Recovery time from VR sickness due to susceptibility: Objective and quantitative evaluation using electroencephalography.

With the increasing use of virtual reality (VR) devices, interest in reducing their negative effects, such as VR sickness, is also increasing. This study used electroencephalography (EEG) to investigate participants' VR sickness recovery time after watching a VR video. We tested 40 participants in advance using a motion sickness susceptibility questionnaire (MSSQ). We classified the participants into two groups (sensitive group/non-sensitive group) depending on their MSSQ scores. We used a simulator sickness questionnaire (SSQ) and EEG to evaluate VR sickness. The SSQ score increased significantly after watching the VR sickness-inducing video (VR video) in both groups (p < 0.001). The recovery time based on the SSQ was 11.3 ± 6.6 min for the sensitive group and 9.1 ± 5.2 min for the non-sensitive group. The difference in recovery time between the two groups was not significant (p > 0.05). EEG results showed that recovery time took an average of 11.5 ± 7.1 min in both groups. The EEG data showed that the delta wave increased significantly across all brain areas (p < 0.01). There was no statistical difference between groups in recovering VR sickness depending on individual characteristics. However, we confirmed that subjective and objective VR recovery required at least 11.5 min. This finding can inform recommendations regarding the VR sickness recovery times.

Detection of the 40 Hz auditory steady-state response with optically pumped magnetometers.

Magnetoencephalography (MEG) is a functional neuroimaging technique that noninvasively detects the brain magnetic field from neuronal activations. Conventional MEG measures brain signals using superconducting quantum interference devices (SQUIDs). SQUID-MEG requires a cryogenic environment involving a bulky non-magnetic Dewar flask and the consumption of liquid helium, which restricts the variability of the sensor array and the gap between the cortical sources and sensors. Recently, miniature optically pumped magnetometers (OPMs) have been developed and commercialized. OPMs do not require cryogenic cooling and can be placed within millimeters from the scalp. In the present study, we arranged six OPM sensors on the temporal area to detect auditory-related brain responses in a two-layer magnetically shielded room. We presented the auditory stimuli of 1 kHz pure-tone bursts with 200 ms duration and obtained the M50 and M100 components of auditory-evoked fields. We delivered the periodic stimuli with a 40 Hz repetition rate and observed the gamma-band power changes and inter-trial phase coherence of auditory steady-state responses at 40 Hz. We found that the OPM sensors have a performance comparable to that of conventional SQUID-MEG sensors, and our results suggest the feasibility of using OPM sensors for functional neuroimaging and brain-computer interface applications.

Electroencephalogram microstates and functional connectivity of cybersickness.

Virtual reality (VR) is a rapidly developing technology that simulates the real world. However, for some cybersickness-susceptible people, VR still has an unanswered problem-cybersickness-which becomes the main obstacle for users and content makers. Sensory conflict theory is a widely accepted theory for cybersickness. It proposes that conflict between afferent signals and internal models can cause cybersickness. This study analyzes the brain states that determine cybersickness occurrence and related uncomfortable feelings. Furthermore, we use the electroencephalogram (EEG) microstates and functional connectivity approach based on the sensory conflict theory. The microstate approach is a time-space analysis method that allows signals to be divided into several temporarily stable states, simultaneously allowing for the exploration of short- and long-range signals. These temporal dynamics can show the disturbances in mental processes associated with neurological and psychiatric conditions of cybersickness. Furthermore, the functional connectivity approach gives us in-depth insight and relationships between the sources related to cybersickness. We recruited 40 males (24.1 ± 2.3 years), and they watched a VR video on a curved computer monitor for 10 min to experience cybersickness. We recorded the 5-min resting state EEG (baseline condition) and 10-min EEG while watching the VR video (task condition). Then, we performed a microstate analysis, focusing on two temporal parameters: mean duration and global explained variance (GEV). Finally, we obtained the functional connectivity data using eLoreta and lagged phase synchronization (LPS). We discovered five sets of microstates (A-E), including four widely reported canonical microstates (A-D), during baseline and task conditions. The average duration increased in microstates A and B, which is related to the visual and auditory networks. The GEV and duration decreased in microstate C, whereas those in microstate D increased. Microstate C is related to the default mode network (DMN) and D to the attention network. The temporal dynamics of the microstate parameters are from cybersickness disturbing the sensory, DMN, and attention networks. In the functional connectivity part, the LPS between the left and right parietal operculum (OP) significantly decreased (p < 0.05) compared with the baseline condition. Furthermore, the connectivity between the right OP and V5 significantly decreased (p < 0.05). These results also support the disturbance of the sensory network because a conflict between the visual (V5) and vestibular system (OP) causes cybersickness. Changes in the microstates and functional connectivity support the sensory conflict theory. These results may provide additional information in understanding brain dynamics during cybersickness.

