How do differences in native language affect out-of-body experiences?

Out-of-body experiences are scientifically inducible cognitive phenomena attracting global attention due to their application in the Metaverse and medical care. Despite previous studies suggesting that one's native language influences one's cognition, the out-of-body experiences of humans with different native languages have not been investigated separately. This study replicated an experiment from a 2007 study to investigate whether differences in native language affect the ability to have scientifically induced out-of-body experiences. A total of 19 age-matched native English and Japanese speakers completed the experiment in two blocks. Thereafter, their experiences were evaluated using questionnaires, and their responses were compared. Importantly, no significant differences between the English and Japanese native-speaker conditions were found. The results showed that out-of-body experiences were induced similarly in both groups, suggesting that people can have out-of-body experiences as a response to similar stimuli, regardless of their native language. However, differences in participants' introspective reports suggested that their experiences may differ qualitatively, possibly, due to the different linguistic backgrounds. The elucidation of the mechanisms of science-assisted out-of-body experiences that consider different cultural and cognitive characteristics, such as native language, could lead to the investigation of their applications in the borderless Metaverse and medicine.

Effects of avatar shape and motion on mirror neuron system activity.

Humanness is an important characteristic for facilitating interpersonal communication, particularly through avatars in the metaverse. In this study, we explored the mirror neuron system (MNS) as a potential neural basis for perceiving humanness in avatars. Although previous research suggests that the MNS may be influenced by human-like shape and motion, the results have been inconsistent due to the diversity and complexity of the MNS investigation. Therefore, this study aims to investigate the effects of shape and motion humanness in avatars on MNS activity. Participants viewed videos of avatars with four different shapes (HumanShape, AngularShape, AbbreviatedShape, and ScatteredShape) and two types of motion (HumanMotion and LinearMotion), and their µ-wave attenuation in the electroencephalogram was evaluated. Results from a questionnaire indicated that HumanMotion was perceived as human-like, while AbbreviatedShape and ScatteredShape were seen as non-human-like. AngularShape's humanity was indefinite. The MNS was activated as expected for avatars with human-like shapes and/or motions. However, for non-human-like motions, there were differences in activity trends depending on the avatar shape. Specifically, avatars with HumanShape and ScatteredShape in LinearMotion activated the MNS, but the MNS was indifferent to AngularShape and AbbreviatedShape. These findings suggest that when avatars make non-human-like motions, the MNS is activated not only for human-like appearance but also for the scattered and exaggerated appearance of the human body in the avatar shape. These findings could enhance inter-avatar communication by considering brain activity.

Cross-Sensory EEG Emotion Recognition with Filter Bank Riemannian Feature and Adversarial Domain Adaptation.

Emotion recognition is crucial in understanding human affective states with various applications. Electroencephalography (EEG)-a non-invasive neuroimaging technique that captures brain activity-has gained attention in emotion recognition. However, existing EEG-based emotion recognition systems are limited to specific sensory modalities, hindering their applicability. Our study innovates EEG emotion recognition, offering a comprehensive framework for overcoming sensory-focused limits and cross-sensory challenges. We collected cross-sensory emotion EEG data using multimodal emotion simulations (three sensory modalities: audio/visual/audio-visual with two emotion states: pleasure or unpleasure). The proposed framework-filter bank adversarial domain adaptation Riemann method (FBADR)-leverages filter bank techniques and Riemannian tangent space methods for feature extraction from cross-sensory EEG data. Compared with Riemannian methods, filter bank and adversarial domain adaptation could improve average accuracy by 13.68% and 8.36%, respectively. Comparative analysis of classification results proved that the proposed FBADR framework achieved a state-of-the-art cross-sensory emotion recognition performance and reached an average accuracy of 89.01% ± 5.06%. Moreover, the robustness of the proposed methods could ensure high cross-sensory recognition performance under a signal-to-noise ratio (SNR) ≥ 1 dB. Overall, our study contributes to the EEG-based emotion recognition field by providing a comprehensive framework that overcomes limitations of sensory-oriented approaches and successfully tackles the difficulties of cross-sensory situations.

