Exploration of the Virtual Reality Teleportation Methods Using Hand-Tracking, Eye-Tracking, and EEG

References

• Al-Qaysi, Z., Zaidan, B., Zaidan, A., & Suzani, M. (2018). A review of disability EEG based wheelchair control system: Coherent taxonomy, open challenges and recommendations. Computer Methods and Programs in Biomedicine, 164, 221–237.
   PubMed Web of Science ®Google Scholar
• Al-Saegh, A., Dawwd, S. A., & Abdul-Jabbar, J. M. (2021). Deep learning for motor imagery EEG-based classification: A review. Biomedical Signal Processing and Control, 63, 102172. https://doi.org/10.1016/j.bspc.2020.102172
   Web of Science ®Google Scholar
• Baker, H. (2020, August). The line review: A wonderful Quest experience with hand tracking. https://uploadvr.com/the-line-oculus-quest-hand-tracking/
   Google Scholar
• Bernal, G., Hidalgo, N., Russomanno, C., & Maes, P. (2022). Galea: A physiological sensing system for behavioral research in virtual environments. In 2022 IEEE Virtual Reality (VR) (pp. 66–76). IEEE.
   Google Scholar
• Blattgerste, J., Renner, P., & Pfeiffer, T. (2018). Advantages of eye-gaze over head-gaze-based selection in virtual and augmented reality under varying field of views. In Proceedings of the Workshop on Communication by Gaze Interaction (pp. 1–9). ACM. https://doi.org/10.1145/3206343.3206349
   Google Scholar
• Boletsis, C. (2017). The new era of virtual reality locomotion: A systematic literature review of techniques and a proposed typology. Multimodal Technologies and Interaction, 1(4), 24. https://doi.org/10.3390/mti1040024
   Google Scholar
• Boletsis, C. (2020). A user experience questionnaire for VR locomotion: Formulation and preliminary evaluation. In International Conference on Augmented Reality, Virtual Reality and Computer Graphics (pp. 157–167). Springer. https://doi.org/10.1007/978-3-030-58465-8_11
   Google Scholar
• Bozgeyikli, E., Raij, A., Katkoori, S., & Dubey, R. (2016a). Locomotion in virtual reality for individuals with autism spectrum disorder. In Proceedings of the 2016 Symposium on Spatial User Interaction (pp. 33–42). ACM. https://doi.org/10.1145/2983310.2985763
   Google Scholar
• Bozgeyikli, E., Raij, A., Katkoori, S., & Dubey, R. (2016b). Point & teleport locomotion technique for virtual reality. In Proceedings of the 2016 annual symposium on computer-human interaction in play (pp. 205–216). ACM.
   Google Scholar
• Bremer, G., Stein, N., & Lappe, M. (2021). Predicting future position from natural walking and eye movements with machine learning. In 2021 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR) (pp. 19–28). IEEE. https://doi.org/10.1109/AIVR52153.2021.00013
   Google Scholar
• Brooke, J. (2013). SUS: A retrospective. Journal of Usability Studies, 8(2), 29–40. https://doi.org/10.5555/2817912.2817913
   Google Scholar
• Buttussi, F., & Chittaro, L. (2021). Locomotion in place in virtual reality: A comparative evaluation of joystick, teleport, and leaning. IEEE Transactions on Visualization and Computer Graphics, 27(1), 125–136. https://doi.org/10.1109/TVCG.2019.2928304
   PubMed Web of Science ®Google Scholar
• Caggianese, G., Capece, N., Erra, U., Gallo, L., & Rinaldi, M. (2020). Freehand-steering locomotion techniques for immersive virtual environments: A comparative evaluation. International Journal of Human–Computer Interaction, 36(18), 1734–1755. https://doi.org/10.1080/10447318.2020.1785151
   Web of Science ®Google Scholar
• Caggianese, G., Gallo, L., & Neroni, P. (2015). Design and preliminary evaluation of free-hand travel techniques for wearable immersive virtual reality systems with egocentric sensing. In International Conference on Augmented and Virtual Reality (pp. 399–408). Springer. https://doi.org/10.1007/978-3-319-22888-4_29
   Google Scholar
• Cao, S. (2021, May). I turned on a lamp with my BRAINWAVES at CES 2020-Here’s how it happened. Observer. https://bit.ly/3sFyCMp
   Google Scholar
• Cardoso, J. C. (2016). Comparison of gesture, gamepad, and gaze-based locomotion for vr worlds. In Proceedings of the 22nd ACM Conference on Virtual Reality Software and Technology (pp. 319–320). ACM.
