Abstract
A sensory feedback was employed for the present work to remap brain signals into sensory information. In particular, sensorimotor rhythms associated with motor imagery were measured as a mean to interact with an extended reality (XR) environment. The aim for such a neurofeedback was to let the user become aware of his/her ability to imagine a movement. A brain-computer interface based on motor imagery was thus implemented by using a consumer-grade electroencephalograph and by taking into account wearable and portable feedback actuators. Visual and vibrotactile sensory feedback modalities were used simultaneously to provide an engaging multimodal feedback in XR. Both a non-immersive and an immersive version of the system were considered and compared. Preliminary validation was carried out with four healthy subjects participating in a total of four sessions on different days. Experiments were conducted according to a wide-spread synchronous paradigm in which an application provides the timing for the motor imagery tasks. Performance was compared in terms of classification accuracy. Overall, subjects preferred the immersive neurofeedback because it allowed higher concentration during experiments, but there was not enough evidence to prove its actual effectiveness and mean classification accuracy resulted about 65%. Meanwhile, classification accuracy resulted higher with the non-immersive neurofeedback, notably it reached about 75%. Future experiments could extend this comparison to more subjects and more sessions, due to the relevance of possible applications in rehabilitation. Moreover, the immersive XR implementation could be improved to provide a greater sense of embodiment.
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
Kok, P., de Lange, F.P.: Predictive coding in sensory cortex. In: Forstmann, B.U., Wagenmakers, E.-J. (eds.) An Introduction to Model-Based Cognitive Neuroscience, pp. 221–244. Springer, New York (2015). https://doi.org/10.1007/978-1-4939-2236-9_11
Škola, F., Tinková, S., Liarokapis, F.: Progressive training for motor imagery brain-computer interfaces using gamification and virtual reality embodiment. Front. Human Neurosci. 329 (2019)
Kabat, H.: Studies of neuromuscular dysfunction; treatment of chronic multiple sclerosis with neostigmine and intensive muscle re-education. Permanent. Found. Med. Bullet. 5(1), 1–14 (1947)
Kollen, B.J., et al.: The effectiveness of the bobath concept in stroke rehabilitation: what is the evidence? Stroke 40(4), e89–e97 (2009)
Thieme, H., et al.: Mirror therapy for improving motor function after stroke. Cochrane Datab. Syst. Rev. 7 (2018)
Pfurtscheller, G., Neuper, C.: Motor imagery and direct brain-computer communication. Proc. IEEE 89(7), 1123–1134 (2001)
Wolpaw, J.R., et al.: Brain-computer interface technology: a review of the first international meeting. IEEE Trans. Rehabilit. Eng. 8(2), 164–173 (2000)
Wen, D., et al.: Combining brain-computer interface and virtual reality for rehabilitation in neurological diseases: a narrative review. Ann. Phys. Rehabilit. Med. 64(1), 101404 (2021)
McCreadie, K.A., Coyle, D.H., Prasad, G.: Learning to modulate sensorimotor rhythms with stereo auditory feedback for a brain-computer interface. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6711–6714. IEEE (2012)
Elbamby, M.S., Perfecto, C., Bennis, M., Doppler, K.: Toward low-latency and ultra-reliable virtual reality. IEEE Network 32(2), 78–84 (2018)
Teplan, M., et al.: Fundamentals of EEG measurement. Measur. Sci. Rev. 2(2), 1–11 (2002)
Leeb, R., Lee, F., Keinrath, C., Scherer, R., Bischof, H., Pfurtscheller, G.: Brain-computer communication: motivation, aim, and impact of exploring a virtual apartment. IEEE Trans. Neural Syst. Rehabilit. Eng. 15(4), 473–482 (2007)
Cuomo, G., et al.: Motor imagery and gait control in Parkinson’s disease: techniques and new perspectives in neurorehabilitation. Expert Rev. Neurotherap. (2022)
Klem, G.H., Lüders, H.O., Jasper, H., Elger, C., et al.: The ten-twenty electrode system of the international federation. Electroencephalogr. Clin. Neurophysiol. 52(3), 3–6 (1999)
Brunner, C., Leeb, R., Müller-Putz, G., Schlögl, A., Pfurtscheller, G.: BCI competition 2008–Graz data set A. In: Institute for Knowledge Discovery (Laboratory of Brain-Computer Interfaces), vol. 16, pp. 1–6. Graz University of Technology (2008)
Ang, K.K., Chin, Z.Y., Wang, C., Guan, C., Zhang, H.: Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Front. Neurosci. 6, 39 (2012)
Acknowledgement
This work was carried out as part of the “ICT for Health” project, which was financially supported by the Italian Ministry of Education, University and Research (MIUR), under the initiative ‘Departments of Excellence’ (Italian Budget Law no. 232/2016), through an excellence grant awarded to the Department of Information Technology and Electrical Engineering of the University of Naples Federico II, Naples, Italy. The authors thank also thank Giovanni D’Errico and Stefania Di Rienzo for supporting system design and data analyses.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Arpaia, P., Coyle, D., Donnarumma, F., Esposito, A., Natalizio, A., Parvis, M. (2022). Non-immersive Versus Immersive Extended Reality for Motor Imagery Neurofeedback Within a Brain-Computer Interfaces. In: De Paolis, L.T., Arpaia, P., Sacco, M. (eds) Extended Reality. XR Salento 2022. Lecture Notes in Computer Science, vol 13446. Springer, Cham. https://doi.org/10.1007/978-3-031-15553-6_28
Download citation
DOI: https://doi.org/10.1007/978-3-031-15553-6_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15552-9
Online ISBN: 978-3-031-15553-6
eBook Packages: Computer ScienceComputer Science (R0)