EEG Dataset for RSVP and P300 Speller Brain-Computer Interfaces.

As attention to deep learning techniques has grown, many researchers have attempted to develop ready-to-go brain-computer interfaces (BCIs) that include automatic processing pipelines. However, to do so, a large and clear dataset is essential to increase the model's reliability and performance. Accordingly, our electroencephalogram (EEG) dataset for rapid serial visual representation (RSVP) and P300 speller may contribute to increasing such BCI research. We validated our dataset with respect to features and accuracy. For the RSVP, the participants (N = 50) achieved about 92% mean target detection accuracy. At the feature level, we observed notable ERPs (at 315 ms in the RSVP; at 262 ms in the P300 speller) during target events compared to non-target events. Regarding P300 speller performance, the participants (N = 55) achieved about 92% mean accuracy. In addition, P300 speller performance over trial repetitions up to 15 was explored. The presented dataset could potentially improve P300 speller applications. Further, it may be used to evaluate feature extraction and classification algorithm effectively, such as for cross-subjects/cross-datasets, and even for the cross-paradigm BCI model.

Estimating objective (EEG) and subjective (SSQ) cybersickness in people with susceptibility to motion sickness.

Cybersickness refers to the uncomfortable side effects, such as headaches, dizziness, and nausea, felt while experiencing virtual reality (VR). This study investigated cybersickness in people with sensitivity to motion sickness using electroencephalography (EEG), the Simulator Sickness Questionnaire (SSQ), and simple VR content. Based on the scores from the Motion Sickness Susceptibility Questionnaire (MSSQ), 40 males in their twenties were selected as the sensitive group (n = 20) and non-sensitive group (n = 20). The experiment contained two conditions: a baseline condition representing a resting state and a cybersickness condition in which watching VR content induced cybersickness. The SSQ score increased significantly after watching the VR content in both groups. The sensitive group showed significantly lower absolute power in the beta and gamma bands than the non-sensitive group. The cybersickness condition showed significantly increased delta and decreased alpha compared to the baseline condition. We evaluated EEG and SSQ to identify subjective symptoms and objective physiological changes associated with cybersickness.

Selective Subject Pooling Strategy to Improve Model Generalization for a Motor Imagery BCI.

Brain-computer interfaces (BCIs) facilitate communication for people who cannot move their own body. A BCI system requires a lengthy calibration phase to produce a reasonable classifier. To reduce the duration of the calibration phase, it is natural to attempt to create a subject-independent classifier with all subject datasets that are available; however, electroencephalogram (EEG) data have notable inter-subject variability. Thus, it is very challenging to achieve subject-independent BCI performance comparable to subject-specific BCI performance. In this study, we investigate the potential for achieving better subject-independent motor imagery BCI performance by conducting comparative performance tests with several selective subject pooling strategies (i.e., choosing subjects who yield reasonable performance selectively and using them for training) rather than using all subjects available. We observed that the selective subject pooling strategy worked reasonably well with public MI BCI datasets. Finally, based upon the findings, criteria to select subjects for subject-independent BCIs are proposed here.

A Systematic Review of Closed-Loop Feedback Techniques in Sleep Studies-Related Issues and Future Directions.

Advances in computer processing technology have enabled researchers to analyze real-time brain activity and build real-time closed-loop paradigms. In many fields, the effectiveness of these closed-loop protocols has proven to be better than that of the simple open-loop paradigms. Recently, sleep studies have attracted much attention as one possible application of closed-loop paradigms. To date, several studies that used closed-loop paradigms have been reported in the sleep-related literature and recommend a closed-loop feedback system to enhance specific brain activity during sleep, which leads to improvements in sleep's effects, such as memory consolidation. However, to the best of our knowledge, no report has reviewed and discussed the detailed technical issues that arise in designing sleep closed-loop paradigms. In this paper, we reviewed the most recent reports on sleep closed-loop paradigms and offered an in-depth discussion of some of their technical issues. We found 148 journal articles strongly related with 'sleep and stimulation' and reviewed 20 articles on closed-loop feedback sleep studies. We focused on human sleep studies conducting any modality of feedback stimulation. Then we introduced the main component of the closed-loop system and summarized several open-source libraries, which are widely used in closed-loop systems, with step-by-step guidelines for closed-loop system implementation for sleep. Further, we proposed future directions for sleep research with closed-loop feedback systems, which provide some insight into closed-loop feedback systems.

