Integrating virtual reality and electroencephalography: applications, opportunities and challenges from a patents review
Resumo
With the increasing availability of physiological sensors and immersive technologies, the integration of electroencephalography (EEG) and virtual reality (VR) has emerged as a promising approach for developing interactive systems that respond to users’ cognitive and emotional states. To better understand the technological directions and industrial strategies shaping this field, this work presents an exploratory search that examines patent records related to the integration of EEG and VR, to identify current applications, opportunities and challenges in this emerging field. A structured search on the Derwent Innovation platform identified 376 patents with application dates between January 2018 and December 2023, analyzed according to classification codes, geographic coverage, assignee profiles, hardware and software references, and application domains. Results indicate a strong focus on medical and cognitive monitoring applications, led by filings from the United States and China, with contributions from both academic and corporate actors. However, limited technical disclosure, reproducibility issues, and persistent challenges in signal quality and validation remain. This study complements academic literature by offering a comprehensive patent-based perspective on the state of the art, highlighting gaps and opportunities for future development.
Palavras-chave:
Electroencephalography (EEG), Virtual Reality (VR), Patent Analysis, Immersive Technologies
Referências
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Aliakbaryhosseinabadi, S. Kamavuako, E. N. Jiang, N. Farina, D. & Mrachacz-Kersting, N. (2020). Prediction of hand movement from EEG signals using artificial neural networks in a virtual reality environment. Journal of Neural Engineering, 17(1), 016046.
Allison, B. Z. & Neuper, C. (2010). Could anyone use a BCI? In Brain-Computer Interfaces (pp. 35–54). Springer, London.
Arns, M. de Ridder, S. Strehl, U. Breteler, R. & Coenen, A. (2009). Efficacy of neurofeedback treatment in ADHD: The effects on inattention, impulsivity and hyperactivity: A meta-analysis. Clinical EEG and Neuroscience, 40(3), 180–189.
Bailenson, J. N. Yee, N. Blascovich, J. Beall, A. C. Lundblad, N. & Jin, M. (2008). The use of immersive virtual reality in the learning sciences: Digital transformations of teachers, students, and social context. The Journal of the Learning Sciences, 17(1), 102–141.
Bashashati, A. Fatourechi, M. Ward, R. K. & Birch, G. E. (2007). A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals. Journal of Neural Engineering, 4(2), R32.
Benitez, A. Morales, D. & Cebolla, A. (2022). EEG and VR integration for cognitive load assessment in virtual learning environments. Computers & Education, 188, 104606.
Biasiucci, A. Leeb, R. Iturrate, I. Perdikis, S. Al-Khodairy, A. Corbet, T. & Millan, J. D. R. (2018). Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke. Nature Communications, 9(1), 2421.
Blum, S. Eickhoff, S. B. & Weiller, C. (2020). Neural correlates of BCI training in VR environments. NeuroImage, 221, 117205.
Bonnet, L. & Arnaldi, B. (2018). The integration of physiological signals into virtual reality systems: A review. IEEE Transactions on Visualization and Computer Graphics, 24(10), 2895–2913.
Brouwer, A. M. van Erp, J. B. & Korteling, J. E. (2015). Neurophysiological measures in adaptive training systems: The case of EEG-based workload assessment. Frontiers in Neuroscience, 9, 358.
Caggianese, G. Neroni, P. & Gallo, L. (2022). Brain–computer interfaces and virtual reality: applications and open challenges. Applied Sciences, 12(19), 9823.
Carvalho, M. R. Dias, J. & Reis, L. P. (2020). Integrating EEG and VR for adaptive gaming experiences. Entertainment Computing, 35, 100373.
Cecotti, H. & Graser, A. (2011). Convolutional neural networks for P300 detection with application to brain–computer interfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(3), 433–445.
Chanel, G. Rebetez, C. Bétrancourt, M. & Pun, T. (2011). Emotion assessment from physiological signals for adaptation of game difficulty. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 41(6), 1052–1063.
Chin-Teng, L. & Lin, C. T. (2019). Brain–computer interfaces in neurorehabilitation: applications and challenges. IEEE Reviews in Biomedical Engineering, 12, 213–230.
Cipresso, P. Riva, G. & Wiederhold, B. K. (2018). The clinical use of virtual reality in neuropsychology: A brief review. Cyberpsychology, Behavior, and Social Networking, 21(2), 153–161.
Coogan, C. G. & He, B. (2018). Brain–computer interface control in virtual reality using multichannel EEG signals. Frontiers in Neuroscience, 12, 365.
Coyle, D. Ward, T. E. & Markham, C. M. (2013). Brain–computer interface using a simplified motor imagery paradigm. Journal of Neural Engineering, 10(2), 026019.
