced 20 different materials. After the tracing task, the participants rated the tactile sensations on a 7-point semantic differential (SD) scale for six basic sensory items that constitute the tactile sensation: roughness, warmness, softness, wetness, friction, and comfort. The measured acceleration data were transformed into spectrograms using short-time Fourier transform (STFT). Using the spectrograms as input data, convolutional neural network (CNN) model was used for machine learning to classify each of the six sensory items into seven levels. When tested on data from new participants, the CNN model achieved classification accuracies of approximately 40-60% for all six sensory evaluation items. However, further evaluation revealed that the accuracy values for "wetness" and "warmness" largely reflected the central tendency bias in the sensory rating, resulting in reduced estimation reliability. In contrast, "friction," "roughness," "softness," and "comfort" showed relatively higher estimation reliability, suggesting that the IMU data more effectively captured the characteristics of these specific sensations.
Arterial stiffness is used to diagnose atherosclerosis. The most commonly used estimation methods are indirect and non-invasive. To develop a method for direct measurement of the stiffness of blood vessels, stiffness estimation was performed on four simulated blood vessels with different stiffnesses values using a balloon catheter employed in the treatment of atherosclerosis. To measure the pressure response during balloon dilation in a simulated blood vessel, a pressurization and pressure measurement device was fabricated to pressurize the balloon catheter at a constant speed while measuring the pressure. To calculate Young's moduli of the simulated vessels, we simultaneously measured the pressure responses of the simulated blood vessels and the changes in circumferential length of the vessels using a microscope. A tensile test was also used to measure the Young's moduli of the simulated vessels. The Young's modulus values obtained by the balloon catheter method and the tensile test were compared. A linear relationship was observed between the Young's moduli determined by the two tests, indicating that the balloon catheter method can be used to estimate the Young's modulus of simulated blood vessels. To develop a simple method for estimating vessel stiffness, we calculated the reaction force received from a simulated vessel using the pressure difference during dilation in three balloon catheters with different diameters. We then evaluated whether the stiffness could be estimated by comparing the measured pressures with the Young's modulus values. Both the peak pressure after pressurization and the steadystate pressure during pressurization were related to the Young's modulus values, although the relationship depended on the diameter of the balloon catheter. Therefore, we suggest that it is possible to obtain values that reflect the Young's moduli of blood vessels using the balloon catheter method. These results suggest that with appropriate optimization of the balloon catheter diameter relative to the vessel diameter, this approach has the potential to provide pressure response values reflecting the stiffness of blood vessels.
Accurate computation of the specific absorption rate (SAR) is crucial to prevent tissue overheating and ensure compliance with radiofrequency (RF) safety regulatory standards in a magnetic resonance imresults, therefore, understanding their impact is vital for comparing studies, optimizing MRI protocols, and adapting to new technologies. Proper normalization also helps tailor safety assessments to individual patients with anatomical variation, ultimately improving MRI safety. We analyzed SAR values using electromagnetic simulations with four human head models (two adults and two children) within a 7T MRI birdcage coil. To account for size variation in patients, two scaled versions of each model were included, resulting in 12 total head models. We compared and analyzed RF safety by applying five B1+-field normalization methods: setting the input power to 1 W and using the B1+-field mean values from a single voxel, a specific volume, the entire slice, and the whole head model within the B1+-field. Our findings indicate that child models exhibited higher B1+-field mean values and normalized SAR1W values (hdSAR and psSAR10g) compared to adult models, primarily due to their smaller size and mass, despite identical input power. While hdSAR decreased with increasing model size, psSAR10g showed no clear linear trend. Additionally, comparisons between SARNF and SAR1W normalization revealed significant discrepancies, emphasizing the importance of considering model size and mass in RF exposure evaluations. This study emphasizes the importance of recognizing differences in SAR values according to normalization methods to ensure accurate quantitative analysis and MRI safety.
