Measuring vection: a review and critical evaluation of different methods for quantifying illusory self-motion.

The sensation of self-motion in the absence of physical motion, known as vection, has been scientifically investigated for over a century. As objective measures of, or physiological correlates to, vection have yet to emerge, researchers have typically employed a variety of subjective methods to quantify the phenomenon of vection. These measures can be broadly categorized into the occurrence of vection (e.g., binary choice yes/no), temporal characteristics of vection (e.g., onset time/latency, duration), the quality of the vection experience (e.g., intensity rating scales, magnitude estimation), or indirect (e.g., distance travelled) measures. The present review provides an overview and critical evaluation of the most utilized vection measures to date and assesses their respective merit. Furthermore, recommendations for the selection of the most appropriate vection measures will be provided to assist with the process of vection research and to help improve the comparability of research findings across different vection studies.

Artificial Intelligence for Multiple Sclerosis Management Using Retinal Images: Pearl, Peaks, and Pitfalls.

Multiple sclerosis (MS) is a complex autoimmune disease characterized by inflammatory processes, demyelination, neurodegeneration, and axonal damage within the central nervous system (CNS). Retinal imaging, particularly Optical coherence tomography (OCT), has emerged as a crucial tool for investigating MS-related retinal injury. The integration of artificial intelligence(AI) has shown promise in enhancing OCT analysis for MS. Researchers are actively utilizing AI algorithms to accurately detect and classify MS-related abnormalities, leading to improved efficiency in diagnosis, monitoring, and personalized treatment planning. The prognostic value of AI in predicting MS disease progression has garnered substantial attention. Machine learning (ML) and deep learning (DL) algorithms can analyze longitudinal OCT data to forecast the course of the disease, providing critical information for personalized treatment planning and improved patient outcomes. Early detection of high-risk patients allows for targeted interventions to mitigate disability progression effectively. As such, AI-driven approaches yielded remarkable abilities in classifying distinct MS subtypes based on retinal features, aiding in disease characterization and guiding tailored therapeutic strategies. Additionally, these algorithms have enhanced the accuracy and efficiency of OCT image segmentation, streamlined diagnostic processes, and reduced human error. This study reviews the current research studies on the integration of AI,including ML and DL algorithms, with OCT in the context of MS. It examines the advancements, challenges, potential prospects, and ethical concerns of AI-powered techniques in enhancing MS diagnosis, monitoring disease progression, revolutionizing patient care, the development of patient screening tools, and supported clinical decision-making based on OCT images.

Mental Stress Classification Based on Selected Electroencephalography Channels Using Correlation Coefficient of Hjorth Parameters.

Electroencephalography (EEG) signals offer invaluable insights into diverse activities of the human brain, including the intricate physiological and psychological responses associated with mental stress. A major challenge, however, is accurately identifying mental stress while mitigating the limitations associated with a large number of EEG channels. Such limitations encompass computational complexity, potential overfitting, and the prolonged setup time for electrode placement, all of which can hinder practical applications. To address these challenges, this study presents the novel CCHP method, aimed at identifying and ranking commonly optimal EEG channels based on their sensitivity to the mental stress state. This method's uniqueness lies in its ability not only to find common channels, but also to prioritize them according to their responsiveness to stress, ensuring consistency across subjects and making it potentially transformative for real-world applications. From our rigorous examinations, eight channels emerged as universally optimal in detecting stress variances across participants. Leveraging features from the time, frequency, and time-frequency domains of these channels, and employing machine learning algorithms, notably RLDA, SVM, and KNN, our approach achieved a remarkable accuracy of 81.56% with the SVM algorithm outperforming existing methodologies. The implications of this research are profound, offering a stepping stone toward the development of real-time stress detection devices, and consequently, enabling clinicians to make more informed therapeutic decisions based on comprehensive brain activity monitoring.

Neural Inference Search for Multiloss Segmentation Models.

