Classification of Emotional and Immersive Outcomes in the Context of Virtual Reality Scene Interactions

The constantly evolving technological landscape of the Metaverse has introduced a significant concern: cybersickness (CS). There is growing academic interest in detecting and mitigating these adverse effects within virtual environments (VEs). However, the development of effective methodologies in this field has been hindered by the lack of sufficient benchmark datasets. In pursuit of this objective, we meticulously compiled a comprehensive dataset by analyzing the impact of virtual reality (VR) environments on CS, immersion levels, and EEG-based emotion estimation. Our dataset encompasses both implicit and explicit measurements. Implicit measurements focus on brain signals, while explicit measurements are based on participant questionnaires. These measurements were used to collect data on the extent of cybersickness experienced by participants in VEs. Using statistical methods, we conducted a comparative analysis of CS levels in VEs tailored for specific tasks and their immersion factors. Our findings revealed statistically significant differences between VEs, highlighting crucial factors influencing participant engagement, engrossment, and immersion. Additionally, our study achieved a remarkable classification performance of 96.25% in distinguishing brain oscillations associated with VR scenes using the multi-instance learning method and 95.63% in predicting emotions within the valence-arousal space with four labels. The dataset presented in this study holds great promise for objectively evaluating CS in VR contexts, differentiating between VEs, and providing valuable insights for future research endeavors.

Exploring neurophysiological correlates of visually induced motion sickness using electroencephalography (EEG)

Visually induced motion sickness (VIMS) is a common phenomenon when using visual devices such as smartphones and virtual reality applications, with symptoms including nausea, fatigue, and headache. To date, the neuro-cognitive processes underlying VIMS are not fully understood. Previous studies using electroencephalography (EEG) delivered mixed findings, with some reporting an increase in delta and theta power, and others reporting increases in alpha and beta frequencies. The goal of the study was to gain further insight into EEG correlates for VIMS. Participants viewed a VIMS-inducing visual stimulus, composed of moving black-and-white vertical bars presented on an array of three adjacent monitors. The EEG was recorded during visual stimulation and VIMS ratings were recorded after each trial using the Fast Motion Sickness Scale. Time-frequency analyses were conducted comparing neural activity of participants reporting minimal VIMS (n = 21) and mild-moderate VIMS (n = 12). Results suggested a potential increase in delta power in the centro-parietal regions (CP2) and a decrease in alpha power in the central regions (Cz) for participants experiencing mild-moderate VIMS compared to those with minimal VIMS. Event-related spectral perturbations (ERSPs) suggested that group differences in EEG activity developed with increasing duration of a trial. These results support the hypothesis that the EEG might be sensitive to differences in information processing in VIMS and minimal VIMS contexts, and indicate that it may be possible to identify neurophysiological correlate of VIMS. Differences in EEG activity related to VIMS may reflect differential processing of conflicting visual and vestibular sensory information.

Brain activity differences between susceptible and non-susceptible populations under visually induced motion sickness based on sensor-space and source-space analyses

The neural mechanisms underlying visually induced motion sickness (VIMS) in different susceptible populations are unclear, as it is not clear how brain activity changes in different susceptible populations during the vection section (VS). This study aimed to analyze the brain activity changes in different susceptible populations during VS. Twenty subjects were included in this study and divided into the VIMS-susceptible group (VIMSSG) and VIMS-resistant group (VIMSRG) based on a motion sickness questionnaire. 64-channel electroencephalogram (EEG) data from these subjects during VS were collected. The brain activities during VS for VIMSSG and VIMSRG were analyzed with time-frequency based sensor-space analysis and EEG source imaging based source-space analysis. Under VS, delta and theta energies were significantly increased in VIMSSG and VIMSRG, while alpha and beta energies were only significantly increased in VIMSRG. Also, the superior and middle temporal were activated in VIMSSG and VIMSRG, while lateral occipital, supramarginal gyrus, and precentral gyrus were activated only in VIMSSG. The spatiotemporal differences in brain activity observed between VIMSSG and VIMSRG may be attributed to the different susceptibility of participants in each group and the different severity of MS symptoms experienced. Long-term vestibular training can effectively improve the ability of anti-VIMS. The knowledge gained from this study helps advance understanding of the neural mechanism of VIMS in different susceptible populations.