This study reports the findings on training methods utilizing the projection-based hands-free augmented reality manual (PHM) that we developed. Eighteen participants who had completed a lecture on artificial organs and consented to participate in this study were enrolled. Participants were informed beforehand that they would assemble a hemodialysis circuit using the PHM and take tests after completing the task. Participants were divided into three groups: individual group (solo learning), conventional group (conventional group learning), and collaborative group (instructed to collaboratively assemble the circuit). After learning, participants were individually tested without using the PHM. Subsequently, a questionnaire survey was conducted to compare educational effects regarding accuracy and time required. The Kruskal-Wallis test (KW) was used to determine differences among the three groups and the Steel-Dwass test (SD) was used to compare between two groups, both with a 5% significance level. A significant difference in learning time was observed among the three groups, with a very large effect size of 0.766 (12: KW). The median correct answer rate in the immediate post-learning test was 66.5 for the collaborative learning group, 61 for the individual learning group, and 59 for the conventional learning group. One week later, the collaborative learning group scored 64.5 (a decrease of 2 points), the individual learning group scored 59 (a decrease of 2.5 points), and the traditional learning group scored 54 (a decrease of 5 points). In the immediate post-learning test, a significant difference in correct answer rate was observed between the collaborative learning group and the traditional learning group, with a very large effect size of 1.11 (d: SD). A comparison among the three groups one week later again showed a very large effect size of 0.16 (12: KW). No significant differences were found in the time required for the immediate and one-week post-learning tests, and in the decline in correct answer rate for the one-week post-learning test. Based on the grading trends, it may be necessary to inform students during class that "a test will be administered after class." This would allow students to deepen their understanding of the teaching material through collaboration during the learning process.
Accurate assessment of gastric motility is essential for improving the safety and efficacy of enteral nutrition therapy in elderly patients and individuals with dysphagia undergoing percutaneous endoscopic gastrostomy (PEG). However, a reliable method for long-term, low-invasive, and stable monitoring of gastric motility is yet to be established. We developed a novel measurement system designed to monitor gastric motility by detecting intragastric pressure fluctuations through a pressure sensor affixed to a gastrostomy tube. This system conceptualizes the stomach as a single balloon, enabling real-time, low-invasive recording of pressure variations associated with gastric contractions. For preliminary evaluation, we evaluated the capability of the system to capture responses comparable to electrogastrography (EGG). We performed simultaneous 30-min recordings of EGG and intragastric pressure signals before and after nutrient infusion in a cohort of 20 PEG patients. Furthermore, we analyzed frequency- and time-frequency-domain features using fast Fourier transform, wavelet transform, and coherence analysis. Before infusion, the average dominant frequencies (DFs) of both signals were comparable, indicating no systematic bias between methods. The average coherence was 0.32 f 0.10, suggesting a low-to-moderate correlation. Following infusion, the DF remained consistent across both methods, with a coherence of 0.30 +/- 0.06. These findings suggest that the intragastric pressure signals reflect gastric motility to a degree similar to that of EGG. In conclusion, the proposed system shows the potential as a practical and effective tool for visualizing gastric motility in PEG patients.
Multitasking challenges faced by nurses are compounded by a lack of clear criteria for planning the order of nursing tasks, with the responsibility left to individual nurses. Systematizing the order of nursing tasks could facilitate the development of support tools, and appropriate education could reduce the burden on nurses. However, knowledge about systematizing task implementation order is limited. As a first step toward addressing this gap, we hypothesized that task perceptions differ between novice and experienced nurses. We therefore aimed to clarify experience-based differences in task perceptions and thought processes, as well as to identify the criteria that novice nurses use when prioritizing tasks. We surveyed nurses working in general wards. The survey included questions on 115 nursing tasks, focusing on task time, difficulty level, priority, and information used to determine execution. Valid responses were obtained from 55 nurses. When comparing nursing tasks between novice and experienced nurses, several tasks demonstrated statistically significant differences in task time, difficulty, and priority. Difficulty and priority showed a positive correlation-stronger among experienced nurses-whereas no significant difference was observed in priority and task time. Compared with experienced nurses, novice nurses tended to underestimate task time, assign more polarized difficulty levels, rely primarily on time constraints when determining task priorities, and depend on a single source of information when performing tasks. These findings contribute to a deeper understanding the complexity inherent in nursing task prioritization and highlight the challenges in developing a support tool for novice nurses.
