Mitigating motion sickness with non-invasive cortical vestibular brain stimulation (vBS) is promising because it does not require redesigning VR content or altering the motion profile of a moving platform. Instead, it aims to enhance individuals' intrinsic resistance to motion sickness. However, the mechanism underlying its online efficacy remains unclear (Here, "online efficacy" means the observed motion sickness reduction during vBS not before or after vBS). Motivated by lower motion sickness susceptibility observed in congenital nystagmus patients, we hypothesized that reductions in motion sickness under vBS condition are associated with increased eye movement instability. Leveraging sham-controlled, sex-balanced eye-tracking data from an open-access database, this study presents the first evaluation of a 20 Hz-vBS approach targeting the vestibular cortices as a countermeasure to motion sickness, using spatiotemporal eye movement metrics derived from eye-tracking data collected in VR. Results show that reduced motion sickness under active vBS indeed increased eye movement instability temporarily and spatially, compared to sham vBS. We also found that the temporal metrics was more strongly associated with female participants, whereas the spatial metric was more strongly associated with male participants.

The increasing use of virtual reality (VR) in public transportation enables travelers to engage in immersive entertainment or productive tasks, thereby enhancing the overall travel experience. However, mismatched VR visual motion and physical car motion can cause motion sickness (MS), leading to nausea, postural instability and reduced time for enjoyment or productive tasks. Thus, the benefits of using VR in transportation systems are currently limited to individuals who do not experience MS. Thus, effective MS mitigation is essential for improving travel safety, maximizing travel time, and expanding VR accessibility. Neuromodulation targeting the vestibular apparatus, such as bone-conducted vibration (BCV), has shown promise in reducing MS. However, it remains unclear whether neuromodulation directly targeting vestibular cortical regions, such as transcranial alternating current stimulation (tACS), is superior. This paper focuses on the design and validation of a novel vestibular cortical neuromodulation approach using tACS in mitigating MS in a novel simulated in-car VR environment. Eighty participants were recruited to evaluate the tACS approach. The results demonstrate that the proposed tACS approach effectively reduces nausea, enhances postural stability, and extends survival time in MS. Compared to BCV, tACS demonstrates a faster onset of action and longer mitigation effects. However, from an applied perspective, the effects of our tACS approach were relatively short-lived and accompanied by side effects such as tingling and itching.

To prevent motion sickness, Virtual Reality (VR) experiences for vehicle passengers typically present “matched motion”: real vehicle movements are replicated 1:1 by movements in VR. This significantly limits virtual applications. We provide foundations for in-car VR experiences that break this constraint by manipulating the passenger’s visual perception of linear velocity through amplifying and reducing the virtual speed. In two on-the-road studies, we examined the application of Vehicular Translational Gain (1.5-9.5x) and Attenuation (0.66-0.14x) to real car speeds (~50km/h) across two VR tasks (reading and gaming), exploring journey perception, impact on motion sickness, travel experience and tasks. We found that vehicular gain/attenuation can be applied without significantly increasing motion sickness. Gain was more noticeable and affected perceived speed, distance, safety, relaxation and excitement, being well-suited to gaming, while attenuation was more suitable for productivity. Our work unlocks new ways that VR applications can enhance and alter the passenger experience through novel perceptual manipulations of vehicle velocity.

This paper presents the first in-car VR motion sickness (VRMS) detection model based on lower face action units (LF-AUs). Initially developed in a simulated in-car environment with 78 participants, the model’s generalizability was later tested in realworld driving conditions. Motion sickness was induced using visual linear motion in the VR headset and physical horizontal rotation via a rotating chair. We used a convolutional neural network (MobileNetV3) to automatically extract LF-AUs from images of the users’ mouth region, captured by the VR headset’s built-in camera. These LF-AUs were then used to train a Support Vector Regression (SVR) model to estimate motion sickness scores. We compared the SVR model’s performance using LF-AUs, pupil diameters, and physiological features (individually and in combination) from the same VR headset. Results showed that both individual LF-AU (right dimple) and combined LF-AUs had significant Pearson correlations with self-reported motion sickness scores and achieved lower root mean squared error compared to pupil diameters. The best detection results were obtained by combining LF-AUs and pupil diameters, while physiological features alone did not yield significant results. The LF-AUs-based model demonstrated encouraging generalizability across different settings in the independent studies.

Cybersickness remains a major drawback of Virtual Reality (VR) headsets, as a breadth of stationary experiences with visual self-motion can result in visually-induced motion sickness. However, not everybody experiences the same intensity or type of adverse symptoms. Here we propose that prior experience with virtual environments can predict ones degree of cybersickness. Video gaming can enhance visuospatial abilities, which in-turn relate negatively to cybersickness - meaning that consistently engaging in virtual environments can result in protective habituation effects. In a controlled stationary VR experiment, we found that 'VR-naive' video gamers experienced significantly less cybersickness in a virtual tunnel-travel task and outperformed 'VR-naive' non-video gamers on a visual attention task. These findings strongly motivate the use of non-VR games for training VR cybersickness resilience, with future research needed to further understand the mechanism(s) by which gamers become cybersickness resilient - potentially expanding access to VR for even the most susceptible participants.

Motion sickness is a problem for many in everyday travel and will become more prevalent with the rise of automated vehicles. Virtual Reality (VR) headsets have shown significant promise in-transit, enabling passengers to engage in immersive entertainment and productivity experiences. In a controlled multi-session motion sickness study using an actuated rotating chair, we examine the potential of multi-sensory visual and auditory motion cues, presented during a VR reading task, for mitigating motion sickness. We found that visual cues are most efficient in reducing symptoms, with auditory cues showing some beneficial effects when combined with the visual. Motion sickness had negative effects on presence as well as task performance, and despite the cognitive demand and multi-sensory cues, motion sickness still reached problematic levels. Our work emphasises the need for effective mitigations and the design of stronger multi-sensory motion cues if VR is to fulfil its potential for passengers.