Classification of mild Parkinson's disease: data augmentation of time-series gait data obtained via inertial measurement units.

Data-augmentation methods have emerged as a viable approach for improving the state-of-the-art performances for classifying mild Parkinson's disease using deep learning with time-series data from an inertial measurement unit, considering the limited amount of training datasets available in the medical field. This study investigated effective data-augmentation methods to classify mild Parkinson's disease and healthy participants with deep learning using a time-series gait dataset recorded via a shank-worn inertial measurement unit. Four magnitude-domain-transformation and three time-domain-transformation data-augmentation methods, and four methods involving mixtures of the aforementioned methods were applied to a representative convolutional neural network for the classification, and their performances were compared. In terms of data-augmentation, compared with baseline classification accuracy without data-augmentation, the magnitude-domain transformation performed better than the time-domain transformation and mixed-data augmentation. In the magnitude-domain transformation, the rotation method significantly contributed to the best performance improvement, yielding accuracy and F1-score improvements of 5.5 and 5.9%, respectively. The augmented data could be varied while maintaining the features of the time-series data obtained via the sensor for detecting mild Parkinson's in gait; this data attribute may have caused the aforementioned trend. Notably, the selection of appropriate data extensions will help improve the classification performance for mild Parkinson's disease.

Influence of Multimodal Emotional Stimulations on Brain Activity: An Electroencephalographic Study.

This study aimed to reveal the influence of emotional valence and sensory modality on neural activity in response to multimodal emotional stimuli using scalp EEG. In this study, 20 healthy participants completed the emotional multimodal stimulation experiment for three stimulus modalities (audio, visual, and audio-visual), all of which are from the same video source with two emotional components (pleasure or unpleasure), and EEG data were collected using six experimental conditions and one resting state. We analyzed power spectral density (PSD) and event-related potential (ERP) components in response to multimodal emotional stimuli, for spectral and temporal analysis. PSD results showed that the single modality (audio only/visual only) emotional stimulation PSD differed from multi-modality (audio-visual) in a wide brain and band range due to the changes in modality and not from the changes in emotional degree. The most pronounced N200-to-P300 potential shifts occurred in monomodal rather than multimodal emotional stimulations. This study suggests that emotional saliency and sensory processing efficiency perform a significant role in shaping neural activity during multimodal emotional stimulation, with the sensory modality being more influential in PSD. These findings contribute to our understanding of the neural mechanisms involved in multimodal emotional stimulation.

Three-dimensional foot trajectory in female patients with end-stage hip osteoarthritis during walking.

Osteoarthritis (OA) is a risk factor for falls. To decrease the fall risk, it is important to evaluate the detailed features of the gait of patients with OA. This study aimed to investigate the spatio-temporal parameters of gait in patients with end-stage hip OA, especially foot trajectory. We measured normal speed gait in patients with hip OA and in healthy controls (HCs) using inertial measurement units attached to shanks. The stride lengths in the affected and unaffected sides in the patients with hip OA were shorter than those in the HCs, but the position of maximum foot clearance was not significantly different between the two groups. The patients with hip OA compensated the position of maximum foot clearance to avoid fall risk. The horizontal plane foot trajectory in patients with hip OA suggests that the lateral bending of the trunk during walking, which is frequently seen in them, was a result of the lateral distance at swing down being located significantly more medially on the unaffected side than on the affected side. Herein, a new gait parameter of lateral distance at swing was discovered by a detailed evaluation of three-dimensional foot trajectory of female patients with end-stage hip OA.

Three-dimensional continuous gait trajectory estimation using single Shank-Worn inertial measurement units and clinical walk test application.