   Google Scholar
• Cattan, G., Mendoza, C., Andreev, A., & Congedo, M. (2018). Recommendations for integrating a p300-based brain computer interface in virtual reality environments for gaming. Computers, 7(2), 34. https://doi.org/10.3390/computers7020034
   Web of Science ®Google Scholar
• Chen, Y., Yang, C., Ye, X., Chen, X., Wang, Y., & Gao, X. (2021). Implementing a calibration-free SSVEP-based BCI system with 160 targets. Journal of Neural Engineering, 18(4), 046094. https://doi.org/10.1088/1741-2552/ac0bfa
   Web of Science ®Google Scholar
• Chumerin, N., Manyakov, N. V., Van Vliet, M., Robben, A., Combaz, A., & Van Hulle, M. M. (2013). Steady-state visual evoked potential-based computer gaming on a consumer-grade EEG device. IEEE Transactions on Computational Intelligence and AI in Games, 5(2), 100–110. https://doi.org/10.1109/TCIAIG.2012.2225623
   Web of Science ®Google Scholar
• Daws, R. (2022, January). HTC uses Private 5G and edge computing for high-fidelity wireless VR. TELECOMS. https://bit.ly/3hD5IpL
   Google Scholar
• Dehais, F., Ladouce, S., Darmet, L., Nong, T.-V., Ferraro, G., Torre Tresols, J., Labedan, P. (2022). Dual Passive Reactive Brain-Computer Interface: A Novel Approach to Human-Machine Symbiosis. Frontiers in Neuroergonomics, 3, 824780. https://doi.org/10.3389/fnrgo.2022.824780
   Google Scholar
• Di Luca, M., Seifi, H., Egan, S., & Gonzalez-Franco, M. (2021). Locomotion vault: The extra mile in analyzing VR locomotion techniques. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1–10). ACM.
   Google Scholar
• Ferracani, A., Pezzatini, D., Bianchini, J., Biscini, G., & Del Bimbo, A. (2016). Locomotion by natural gestures for immersive virtual environments. In Proceedings of the 1st international workshop on multimedia alternate realities (pp. 21–24). ACM. https://doi.org/10.1145/2983298.2983307
   Google Scholar
• Freiwald, J. P., Ariza, O., Janeh, O., & Steinicke, F. (2020). Walking by cycling: A novel in-place locomotion user interface for seated virtual reality experiences. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–12). ACM. https://doi.org/10.1145/3313831.3376574
   Google Scholar
• Gehrke, L., Akman, S., Lopes, P., Chen, A., Singh, A. K., & Chen, H.-T. (2019). Detecting visuo-haptic mismatches in virtual reality using the prediction error negativity of event-related brain potentials. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–11). ACM. https://doi.org/10.1145/3290605.3300657
   Google Scholar
• Hayhoe, M. M., & Ballard, D. H. (2011). Mechanisms of gaze control in natural vision. In S. P. Liversedge, I. D. Gilchrist, & S. Everling (Eds.), The Oxford handbook of eye movements (pp. 607–617). Oxford University Press.
   Google Scholar
• Hollands, M. A., Patla, A. E., & Vickers, J. N. (2002). “Look where you’re going!”: Gaze behaviour associated with maintaining and changing the direction of locomotion. Experimental Brain Research, 143(2), 221–230.
   PubMed Web of Science ®Google Scholar
• Ian, H. (2020, May). Waltz of the wizard quest hand-tracking impressions: Magic in-hand. https://uploadvr.com/waltz-wizard-magic-quest/
   Google Scholar
• IJsselsteijn, W. A., de Kort, Y. A. W., & Poels, K. (2013). The game experience questionnaire. Technische Universiteit Eindhoven.
   Google Scholar
• Jacob, R. J. (1990). What you look at is what you get: Eye movement-based interaction techniques. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 11–18). ACM. https://doi.org/10.1145/97243.97246
   Google Scholar
• Jacob, R. J. (1991). The use of eye movements in human-computer interaction techniques: what you look at is what you get. ACM Transactions on Information Systems, 9(2), 152–169. https://doi.org/10.1145/123078.128728
   Web of Science ®Google Scholar
• Jacob, R. J. (1995). Eye tracking in advanced interface design. Virtual Environments and Advanced Interface Design, 258, 288.
   Google Scholar
• Jeong, D. K., Yoo, S., & Jang, Y. (2018). VR sickness measurement with EEG using DNN algorithm. In Proceedings of the 24th ACM Symposium on Virtual Reality Software and Technology (pp. 1–2). ACM.