De Vries, S. Mulder, T. & Robles-Garcia, V. (2016). The potential of VR combined with EEG for cognitive rehabilitation: a pilot study. NeuroRehabilitation, 39(2), 219–231.
Delorme, A. & Makeig, S. (2004). EEGLAB: an open-source toolbox for analysis of single-trial EEG dynamics. Journal of Neuroscience Methods, 134(1), 9–21.
Ding, Y. Robinson, N. & Jirakittayakorn, N. (2019). The applications of EEG-based BCI systems in educational settings: a systematic review. Computers & Education, 141, 103634.
Eren, E. & Subasi, A. (2021). Classification of mental workload levels using EEG and deep learning. Neural Computing and Applications, 33(10), 4669–4683.
Fernandez, E. & Pelayo, F. (2019). Brain–computer interfaces: from basic research to clinical applications. Current Opinion in Neurology, 32(6), 847–856.
Freeman, D. Haselton, P. & Freeman, J. (2018). Virtual reality in mental health: Progress and challenges. Psychological Medicine, 48(4), 481–485.
Gargiulo, G. & McEwan, A. (2019). Dry and noncontact electrodes for neurophysiological recording. IEEE Reviews in Biomedical Engineering, 12, 312–331.
Guger, C. Allison, B. & Edlinger, G. (Eds.). (2012). Brain–computer interface research: A state-of-the-art summary. Springer Science & Business Media.
Han, C. H. Song, H. & Lee, J. (2020). Development of a VR neurofeedback system using EEG and its application for anxiety reduction. Sensors, 20(12), 3473.
He, B. & Gao, S. (2014). Brain–computer interfaces: From laboratory to real-world applications. IEEE Reviews in Biomedical Engineering, 7, 1–6.
Hoffman, H. G. Chambers, G. T. Meyer, W. J. Arceneaux, L. L. Russell, W. J. Seibel, E. J. Richards, T. L. Sharar, S. R. & Patterson, D. R. (2011). Virtual reality as an adjunctive non-pharmacologic analgesic for acute burn pain during medical procedures. Annals of Behavioral Medicine, 41(2), 183–191.
Huang, Q. Zhang, Z. & He, H. (2019). Sparse representation for EEG signal classification in VR-based motor imagery. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(6), 1180–1189.
Janssen, T. & Polich, J. (2021). Neurophysiological correlates of immersive virtual environments: an EEG review. Frontiers in Human Neuroscience, 15, 719832.
Kapeller, C. & Bauer, R. (2020). Hybrid BCIs and their potential for rehabilitation: combining EEG, EMG, and VR. Frontiers in Neuroscience, 14, 580341.
Kim, K. Kim, J. & Lee, S. (2018). EEG-based stress level assessment in VR environments. Applied Sciences, 8(2), 250.
Lau, C. & Mak, J. (2020). EEG-based brain–computer interfaces: a review of recent developments. Sensors, 20(21), 6258.
Lee, M. H. Williamson, J. & Park, C. (2019). Combining EEG and VR for real-time affective computing: challenges and perspectives. IEEE Access, 7, 86412–86423.
Li, Y. Pan, J. Wang, F. & Yu, Z. (2019). A hybrid BCI system for VR control. Sensors, 19(11), 2497.
Lin, C. T. & Ko, L. W. (2010). Neuroergonomics: integrating brain and behavior in the workplace. Theoretical Issues in Ergonomics Science, 11(5), 451–465.
Lotte, F. Congedo, M. Lecuyer, A. Lamarche, F. & Arnaldi, B. (2007). A review of classification algorithms for EEG-based brain–computer interfaces. Journal of Neural Engineering, 4(2), R1.
Makeig, S. Debener, S. Onton, J. & Delorme, A. (2004). Mining event-related brain dynamics. Trends in Cognitive Sciences, 8(5), 204–210.
Marquez, J. & Mendez, J. (2021). Neurofeedback training in virtual environments for ADHD treatment. Frontiers in Human Neuroscience, 15, 637218.
Martini, M. Perez-Marcos, D. & Sanchez-Vives, M. V. (2014). What color is my arm? Changes in skin color of an embodied virtual arm modulate pain threshold. Frontiers in Human Neuroscience, 8, 438.
Miller, M. R. Herrera, F. Jun, H. Landay, J. A. & Bailenson, J. N. (2020). Personal space regulation in immersive virtual environments. PLoS One, 15(5), e0233260.
Nijboer, F. & Furdea, A. (2015). The future of BCIs in neurorehabilitation. Frontiers in Neuroengineering, 8, 15.
Noor, M. & Moghavvemi, S. (2020). EEG signals as an indicator of emotional states in VR therapy. IEEE Access, 8, 221073–221084.
