Despite improvement in the computational capabilities of visual displays in flight simulators, intersensory visual-vestibular conflict remains the leading cause of simulator sickness (SS). By using galvanic vestibular stimulation (GVS), the vestibular system can be synchronized with a moving visual field in order to lessen the mismatch of sensory inputs thought to result in SS. A multisite electrode array was used to deliver combinations of GVS in 21 normal subjects. Optimal electrode combinations were identified and used to establish GVS dose-response predictions for the perception of roll, pitch, and yaw. Based on these data, an algorithm was then implemented in flight simulator hardware in order to synchronize visual and GVS-induced vestibular sensations (oculo-vestibular-recoupled or OVR simulation). Subjects were then randomly exposed to flight simulation either with or without OVR simulation. A self-report SS checklist was administered to all subjects after each session. An overall SS score was calculated for each category of symptoms for both groups. The analysis of GVS stimulation data yielded six unique combinations of electrode positions inducing motion perceptions in the three rotational axes. This provided the algorithm used for OVR simulation. The overall SS scores for gastrointestinal, central, and peripheral categories were 17%, 22.4%, and 20% for the Control group and 6.3%, 20%, and 8% for the OVR group, respectively. When virtual head signals produced by GVS are synchronized to the speed and direction of a moving visual field, manifestations of induced SS in a cockpit flight simulator are significantly reduced.

Cevette MJ, Pradhan GN, Cocco D, Crowell MD, Galea AM, Bartlett J, Stepanek J. Electrogastrographic and autonomic responses during oculovestibular recoupling in flight simulation. Aviat Space Environ Med 2014; 85:15–24. Introduction: Simulator sickness causes vestibulo-autonomic responses that increase sympathetic activity and decrease parasympathetic activity. The purpose of the study was to quantify these responses through electrogastrography and cardiac interbeat intervals during flight simulation. Methods: There were 29 subjects that were randomly assigned to 2 parallel arms: 1) oculovestibular recoupling, where galvanic vestibular stimulation was synchronous with the visual field; and 2) control. Electrogastrography and interbeat interval data were collected during baseline, simulation, and post-simulation periods. A simulator sickness questionnaire was administered. Results: Statistically significant differences were observed in percentage of recording time with the dominant frequency of electrogastrography in normogastric and bradygastric domains between the oculovestibular recoupling and control groups. Normogastria was dominant during simulation in the oculovestibular recoupling group. In the control group, the percentage of recording time with the dominant frequency decreased by 22% in normogastria and increased by 20% in bradygastria. The percentage change of the dominant power instability coefficient from baseline to simulation was 26% in the oculovestibular recoupling group vs. 108% in the control group. The power of high-frequency components for interbeat intervals did not change significantly in the oculovestibular recoupling group and was decreased during simulation in the control group. Discussion: Electrogastrography and interbeat intervals are sensitive indices of autonomic changes in subjects undergoing flight simulation. These data demonstrate the potential of oculovestibular recoupling to stabilize gastric activity and cardiac autonomic changes altered during simulator and motion sickness.

Background/Objectives: Galvanic vestibular stimulation (GVS) integrated into virtual reality (VR) environments enhances immersion and mitigates cybersickness. It is well known that GVS can affect standing balance. Most studies have investigated the effects of GVS in VR in seated conditions. The purpose of this study was to evaluate the impact of joint GVS and VR with moving visual stimulus on standing balance. Methods: Using a repeated measures counter-balanced design, motion sickness, postural sway, and velocity utilizing the center of pressure (COP) along the mediolateral (ML) and anteroposterior (AP) axes were obtained in 18 subjects during optokinetic (OPK) stimulus (black and white vertical bars moving from left to the right) in VR across three interventions: GVS in the same direction of visual stimulus—left to right ear (Positive GVS), GVS in the opposite direction of visual stimulus—right to left ear (Negative GVS), and without GVS (Null GVS). Motion sickness symptom scoring was obtained using the Pensacola Diagnostic Index. Results: The PDI score was increased significantly in the Negative GVS. The root mean square and sway range of COP along ML was greater during the Positive GVS and Negative GVS than the Null GVS, while, along AP, it was only greater during Negative GVS. During Positive GVS, mean positive and negative peak velocities, only in ML, were increased and decreased, respectively. During Negative GVS, only negative peak velocities in both ML and AP directions were decreased. Conclusions: This research highlights the importance of testing combined VR and GVS to assess standing balance while mitigating cybersickness.

Galvanic stimulation has long been used as a nonmechanical means of activating the vestibular apparatus through direct action on the vestibular nerve endings. This stimulation has been reported to be safe, but no studies have examined the potential changes in the corresponding cochlear receptors. The aim of the present study was to evaluate the effect of galvanic vestibular stimulation (GVS) on distortion product otoacoustic emissions (DPOAEs). Fourteen subjects underwent DPOAEs during several conditions of GVS. The DPOAEs ranged from ∼ 1 kHz to ∼ 8 kHz at 65/55 dB for f1/f2 and with an f2/f1 ratio of 1.2. The subjects were evaluated at 10 stimulation conditions that ranged from −2.0 mA to +2.0 mA for each frequency. Statistical analysis showed no significant differences in DPOAE amplitudes for all conditions with and without GVS. Results also showed no significant differences between DPOAE amplitudes before and after GVS. Multivariate analysis found subject variability in DPOAE amplitude, which was not thought to be GVS related. Results indicated that GVS produced neither temporary nor permanent changes in DPOAEs.

Background/Objectives: Mild cognitive impairment (MCI) affects multiple functional and cognitive domains, rendering it challenging to diagnose. Brief mental status exams are insensitive while detailed neuropsychological testing is time-consuming and presents accessibility issues. By contrast, the Oculo-Cognitive Addition Test (OCAT) is a rapid, objective tool that measures oculometric features during mental addition tasks under one minute. This study aims to develop artificial intelligence (AI)-derived predictive models using OCAT eye movement and time-based features for the early detection of those at risk for MCI, requiring more thorough assessment. Methods: The OCAT with integrated eye tracking was completed by 250 patients at the Mayo Clinic Arizona Department of Neurology. Raw gaze data analysis yielded time-related and eye movement features. Random Forest and univariate decision trees were the feature selection methods used to identify predictors of Dementia Rating Scale (DRS) outcomes. Logistic regression (LR) and K-nearest neighbors (KNN) supervised models were trained to classify PMCI using three feature sets: time-only, eye-only, and combined. Results: LR models achieved the highest performance using the combined time and eye movement features, with an accuracy of 0.97, recall of 0.91, and an AUPRC of 0.95. The eye-only and time-only LR models also performed well (accuracy = 0.93), though with slightly lower F1-scores (0.87 and 0.86, respectively). Overall, models leveraging both time and eye movement features consistently outperformed those using individual feature sets. Conclusions: Machine learning models trained on OCAT-derived features can reliably predict DRS outcomes (PASS/FAIL), offering a promising approach for early MCI identification. With further refinement, OCAT has the potential to serve as a practical and scalable cognitive screening tool, suitable for use in clinics, at the bedside, or in remote and resource-limited settings.