   Google Scholar
• Jo, A., & Chae, B. Y. (2020). Introduction to real time user interaction in virtual reality powered by brain computer interface technology. In ACM SIGGRAPH 2020 Real-Time Live! (pp. 1–1). ACM. https://doi.org/10.1145/3407662.3407754
   Google Scholar
• Kappenman, E. S., Farrens, J. L., Zhang, W., Stewart, A. X., & Luck, S. J. (2021). ERP core: An open resource for human event-related potential research. NeuroImage, 225, 117465. https://doi.org/10.1016/j.neuroimage.2020.117465
   PubMed Web of Science ®Google Scholar
• Kim, D., Kim, J., Shin, J.-E., Yoon, B., Lee, J., & Woo, W. (2022). Effects of virtual room size and objects on relative translation gain thresholds in redirected walking. In 2022 IEEE Conference on Virtual Reality and 3D User Interfaces (VR) (pp. 379–388). IEEE.
   Google Scholar
• Kim, H., & Im, C.-H. (2021). Influence of the number of channels and classification algorithm on the performance robustness to electrode shift in steady-state visual evoked potential-based brain-computer interfaces. Frontiers in Neuroinformatics, 15, 750839. https://doi.org/10.3389/fninf.2021.750839
   PubMed Web of Science ®Google Scholar
• Kim, Y. M., Rhiu, I., & Yun, M. H. (2020). A systematic review of a virtual reality system from the perspective of user experience. International Journal of Human–Computer Interaction, 36(10), 893–910. https://doi.org/10.1080/10447318.2019.1699746
   Web of Science ®Google Scholar
• Kim, Y. R., Choi, H., Chang, M., & Kim, G. J. (2020). Applying touchscreen based navigation techniques to mobile virtual reality with open clip-on lenses. Electronics, 9(9), 1448. https://doi.org/10.3390/electronics9091448
   Web of Science ®Google Scholar
• Kitson, A., Hashemian, A. M., Stepanova, E. R., Kruijff, E., & Riecke, B. E. (2017). Lean into it: Exploring leaning-based motion cueing interfaces for virtual reality movement. In 2017 IEEE Virtual Reality (VR) (pp. 215–216). IEEE. https://doi.org/10.1109/VR.2017.7892253
   Google Scholar
• Klamka, K., Siegel, A., Vogt, S., Göbel, F., Stellmach, S., & Dachselt, R. (2015). Look & pedal: Hands-free navigation in zoomable information spaces through gaze-supported foot input. In Proceedings of the 2015 ACM on International Conference on Multimodal Interaction (pp. 123–130). ACM.
   Google Scholar
• Kozhemiako, N., Nunes, A. S., Samal, A., Rana, K. D., Calabro, F. J., Hämäläinen, M. S., Khan, S., & Vaina, L. M. (2020). Neural activity underlying the detection of an object movement by an observer during forward self-motion: Dynamic decoding and temporal evolution of directional cortical connectivity. Progress in Neurobiology, 195, 101824. https://doi.org/10.1016/j.pneurobio.2020.101824
   PubMed Web of Science ®Google Scholar
• Kytö, M., Ens, B., Piumsomboon, T., Lee, G. A., & Billinghurst, M. (2018). Pinpointing: Precise head-and eye-based target selection for augmented reality. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1–14). ACM.
   Google Scholar
• Lee, J., Leonard, C. J., Luck, S. J., & Geng, J. J. (2018). Dynamics of feature-based attentional selection during color–shape conjunction search. Journal of Cognitive Neuroscience, 30(12), 1773–1787. https://doi.org/10.1162/jocn_a_01318
   PubMed Web of Science ®Google Scholar
• Li, C., Zheng, S., & Pan, X. (2021). Using eye gaze for navigation: A user study inspired by lucid dreams. In 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) (pp. 665–666). IEEE. https://doi.org/10.1109/VRW52623.2021.00215
   Google Scholar
• Li, Y., Bin, G., Gao, X., Hong, B., & Gao, S. (2011). Analysis of phase coding SSVEP based on canonical correlation analysis (CCA). In 2011 5th International IEEE/EMBS Conference on Neural Engineering (pp. 368–371). IEEE.