State-of-the-art estimation methods using inertial measurement units (IMUs) for global continuous gait path and local stepwise gait trajectory during walking have been developed. However, estimation methods for continuous gait trajectory integrating both these aspects with high accuracy are almost lacking. Thus, continuous gait trajectory estimation using a single shank-worn IMU with high accuracy is proposed in this study. This method calculates three-dimensional local stepwise gait trajectory based on IMU measurement data extracted between adjacent middle points of stance phases during walking. Continuous gait trajectory is estimated by concatenating adjacent local stepwise gait trajectories based on relative angles determined according to stride vectors and shank orientations. Evaluation experiments results obtained using the optical motion capture system with 12 healthy participants demonstrated estimation errors in the stride length (- 0.027 (- 0.054 to - 0.006) m) and turning angle (0.7 (- 0.2-1.7)°), and normalized endpoint position error (0.029 (0.019-0.04) m). Comparing with previous reports, the proposed method integrally achieves a continuous gait trajectory with a low estimation error level in both local and global aspects despite the continuous measurement of multiple gait cycles. The proposed simple and low-cost method can be applied in the medical field and contribute to expansion of the application of precise gait information in daily life.

Effect of Interpersonal Interaction on Festinating Gait Rehabilitation in Patients with Parkinson's Disease.

UNLABELLED: Although human walking gait rhythms are generated by native individual gait dynamics, these gait dynamics change during interactions between humans. A typical phenomenon is synchronization of gait rhythms during cooperative walking. Our previous research revealed that fluctuation characteristics in stride interval of subjects with Parkinson's disease changed from random to 1/f fluctuation as fractal characteristics during cooperative walking with the gait assist system Walk-Mate, which emulates a human interaction using interactive rhythmic cues. Moreover, gait dynamics were relearned through Walk-Mate gait training. However, the system's clinical efficacy was unclear because the previous studies did not focus on specific gait rhythm disorder symptoms. Therefore, this study aimed to evaluate the effect of Walk-Mate on festinating gait among subjects with Parkinson's disease. Three within-subject experimental conditions were used: (1) preinteraction condition, (2) interaction condition, and (3) postinteraction condition. The only difference between conditions was the interactive rhythmic cues generated by Walk-Mate. Because subjects with festinating gait gradually and involuntarily decreased their stride interval, the regression slope of stride interval as an index of severity of preinteraction festinating gait was elevated. The regression slope in the interaction condition was more gradual than during the preinteraction condition, indicating that the interactive rhythmic cues contributed to relieving festinating gait and stabilizing gait dynamics. Moreover, the gradual regression slope was carried over to the postinteraction condition, indicating that subjects with festinating gait have the potential to relearn stable gait dynamics. These results suggest that disordered gait dynamics are clinically restored through interactive rhythmic cues and that Walk-Mate may have the potential to assist therapists in more effective rehabilitation. TRIAL REGISTRATION: UMIN Clinical Trials Registry UMIN000012591.

Relationship between neural rhythm generation disorders and physical disabilities in Parkinson's disease patients' walking.

Walking is generated by the interaction between neural rhythmic and physical activities. In fact, Parkinson's disease (PD), which is an example of disease, causes not only neural rhythm generation disorders but also physical disabilities. However, the relationship between neural rhythm generation disorders and physical disabilities has not been determined. The aim of this study was to identify the mechanism of gait rhythm generation. In former research, neural rhythm generation disorders in PD patients' walking were characterized by stride intervals, which are more variable and fluctuate randomly. The variability and fluctuation property were quantified using the coefficient of variation (CV) and scaling exponent α. Conversely, because walking is a dynamic process, postural reflex disorder (PRD) is considered the best way to estimate physical disabilities in walking. Therefore, we classified the severity of PRD using CV and α. Specifically, PD patients and healthy elderly were classified into three groups: no-PRD, mild-PRD, and obvious-PRD. We compared the contributions of CV and α to the accuracy of this classification. In this study, 45 PD patients and 17 healthy elderly people walked 200 m. The severity of PRD was determined using the modified Hoehn-Yahr scale (mH-Y). People with mH-Y scores of 2.5 and 3 had mild-PRD and obvious-PRD, respectively. As a result, CV differentiated no-PRD from PRD, indicating the correlation between CV and PRD. Considering that PRD is independent of neural rhythm generation, this result suggests the existence of feedback process from physical activities to neural rhythmic activities. Moreover, α differentiated obvious-PRD from mild-PRD. Considering α reflects the intensity of interaction between factors, this result suggests the change of the interaction. Therefore, the interaction between neural rhythmic and physical activities is thought to plays an important role for gait rhythm generation. These characteristics have potential to evaluate the symptoms of PD.