Super-Resolution for Improving EEG Spatial Resolution using Deep Convolutional Neural Network-Feasibility Study.

Electroencephalography (EEG) has relatively poor spatial resolution and may yield incorrect brain dynamics and distort topography; thus, high-density EEG systems are necessary for better analysis. Conventional methods have been proposed to solve these problems, however, they depend on parameters or brain models that are not simple to address. Therefore, new approaches are necessary to enhance EEG spatial resolution while maintaining its data properties. In this work, we investigated the super-resolution (SR) technique using deep convolutional neural networks (CNN) with simulated EEG data with white Gaussian and real brain noises, and experimental EEG data obtained during an auditory evoked potential task. SR EEG simulated data with white Gaussian noise or brain noise demonstrated a lower mean squared error and higher correlations with sensor information, and detected sources even more clearly than did low resolution (LR) EEG. In addition, experimental SR data also demonstrated far smaller errors for N1 and P2 components, and yielded reasonable localized sources, while LR data did not. We verified our proposed approach's feasibility and efficacy, and conclude that it may be possible to explore various brain dynamics even with a small number of sensors.

P300 Speller Performance Predictor Based on RSVP Multi-feature.

Brain-computer interface (BCI) systems were developed so that people can control computers or machines through their brain activity without moving their limbs. The P300 speller is one of the BCI applications used most commonly, as is very simple and reliable and can achieve satisfactory performance. However, like other BCIs, the P300 speller still has room for improvements in terms of its practical use, for example, selecting the best compromise between spelling accuracy and information transfer rate (ITR; speed) so that the P300 speller can maintain high accuracy while increasing spelling speed. Therefore, seeking correlates of, and predicting, the P300 speller's performance is necessary to understand and improve the technique. In this work, we investigated the correlations between rapid serial visual presentation (RSVP) task features and the P300 speller's performance. Fifty-five subjects participated in the RSVP and conventional matrix P300 speller tasks and RSVP behavioral and electroencephalography (EEG) features were compared in the P300's speller performance. We found that several of the RSVP's event-related potential (ERP) and behavioral features were correlated with the P300 speller's offline binary classification accuracy. Using these features, we propose a simple multi-feature performance predictor (r = 0.53, p = 0.0001) that outperforms any single feature performance predictor, including that of the conventional RSVP T1% predictor (r = 0.28, p = 0.06). This result demonstrates that selective multi-features can predict BCI performance better than a single feature alone.

Interbrain phase synchronization during turn-taking verbal interaction-a hyperscanning study using simultaneous EEG/MEG.

Recently, neurophysiological findings about social interaction have been investigated widely, and hardware has been developed that can measure multiple subjects' brain activities simultaneously. These hyperscanning studies have enabled us to discover new and important evidences of interbrain interactions. Yet, very little is known about verbal interaction without any visual input. Therefore, we conducted a new hyperscanning study based on verbal, interbrain turn-taking interaction using simultaneous EEG/MEG, which measures rapidly changing brain activities. To establish turn-taking verbal interactions between a pair of subjects, we set up two EEG/MEG systems (19 and 146 channels of EEG and MEG, respectively) located ∼100 miles apart. Subjects engaged in verbal communication via condenser microphones and magnetic-compatible earphones, and a network time protocol synchronized the two systems. Ten subjects participated in this experiment and performed verbal interaction and noninteraction tasks separately. We found significant oscillations in EEG alpha and MEG alpha/gamma bands in several brain regions for all subjects. Furthermore, we estimated phase synchronization between two brains using the weighted phase lag index and found statistically significant synchronization in EEG and MEG data. Our novel paradigm and neurophysiological findings may foster a basic understanding of the functional mechanisms involved in human social interactions. Hum Brain Mapp 39:171-188, 2018. © 2017 Wiley Periodicals, Inc.

Cognitive responses and cortical oscillatory processing at various stereoscopic depths - a simultaneous EEG/MEG study.

