Abstract
Although wearable sensors are omnipresent in consumer market nowadays, due to wearability and comfort issues they often avoid human head. Reliable and robust estimation of human cognitive states and traits, unfortunately, requires positioning electrodes at this sensitive location to capture electrical activity of the brain, eyes, and/or (facial) muscles. Novel augmented and virtual reality (AR/VR) use cases provide a new area that could lower the acceptance threshold of users toward head-worn devices and open new application spaces. To foster this new trend, head-mounted solutions have to offer clear benefits to users and also provide convenience and wearing comfort. Two prototype solutions are discussed in more detail, an AR-enhanced glasses and a VR-compatible electroencephalography (EEG) headset, along with the methods used to assess and improve data quality such that reliable information on brain and eye activity can be extracted. Systems and methods are evaluated through large-scale user experiments during a music festival (Lowlands) in case of AR glasses and a simulated real-life scenario for VR EEG solution. Improvements achieved in ergonomics and signal integrity are clear over state of the art, however, further adaptations of the solutions toward final use case could lead to increased end-user benefits.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Barbara, N., & Camilleri, T. (2016). Interfacing with a speller using EOG glasses. In IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 1069–1074). Budapest: IEEE.
Bulling, A., Roggen, D., & Tröster, G. (2009). Wearable EOG goggles: Eye-based interaction in everyday environments. In Extended Abstracts on Human Factors in Computing Systems (CHI EA ‘09) (pp. 3259–3264). New York: ACM.
Chen, Y.-H., Op de Beeck, M., Vanderheyden, L., Carrette, E., Mihajlović, V., Vanstreels, K., … Hoof, C. (2014). Soft, comfortable polymer dry electrodes for high quality ECG and EEG recording. Sensors, 23758–23780.
Fitzpatrick, T. (1988). The validity and practicality of sun-reactive skin types I through VI. Archives of Dermatology, 124(6), 869–871.
Grillon, C., & Buchsbaum, M. (1986). Computed EEG topogaphy of response to visual and auditory stimuli. Electroencephalography and Clinical Neurophysiology, 42–53.
Huotilainen, M., Winkler, I., Alho, K., Escera, C., Virtanen, J., Ilmoniemi, R., … Naatanen, R. (1998). Combined mapping of human auditory EEG and MEG responses. Electroencephalography and Clinical Neurophysiology, 370–379.
Iáñez, E., Azorin, J., & Perez-Vidal, C. (2013). Using eye movement to control a computer: A design for a lightweight electro-oculogram electrode array and computer interface. PLoS One, e6709.
Ishimaru, S., Kunze, K., Uema, Y., Kise, K., Inam, M., & Tanaka, K. (2014). Smarter eyewear: Using commercial EOG glasses for activity recognition. In ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication (UbiComp ‘14 Adjunct) (pp. 239–242). New York: ACM.
Jantz, J., Molnar, A., & Alcaide, R. (2017). A brain-computer interface for extended reality interfaces. In ACM SIGGRAPH 2017 VR Village (SIGGRAPH ‘17) (p. 2). New York: ACM.
Jins Meme. (2019, February 2). Retrieved from Jins Meme: https://jins-meme.com.
Jung, T.-P., Makeig, S., Humphries, C., Lee, T.-W., McKeown, M., Iragui, V., & Sejnowski, T. (2000). Removing electroencephalographic artifacts by blind source separation. Psychophysiology, 163–178.
Looxid Labs. (2019, February 2). Retrieved from Looxid Labs: https://looxidlabs.com/.
Lupu, R., Irimia, D., Ungureanu, F., Poboroniuc, M., & Moldoveanu, A. (2018). BCI and FES based therapy for stroke rehabilitation using VR facilities. Wireless Communications and Mobile Computing, 8.
MacAskill, M. R., & Anderson, T. J. (2016). Eye movements in neurodegenerative diseases. Current Opinion in Neurology, 61–68.