Pfurtscheller, G. & Neuper, C. (2001). Motor imagery and direct brain–computer communication. Proceedings of the IEEE, 89(7), 1123–1134.
Ramos-Murguialday, A. & Birbaumer, N. (2015). Brain–machine interfaces in stroke neurorehabilitation. Current Opinion in Neurology, 28(6), 596–603.
Rao, R. P. & Scherer, R. (2010). Brain–computer interfacing: An introduction. Cambridge University Press.
Rebsamen, B. Guan, C. Zhang, H. Wang, C. Teo, C. Ang, M. Burdet, E. (2010). A brain–controlled wheelchair based on P300 and SSVEP. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18(6), 596–606.
Rohani, D. A. & Puthusserypady, S. (2015). BCI-based rehabilitation of stroke patients: Motor imagery combined with virtual reality. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23(6), 1019–1030.
Ros, T. Enriquez-Geppert, S. & Gruzelier, J. (2020). The neurophysiology of VR-based neurofeedback. Neuroscience & Biobehavioral Reviews, 118, 77–85.
Slater, M. & Sanchez-Vives, M. V. (2016). Enhancing our lives with immersive virtual reality. Frontiers in Robotics and AI, 3, 74.
Vourvopoulos, A. & Bermudez i Badia, S. (2016). Motor priming in VR with EEG-based neurofeedback. Journal of Neuroengineering and Rehabilitation, 13(1), 1–11.
Wolpaw, J. R. & Wolpaw, E. W. (Eds.). (2012). Brain–computer interfaces: Principles and practice. Oxford University Press.
Zhang, R. & Wang, C. (2019). A survey on EEG signal processing methods for BCI applications. BioMedical Engineering Online, 18(1), 34.
Zhu, F. & Luo, Y. (2021). Real-time EEG-based attention detection in VR classrooms. Computers & Education, 172, 104264.
Alcaide-Aguirre, R. E. Gonzalez-Franco, M. & Peon-Fernandez, M. (2019). EEG-based neurofeedback in virtual environments for anxiety reduction: a systematic review. Frontiers in Psychology, 10, 1043.
Aliakbaryhosseinabadi, S. Kamavuako, E. N. Jiang, N. Farina, D. & Mrachacz-Kersting, N. (2020). Prediction of hand movement from EEG signals using artificial neural networks in a virtual reality environment. Journal of Neural Engineering, 17(1), 016046.
Allison, B. Z. & Neuper, C. (2010). Could anyone use a BCI? In Brain-Computer Interfaces (pp. 35–54). Springer, London.
Arns, M. de Ridder, S. Strehl, U. Breteler, R. & Coenen, A. (2009). Efficacy of neurofeedback treatment in ADHD: The effects on inattention, impulsivity and hyperactivity: A meta-analysis. Clinical EEG and Neuroscience, 40(3), 180–189.
Bailenson, J. N. Yee, N. Blascovich, J. Beall, A. C. Lundblad, N. & Jin, M. (2008). The use of immersive virtual reality in the learning sciences: Digital transformations of teachers, students, and social context. The Journal of the Learning Sciences, 17(1), 102–141.
Bashashati, A. Fatourechi, M. Ward, R. K. & Birch, G. E. (2007). A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals. Journal of Neural Engineering, 4(2), R32.
Benitez, A. Morales, D. & Cebolla, A. (2022). EEG and VR integration for cognitive load assessment in virtual learning environments. Computers & Education, 188, 104606.
Biasiucci, A. Leeb, R. Iturrate, I. Perdikis, S. Al-Khodairy, A. Corbet, T. & Millan, J. D. R. (2018). Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke. Nature Communications, 9(1), 2421.
Blum, S. Eickhoff, S. B. & Weiller, C. (2020). Neural correlates of BCI training in VR environments. NeuroImage, 221, 117205.
Bonnet, L. & Arnaldi, B. (2018). The integration of physiological signals into virtual reality systems: A review. IEEE Transactions on Visualization and Computer Graphics, 24(10), 2895–2913.
Brouwer, A. M. van Erp, J. B. & Korteling, J. E. (2015). Neurophysiological measures in adaptive training systems: The case of EEG-based workload assessment. Frontiers in Neuroscience, 9, 358.
Caggianese, G. Neroni, P. & Gallo, L. (2022). Brain–computer interfaces and virtual reality: applications and open challenges. Applied Sciences, 12(19), 9823.
Carvalho, M. R. Dias, J. & Reis, L. P. (2020). Integrating EEG and VR for adaptive gaming experiences. Entertainment Computing, 35, 100373.
Cecotti, H. & Graser, A. (2011). Convolutional neural networks for P300 detection with application to brain–computer interfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(3), 433–445.