Objective: Automatic detection of P-waves in electrocardiograms (ECGs) is difficult because of their small amplitudes and interference by ventricular activity and noise. This study aimed to improve P-wave detection using the periodic nature of sinoatrial (SA) node excitation. Methods: This study developed a phase regression deep neural network (PRDNN) that estimated the continuous phase of SA node-derived P-waves by assigning 0 rad at each P-wave onset. This architecture included bidirectional long short-term memory (BLSTM) layers, feature pyramid networks (FPN), and linear layers. The model was trained using 170 ECGs from 17 patients with second-degree atrioventricular (AV) blocks annotated by experts. Results: When tested on 22 ECGs (240 P-waves) from four AV block patients, the PRDNN achieved 98.8% recall, 99.2% precision, and F-measure of 0.990. The segmentation-based deep neural network (DNN) model used for comparison achieved 88.8% recall, 100% precision, and F-measure of 0.940. The PRDNN significantly improved sensitivity with a minimal loss in precision. Conclusion: By leveraging phase information, the proposed model improves the P-wave detection performance in automatic ECG analysis, potentially enhancing diagnostic support for clinicians.
Itch is an unpleasant sensation that evokes the urge to scratch and can lead to reduced concentration, sleep disturbances, and decreased quality of life. Conventional assessment methods, such as the visual analog scale (VAS) and verbal rating scale (VRS), are widely used to evaluate itch intensity; however, these methods are subjective and may be influenced by the participants' perceptions or evaluators' interpretations. This study aimed to examine the feasibility of objectively quantifying itch intensity using transcutaneous electrical stimulation. In Experiments 1 and 2, two types of itch perception thresholds, the initial itch perception threshold (IPT) and the maximum itch perception threshold (IMT), were measured and compared with each participant's current perception threshold (CPT). Both IPT and IMT showed strong correlations (r or p >= 0.81) and positive regression relationships (R-2 >= 0.68) with CPT values. In addition, subjective evaluations revealed a strong positive correlation between VAS and VRS scores (p >= 0.61). In Experiment 3, a 20-Hz square wave electrical stimulus at a 50% duty cycle was applied while incrementally increasing the stimulation intensity from 1.2 to 2.0 times each participant's IPT. VAS scores increased significantly with increasing stimulation intensity, and statistical analysis revealed a significant main effect of stimulation level (p < 0.001). In Experiment 4, itch was induced by topical application of yam (calcium oxalate) to the skin. Itch intensity was quantified using the proposed Itch Index calculated from the ratio of IPT to CPT, as well as the VAS. A significant positive correlation was observed between the Itch Index and VAS score (r = 0.77, p = 0.01), supporting the validity of the proposed evaluation method. In contrast, no clear correlation between the Itch Index and VAS score was observed in Experiment 2, which may be attributable to inter-individual differences in sensitivity to electrically induced itch. Further investigation is required to clarify this relationship. Overall, the Itch Index based on transcutaneous electrical stimulation represents a promising approach for objective quantification of itch. This method complements conventional subjective assessment tools and may contribute to improved evaluation of pruritic symptoms.
Metabolic dysfunction-associated steatohepatitis (MASH) is a disease characterized by accumulation of fat droplets and fibrous tissue in the liver. This disease has attracted attention due to its high incidence and risk of severe complications. High-frequency quantitative ultrasound (QUS) has demonstrated strong potential in the diagnosis of liver diseases. However, previous studies focused only on evaluating lipid droplets in fatty liver, without accounting for the interference of other components such as fibrous tissue. This study aimed to clarify the scattered signals from various tissues by analyzing six numerical computer phantoms simulating normal liver, fatty liver and hepatitis, using an amplitude envelope statistical method, the double Nakagami (DN) model. The simulation system consisted of an ultrasound platform and a linear probe with center frequency of 31.25 MHz. Eleven plane waves ranging from-15 degrees to +15 degrees were transmitted and received using a compound plane-wave imaging method. The results show that the DN model matches the amplitude envelope of the original echo signals and effectively distinguishes independent signal components arising from different tissues. In fatty liver phantoms, the DN model parameter awF showed a strong positive correlation (r = 0.8434, p = 0.0472) with fat volume. In hepatitis phantoms, awF increased with increase in the fibrous tissue mixture ratio in the regions of interest, while uL, uF were close to 1, reflecting the cell distribution patterns and tissue characteristics. These results indicate that echo signals exhibit different properties when the scatterer density is high or when scattering intensity is strong, supporting the feasibility of using the DN model to differentiate fat and fibrous tissues from normal liver. However, real clinical cases are more complex, as fatty and fibrous tissue intermix in the liver, requiring further validation in the future. Nevertheless, the results show the potential of this method for real-time, noninvasive quantitative characterization of tissue properties.
