Abstract
This study integrates virtual reality (VR) and electroencephalography technology in the design of a virtual space for multi-user rehabilitation. It allows therapists to conduct remote care and provides easy-to-use functions for stroke patients during their rehabilitation process. To analyse patient rehabilitation data, Azure ML Studio, a cloud service for big data prediction and analysis was employed to evaluate and validate the model. The aim of this study is to provide appropriate rehabilitation procedures and plans through a personalised numerical analysis and improve the immediacy of the clinical evaluation of physical therapy. This study covers rehabilitation mechanisms ranging from individual home-based rehabilitation to remote multi-user virtual network rehabilitation. A brain-computer interface is integrated into the design of the VR rehabilitation function and introduces a personalised music recommendation mechanism, allowing patients to adjust their rehabilitation plans according to their physical movements and specific changes in brainwaves during the rehabilitation process. The system can be used to provide healthcare services for seniors in their communities. With the global spread of COVID-19, many medical institutions in Taiwan and other countries have established VR medical simulation centres in the post-pandemic era to provide educational training for medical staff. By investing artificial intelligence (AI) and VR technology, the remote care of smart hospitals can result in innovative processes and opportunities.
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
Similar content being viewed by others
References
Zweifach, S.M., Triola, M.M.: Extended reality in medical education: driving adoption through provider-centered design. Digit. Biomarkers 3(1), 14–21 (2019)
Seyed Esfahani, M., Heydari Khajehpour, S., Manku, T.: Virtual reality in healthcare, what stops hospitals and patients adopting the technology? Case study of National Health Service in Dorset. In: International Society of Professional Innovation Management Conference (2019)
Pinter, D., Kober, S.E., Fruhwirth, V., Berger, L., Damulina, A., Khalil, M., Enzinger, C.: MRI correlates of cognitive improvement after home-based EEG neurofeedback training in patients with multiple sclerosis: a pilot study. J. Neurol. 1–9 (2021)
Hunt, A.M., Fachner, J., Clark-Vetri, R., Raffa, R.B., Rupnow-Kidd, C., Maidhof, C., Dileo, C.: Neuronal effects of listening to entrainment music versus preferred music in patients with chronic cancer pain as measured via EEG and LORETA Imaging. Front. Psychol. 12, 390 (2021)
Chang, W.C., Ko, L.W., Yu, K.H., Ho, Y.C., Chen, C.H., Jong, Y.J., Huang, Y.P.: EEG analysis of mixed-reality music rehabilitation system for post-stroke lower limb therapy. J. Soc. Inform. Display 27(6), 372–380 (2019)
Li, J., Jin, Y., Lu, S., Wu, W., Wang, P.: Building environment information and human perceptual feedback collected through a combined virtual reality (VR) and electroencephalogram (EEG) method. Energy Build. 224, 110259 (2020)
Camargo, A., Carmo, J.F., Rosa-Castro, R.M., Rodrigues, C., Mazzei, L.G., Scalha, T.B., Andrade, A.C.N.B.: The influence of virtual reality on rehabilitation of upper limbs and gait after stroke. J. Innov. Healthcare Manage. 2, 1–16 (2019)
Kern, F., Winter, C., Gall, D., Käthner, I., Pauli, P., Latoschik, M.E.: Immersive virtual reality and gamification within procedurally generated environments to increase motivation during gait rehabilitation. In: 26th IEEE Conference on Virtual Reality and 3D User Interfaces on Proceedings, Osaka, Japan, pp. 500–509 (2019)
Pereira, F., i Badia, S.B., Jorge, C., Cameirão, M.S.: The use of game modes to promote engagement and social involvement in multi-user serious games: a within-person randomized trial with stroke survivors. J. NeuroEng. Rehabil. 18(1), 1–15 (2021)
López-Hernández, J.L., González-Carrasco, I., López-Cuadrado, J.L., Ruiz-Mezcua, B.: Framework for the classification of emotions in people with visual disabilities through brain signals. Front. Neuroinf. 15 (2021)
Caldas, O.I., Aviles, O.F., Rodriguez-Guerrero, C.: Effects of presence and challenge variations on emotional engagement in immersive virtual environments. IEEE Trans. Neural Syst. Rehabil. Eng. 28(5), 1109–1116 (2020)
Marimpis, A.D., Dimitriadis, S.I., Goebel, R.: A multiplex connectivity map of valence-arousal emotional model. IEEE Access 8, 170928–170938 (2020)
Elmali, A.D., Ekizoglu, E., Ciftci, I., Yesilot, N., Coban, O., Baykan, B.: Periodic electroclinical seizures following an ischemic stroke revealed by continuous-EEG. Epilepsy & Behav. Rep. 15, 100428 (2021)
Zabcikova, M.: Visual and auditory stimuli response, measured by Emotiv insight headset. In: MATEC Web of Conferences, vol. 292, pp. 01024. EDP Sciences (2019)
Kumar, J.L.M., Rashid, M., Musa, R.M., Razman, M.A.M., Sulaiman, N., Jailani, R., Majeed, A.P.A.: The classification of EEG-based wink signals: a CWT-transfer learning pipeline. ICT Express, 1–5 (2021)
Acknowledgements
The authors would like to thank the Ministry of Science and Technology (MOST) of Taiwan for financially supporting this research project under grant numbers MOST 109-2221-E-241-006. The authors would also like to thank Prof. Han Yu Chen, Mr. Shing Fai Steven Lam, Yu-Ming Huang, and Cho-Jung Chang, for their valuable comments, which helped improve the quality of this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Lin, PJ., Lam, ML. (2022). Development of Virtual Reality Training System Based on EEG Biofeedback. In: Nagar, A.K., Jat, D.S., MarÃn-Raventós, G., Mishra, D.K. (eds) Intelligent Sustainable Systems. Lecture Notes in Networks and Systems, vol 333. Springer, Singapore. https://doi.org/10.1007/978-981-16-6309-3_26
Download citation
DOI: https://doi.org/10.1007/978-981-16-6309-3_26
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-6308-6
Online ISBN: 978-981-16-6309-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)