Current attentional guidance systems in Virtual Reality (VR) typically treat the user as a static receiver, employing fixed visual cues that fail to account for the fluctuating cognitive state. We address this limitation by positing that effective guidance requires modulating external stimuli to correspond with the brain’s intrinsic attentional sampling mechanism. Using EEG in an immersive environment, we provide direct evidence that this sampling periodicity is not fixed; specifically, the intensified sensorimotor integration load of hand-eye coordination drives the endogenous rhythm to decelerate from the alpha band (∼8 Hz) to the theta band (∼4 Hz). This neural adaptation dictates the temporal requirements for external guidance: identifying optimal parameters via Pareto optimization, we demonstrate that while a 4 Hz cue suffices for gaze, the computationally demanding coordination task requires a higher-frequency (7 Hz) cue to ensure sufficient temporal signal density. Validated in ecological VR scenarios, our adaptive strategy significantly enhanced interaction efficiency without increasing cognitive load. Beyond the specific implementation of flicker, this work establishes a critical design principle for next-generation attention-aware interfaces: maximizing performance by synchronizing information presentation with the user’s task-induced sensorimotor state.

Accurate 3D target selection in virtual reality (VR) is fundamentally impeded by pointing uncertainty along the depth axis, a challenge that existing 2D pointing models fail to address due to the complexities of depth perception. Near-eye interactions in VR are influenced by binocular depth cues and vergence-accommodation conflicts (VAC), which introduce significant depth perception biases that impair predictive performance. To address this issue, we first investigate these factors and derive a Gaussian distribution to model near-field depth biases within a 2.5m range. Second, to analyze pointing performance across this extended depth range, we classify 3D target motions into three distinct types: motion-indepth, motion-in-plane, and combined motion. Our analysis identifies that interaction depth and motion amplitude are the two most critical factors influencing pointing accuracy. Accordingly, by incorporating these factors alongside our perceptual bias Gaussian into the Ternary-Gaussian framework, we demonstrate significantly improved predictive performance across diverse 3D motion scenarios. These findings enhance the understanding of user perception in virtual environments and support the development of precise, context-aware interaction cues. Future research can extend these models to design real-time adaptive interfaces, thereby elevating user experiences in VR.

Virtual Reality (VR) technologies in fields such as telehealth, teleconferencing, and virtual education are significantly affected by end-to-end latency, which notably impacts users' interactive experience and performance. Previous research suggests that a perceptual threshold may exist-once latency is reduced below a certain level, users no longer perceive it, and their interactive performance remains largely unaffected. However, there is no consensus on the exact value of this absolute latency perception threshold. In this study, we employed an experimental design based on Fitts' law to investigate whether interaction strategies and task difficulty can alter the latency perception threshold (LPT), and how variations in this threshold influence users' interactive performance. The results show that the LPT is approximately 130-170 ms, and that when interaction strategies prioritize speed or when tasks become more challenging, users exhibit heightened sensitivity to latency. Due to the presence of the LPT, the effect of latency on interactive performance follows a nonlinear pattern, and building on this finding, we refined a Fitts' law model to incorporate the influence of latency. Notably, electroencephalogram (EEG) signals can still capture users' perception of latency when they are unaware of minor latency, demonstrating a level of sensitivity that exceeds conscious awareness. Our findings provide insights into latency effects on performance and perception, guiding the design of more responsive VR interaction systems.