Early decompression for traumatic spinal cord injury (SCI) benefits neurological recovery, yet executing timely surgery demands precise, safe bone cutting adjacent to the cord. We developed and evaluated a skills training system for cervical laminoplasty that provides real-time visual feedback on drill pressing force and spinal cord contact, using an expert-derived "teacher" waveform (median rhythm approximate to 1.06 Hz) and shadowing to guide novice performance. Twenty students without surgical experience were randomized to a training group (n = 10) or control group (n = 10). Both groups performed pre- and post-training cutting trials on standardized 3D-printed cervical models. The system measured vertical pressing force (load cell, 40 Hz) and flagged spinal cord contact via an optical threshold. Over two weeks, the training group completed six shadowing sessions with metronome guidance and on-screen warnings when exceeding force/contact thresholds; the control group received only one expert video critique during the study period. Primary outcomes were cutting time, maximum pressing force, and number of spinal cord contacts. The results were analyzed using nonparametric tests. Cutting time decreased within both groups, but was not significant (control: median time 38.0 to 28.5 s, p = 0.160; training: 47.8 to 31.7 s, p = 0.064), and the between-group difference after training was also not significant (p = 0.353). Maximum pressing force showed no significant change within groups (control: 4.2 to 3.6 N, p = 0.241; training: 3.5 to 4.0 N, p = 0.554) and also no significant difference between groups after training (p = 0.519). In contrast, post-training spinal cord contacts were significantly fewer in the training group than in the control group (median 0 vs 1.5, p = 0.006), with no significant within-group changes. This laminoplasty-specific training system prioritizes the safety goal of "cutting without contacting the cord" while maintaining efficiency and output. Visual feedback based on expert force patterns reduces spinal cord contact events and provides a reproducible, quantitative framework to accelerate safe skill acquisition in decompressive cervical surgery.
Turning over in bed, especially turning over at night, is a vital human unconscious behavior. Clinically, this movement disperses pressure between the body and bed, thus preventing bedsores. Several devices, such as acceleration and pressure sensors, can count turning overs automatically; however, they often require installation on the patients or in the bed. The simplest and noninvasive method to count turning overs is to record and count on video images, but this method cannot protect privacy. Images obtained using thermal sensors have been used to protect privacy; however, there are no reports of counting turning overs automatically using low-resolution sensors. We developed a novel device equipped with four low-resolution thermal sensors, with each sensor recording only an 8x8-pixel thermal image. The original data can protect patient privacy because the resolution is only similar to 28.8 x 28.8 cm per body, which is the lowest resolution compared to previous reports using thermal images. Using four sensors simultaneously enables us to collect sufficient data for automatic identification. We first used the bilinear interpolation method employed in a previous report to count turning overs; however, the results were unsatisfactory because turning overs produced extremely subtle changes in the original data compared with postural changes such as falls. After several attempts, we finally developed a unique identification program that interleaved all data from four sensors and then identified turning overs using residual neural network-18. Using the new system, the accuracy, recall, and precision of counting turning overs in bed improved to approximately 90% with an acceptable computation load in an experiment conducted on volunteers. This study demonstrated the feasibility of our device to count turning overs in clinical settings by the new identification program using four 8x8-pixel thermal images per frame, which have sufficiently low resolution to protect patient privacy.
