Mixed Reality (MR) interfaces increasingly rely on gaze for interaction, yet distinguishing visual attention from intentional action remains difficult, leading to the Midas Touch problem. Existing solutions require explicit confirmations, while brain–computer interfaces may provide an implicit marker of intention using Stimulus-Preceding Negativity (SPN). We investigated how Intention (Select vs. Observe) and Feedback (With vs. Without) modulate SPN during gaze-based MR interactions. During realistic selection tasks, we acquired EEG and eye-tracking data from 28 participants. SPN was robustly elicited and sensitive to both factors: observation without feedback produced the strongest amplitudes, while intention to select and expectation of feedback reduced activity, suggesting SPN reflects anticipatory uncertainty rather than motor preparation. Complementary decoding with deep learning models achieved reliable person-dependent classification of user intention, with accuracies ranging from 75% to 97% across participants. These findings identify SPN as an implicit marker for building intention-aware MR interfaces that mitigate the Midas Touch.

Difficulty spillover and suboptimal help-seeking challenge the sequential, knowledge-intensive nature of digital tasks. In online surveys, tough questions can drain mental energy and hurt performance on later questions, while users often fail to recognize when they need assistance or may satisfy, lacking motivation to seek help. We developed a proactive, adaptive system using electrodermal activity and mouse movement to predict when respondents need support. Personalized classifiers with a rule-based threshold adaptation trigger timely LLM-based clarifications and explanations. In a within-subjects study (N=32), aligned-adaptive timing was compared to misaligned-adaptive and random-adaptive controls. Aligned-adaptive assistance improved response accuracy by 21%, reduced false negative rates from 50.9% to 22.9%, and improved perceived efficiency, dependability, and benevolence. Properly timed interventions prevent cascades of degraded responses, showing that aligning support with cognitive states improves both the outcomes and the user experience. This enables more effective, personalized large language model (LLM) support in survey-based research.

With the increasing spread of AR head-mounted displays suitable for everyday use, interaction with information becomes ubiquitous, even while walking. However, this requires constant shifts of our attention between walking and interacting with virtual information to fulfill both tasks adequately. Accordingly, we as a community need a thorough understanding of the mutual influences of walking and interacting with digital information to design safe yet effective interactions. Thus, we systematically investigate the effects of different AR anchors (hand, head, torso) and task difficulties on user experience and performance. We engage participants (n=26) in a dual-task paradigm involving a visual working memory task while walking. We assess the impact of dual-tasking on both virtual and walking performance, and subjective evaluations of mental and physical load. Our results show that head-anchored AR content least affected walking while allowing for fast and accurate virtual task interaction, while hand-anchored content increased reaction times and workload.

As autonomous agents, from self-driving cars to virtual assistants, become increasingly present in everyday life, safe and effective collaboration depends on human understanding of agents’ intentions. Current intent communication approaches are often rigid, agent-specific, and narrowly scoped, limiting their adaptability across tasks, environments, and user preferences. A key gap remains: existing models of what to communicate are rarely linked to systematic choices of how and when to communicate, preventing the development of generalizable, multi-modal strategies. In this paper, we introduce a multidimensional design space for intent communication structured along three dimensions: Transparency (what is communicated), Abstraction (when), and Modality (how). We apply this design space to three distinct human-agent collaboration scenarios: (a) bystander interaction, (b) cooperative tasks, and (c) shared control, demonstrating its capacity to generate adaptable, scalable, and cross-domain communication strategies. By bridging the gap between intent content and communication implementation, our design space provides a foundation for designing safer, more intuitive, and more transferable agent-human interactions.

Virtual reality training systems require the careful design of content presentation, user embodiment, and overall user experience. We explore the impact of different perspectives (first-person and third-person) and virtual self-visualization techniques (VSVTs: mirrors and external avatars) on user embodiment, performance and experience. In a study with 28 participants learning karate movements, we tested four combinations of these factors. Results indicate that perspective influences visual focus and embodiment, while VSVTs affect movement execution, particularly in the third-person avatar condition. Measurements of physiological activity, workload, presence, and enjoyment found no significant overall advantages for any of the conditions. Interviews revealed that most participants preferred the familiar first-person mirror combination, although participants in third-person perspective focused more on their own body and noted the helpfulness of this viewpoint. The study demonstrates that alternative perspectives and visualization techniques offer valuable training options, as these conditions did not produce significant differences in measured cognitive load when compared with each other. Future VR training systems should incorporate interactive feedback and customization options to accommodate individual preferences and optimize learning experiences.