Virtual Reality (VR) offers a valuable platform for real-life skills training. However, previous research has indicated that human’s perception of depth in VR differs from that of the real world. Such perceptual conflicts can impact immersion and the learning of skills, thus attracting widespread attention. Various methods have been proposed to enhance users’ depth perception, yet the underlying mechanisms of depth perception conflicts still require further research. In this paper, we used Error-Related Potentials (ErrPs) from electroencephalography (EEG) data to investigate the differences in participants’ perceptions at varying depths within the near-field. We designed a within-subjects experiment to successfully introduce depth perception conflicts. From participants exposed to three distinct depths, we collected questionnaire results, performance data, and EEG data. Our findings showed that EEG can effectively detect depth perception conflicts and, following each conflict, participants’ behavioral patterns showed significant changes. In situations with shallower depths, participants exhibited stronger responses to the designed conflicts. This increased sensitivity correlates with their accuracy in depth estimation. This study represents a novel approach to depth perception in VR using ErrPs, setting the stage for further use of physiological signals to measure the granularity of depth perception in VR/AR environments.

Stereoscopic visual fatigue (SVF) due to prolonged immersion in the virtual environment can lead to negative user experience, thus hindering the development of virtual reality (VR) industry. Previous studies have focused on investigating the evaluation indicators associated with SVF, while few studies have been conducted to reveal the underlying neural mechanism, especially in VR applications. In this paper, a modified Go/NoGo paradigm was adopted to induce SVF in VR environment with Go trials for maintaining participants' attention and NoGo trials for investigating the neural effects under SVF. Random dot stereograms (RDSs) with 11 disparities were presented to evoke the depth-related visual evoked potentials (DVEPs) during 64-channel EEG recordings. EEG datasets collected from 15 participants in NoGo trials were selected to conduct individual processing and group analysis, in which the characteristics of the DVEPs components for various fatigue degrees were compared and independent components were clustered to explore the original cortex areas related to SVF. Point-by-point permutation statistics revealed that DVEPs sample points from 230 ms to 280 ms (component P2) in most brain areas changed significantly when SVF increased. Additionally, independent component analysis (ICA) identified that component P2 which originated from posterior cingulate cortex and precuneus, was associated statistically with SVF. We believe that SVF is rather a conscious status concerning the changes of self-awareness or self-location awareness than the performance reduction of retinal image processing. Moreover, we suggest that indicators representing higher conscious state may be a better indicator for SVF evaluation in VR environments.

A brain-computer interface (BCI) facilitates bypassing the peripheral nervous system and directly communicating with surrounding devices. Navigation technology using BCI has developed—from exploring the prototype paradigm in the virtual environment (VE) to accurately completing the locomotion intention of the operator in the form of a powered wheelchair or mobile robot in a real environment. This paper summarizes BCI navigation applications that have been used in both real and VEs in the past 20 years. Horizontal comparisons were conducted between various paradigms applied to BCI and their unique signal-processing methods. Owing to the shift in the control mode from synchronous to asynchronous, the development trend of navigation applications in the VE was also reviewed. The contrast between highlevel commands and low-level commands is introduced as the main line to review the two major applications of BCI navigation in real environments: mobile robots and unmanned aerial vehicles (UAVs). Finally, applications of BCI navigation to scenarios outside the laboratory; research challenges, including human factors in navigation application interaction design; and the feasibility of hybrid BCI for BCI navigation are discussed in detail.

With the rapid development of mobile learning technologies as well as relevant hardware and software platforms, there is a bright prospect for mixed reality (MR) applying in the field of education. However, appropriate knowledge navigation and concept arrangement methods are urgent to diminish students' cognitive load in the virtual learning environment. In this preliminary study, concept maps were selected as scaffolding tools to help students navigate through the MR learning space, and an educational game prototype named MMRCM integrating MR and concept maps was developed to investigate students' technology acceptance about it. Considering the novelty of this new type of MR application, an extended version of the Technology Acceptance Model (TAM) was constructed with the MR game design elements and the concept map usefulness as external factors. A middle school physics experiment using MMRCM was conducted to help students learn the abstract concepts of friction. The evaluation results showed that the model's external factors have significant correlations with both perceived ease of use and perceived usefulness. The findings indicated explicit intention to use MMRCM, which implied that MR gaming and concept maps could be integrated as an effective instructional tool in science education.