Assessing Passengers' Motion Sickness Levels Based on Cerebral Blood Oxygen Signals and Simulation of Actual Ride Sensation

(1) Background: After motion sickness occurs in the ride process, this can easily cause passengers to have a poor mental state, cold sweats, nausea, and even vomiting symptoms. This study proposes to establish an association model between motion sickness level (MSL) and cerebral blood oxygen signals during a ride. (2) Methods: A riding simulation platform and the functional near-infrared spectroscopy (fNIRS) technology are utilized to monitor the cerebral blood oxygen signals of subjects in a riding simulation experiment. The subjects' scores on the Fast Motion sickness Scale (FMS) are determined every minute during the experiment as the dependent variable to manifest the change in MSL. The Bayesian ridge regression (BRR) algorithm is applied to construct an assessment model of MSL during riding. The score of the Graybiel scale is adopted to preliminarily verify the effectiveness of the MSL evaluation model. Finally, a real vehicle test is developed, and two driving modes are selected in random road conditions to carry out a control test. (3) Results: The predicted MSL in the comfortable mode is significantly less than the MSL value in the normal mode, which is in line with expectations. (4) Conclusions: Changes in cerebral blood oxygen signals have a huge correlation with MSL. The MSL evaluation model proposed in this study has a guiding significance for the early warning and prevention of motion sickness.

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.

Neural networks involved in nausea in adult humans: A systematic review

Nausea is a common clinical symptom, poorly managed with anti-emetic drugs. To identify potential brain regions which may be therapeutic targets we systematically reviewed brain imaging in subjects reporting nausea. The systematic review followed PRISMA statements with methodological quality (MINORS) and risk of bias (ROBINS-I) assessed. Irrespective of the nauseagenic stimulus the common (but not only) cortical structures activated were the inferior frontal gyrus (IFG), the anterior cingulate cortex (ACC) and the anterior insula (AIns) with some evidence for lateralization (Left-IFG, Right-AIns, Right-ACC). Basal ganglia structures (e.g., putamen) were also consistently activated. Inactivation was rarely reported but occurred mainly in the cerebellum and occipital lobe. During nausea, functional connectivity increased, mainly between the posterior and mid- cingulate cortex. Limitations include, a paucity of studies and stimuli, subject demographics, inconsistent definition and measurement of nausea. Structures implicated in nausea are discussed in the context of knowledge of central pathways for interoception, emotion and autonomic control. Comparisons are made between nausea and other aversive sensations as multimodal aversive conscious experiences.

Machine learning methods for the study of cybersickness: a systematic review

This systematic review offers a world-first critical analysis of machine learning methods and systems, along with future directions for the study of cybersickness induced by virtual reality (VR). VR is becoming increasingly popular and is an important part of current advances in human training, therapies, entertainment, and access to the metaverse. Usage of this technology is limited by cybersickness, a common debilitating condition experienced upon VR immersion. Cybersickness is accompanied by a mix of symptoms including nausea, dizziness, fatigue and oculomotor disturbances. Machine learning can be used to identify cybersickness and is a step towards overcoming these physiological limitations. Practical implementation of this is possible with optimised data collection from wearable devices and appropriate algorithms that incorporate advanced machine learning approaches. The present systematic review focuses on 26 selected studies. These concern machine learning of biometric and neuro-physiological signals obtained from wearable devices for the automatic identification of cybersickness. The methods, data processing and machine learning architecture, as well as suggestions for future exploration on detection and prediction of cybersickness are explored. A wide range of immersion environments, participant activity, features and machine learning architectures were identified. Although models for cybersickness detection have been developed, literature still lacks a model for the prediction of first-instance events. Future research is pointed towards goal-oriented data selection and labelling, as well as the use of brain-inspired spiking neural network models to achieve better accuracy and understanding of complex spatio-temporal brain processes related to cybersickness.