Virtual reality (VR) finds various applications in productivity, entertainment, and training, often requiring substantial working memory and attentional resources. Effective task performance in VR relies on prioritizing relevant information and suppressing distractions through internal attention. However, current VR systems fail to account for the impact of working memory loads, leading to over or under-stimulation. In this work, we designed an adaptive system using Electroencephalography (EEG) correlates of external and internal attention to support working memory tasks. Participants engaged in a visual working memory N-Back task, where we adapted the visual complexity of distracting elements. Our study demonstrated that EEG frontal theta and parietal alpha frequency bands effectively adjust dynamic visual complexity. The adaptive system improved task performance and reduced perceived workload compared to a reverse adaptation. Furthermore, we trained a Linear Discriminant Analysis (LDA) model and achieved a classification accuracy of 79.4% for distinguishing internal and external attention states using EEG frequency features, demonstrating the feasibility of EEG-based models for real-time attention state detection. These results highlight the potential of EEG-based adaptive systems to balance distraction management and maintain user engagement without causing cognitive overload. • Developed a VR system using EEG to balance performance and engagement. • Improved task performance with subtle, natural adaptations to environment factors. • Achieved 79.4% accuracy classifying attention states using an LDA model.

Physiologically-adaptive Virtual Reality systems dynamically adjust virtual content based on users’ physiological signals to enhance interaction and achieve specific goals. However, as different users’ cognitive states may underlie multivariate physiological patterns, adaptive systems necessitate a multimodal evaluation to investigate the relationship between input physiological features and target states for efficient user modeling. Here, we investigated a multimodal dataset (EEG, ECG, and EDA) while interacting with two different adaptive systems adjusting the environmental visual complexity based on EDA. Increased visual complexity led to increased alpha power and alpha-theta ratio, reflecting increased mental fatigue and workload. At the same time, EDA exhibited distinct dynamics with increased tonic and phasic components. Integrating multimodal physiological measures for adaptation evaluation enlarges our understanding of the impact of system adaptation on users’ physiology and allows us to account for it and improve adaptive system design and optimization algorithms.

When using Head-Mounted Displays (HMDs), users may not always notice or report visual discomfort by blurred vision through unadjusted lenses, motion sickness, and increased eye strain. Current measures for visual discomfort rely on users’ self-reports those susceptible to subjective differences and lack of real-time insights. In this work, we investigate if Electroencephalography (EEG) can objectively measure visual discomfort by sensing Event-Related Potentials (ERPs). In a user study ($\mathrm{N}=20$), we compare four different levels of Gaussian blur in a user study while measuring ERPs at occipito-parietal EEG electrodes. The findings reveal that specific ERP components (i.e., P1, N2, and P3) discriminated discomfort-related visual stimuli and indexed increased load on visual processing and fatigue. We conclude that time-locked brain activity can be used to evaluate visual discomfort and propose EEG-based automatic discomfort detection and prevention tools.

Future VR environments envision adaptive and personalized interactions.To this aim, attention detection in VR settings would allow for diverse applications and improved usability.However, attention-aware VR systems based on EEG data suffer from long training periods, hindering generalizability and widespread adoption.This work addresses the challenge of person-independent, training-free VR BCI classifying internal and external attention in VR.We compared the performance of four classifiers on an EEG dataset (N=24) featuring internal and external attention labeled classes.With the goal of online adaptation, we tested overall accuracy, different window lengths of the data, and training split to optimize the trade-off between window length and classification accuracy.Our results show that models using a complete EEG band combination consistently achieve the highest accuracy, with Linear Discriminant Analysis particularly benefiting from full-band data.The window length impacts most models' performance with short windows.LDA achieved optimal accuracy around 6.3 seconds, SVM and NN around 6.5 and 6 seconds, respectively, and RF reached stability at 6 seconds.Lastly, increasing training data ratios improved accuracy gains consistently across models.We discuss the potential of machine learning to model EEG correlates of internal and external attention as online inputs for adaptive VR systems.

Biosensing technologies are on their way to becoming ubiquitous in multimedia interaction.These technologies capture physiological data, such as heart rate, breathing, skin conductance, and brain activity.Researchers are exploring biosensing from perspectives including engineering, design, medicine, mental health, consumer products, and interactive art.These technologies can enhance our interactions, allowing us to connect with our bodies and others around us across diverse application areas.However, the integration of biosignals in HCI presents new challenges pertaining to choosing what data we capture, interpreting these data, its representation, application areas, and ethics.There is a need to synthesize knowledge across diverse perspectives of researchers and designers spanning multiple domains and to map a landscape of the challenges and opportunities of this research area.The goal of this workshop is to exchange knowledge in the research community, introduce novices to this emerging field, and build a future research agenda.