Due to the recent explosion in various forms of 3D content, the evaluation of such content from a neuroscience perspective is quite interesting. However, existing investigations of cortical oscillatory responses in stereoscopic depth perception are quite rare. Therefore, we investigated spatiotemporal and spatio-temporo-spectral features at four different stereoscopic depths within the comfort zone. We adopted a simultaneous EEG/MEG acquisition technique to collect the oscillatory responses of eight participants. We defined subject-specific retinal disparities and designed a single trial-based stereoscopic viewing experimental paradigm. In the group analysis, we observed that, as the depth increased from Level 1 to Level 3, there was a time-locked increase in the N200 component in MEG and the P300 component in EEG in the occipital and parietal areas, respectively. In addition, initial alpha and beta event-related desynchronizations (ERD) were observed at approximately 500 to 1000 msec, while theta, alpha, and beta event-related synchronizations (ERS) appeared at approximately 1000 to 2000 ms. Interestingly, there was a saturation point in the increase in cognitive responses, including N200, P300, and alpha ERD, even when the depth increased only within the comfort zone. Meanwhile, the magnitude of low beta ERD decreased in the dorsal pathway as depth increased. From these findings, we concluded that cognitive responses are likely to become saturated in the visual comfort zone, while perceptual load may increase with depth.

EEG datasets for motor imagery brain-computer interface.

Background: Most investigators of brain-computer interface (BCI) research believe that BCI can be achieved through induced neuronal activity from the cortex, but not by evoked neuronal activity. Motor imagery (MI)-based BCI is one of the standard concepts of BCI, in that the user can generate induced activity by imagining motor movements. However, variations in performance over sessions and subjects are too severe to overcome easily; therefore, a basic understanding and investigation of BCI performance variation is necessary to find critical evidence of performance variation. Here we present not only EEG datasets for MI BCI from 52 subjects, but also the results of a psychological and physiological questionnaire, EMG datasets, the locations of 3D EEG electrodes, and EEGs for non-task-related states. Findings: We validated our EEG datasets by using the percentage of bad trials, event-related desynchronization/synchronization (ERD/ERS) analysis, and classification analysis. After conventional rejection of bad trials, we showed contralateral ERD and ipsilateral ERS in the somatosensory area, which are well-known patterns of MI. Finally, we showed that 73.08% of datasets (38 subjects) included reasonably discriminative information. Conclusions: Our EEG datasets included the information necessary to determine statistical significance; they consisted of well-discriminated datasets (38 subjects) and less-discriminative datasets. These may provide researchers with opportunities to investigate human factors related to MI BCI performance variation, and may also achieve subject-to-subject transfer by using metadata, including a questionnaire, EEG coordinates, and EEGs for non-task-related states.

Cortical Responses and Shape Complexity of Stereoscopic Image - A Simultaneous EEG/MEG Study.

BACKGROUND/AIMS: In exploring human factors, stereoscopic 3D images have been used to investigate the neural responses associated with excessive depth, texture complexity, and other factors. However, the cortical oscillation associated with the complexity of stereoscopic images has been studied rarely. Here, we demonstrated that the oscillatory responses to three differently shaped 3D images (circle, star, and bat) increase as the complexity of the image increases. METHODS: We recorded simultaneous EEG/MEG for three different stimuli. Spatio-temporal and spatio-spectro-temporal features were investigated by non-parametric permutation test. RESULTS: The results showed that N300 and alpha inhibition increased in the ventral area as the shape complexity of the stereoscopic image increased. CONCLUSION: It seems that the relative disparity in complex stereoscopic images may increase cognitive processing (N300) and cortical load (alpha inhibition) in the ventral area.

Herbal Extracts That Reduce Ocular Oxidative Stress May Enhance Attentive Performance in Humans.

We used herbal extracts in this study to investigate the effects of blue-light-induced oxidative stress on subjects' attentive performance, which is also associated with work performance. We employed an attention network test (ANT) to measure the subjects' work performance indirectly and used herbal extracts to reduce ocular oxidative stress. Thirty-two subjects participated in either an experimental group (wearing glasses containing herbal extracts) or a control group (wearing glasses without herbal extracts). During the ANT experiment, we collected electroencephalography (EEG) and electrooculography (EOG) data and measured button responses. In addition, electrocardiogram (ECG) data were collected before and after the experiments. The EOG results showed that the experimental group exhibited a reduced number of eye blinks per second during the experiment and faster button responses with a smaller variation than did the control group; this group also showed relatively more sustained tension in their ECG results. In the EEG analysis, the experimental group had significantly greater cognitive processing, with larger P300 and parietal 2-6 Hz activity, an orienting effect with neural processing of frontal area, high beta activity in the occipital area, and an alpha and beta recovery process after the button response. We concluded that reducing blue-light-induced oxidative stress with herbal extracts may be associated with reducing the number of eye blinks and enhancing attentive performance.