Makeig, S., Westerfield, M., Jung, T., Enghoff, S., Townsend, J., Courchesne, E., & Sejnowski, T. (2002). Dynamic brain sources of visual evoked responses. Science, 690–694.
Mani, R., Asper, L., & Khuu, S. K. (2018). Deficits in saccades and smooth-pursuit eye movements in adults with traumatic brain injury: A systematic review and meta-analysis. Brain Injury, 1–22.
McEvoy, L., Smith, M., & Gevins, A. (2000). Test-retest reliability of cognitive EEG. Clinical Neurophysiology, 457–463.
Mihajlović, V., Grundlehner, B., Vullers, R., & Penders, J. (2015). Wearable, wireless EEG solutions in daily life applications: What are we missing? IEEE Journal of Biomedical and Health Informatics, 6–21.
Mihajlović, V., Li, H., Grundlehner, B., Penders, J., & Schouten, A. (2013). Investigating the impact of force and movements on impedance magnitude and EEG. Engineering in Medicine and Biology Society (EMBC) (pp. 1466–1469). IEEE.
Mihajlović, V., Patki, S., & Grundlehner, B. (2014). The impact of head movements in EEG and contact impedance: An adaptive filtering solution for motion artifact reduction. Engineering in Medicine and Biology Society (EMBC) (pp. 5064–5067). IEEE.
Neurable. (2019, February 2). Retrieved from Neurable: http://neurable.com/.
Nicander, I., Nyren, M., Emtestam, L., & Ollmar, S. (2006). Baseline electrical impedance measurements at various skin sites—related to age and sex. Skin Research & Technology, 252–258.
Papousek, I., & Schulter, G. (2004). Manipulation of frontal brain asymmetry by cognitive tasks. Brain and Cognition, 43–51.
Pretegiani, E., & Optican, L. M. (2017). Eye movements in Parkinson’s disease and inherited parkinsonian syndromes. Frontiers in Neurology, 1–7.
Sabatos-DeVito, M., Schipul, S., Bulluck, J., Belger, A., & Baranek, G. (2016). Eye tracking reveals impaired attentional disengagement associated with sensory response patterns in children with autism. Journal of Autism and Developmental Disorders, 1319–1333.
Shallice, T., & Evans M E. (1978). The involvement of the frontal lobes in cognitive estimation. Cortex, 294–303.
Sharon, D., Hamalainen, M., Tootell, R., Halgren, E., & Belliveau, J. (2007). The advantage of combining MEG and EEG: Comparison to fMRI in focally-stimulated visual cortex. Neuroimage, 1225–1235.
Tromp, J., Peeters, D., Meyer, A., & Hagoort, P. (2017). The combined use of virtual reality and EEG to study language processing in naturalistic environments. Behavior Research Methods, 862–869.
Witteveen, J., Pradhapan, P., & Mihajlović, V. (2019). Comparison of a Pragmatic and Regression Approach for Wearable EEG Signal Quality Assessment. IEEE Journal of Biomedical and Health Informatics, 1–1.
Xu, J., Mitra, S., Matsumoto, A., Patki, S., Van Hoof, C., Makinwa, K., & Yazicioglu, R. (2014). A wearable 8-channel active-electrode EEG/ETI acquisition system for body area networks. IEEE Journal of Solid-State Circuits, 2005–2016.
Zargari Marandi, R., Madeleine, P., Omland, Ø., Vuillerme, N., & Samani, A. (2018). Eye movement characteristics reflected fatigue development in both young and elderly individuals. Scientific Reports, 1–10.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Pradhapan, P., Witteveen, J., Shahriari, N., Meroni, A., Mihajlović, V. (2020). Neuroergonomic Solutions in AR and VR Applications. In: Nam, C. (eds) Neuroergonomics. Cognitive Science and Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-34784-0_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-34784-0_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34783-3
Online ISBN: 978-3-030-34784-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)