Physical training in virtual environments, such as VR, has gained popularity, especially due to the coronavirus pandemic. VR training offers new opportunities compared to traditional methods, including the use of different perspectives, mirrors, and avatars to enhance the understanding of personal movements. However, the interaction of these elements has been less studied. To address this, we developed VReflect, a VR environment that uses mirrors and avatars as virtual self-visualization techniques (VSVT) to improve self-awareness during movement training. In a preliminary study on learning beginner karate movements, we tested four combinations of perspectives and VSVTs. The results indicate that neither first-person nor third-person perspectives can be universally recommended, which is in alignment with previous work. Interviews revealed a preference for the traditional combination of mirrors and first-person perspective.

While Mixed Reality allows the seamless blending of digital content in users' surroundings, it is unclear if its fusion with physical information impacts users' perceptual and cognitive resources differently. While the fusion of digital and physical objects provides numerous opportunities to present additional information, it also introduces undesirable side effects, such as split attention and increased visual complexity. We conducted a visual search study in three manifestations of mixed reality (Augmented Reality, Augmented Virtuality, Virtual Reality) to understand the effects of the environment on visual search behavior. We conducted a multimodal evaluation measuring Fixation-Related Potentials (FRPs), alongside eye tracking to assess search efficiency, attention allocation, and behavioral measures. Our findings indicate distinct patterns in FRPs and eye-tracking data that reflect varying cognitive demands across environments. Specifically, AR environments were associated with increased workload, as indicated by decreased FRP - P3 amplitudes and more scattered eye movement patterns, impairing users' ability to identify target information efficiently. Participants reported AR as the most demanding and distracting environment. These insights inform design implications for MR adaptive systems, emphasizing the need for interfaces that dynamically respond to user cognitive load based on physiological inputs.

Mixed reality enables users to immerse themselves in high-workload interaction spaces like office work scenarios. We envision physiologically adaptive systems that can move users into different mixed reality manifestations, to improve their focus on the primary task. However, it is unclear which manifestation is most conducive for high productivity and engagement. In this work, we evaluate whether physiological indicators for engagement can be discriminated for different manifestations. For this, we engaged participants in a typing task in three different mixed reality manifestations (augmented reality, augmented virtuality, virtual reality) and monitored physiological correlates (EEG, ECG, and eye tracking) of users' engagement and workload. We found that users achieved best typing performances in augmented reality and augmented virtuality. At the same time, physiological engagement peaked in augmented virtuality, while workload decreased. We conclude that augmented virtuality strikes a good balance between the different manifestations, as it facilitates displaying the physical keyboard for improved typing performance and, at the same time, allows one to block out the real world, removing many real-world distractors.

Mixed Reality allows us to integrate virtual and physical content into users' environments seamlessly. Yet, how this fusion affects perceptual and cognitive resources and our ability to find virtual or physical objects remains uncertain. Displaying virtual and physical information simultaneously might lead to divided attention and increased visual complexity, impacting users' visual processing, performance, and workload. In a visual search task, we asked participants to locate virtual and physical objects in Augmented Reality and Augmented Virtuality to understand the effects on performance. We evaluated search efficiency and attention allocation for virtual and physical objects using event-related potentials, fixation and saccade metrics, and behavioral measures. We found that users were more efficient in identifying objects in Augmented Virtuality, while virtual objects gained saliency in Augmented Virtuality. This suggests that visual fidelity might increase the perceptual load of the scene. Reduced amplitude in distractor positivity ERP, and fixation patterns supported improved distractor suppression and search efficiency in Augmented Virtuality. We discuss design implications for mixed reality adaptive systems based on physiological inputs for interaction.

During listening to music, the brain expects specific acoustic events based on learned musical rules. During music performance expectancy is additionally created based on motor action by linking keypresses to their sounds. We investigated EEG (Electroencephalography) signals to auditory expectancy violations in piano performance and perception. In our study, pianists experienced manipulations of different acoustic features, such as pitch and loudness, during playing and listening to piano sequences. We found that manipulations during performance elicited deflections with stronger amplitudes compared to manipulations during perception indicating that the action of producing sounds strengthens auditory expectancy. Loudness manipulations, violating musical regularity, elicited deflections with smaller latencies compared to pitch manipulations, which violate harmonic expectancy, suggesting that the brain processes expectancy violations of distinct acoustic features in a different way. These EEG signatures may prove useful for applications in intelligent music interfaces by providing information about sensory-motor accuracy.