In developed countries, the aging population is increasing steadily, raising concerns related to healthcare and patient safety. One critical issue is the growing number of bed falls and injury incidents in hospitals and nursing homes, often occurring when patients attempt to get out of bed unassisted. Accurately detecting such movements and promptly notifying nursing staff are essential for preventing accidents. This work proposes a camera-based bed fall prevention system that utilizes the YOLOv11 deep learning object detection model and a single infrared camera to detect four key patient postures: supine, side-lying, getting up, and edge-sitting. Previous research has shown that factors such as blanket occlusion and presence of caregiver complicate accurate posture recognition. To address these challenges, we developed a multilabel dataset and trained a custom YOLOv11 model capable of simultaneously detecting patient posture, head position, and bed location. Training data were collected in four different scenarios, and system evaluation was conducted using three datasets: (1) simulated laboratory data from 19 participants, (2) real hospital data from five elderly participants; and (3) hospital data captured under various lighting conditions. The system successfully identifies the "getting-up" posture, tracks head movement beyond bed boundaries, issues alerts, estimates the number of visible heads, and recognizes safety modes. Experimental results demonstrate high performance, with an average accuracy of 99.4%, sensitivity of 98.8%, and specificity of 99.7%. The proposed system offers a practical and cost-effective solution for bed fall prevention, with the potential to reduce the workload of clinical staff and improve patient safety.
This study aimed to verify whether the natural frequency of the medial gastrocnemius muscle during quiet standing & horbar;proportional to the square root of muscle stiffness & horbar;estimated from the electrically induced mechanomyogram (MMG), matched that derived from the anterior-posterior torque. Six healthy young male volunteers participated in the experiment. Each participant stood quietly on a force plate while electrical stimulation was applied to the gastrocnemius muscle via disposable surface electrodes. The electrically induced MMG and anterior-posterior torque were recorded and synchronously averaged to eliminate body-sway components from the measured signals. The averaged MMG and torque waveforms were then modeled using a single second-order system and two coupled second-order systems, respectively, and the natural frequency was estimated using a nonlinear least-squares optimization method. The estimated natural frequencies were 2.07 +/- 0.15 Hz for the MMG and 2.16 +/- 0.14 Hz for the torque. These findings verify that the natural frequency of the medial gastrocnemius muscle derived from the electrically induced MMG closely matches that obtained from the anterior-posterior torque, demonstrating supporting the validity of both approaches for noninvasive assessment of muscle mechanical properties during quiet standing.
Blood oxygen saturation (SpO2) is one of the most important vital sign parameters. The conventional measurement method is contact-based photoplethysmography (PPG), which includes finger pulse oximetry. While PPG is usually used to measure vital signs, this method can be uncomfortable for people with sensitive skin, such as infants and critically ill individuals. Recently, research has been conducted to improve noncontact SpO2 estimation techniques using facial videos and enhance remote photoplethysmography (r-PPG) signals to extract significant information. However, r-PPG signals are often degraded by lighting, motion, and skin tone variability. This study aimed to enhance the quality of r-PPG signal from facial videos, using feature selection to reduce complexity and overfitting, and applying random forest (RF)-based methods to model the r-PPG signals. Furthermore, we developed an innovative machine learning-based method for estimating SpO2 level using a web camera to capture r-PPG signals from defined facial videos in a controlled laboratory environment. Initially, an AI-driven framework for face mesh detection and region-of-interest (ROI) tracker algorithm were utilized to enhance r-PPG signal quality and reduce the noise caused by ambient light and subject's motions. Face detection and ROT tracker vibration was smoothed using a Kalman filter. The resulting time-series data were processed using signal filtering. Thereafter, the RF algorithm, a supervised machine learning method, was used to predict SpO2 values from these components. For the experiment, red-green-blue (RGB) facial video data were collected from 11 subjects with different skin tones and genders to train the RF algorithm, using a smaller dataset than those utilized in other studies. The results showed mean square error of 0.95%, root mean square error of 0.98%, mean absolute error of 0.77%, and Pearson's correlation coefficient of 0.80. Despite a small training dataset, the proposed method demonstrated notable feasibility. The RGB color values from facial videos were potentially useful for accurate estimation of SpO2 levels in a stable environment. The proposed noncontact method is a promising alternative to traditional pulse oximetry, and has potential applications in clinical settings, particularly in remote patient monitoring, critical care monitoring, early disease detection, and telemedicine.