Interactive rhythmic cue facilitates gait relearning in patients with Parkinson's disease.

To develop a method for cooperative human gait training, we investigated whether interactive rhythmic cues could improve the gait performance of Parkinson's disease patients. The interactive rhythmic cues ware generated based on the mutual entrainment between the patient's gait rhythms and the cue rhythms input to the patient while the patient walked. Previously, we found that the dynamic characteristics of stride interval fluctuation in Parkinson's disease patients were improved to a healthy 1/f fluctuation level using interactive rhythmic cues and that this effect was maintained in the short term. However, two problems remained in our previous study. First, it was not clear whether the key factor underpinning the effect was the mutual entrainment between the gait rhythms and the cue rhythms or the rhythmic cue fluctuation itself. Second, it was not clear whether or not the gait restoration was maintained longitudinally and was relearned after repeating the cue-based gait training. Thus, the present study clarified these issues using 32 patients who participated in a four-day experimental program. The patients were assigned randomly to one of four experimental groups with the following rhythmic cues: (a) interactive rhythmic cue, (b) fixed tempo cue, (c) 1/f fluctuating tempo cue, and (d) no cue. It has been reported that the 1/f fluctuation of stride interval in healthy gait is absent in Parkinson's disease patients. Therefore, we used this dynamic characteristic as an evaluation index to analyze gait relearning in the four different conditions. We observed a significant effect in condition (a) that the gait fluctuation of the patients gradually returned to a healthy 1/f fluctuation level, whereas this did not occur in the other conditions. This result suggests that the mutual entrainment can facilitate gait relearning effectively. It is expected that interactive rhythmic cues will be widely applicable in the fields of rehabilitation and assistive technology.

Interactive rhythmic auditory stimulation reinstates natural 1/f timing in gait of Parkinson's patients.

Parkinson's disease (PD) and basal ganglia dysfunction impair movement timing, which leads to gait instability and falls. Parkinsonian gait consists of random, disconnected stride times--rather than the 1/f structure observed in healthy gait--and this randomness of stride times (low fractal scaling) predicts falling. Walking with fixed-tempo Rhythmic Auditory Stimulation (RAS) can improve many aspects of gait timing; however, it lowers fractal scaling (away from healthy 1/f structure) and requires attention. Here we show that interactive rhythmic auditory stimulation reestablishes healthy gait dynamics in PD patients. In the experiment, PD patients and healthy participants walked with a) no auditory stimulation, b) fixed-tempo RAS, and c) interactive rhythmic auditory stimulation. The interactive system used foot sensors and nonlinear oscillators to track and mutually entrain with the human's step timing. Patients consistently synchronized with the interactive system, their fractal scaling returned to levels of healthy participants, and their gait felt more stable to them. Patients and healthy participants rarely synchronized with fixed-tempo RAS, and when they did synchronize their fractal scaling declined from healthy 1/f levels. Five minutes after removing the interactive rhythmic stimulation, the PD patients' gait retained high fractal scaling, suggesting that the interaction stabilized the internal rhythm generating system and reintegrated timing networks. The experiment demonstrates that complex interaction is important in the (re)emergence of 1/f structure in human behavior and that interactive rhythmic auditory stimulation is a promising therapeutic tool for improving gait of PD patients.