   Google Scholar
• Liao, L.-D., Chen, C.-Y., Wang, I.-J., Chen, S.-F., Li, S.-Y., Chen, B.-W., Chang, J.-Y., & Lin, C.-T. (2012). Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors. Journal of Neuroengineering and Rehabilitation, 9(1), 5–12. https://doi.org/10.1186/1743-0003-9-5
   PubMedGoogle Scholar
• Lim, H. K., Ji, K., Woo, Y. S., Han, D.-U., Lee, D.-H., Nam, S. G., & Jang, K.-M. (2021). Test-retest reliability of the virtual reality sickness evaluation using electroencephalography (EEG). Neuroscience Letters, 743, 135589. https://doi.org/10.1016/j.neulet.2020.135589
   PubMed Web of Science ®Google Scholar
• Linn, A. (2017). Gaze teleportation in virtual reality.
   Google Scholar
• Loup, G., & Loup-Escande, E. (2019). Effects of travel modes on performances and user comfort: A comparison between ArmSwinger and teleporting. International Journal of Human–Computer Interaction, 35(14), 1270–1278. https://doi.org/10.1080/10447318.2018.1519164
   Web of Science ®Google Scholar
• Lu, X., Yu, D., Liang, H.-N., Xu, W., Chen, Y., & Li, X. (2020). Exploration of hands-free text entry techniques for virtual reality. In 2020 IEEE International Symposium on Mixed and Augmented Reality (ISMAR) (pp. 344–349). IEEE. https://doi.org/10.1109/ISMAR50242.2020.00061
   Google Scholar
• Marius’t Hart, B., & Einhäuser, W. (2012). Mind the step: Complementary effects of an implicit task on eye and head movements in real-life gaze allocation. Experimental Brain Research, 223(2), 233–249.
   PubMed Web of Science ®Google Scholar
• Marín-Morales, J., Higuera-Trujillo, J. L., Greco, A., Guixeres, J., Llinares, C., Scilingo, E. P., Alcañiz, M., & Valenza, G. (2018). Affective computing in virtual reality: Emotion recognition from brain and heartbeat dynamics using wearable sensors. Scientific Reports, 8(1), 1–15. https://doi.org/10.1038/s41598-018-32063-4
   PubMedGoogle Scholar
• Marín-Morales, J., Llinares, C., Guixeres, J., & Alcañiz, M. (2020). Emotion recognition in immersive virtual reality: From statistics to affective computing. Sensors, 20(18), 5163. https://doi.org/10.3390/s20185163
   PubMed Web of Science ®Google Scholar
• Meta. (2022, May). Project cambria preview - mixed reality with presence platform. https://www.youtube.com/watch?v=tgJ7m0Phd64
   Google Scholar
• Mirza, I. A., Tripathy, A., Chopra, S., Michelle, D., Rajagopalan, K., & D’Souza, A. (2015). Mind-controlled wheelchair using an EEG headset and arduino microcontroller. In 2015 International Conference on Technologies for Sustainable Development (ICTSD) (pp. 1–5). IEEE. https://doi.org/10.1109/ICTSD.2015.7095887
   Google Scholar
• Pan, J., Gao, X., Duan, F., Yan, Z., & Gao, S. (2011). Enhancing the classification accuracy of steady-state visual evoked potential-based brain–computer interfaces using phase constrained canonical correlation analysis. Journal of Neural Engineering, 8(3), 036027.
   PubMed Web of Science ®Google Scholar
• Sauro, J. (2011). A practical guide to the system usability scale: Background, benchmarks & best practices. Measuring Usability LLC.
   Google Scholar
• Schäfer, A., Reis, G., & Stricker, D. (2021). Controlling teleportation-based locomotion in virtual reality with hand gestures: A comparative evaluation of two-handed and one-handed techniques. Electronics, 10(6), 715. https://doi.org/10.3390/electronics10060715
   Web of Science ®Google Scholar
• Shim, J.-Y., & Min, J.-S. (2021). Design of reality object and virtual object control system using eeg. Journal of Korea Game Society, 21(1), 91–98. https://doi.org/10.7583/JKGS.2021.21.1.91
   Google Scholar
• Si-Mohammed, H., Lopes-Dias, C., Duarte, M., Argelaguet, F., Jeunet, C., & Casiez, G. (2020). Detecting system errors in virtual reality using EEG through error-related potentials. In 2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR) (pp. 653–661). IEEE. https://doi.org/10.1109/VR46266.2020.00088
   Google Scholar
• Si-Mohammed, H., Petit, J., Jeunet, C., Argelaguet, F., Spindler, F., Evain, A., Roussel, N., Casiez, G., & Lecuyer, A. (2018). Towards BCI-based interfaces for augmented reality: Feasibility, design and evaluation. IEEE Transactions on Visualization and Computer Graphics, 26(3), 1608–1621. https://doi.org/10.1109/TVCG.2018.2873737
   PubMed Web of Science ®Google Scholar
• Singh, A. K., Chen, H.-T., Cheng, Y.-F., King, J.-T., Ko, L.-W., Gramann, K., & Lin, C.-T. (2018). Visual appearance modulates prediction error in virtual reality. IEEE Access, 6, 24617–24624. https://doi.org/10.1109/ACCESS.2018.2832089
   Web of Science ®Google Scholar
• Stein, N., Bremer, G., & Lappe, M. (2022). Eye tracking-based lstm for locomotion prediction in VR. In 2022 IEEE Virtual Reality (VR) (pp. 493–503). IEEE.