EEG-based analysis of various sensory stimulation effects to reduce visually induced motion sickness in virtual reality

The use of virtual reality (VR) is frequently accompanied by motion sickness, and approaches for preventing it are not yet well established. We explored the effects of synchronized presentations of sound and motion on visually induced motion sickness (VIMS) in order to reduce VIMS. A total of 25 participants bicycle riding for 5 min with or without sound and motion synchronization presented on a head-mounted display. As a result, the VIMS scores measured by the fast motion sickness scale and simulator sickness questionnaire were significantly lower in the participants who experienced the riding scene with sound and motion than those who experienced the riding scene with sound only, motion only, or neither. Furthermore, analysis of the EEG signal showed that the higher the VIMS, the significant increase in alpha and theta waves in the parietal and occipital lobes. Therefore, we demonstrate that the simultaneous presentation of sound and motion, closely associated with synchronous and visual flow speed, is effective in reducing VIMS while experiencing simulated bicycle riding in a VR environment.

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.

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.

EEG-based evaluation of motion sickness and reducing sensory conflict in a simulated autonomous driving environment

Autonomous driving offers significant potential for changes in the automotive industry. However, sensory conflict during autonomous driving can lead to motion sickness. Quantitative evaluation and effective preventions to predict and reduce motion sickness are needed. The goal of this study is to verify the objective indicator of motion sickness level based on encephalography (EEG) that we proposed before and investigate the influence of attenuating sensory conflict on motion sickness. A 6-degree of freedom (DOF) driving simulator platform was used to provide an autonomous driving environment to the subjects, and the subjective motion sickness level (MSL), as well as the EEG signals of 15 healthy subjects, were collected simultaneously during 3 conditions, i) autonomous driving, ii) autonomous driving with eyes blindfolded and iii) active driving. The MSLs were reported by the subjects every two minutes, providing a reference to the recorded EEG signals. The EEG signals were analyzed and compared among different conditions. Average MSLs were higher in autonomous driving than in autonomous driving with eyes blindfolded and active driving, together with the increase of the mean EEG frequency of theta band in the central, parietal and occipital areas (FC5, Cz, CP5, P3, and POz). These findings validated that EEG mean frequency of theta band could be an indicator of motion sickness, besides an attenuated visual input or active control of the vehicle can effectively reduce the generation of motion sickness.

Multi-Dimensional and Objective Assessment of Motion Sickness Susceptibility Based on Machine Learning

Background

As human transportation, recreation, and production methods change, the impact of motion sickness (MS) on humans is becoming more prominent. The susceptibility of people to MS can be accurately assessed, which will allow ordinary people to choose comfortable transportation and entertainment and prevent people susceptible to MS from entering provocative environments. This is valuable for maintaining public health and the safety of tasks.

Objective

To develop an objective multi-dimensional MS susceptibility assessment model based on physiological indicators that objectively reflect the severity of MS and provide a reference for improving the existing MS susceptibility assessment methods.

Methods

MS was induced in 51 participants using the Coriolis acceleration stimulation. Some portable equipment were used to digitize the typical clinical manifestations of MS and explore the correlations between them and Graybiel's diagnostic criteria. Based on significant objective parameters and selected machine learning (ML) algorithms, several MS susceptibility assessment models were developed, and their performances were compared.

Results

Gastric electrical activity, facial skin color, skin temperature, and nystagmus are related to the severity of MS. Among the ML assessment models based on these variables, the support vector machine classifier had the best performance with an accuracy of 88.24%, sensitivity of 91.43%, and specificity of 81.25%.

Conclusion

The severity of symptoms and signs of MS can be objectively quantified using some indicators. Multi-dimensional and objective assessment models for MS susceptibility based on ML can be successfully established.