Motor imagery brain-computer interfaces have a wide range of promising applications in medical, entertainment and home applications, however, such problems as few motor imagery brain-computer paradigms and weak EEG features need to be addressed. In order to explore good experimental paradigms to induce users to perform motor imagery tasks more effectively, provide more effective EEG features and save training time and increase system robustness, this paper designs a novel motor imagination brain-computer interaction paradigm, which combines virtual reality and haptic stimulation to provide synchronized visual-haptic feedback to the system and enhance the user's motor imagination ability. The experimental results show that there are significant differences between the traditional paradigms and the proposed paradigm in terms of event-related spectral perturbations, which are more obvious in the 10-20Hz and 25-30Hz in the proposed, and the embodiment scores in the proposed paradigm are also higher. In summary, the proposed paradigm can improve users' motor imagery ability by enhancing their embodiments.

Accurate estimation of coupling coefficient between the transmitter and the receiver is a challenge faced in the application of magnetic coupling wireless power transfer (WPT) system. This paper proposes a time domain method to estimate the coupling coefficient only with the requirement of transmitter side instantaneous input voltage and current. The method first provides a systematic way to generate a time domain mathematical model of the WPT system, where the coupling coefficient is introduced as one of the time-varying states of the system. The model is built in such a way that all the physical laws that the WPT system should obey are carefully considered. Afterwards, a standard dynamic state estimation based method is introduced to accurately solve the states of the system including the coupling coefficient. The method does not have further assumptions on the WPT topology as well as system operating conditions. The hardware experiments of an example prototype WPT system demonstrate the feasibility of the proposed coupling coefficient estimation method.

The inductive power transfer (IPT) technique has many merits in battery charging compared to traditional charging methods. However, in low-power applications, most of the existing wireless chargers would employ redundant power conversion and isolation stages in order to be compatible with the wire-connected chargers, which leads to low overall charging efficiency. This paper proposes a novel wireless fast charger by using a two-stage configuration. An unregulated IPT stage can not only offer galvanic isolation but also provide high step-down conversion ratio, and it works like a transformer-based isolated converter. Then the whole charger only needs one more regulation stage to meet the output requirement. This paper focus on the IPT stage with high-input voltage and low-output voltage. The LCC-C compensation is selected for the voltage step-down purpose. In the analysis, the influence of layout size, resonance frequency, and the power level are discussed, based on which the circuit parameters are optimally designed. Finally, a 2MHz 25W IPT stage is used as an example to achieve 120V /12V conversion. The measured peak efficiency is 82 %, which is 9 % higher than the stage of the art product (5W, 5V/5V conversion).

In the past decade, Electroencephalogram (EEG) has been applied in many fields, such as Motor Imagery (MI) and Emotion Recognition. Traditionally, for classification tasks based on EEG, researchers would extract features from raw signals manually which is often time consuming and requires adequate domain knowledge. Besides that, features manually extracted and selected may not generalize well due to the limitation of human. Convolutional Neural Networks (CNNs) plays an important role in the wave of deep learning and achieve amazing results in many areas. One of the most attractive features of deep learning for EEG-based tasks is the end-to-end learning. Features are learned from raw signals automatically and the feature extractor and classifier are optimized simultaneously. There are some researchers applying deep learning methods to EEG analysis and achieving promising performances. However, supervised deep learning methods often require large-scale annotated dataset, which is almost impossible to acquire in EEG-based tasks. This problem limits the further improvements of deep learning models for classification based on EEG. In this paper, we propose a novel deep learning method DMTL-BCI based on Multi-Task Learning framework for EEG-based classification tasks. The proposed model consists of three modules, the representation module, the reconstruction module and the classification module. Our model is proposed to improve the classification performance with limited EEG data. Experimental results on benchmark dataset, BCI Competition IV dataset 2a, show that our proposed method outperforms the state-of-the-art method by 3.0%, which demonstrates the effectiveness of our model.