Measuring neural activity during communication is essential for understanding social brain processes and contributes to improving the diagnosis and treatment of communication disorders. Hyperscanning (simultaneous recording of brain activity from multiple individuals) is a highly effective method for this purpose. Magnetoencephalography (MEG) hyperscanning is particularly suitable owing to its high spatiotemporal resolution. However, conventional MEG with superconducting quantum interference devices (SQUIDs) is cumbersome and requires liquid helium to maintain superconductivity. Moreover, the rising cost of liquid helium is currently a major obstacle in SQUID-based MEG operation. In contrast, novel optically pumped magnetometer (OPM)-MEG is gaining attention because it is compact, portable, and does not require liquid helium for operation. However, OPM-MEG remains costly, and its widespread adoption is expected to take time. Therefore, until OPM-MEG becomes more widely available, a hybrid hyperscanning approach combining OPM-MEG and SQUID-MEG may be a more practical solution. In the present study, we constructed a hyperscanning system using a 48-channel OPM-MEG (HEDscan, FieldLine) and a 306-channel SQUID-MEG (Vectorview, ElektaNeuromag). Twelve pairs of adults (14 women and 10 men), aged 21.5 f 2.7 years, participated in a turn-taking verbal communication task with meaningful words and meaningless syllables. We calculated normalized amplitudes of alpha band activity during the 2-s pre-speech interval and performed a two-way mixed-design analysis of variance (ANOVA) with MEG type (OPM and SQUID) as the between-subjects factor and condition (meaningful and meaningless) as the within-subjects factor. ANOVA revealed no significant interaction and no main effect related to MEG type [F(1, 22) = 0.171, p = 0.684; F(1, 22) = 0.061, p = 0.807, respectively]. In contrast, there was a significant main effect of condition [F(1, 22) = 11.489, p = 0.003], which indicated that the normalized amplitude of alpha band activity during the meaningless condition was larger than that of the meaningful condition. This finding is consistent with previous research using a SQUID-MEG hyperscanning system, indicating successful measurement of differing brain activities across conditions regardless of MEG sensors. This study demonstrates the feasibility of hyperscanning using OPM-MEG and SQUID-MEG.
Magnetoencephalography with optically pumped magnetometers (OPM-MEG) has become a focus of research. Signal sources can be estimated using OPM-MEG, where the sources are approximated as current dipoles, such as those associated with sensory evoked fields (SEFs). However, previous studies have typically used around 100 OPM-MEG sensors within high-performance magnetically shielded rooms (MSR) with active shielding. In this study, we aimed to develop OPM-MEG with a reduced number of sensors in a moderate MSR. To minimize the effects of magnetic field gradients in this environment, we fixed the sensor helmet to the MSR. Either 48 or 16 OPM-MEG sensors were used, depending on the configuration. Using these setups, we measured SEFs and compared the estimated locations of signal sources with those obtained from a conventional 306-channel MEG with a superconducting quantum interference device (SQUID-MEG). Five adults (one woman, four men), aged 33.6 f 8.02 years (mean f SD) participated in this experiment. To co-register the sensor positions to the participant's head coordinates, head position indicator coils were used in the 48-channel configuration, whereas reference points embedded in the sensor helmet were used in the 16-channel configuration. Signal source estimation was performed using minimum norm estimation with dynamic statistical parametric mapping on a 5-mm grid-based volume model. SEF peak latencies were determined at the peaks of the global field power around 20 ms (N20m) from the stimulus onset. The SEF signal source locations were defined as the voxel coordinates with the highest dSPM values within the somatosensory or motor cortex at the SEF peak latencies. The SEF signal source locations estimated by the 48-channel and 16-channel OPM-MEG differed from those estimated using SQUID-MEG by 10.81 f 5.42 mm and 11.95 f 4.66 mm (mean f SD), respectively. Based on previous studies comparing source estimates across multiple SQUID-MEGs, this level of discrepancy is considered acceptable. These results suggest that the source locations of SEFs can be estimated reliably even with a 16-channel OPM-MEG operating in a moderate MSR.