Changes in Electroencephalography Activity of Sensory Areas Linked to Car Sickness in Real Driving Conditions

Car sickness is a major concern for car passengers, and with the development of autonomous vehicles, increasing numbers of car occupants are likely to be affected. Previous laboratory studies have used EEG measurements to better understand the cerebral changes linked to symptoms. However, the dynamics of motion in labs/simulators differ from those of a real car. This study sought to identify specific cerebral changes associated with the level of car sickness experienced in real driving conditions. Nine healthy volunteers participated as front passengers in a slalom session inducing lateral movements at very low frequency (0.2 Hz). They were continuously monitored via EEG recordings and subjectively rated their level of symptoms after each slalom, using a 5-point likert scale. Car-sickness symptoms evolved concomitantly with changes in theta and alpha power in the occipital and parietal areas. These changes may reflect altered sensory integration, as well as a possible influence of sleepiness mitigating symptoms.

Detection of unrecognized spatial disorientation: A theoretical perspective

BACKGROUND:

Spatial disorientation (SD) is a problem that pilots often encounter during a flight. One reason for this problem is that among the three types of SD, there is no validated method to detect the Type I (unrecognized) SD.

OBJECTIVE:

In this pursuit, initially we reviewed the problems and the evaluation methods of associated with SD. Subsequently, we discussed the advantages and disadvantages of the subjective questionnaire evaluation method and the behavior evaluation method.

METHODS:

On the basis of these analyses, we proposed a method to detect the unrecognized SD that improved the assessment of SD to a significant extent. We developed a new direction to study the unrecognized SD based on the subjective report and the center of pressure (CoP).

RESULTS:

The proposed evaluation method can assist the pilots to understand the feelings and physical changes, when exposed to unrecognized SD.

CONCLUSION:

We hope that this evaluation method can provide a strong support in developing a countermeasure against the unrecognized SD and fundamentally solve the severe flight accidents arising due to them.

Mismatch of Visual-Vestibular Information in Virtual Reality: Is Motion Sickness Part of the Brains Attempt to Reduce the Prediction Error?

Visually induced motion sickness (VIMS) is a relevant limiting factor in the use of virtual reality (VR) devices. Understanding the origin of this problem might help to develop strategies to circumvent this limitation. Previous studies have attributed VIMS to a mismatch between visual, and vestibular information, causing ambiguity of the position of the body in relation to its surrounding. Studies using EEG have shown a shift of the power spectrum to lower frequencies while VIMS is experienced. However, little is known about the relationship between the intensity of the VIMS and the changes in these power spectra. Moreover, the effect of different varieties of VIMS on the causal relationship between brain areas is largely unknown. Here, we used EEG to study 14 healthy subjects in a VR environment who were exposed to increasing levels of mismatch between vestibular and visual information. The frequency power and the bivariate transfer entropy as a measure for the information transfer were calculated. We found a direct association between increasing mismatch levels and subjective VIMS. With increasing VIMS, the proportion of slow EEG waves (especially 1–10 Hz) increases, especially in temporo-occipital regions. Furthermore, we found a general decrease in the information flow in most brain areas but especially in brain areas involved in the processing of vestibular signals and the detection of self-motion. We hypothesize that the general shift of frequency power and the decrease in information flow while experiencing high intensity VIMS represent a brain state of a reduced ability to receive, transmit and process information. We further hypothesize that the mechanism of reduced information flow is a general reaction of the brain to an unresolvable mismatch of information. This reaction aims on transforming a currently unstable model with a high prediction error into a stable model in an environment of minimal contradictory information.

Stress Assessment of Vestibular Endurance Training for Civil Aviation Flight Students Based on EEG

Objective: The main goal of this study is to clarify the electroencephalogram (EEG) characteristics of the stress response caused by vestibular endurance training under real conditions.

Methods: Ten pilot trainees received a series of acute anti-vertigo training stimulations on the rotary ladder while recording electroencephalographic data (64 electrodes). Afterward, the anti-vertigo ability of the subject was tested for the best performance after 1 month of training and verified whether it is related to the EEG signals we collected before.

Results: (1) The absolute power of α waves in the C3 and C4 regions is the same as the difference between 1 min before and 2 min after stimulation, and their activity is enhanced by stimulation. Otherwise, the activation of the C3 region after 5 min of stimulation is still significantly changed. (2) Through Spearman's correlation analysis, we found that the α waves in the C3 and C4 the greater the power change, the better the performance of the subject in the proficient stage.