Semantic segmentation is vital for many emerging surveillance applications, but current models cannot be relied upon to meet the required tolerance, particularly in complex tasks that involve multiple classes and varied environments. To improve performance, we propose a novel algorithm, neural inference search (NIS), for hyperparameter optimization pertaining to established deep learning segmentation models in conjunction with a new multiloss function. It incorporates three novel search behaviors, i.e., Maximized Standard Deviation Velocity Prediction, Local Best Velocity Prediction, and n -dimensional Whirlpool Search. The first two behaviors are exploratory, leveraging long short-term memory (LSTM)-convolutional neural network (CNN)-based velocity predictions, while the third employs n -dimensional matrix rotation for local exploitation. A scheduling mechanism is also introduced in NIS to manage the contributions of these three novel search behaviors in stages. NIS optimizes learning and multiloss parameters simultaneously. Compared with state-of-the-art segmentation methods and those optimized with other well-known search algorithms, NIS-optimized models show significant improvements across multiple performance metrics on five segmentation datasets. NIS also reliably yields better solutions as compared with a variety of search methods for solving numerical benchmark functions.

A virtual reality study investigating the train illusion.

The feeling of self-movement that occurs in the absence of physical motion is often referred to as vection, which is commonly exemplified using the train illusion analogy (TIA). Limited research exists on whether the TIA accurately exemplifies the experience of vection in virtual environments (VEs). Few studies complemented their vection research with participants' qualitative feedback or by recording physiological responses, and most studies used stimuli that contextually differed from the TIA. We investigated whether vection is experienced differently in a VE replicating the TIA compared to a VE depicting optic flow by recording subjective and physiological responses. Additionally, we explored participants' experience through an open question survey. We expected the TIA environment to induce enhanced vection compared to the optic flow environment. Twenty-nine participants were visually and audibly immersed in VEs that either depicted optic flow or replicated the TIA. Results showed optic flow elicited more compelling vection than the TIA environment and no consistent physiological correlates to vection were identified. The post-experiment survey revealed discrepancies between participants' quantitative and qualitative feedback. Although the dynamic content may outweigh the ecological relevance of the stimuli, it was concluded that more qualitative research is needed to understand participants' vection experience in VEs.

Nutritional and Behavioral Countermeasures as Medication Approaches to Relieve Motion Sickness: A Comprehensive Review.

The mismatch in signals perceived by the vestibular and visual systems to the brain, also referred to as motion sickness syndrome, has been diagnosed as a challenging condition with no clear mechanism. Motion sickness causes undesirable symptoms during travel and in virtual environments that affect people negatively. Treatments are directed toward reducing conflicting sensory inputs, accelerating the process of adaptation, and controlling nausea and vomiting. The long-term use of current medications is often hindered by their various side effects. Hence, this review aims to identify non-pharmacological strategies that can be employed to reduce or prevent motion sickness in both real and virtual environments. Research suggests that activation of the parasympathetic nervous system using pleasant music and diaphragmatic breathing can help alleviate symptoms of motion sickness. Certain micronutrients such as hesperidin, menthol, vitamin C, and gingerol were shown to have a positive impact on alleviating motion sickness. However, the effects of macronutrients are more complex and can be influenced by factors such as the food matrix and composition. Herbal dietary formulations such as Tianxian and Tamzin were shown to be as effective as medications. Therefore, nutritional interventions along with behavioral countermeasures could be considered as inexpensive and simple approaches to mitigate motion sickness. Finally, we discussed possible mechanisms underlying these interventions, the most significant limitations, research gaps, and future research directions for motion sickness.

A systematic review and meta-analysis on the use of tactile stimulation in vection research.