   Google Scholar
• Stellmach, S., & Dachselt, R. (2012). Designing gaze-based user interfaces for steering in virtual environments. In Proceedings of the Symposium on Eye Tracking Research and Applications (pp. 131–138). ACM. https://doi.org/10.1145/2168556.2168577
   Google Scholar
• Tariq, M., Trivailo, P. M., & Simic, M. (2018). EEG-based BCI control schemes for lower-limb assistive-robots. Frontiers in Human Neuroscience, 12, 312. https://doi.org/10.3389/fnhum.2018.00312
   PubMed Web of Science ®Google Scholar
• Templeman, J. N., Denbrook, P. S., & Sibert, L. E. (1999). Virtual locomotion: Walking in place through virtual environments. Presence: Teleoperators and Virtual Environments, 8(6), 598–617. https://doi.org/10.1162/105474699566512
   Web of Science ®Google Scholar
• Tregillus, S., Al Zayer, M., & Folmer, E. (2017). Handsfree omnidirectional VR navigation using head tilt. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 4063–4068). ACM.
   Google Scholar
• Tremmel, C., Herff, C., Sato, T., Rechowicz, K., Yamani, Y., & Krusienski, D. J. (2019). Estimating cognitive workload in an interactive virtual reality environment using EEG. Frontiers in Human Neuroscience, 13, 401. Frontiers. https://doi.org/10.3389/fnhum.2019.00401
   PubMed Web of Science ®Google Scholar
• von Willich, J., Schmitz, M., Müller, F., Schmitt, D., & Mühlhäuser, M. (2020). Podoportation: Foot-based locomotion in virtual reality. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–14). ACM.
   Google Scholar
• Vuillerme, N., Nougier, V., & Camicioli, R. (2002). Veering in human locomotion: Modulatory effect of attention. Neuroscience Letters, 331(3), 175–178. https://doi.org/10.1016/s0304-3940(02)00876-5
   PubMed Web of Science ®Google Scholar
• Ware, C., & Mikaelian, H. H. (1986). An evaluation of an eye tracker as a device for computer input2. In Proceedings of the SIGCHI/GI Conference on Human Factors in Computing Systems and Graphics Interface (pp. 183–188). ACM. https://doi.org/10.1145/30851.275627
   Google Scholar
• Wen, T., & Zhang, Z. (2018). Deep convolution neural network and autoencoders-based unsupervised feature learning of EEG signals. IEEE Access, 6, 25399–25410. https://doi.org/10.1109/ACCESS.2018.2833746
   Web of Science ®Google Scholar
• Witmer, B. G., & Singer, M. J. (1998). Measuring presence in virtual environments: A presence questionnaire. Presence: Teleoperators and Virtual Environments, 7(3), 225–240. https://doi.org/10.1162/105474698565686
   Web of Science ®Google Scholar
• Zank, M., & Kunz, A. (2016). Eye tracking for locomotion prediction in redirected walking. In 2016 IEEE Symposium on 3D User Interfaces (3DUI) (pp. 49–58). IEEE. https://doi.org/10.1109/3DUI.2016.7460030
   Google Scholar
• Zhang, F., Chu, S., Pan, R., Ji, N., Xi, L. (2017). Double hand-gesture interaction for walk-through in VR environment. In 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) (pp. 539–544) IEEE. https://doi.org/10.1109/ICIS.2017.7960051
   Google Scholar
• Zhu, D., Bieger, J., Garcia Molina, G., & Aarts, R. M. (2010). A survey of stimulation methods used in SSVEP-based BCIS. Computational Intelligence and Neuroscience, 2010, 1–12. https://doi.org/10.1155/2010/702357
   Google Scholar