Conclusion: C3 and C4 areas are specific brain regions of the stress response of anti-vertigo endurance training, and the absolute power of the α wave can be used as a parameter for identifying the degree of motion sickness (MS). The absolute power changes of α waves in the C3 and C4 areas are positively correlated with their anti-vertigo potential.

Significance: Increasing the absolute power of α wave in the C3 and C4 is a manifestation of MS stress adaptability.

Non-Contact Measurement of Motion Sickness Using Pupillary Rhythms from an Infrared Camera

Both physiological and neurological mechanisms are reflected in pupillary rhythms via neural pathways between the brain and pupil nerves. This study aims to interpret the phenomenon of motion sickness such as fatigue, anxiety, nausea and disorientation using these mechanisms and to develop an advanced non-contact measurement method from an infrared webcam. Twenty-four volunteers (12 females) experienced virtual reality content through both two-dimensional and head-mounted device interpretations. An irregular pattern of the pupillary rhythms, demonstrated by an increasing mean and standard deviation of pupil diameter and decreasing pupillary rhythm coherence ratio, was revealed after the participants experienced motion sickness. The motion sickness was induced while watching the head-mounted device as compared to the two-dimensional virtual reality, with the motion sickness strongly related to the visual information processing load. In addition, the proposed method was verified using a new experimental dataset for 23 participants (11 females), with a classification performance of 89.6% (n = 48) and 80.4% (n = 46) for training and test sets using a support vector machine with a radial basis function kernel, respectively. The proposed method was proven to be capable of quantitatively measuring and monitoring motion sickness in real-time in a simple, economical and contactless manner using an infrared camera.

Test-retest reliability of the virtual reality sickness evaluation using electroencephalography (EEG)

No reliable quantitative and objective measurement method for virtual reality (VR) sickness has been firmly established to date. Electroencephalography (EEG) may be a strong candidate to evaluate VR sickness objectively. However, no test-retest evaluation has been made for VR sickness using EEG. To recruit VR sickness-sensitive participants, we tested 858 participants (age = 20's-50's) using the Motion Sickness Susceptibility Questionnaire (MSSQ). Among them, we recruited 21 males (average age = 25.0) who obtained the 75th percentile of scores on the MSSQ (32.9 ± 5.7). VR sickness was evaluated twice (one week apart) using EEG with VR video content designed to cause VR sickness. A Simulation Sickness Questionnaire (SSQ) was also used to evaluate VR sickness. In terms of the reliability of EEG, ICC and Cronbach's alpha analyses showed that three waves (delta, theta, and alpha) were consistent in two areas (frontal and central). A significant difference in EEG was also found repeatedly between the baseline and VR sickness (delta, theta, and alpha) in two areas (frontal and central). We evaluated EEG for its reliability and found specific waves and areas that showed good consistency and significant changes associated with VR sickness. These findings may support further research of VR sickness evaluation.

Temporal Dynamics of Visually Induced Motion Perception and Neural Evidence of Alterations in the Motion Perception Process in an Immersive Virtual Reality Environment

Even though reciprocal inhibitory vestibular interactions following visual stimulation have been understood as sensory-reweighting mechanisms to stabilize motion perception; this hypothesis has not been thoroughly investigated with temporal dynamic measurements. Recently, virtual reality technology has been implemented in different medical domains. However, exposure in virtual reality environments can cause discomfort, including nausea or headache, due to visual-vestibular conflicts. We speculated that self-motion perception could be altered by accelerative visual motion stimulation in the virtual reality situation because of the absence of vestibular signals (visual-vestibular sensory conflict), which could result in the sickness. The current study investigated spatio-temporal profiles for motion perception using immersive virtual reality. We demonstrated alterations in neural dynamics under the sensory mismatch condition (accelerative visual motion stimulation) and in participants with high levels of sickness after driving simulation. Additionally, an event-related potentials study revealed that the high-sickness group presented with higher P3 amplitudes in sensory mismatch conditions, suggesting that it would be a substantial demand of cognitive resources for motion perception on sensory mismatch conditions.