Vection is classically defined as the illusory perception of self-motion induced via visual stimuli. The utility of vection research lies in its potential to enhance simulation fidelity, as measured through presence, and reduce the probability that motion sickness symptoms occur. Studies have shown a multimodal interaction of various sensory systems in facilitating vection, and the utility of co-stimulating some of these sensory systems along with the presentation of visual stimuli have been reviewed. However, a review on the use of tactile stimulation in vection research appears to be missing from literature. The purpose of this review was to evaluate the current methodologies, and outcomes, of tactile stimulation in vection research. We searched for articles through EBSCOHost, Scopus and Web of Science. Studies were included only if they detailed an experiment on the effect of tactile stimulation on vection. Twenty-four studies were obtained and distilled in tabular form. Eighteen studies contained sufficient information to be included in a meta-analysis. We identified that tactile stimulation has mostly been applied in the form of vibrational stimulation to the feet. Furthermore, tactile stimulation is most effective when it is presented in a temporally congruent manner to other sensory cues, whereas tactile stimulation as a unisensory stimulus does not appear to be effective in eliciting vection. We discuss the need for more qualitative research to reduce methodological inhomogeneities and recommend future research in tactile-mediated vection to investigate stimulation to the torso and investigate the use of forces as a tactile stimulus.

Multiobjective and Interactive Genetic Algorithms for Weight Tuning of a Model Predictive Control-Based Motion Cueing Algorithm.

Driving simulators are effective tools for training, virtual prototyping, and safety assessment which can minimize the cost and maximize road safety. Despite the aim of a realistic motion generation for the impression of real-world driving, motion simulators are bound in a limited workspace. Motion cueing algorithms (MCAs) aim to plan an acceptable motion feeling for drivers, without infringing the simulated boundaries. Recently, model predictive control (MPC) has been widely used in MCAs; however, the tuning process for finding the best weights of the MPC optimization is still a challenge. As there are several objectives for the optimization without any standard weighting for solution evaluations, a nonbiased scalarization of solutions for the purpose of comparison is impossible. In this paper, a clear method for obtaining the best MPC weighting has been proposed. This method searches for the best tune of MPC cost function weights, reduces the user burden for weight tuning while receiving feedback from the user satisfaction. The MPC-based MCA weights are optimized using a multiobjective genetic algorithm (GA) considering objectives, such as minimization of motion inputs (linear acceleration and angular velocity), input rates, output displacements and the sensed motion errors. Any process based on trial-and-error has been omitted. The adjusted weights have to satisfy a set of predefined conditions related to maximum tolerated error and maximum displacement. The obtained Pareto-front is used for decision making via an interactive GA (IGA), aiming for maximization of the decision maker's satisfaction. A Web interface is developed to interact with the IGA and to influence the region of searching. Simulation results show the superiority of the proposed method compared with the previous empirical tuning method. The sensed motion error is minimized using the proposed method and with the same available workspace, a more realistic motion can be rendered to the driver.

Semicircular canal modeling in human perception.

The human vestibular system is a sensory and equilibrium system that manages and controls the human sense of balance and movement. It is the main sensor humans use to perceive rotational and linear motions. Determining an accurate mathematical model of the human vestibular system is significant for research pertaining to motion perception, as the quality and effectiveness of the motion cueing algorithm (MCA) directly depends on the mathematical model used in its design. This paper describes the history and analyses the development process of mathematical semicircular canal models. The aim of this review is to determine the most consistent and reliable mathematical semicircular canal models that agree with experimental results and theoretical analyses, and offer reliable approximations for the semicircular canal functions based on the existing studies. Selecting and formulating accurate mathematical models of semicircular canals are essential for implementation into the MCA and for ensuring effective human motion perception modeling.

A review on otolith models in human perception.

The vestibular system, which consists of semicircular canals and otolith, are the main sensors mammals use to perceive rotational and linear motions. Identifying the most suitable and consistent mathematical model of the vestibular system is important for research related to driving perception. An appropriate vestibular model is essential for implementation of the Motion Cueing Algorithm (MCA) for motion simulation purposes, because the quality of the MCA is directly dependent on the vestibular model used. In this review, the history and development process of otolith models are presented and analyzed. The otolith organs can detect linear acceleration and transmit information about sensed applied specific forces on the human body. The main purpose of this review is to determine the appropriate otolith models that agree with theoretical analyses and experimental results as well as provide reliable estimation for the vestibular system functions. Formulating and selecting the most appropriate mathematical model of the vestibular system is important to ensure successful human perception modelling and simulation when implementing the model into the MCA for motion analysis.