Social media platforms use short, highly engaging videos to catch users’ attention. While the short-form video feeds popularized by TikTok are rapidly spreading to other platforms, we do not yet understand their impact on cognitive functions. We conducted a between-subjects experiment (N = 60) investigating the impact of engaging with TikTok, Twitter, and YouTube while performing a Prospective Memory task (i.e., executing a previously planned action). The study required participants to remember intentions over interruptions. We found that the TikTok condition significantly degraded the users’ performance in this task. As none of the other conditions (Twitter, YouTube, no activity) had a similar effect, our results indicate that the combination of short videos and rapid context-switching impairs intention recall and execution. We contribute a quantified understanding of the effect of social media feed format on Prospective Memory and outline consequences for media technology designers to not harm the users’ memory and wellbeing.

Virtual reality experiences increasingly use physiological data for virtual environment adaptations to evaluate user experience and immersion. Previous research required complex medical-grade equipment to collect physiological data, limiting real-world applicability. To overcome this, we present SensCon for skin conductance and heart rate data acquisition. To identify the optimal sensor location in the controller, we conducted a first study investigating users' controller grasp behavior. In a second study, we evaluated the performance of SensCon against medical-grade devices in six scenarios regarding user experience and signal quality. Users subjectively preferred SensCon in terms of usability and user experience. Moreover, the signal quality evaluation showed satisfactory accuracy across static, dynamic, and cognitive scenarios. Therefore, SensCon reduces the complexity of capturing and adapting the environment via real-time physiological data. By open-sourcing SensCon, we enable researchers and practitioners to adapt their virtual reality environment effortlessly. Finally, we discuss possible use cases for virtual reality-embedded physiological sensing.

Biocybernetic loops encompass users' state detection and system adaptation based on physiological signals. Current adaptive systems limit the adaptation to task features such as task difficulty or multitasking demands. However, virtual reality allows the manipulation of task-irrelevant elements in the environment. We present a physiologically adaptive system that adjusts the virtual environment based on physiological arousal, i.e., electrodermal activity. We conducted a user study with our adaptive system in social virtual reality to verify improved performance. Here, participants completed an n-back task, and we adapted the visual complexity of the environment by changing the number of non-player characters. Our results show that an adaptive virtual reality can control users' comfort, performance, and workload by adapting the visual complexity based on physiological arousal. Thus, our physiologically adaptive system improves task performance and perceived workload. Finally, we embed our findings in physiological computing and discuss applications in various scenarios.

Social games benefit from social connectedness between players because it improves the gaming experience and increases enjoyment. In virtual reality (VR), various approaches, such as avatars, are developed for multi-player games to increase social connectedness. However, these approaches are lacking in single-player games. To increase social connectedness in such games, our work explores the visualization of physiological data from asynchronous players, i.e., electrocardiogram (ECG). We identified two visualization dimensions, the number of players, and the visualization style, after a design workshop with experts (N=4) and explored them in a single-user virtual escape room game. We spatially and temporally integrated the visualizations and compared two times two visualizations against a baseline condition without visualization in a within-subject lab study (N=34). All but one visualization significantly increased participants’ feelings of social connectedness. Heart icons triggered the strongest feeling of connectedness, understanding, and perceived support in playing the game.

Augmented Reality (AR) embeds virtual content in physical spaces, including virtual agents that are known to exert a social presence on users. Existing design guidelines for AR rarely consider the social implications of an agent’s personal space (PS) and that it can impact user behavior and arousal. We report an experiment (N=54) where participants interacted with agents in an AR art gallery scenario. When participants approached six virtual agents (i.e., two males, two females, a humanoid robot, and a pillar) to ask for directions, we found that participants respected the agents’ PS and modulated interpersonal distances according to the human-like agents’ perceived gender. When participants were instructed to walk through the agents, we observed heightened skin-conductance levels that indicate physiological arousal. These results are discussed in terms of proxemic theory that result in design recommendations for implementing pervasive AR experiences with virtual agents.

Dynamic balance is an essential skill for the human upright gait; therefore, regular balance training can improve postural control and reduce the risk of injury. Even slight variations in walking conditions like height or ground conditions can significantly impact walking performance. Virtual reality is used as a helpful tool to simulate such challenging situations. However, there is no agreement on design strategies for balance training in virtual reality under stressful environmental conditions such as height exposure. We investigate how two different training strategies, imitation learning, and gamified learning, can help dynamic balance control performance across different stress conditions. Moreover, we evaluate the stress response as indexed by peripheral physiological measures of stress, perceived workload, and user experience. Both approaches were tested against a baseline of no instructions and against each other. Thereby, we show that a learning-by-imitation approach immediately helps dynamic balance control, decreases stress, improves attention focus, and diminishes perceived workload. A gamified approach can lead to users being overwhelmed by the additional task. Finally, we discuss how our approaches could be adapted for balance training and applied to injury rehabilitation and prevention.