Motion sickness-susceptible participants exposed to coherent rotating dot patterns show excessive N2 amplitudes and impaired theta-band phase synchronization

Visually induced motion sickness (VIMS) can occur via prolonged exposure to visual stimulation that generates the illusion of self-motion (vection). Not everyone is susceptible to VIMS and the neural mechanism underlying susceptibility is unclear. This study explored the differences of electroencephalographic (EEG) signatures between VIMS-susceptible and VIMS-resistant groups. Thirty-two-channel EEG data were recorded from 12 VIMS-susceptible and 15 VIMS-resistant university students while they were watching two patterns of moving dots: (1) a coherent rotation pattern (vection-inducing and potentially VIMS-provoking pattern), and (2) a random movement pattern (non-VIMS-provoking control). The VIMS-susceptible group exhibited a significantly larger increase in the parietal N2 response when exposed to the coherent rotating pattern than when exposed to control patterns. In members of the VIMS-resistant group, before vection onset, global connectivity from all other EEG electrodes to the right-temporal-parietal and to the right-central areas increased, whereas after vection onset the global connectivity to the right-frontal area reduced. Such changes were not observed in the susceptible group. Further, the increases in N2 amplitude and the identified phase synchronization index were significantly correlated with individual motion sickness susceptibility. Results suggest that VIMS susceptibility is associated with systematic impairment of dynamic cortical coordination as captured by the phase synchronization of cortical activities. Analyses of dynamic EEG signatures could be a means to unlock the neural mechanism of VIMS.

Psychophysiological Alteration After Virtual Reality Experiences Using Smartphone-Assisted Head Mount Displays: An EEG-Based Source Localization Study

Brain functional changes could be observed in people after an experience of virtual reality (VR). The present study investigated cyber sickness and changes of brain regional activity using electroencephalogram (EEG)-based source localization, before and after a VR experience involving a smartphone-assisted head mount display. Thirty participants (mean age = 25 years old) were recruited. All were physically healthy and had no ophthalmological diseases. Their corrected vision was better than 20/20. Resting state EEG and the simulator sickness questionnaire (SSQ) were measured before and after the VR experience. Source activity of each frequency band was calculated using the sLORETA program. After the VR experience, the SSQ total score and sub scores (nausea, oculomotor symptoms, and disorientation) were significantly increased, and brain source activations were significantly increased: alpha1 activity in the cuneus and alpha2 activity in the cuneus and posterior cingulate gyrus (PCG). The change of SSQ score (after–before) showed significant negative correlation with the change of PCG activation (after–before) in the alpha2 band. The study demonstrated increased cyber sickness and increased alpha band power in the cuneus and PCG after the VR experience. Reduced PCG activation in alpha band may be associated with the symptom severity of cyber sickness.

Neural Comodulation of Independent Brain Processes Related to Multitasking

Distracted driving is regarded as an integrated task requiring different regions of the brain to receive sensory data, coordinate information, make decisions, and synchronize movements. In this paper, we applied an independent modulator analysis (IMA) method to temporally independent electroencephalography (EEG) components to understand how the human executive control system coordinates different brain regions to simultaneously perform multiple tasks with distractions presented in different modalities. The behavioral results showed that the reaction time (RT) in response to traffic events increased while multitasking. Moreover, the RT was longer when the distractor was presented in an auditory form versus a visual form. The IMA results showed that there were performance-related IMs coordinating different brain regions during distracted driving. The component spectral fluctuations affected by the modulators were distinct between the single- and dual-task conditions. Specifically, more modulatory weight was projected to the occipital region to address the additional distracting stimulus in both visual and auditory modality in the dual-task conditions. A comparison of modulatory weights between auditory and visual distractors showed that more modulatory weight was projected to the frontal region during the processing of the auditory distractor. This paper provides valuable insights into the temporal dynamics of attentional modulation during multitasking as well as an understanding of the underlying brain mechanisms that mediate the synchronization across brain regions and govern the allocation of attention in distracted driving.