VR (Virtual Reality) Motion Sickness (VRMS) refers to purely visually-induced motion sickness. Not everyone is susceptible to VRMS, but if experienced, nausea will often lead users to withdraw from the ongoing VR applications. VRMS represents a serious challenge in the field of VR ergonomics and human factors. Like other neuro-ergonomics researchers did before, this paper considers VRMS as a brain state problem as various etiologies of VRMS support the claim that VRMS is caused by disagreement between the vestibular and visual sensory inputs. However, what sets this work apart from the existing literature is that it explores anti-VRMS brain patterns via electroencephalogram (EEG) in VRMS-resistant individuals. Based on existing datasets of a previous study, we found enhanced theta activity in the left parietal cortex in VRMS-resistant individuals (N= 10) compared to VRMS-susceptible individuals (N=10). Even though the sample size per se is not large, this finding achieved medium effect size. This finding offers new hypotheses regarding how to reduce VRMS by the enhancement of brain functions per se (e.g., via non-invasive transcranial electrostimulation techniques) without the need to redesign the existing VR content.

Virtual reality (VR) has the potential to induce cybersickness (CS), which impedes CS-susceptible VR users from the benefit of emerging VR applications. To better detect CS, the current study investigated whether/how the newly proposed human vestibular network (HVN) is involved in flagship consumer VR-induced CS by simultaneously recording autonomic physiological signals as well as neural signals generated in sensorimotor and cognitive domains. The VR stimuli were made up of one or two moderate CS-inducing entertaining task(s) as well as a mild CS-inducing cognitive task implemented before and after the moderate CS task(s). Results not only showed that CS impaired cognitive control ability, represented by the degree of attentional engagement, but also revealed that combined indicators from all three HVN domains could together establish the best regression relationship with CS ratings. More importantly, we found that every HVN domain had its unique advantage with the dynamic changes in CS severity and time. These results provide evidence for involvement of the HVN in CS and indicate the necessity of HVN-based CS detection.

Vigilance decrement in driving tasks has been reported to be a major factor in fatal accidents and could severely endanger public transportation safety. However, efficient approaches for estimating vigilance in real driving environment are still lacking. In this paper, we propose a novel approach for implementing continuous vigilance estimation using forehead electrooculograms (EOGs) acquired by wearable dry electrodes in both simulated and real driving environments. To improve the feasibility of this approach for real-world applications, a forehead EOG-based electrode placement with only four electrodes is designed. Flexible dry electrodes and an acquisition board are integrated as a wearable device for recording EOGs. Twenty and ten subjects participated in the simulated and real-world driving environment experiments, respectively. Accurate eye movement parameters from eye-tracking glasses are extracted to calculate the PERCLOS index for vigilance annotation. This is because the vigilance state is a temporally dynamic process, and a continuous conditional random field and a continuous conditional neural field are introduced to construct more accurate vigilance estimation models. To evaluate the efficiency of our system, systematic experiments are performed in real scenarios under various illumination and weather conditions following laboratory simulations as preliminary studies. The experimental results demonstrate that the wearable dry electrode prototype, which has a relatively comfortable forehead setup, can efficiently capture vigilance dynamics. The best mean correlation coefficients achieved by our proposed approach are 71.18% and 66.20% in laboratory simulations and real-world driving environments, respectively. The cross-environment experiments are performed to evaluate the simulated-to-real generalization and a best mean correlation coefficient of 53.96% is achieved.

In this paper, we present the design and implementation of a wireless, wearable brain machine interface (BMI) system dedicated to signal sensing and processing for driver drowsiness detection (DDD). Owing to the importance of driver drowsiness and the possibility for brainwaves-based DDD, many electroencephalogram (EEG)-based approaches have been proposed. However, few studies focus on the early detection of driver drowsiness and on the early management of driver drowsiness using a closed-loop algorithm. The reported wireless and wearable BMI system is used for 1) simultaneous EEG and gyroscope-based head movement measurement for the early detection of driver drowsiness and 2) simultaneous EEG and transcranial direct current stimulation (tDCS) for the early management of driver drowsiness. To achieve the purposes of easy-to-use and distraction-free driving, a Bluetooth low-energy module is embedded in this BMI system and used to communicate with a fully wearable consumer device, a smartwatch, which coordinates the work of drowsiness monitoring and brain stimulation with its embedded closed-loop algorithm. The proposed system offers a 128 Hz sampling rate per channel, 12-bit and 16-bit resolution for a single-channel EEG and a three-channel gyroscope, and a maximum 2 mA current for the tDCS. The current consumption of the whole headset system is 56 mA. The battery life of the smartwatch is 9 h. The DDD experimental results show that the proposed system obtained a 93.67% five-level overall accuracy, a 96.15% two-level (alert versus slightly drowsy) accuracy, and maximum 16- to 23-min wakefulness maintenance.

The development and realization of a portable wireless electroencephalogram (EEG) monitor has become feasible due to the recent active research activities in the area of Wireless Sensor Networks (WSN). In this paper, we propose an EEG monitor design based on a commercially available WSN device, Body Sensor Networks node (BSN node). The device utilizes Zigbee technology and operates on the TinyOS operating system. The EEG signal from subjects is detected and radioed to a PC for monitoring. This study validates our approach and suggests the feasibility of future enhancements.