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Modeling the Intent to Interact With VR Using Physiological Features
Abstract
OBJECTIVE: Mixed-Reality (XR) technologies promise a user experience (UX) that rivals the interactive experience with the real-world. The key facilitators in the design of such a natural UX are that the interaction has zero lag and that users experience no excess mental load. This is difficult to achieve due to technical constraints such as motion-to-photon latency as well as false-positives during gesture-based interaction. METHODS: In this paper, we explored the use of physiological features to model the user's intent to interact with a virtual reality (VR) environment. Accurate predictions about when users want to express an interaction intent could overcome the limitations of an interactive device that lags behind the intention of a user. We computed time-domain features from electroencephalography (EEG) and electromyography (EMG) recordings during a grab-and-drop task in VR and cross-validated a Linear Discriminant Analysis (LDA) for three different combinations of (1) EEG, (2) EMG and (3) EEG-EMG features. RESULTS & CONCLUSION: We found the classifiers to detect the presence of a pre-movement state from background idle activity reflecting the users' intent to interact with the virtual objects (EEG: 62 % ± 10 %, EMG: 72 % ± 9 %, EEG-EMG: 69 % ± 10 %) above simulated chance level. The features leveraged in our classification scheme have a low computational cost and are especially useful for fast decoding of users' mental states. Our work is a further step towards a useful classification of users' intent to interact, as a high temporal resolution and speed of detection is crucial. This facilitates natural experiences through zero-lag adaptive interfaces.
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Immersive interactions in virtual reality and driver behavior monitoring in intelligent vehicles rely on HMIP technology to accurately capture complex motion intentions.
“Additionally, immersive interactions in virtual reality (Fig. 1g, h) 50,51 and driver behavior monitoring in intelligent vehicles (Fig. 1i) [52][53][54] rely on HMIP technology to accurately capture complex motion intentions.”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [50]
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- Jonker, The role of AI in mixed and augmented reality interactions
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Chen, S., Peng, C., Yang, B., Lin, J., Zhou, L., Jiang, Z., Liu, Z., Liu, Y., & Tang, L. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-026-00083-5 · Cited by 9
Metadata
- Authors
- Nguyen, W., Gramann, K., & Gehrke, L.
- Affiliations
- Université Paris-Saclay (FR)
- Technische Universität Berlin (DE)
- Venue
- IEEE Transactions on Visualization and Computer Graphics
- Published
- 2024-08
- Volume
- 30
- Issue
- 8
- Pages
- 5893-5900
- ISSN
- 1077-2626 (ISSN-L)
- 1941-0506
- 2160-9306
- OpenAlex
- W4386176470
- Cited by
- 12
- References
- 42
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 2
- FWCI
- 1.80
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Visualization and Computer Graphics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (GR 2627/13-1)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Physical Sciences › Computer Science › Human-Computer Interaction › Virtual Reality Applications and Impacts
- Keywords
- virtual reality interaction, user intention recognition, physiological traits, EEG, electromyography, EEG-EMG fusion, gesture-based interaction, motion-to-photon latency, linear discriminant analysis, adaptive user interfaces
- MeSH terms
- Virtual Reality, Adult, Computer Graphics, Electroencephalography, Electromyography, Female, Humans, Male, Signal Processing, Computer-Assisted, User-Computer Interface, Intention, Young Adult
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