A Dynamic Window Recognition Algorithm for SSVEP-Based Brain–Computer Interfaces Using a Spatio-Temporal Equalizer

The past decade has witnessed rapid development in the field of brain–computer interfaces (BCIs). While the performance is no longer the biggest bottleneck in the BCI application, the tedious training process and the poor ease-of-use have become the most significant challenges. In this study, a spatio-temporal equalization dynamic window (STE-DW) recognition algorithm is proposed for steady-state visual evoked potential (SSVEP)-based BCIs. The algorithm can adaptively control the stimulus time while maintaining the recognition accuracy, which significantly improves the information transfer rate (ITR) and enhances the adaptability of the system to different subjects. Specifically, a spatio-temporal equalization algorithm is used to reduce the adverse effects of spatial and temporal correlation of background noise. Based on the theory of multiple hypotheses testing, a stimulus termination criterion is used to adaptively control the dynamic window. The offline analysis which used a benchmark dataset and an offline dataset collected from 16 subjects demonstrated that the STE-DW algorithm is superior to the filter bank canonical correlation analysis (FBCCA), canonical variates with autoregressive spectral analysis (CVARS), canonical correlation analysis (CCA) and CCA reducing variation (CCA-RV) algorithms in terms of accuracy and ITR. The results show that in the benchmark dataset, the STE-DW algorithm achieved an average ITR of 134 bits/min, which exceeds the FBCCA, CVARS, CCA and CCA-RV. In off-line experiments, the STE-DW algorithm also achieved an average ITR of 116 bits/min. In addition, the online experiment also showed that the STE-DW algorithm can effectively expand the number of applicable users of the SSVEP-based BCI system. We suggest that the STE-DW algorithm can be used as a reliable identification algorithm for training-free SSVEP-based BCIs, because of the good balance between ease of use, recognition accuracy, ITR and user applicability.

Neural Oscillation Correlates Chemistry Decision-Making

This study explored the electroencephalography (EEG) dynamics during a chemistry-related decision-making task and further examined whether the correctness of the decision-making performance could be reflected by EEG activity. A total of 66 undergraduate students’ EEG were collected while they participated in a chemistry-related decision-making task in which they had to retrieve the relevant chemistry concepts in order to make correct decisions for each task item. The results showed that it was only in the anterior cingulate cortex (ACC) cluster that distinct patterns in EEG dynamics were displayed for the correct and incorrect responses. The logistic regression results indicated that ACC theta power from 300[Formula: see text]ms to 250[Formula: see text]ms before stimulus onset was the most informative factor for estimating the likelihood of making correct decisions in the chemistry-related decision-making task, while it was the ACC low beta power from 150[Formula: see text]ms to 250[Formula: see text]ms after stimulus onset. The results suggested that the ACC theta augmentation before the stimulus onset serves to actively maintain the relevant information for retrieval from long-term memory, while the ACC low beta augmentation after the stimulus onset may serve the function of mapping the encoded stimulus onto the relevant criteria that the given participant has held within his or her mind to guide the decision-making responses.

What is nausea? A historical analysis of changing views

The connotation of "nausea" has changed across several millennia. The medical term 'nausea' is derived from the classical Greek terms ναυτια and ναυσια, which designated the signs and symptoms of seasickness. In classical texts, nausea referred to a wide range of perceptions and actions, including lethargy and disengagement, headache (migraine), and anorexia, with an awareness that vomiting was imminent only when the condition was severe. However, some recent articles have limited the definition to the sensations that immediately precede emesis. Defining nausea is complicated by the fact that it has many triggers, and can build-up slowly or rapidly, such that the prodromal signs and symptoms can vary. In particular, disengagement responses referred to as the "sopite syndrome" are typically present only when emetic stimuli are moderately provocative, and do not quickly culminate in vomiting or withdrawing from the triggering event. This review considers how the definition of "nausea" has evolved over time, and summarizes the physiological changes that occur prior to vomiting that may be indicative of nausea. Also described are differences in the perception of nausea, as well as the accompanying physiological responses, that occur with varying stimuli. This information is synthesized to provide an operational definition of nausea.