Chanel, G. Rebetez, C. Bétrancourt, M. & Pun, T. (2011). Emotion assessment from physiological signals for adaptation of game difficulty. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 41(6), 1052–1063.
Chin-Teng, L. & Lin, C. T. (2019). Brain–computer interfaces in neurorehabilitation: applications and challenges. IEEE Reviews in Biomedical Engineering, 12, 213–230.
Cipresso, P. Riva, G. & Wiederhold, B. K. (2018). The clinical use of virtual reality in neuropsychology: A brief review. Cyberpsychology, Behavior, and Social Networking, 21(2), 153–161.
Coogan, C. G. & He, B. (2018). Brain–computer interface control in virtual reality using multichannel EEG signals. Frontiers in Neuroscience, 12, 365.
Coyle, D. Ward, T. E. & Markham, C. M. (2013). Brain–computer interface using a simplified motor imagery paradigm. Journal of Neural Engineering, 10(2), 026019.
De Vries, S. Mulder, T. & Robles-Garcia, V. (2016). The potential of VR combined with EEG for cognitive rehabilitation: a pilot study. NeuroRehabilitation, 39(2), 219–231.
Delorme, A. & Makeig, S. (2004). EEGLAB: an open-source toolbox for analysis of single-trial EEG dynamics. Journal of Neuroscience Methods, 134(1), 9–21.
Ding, Y. Robinson, N. & Jirakittayakorn, N. (2019). The applications of EEG-based BCI systems in educational settings: a systematic review. Computers & Education, 141, 103634.
Eren, E. & Subasi, A. (2021). Classification of mental workload levels using EEG and deep learning. Neural Computing and Applications, 33(10), 4669–4683.
Fernandez, E. & Pelayo, F. (2019). Brain–computer interfaces: from basic research to clinical applications. Current Opinion in Neurology, 32(6), 847–856.
Freeman, D. Haselton, P. & Freeman, J. (2018). Virtual reality in mental health: Progress and challenges. Psychological Medicine, 48(4), 481–485.
Gargiulo, G. & McEwan, A. (2019). Dry and noncontact electrodes for neurophysiological recording. IEEE Reviews in Biomedical Engineering, 12, 312–331.
Guger, C. Allison, B. & Edlinger, G. (Eds.). (2012). Brain–computer interface research: A state-of-the-art summary. Springer Science & Business Media.
Han, C. H. Song, H. & Lee, J. (2020). Development of a VR neurofeedback system using EEG and its application for anxiety reduction. Sensors, 20(12), 3473.
He, B. & Gao, S. (2014). Brain–computer interfaces: From laboratory to real-world applications. IEEE Reviews in Biomedical Engineering, 7, 1–6.
Hoffman, H. G. Chambers, G. T. Meyer, W. J. Arceneaux, L. L. Russell, W. J. Seibel, E. J. Richards, T. L. Sharar, S. R. & Patterson, D. R. (2011). Virtual reality as an adjunctive non-pharmacologic analgesic for acute burn pain during medical procedures. Annals of Behavioral Medicine, 41(2), 183–191.
Huang, Q. Zhang, Z. & He, H. (2019). Sparse representation for EEG signal classification in VR-based motor imagery. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(6), 1180–1189.
Janssen, T. & Polich, J. (2021). Neurophysiological correlates of immersive virtual environments: an EEG review. Frontiers in Human Neuroscience, 15, 719832.
Kapeller, C. & Bauer, R. (2020). Hybrid BCIs and their potential for rehabilitation: combining EEG, EMG, and VR. Frontiers in Neuroscience, 14, 580341.
Kim, K. Kim, J. & Lee, S. (2018). EEG-based stress level assessment in VR environments. Applied Sciences, 8(2), 250.
Lau, C. & Mak, J. (2020). EEG-based brain–computer interfaces: a review of recent developments. Sensors, 20(21), 6258.
Lee, M. H. Williamson, J. & Park, C. (2019). Combining EEG and VR for real-time affective computing: challenges and perspectives. IEEE Access, 7, 86412–86423.
Li, Y. Pan, J. Wang, F. & Yu, Z. (2019). A hybrid BCI system for VR control. Sensors, 19(11), 2497.
Lin, C. T. & Ko, L. W. (2010). Neuroergonomics: integrating brain and behavior in the workplace. Theoretical Issues in Ergonomics Science, 11(5), 451–465.
Lotte, F. Congedo, M. Lecuyer, A. Lamarche, F. & Arnaldi, B. (2007). A review of classification algorithms for EEG-based brain–computer interfaces. Journal of Neural Engineering, 4(2), R1.
Makeig, S. Debener, S. Onton, J. & Delorme, A. (2004). Mining event-related brain dynamics. Trends in Cognitive Sciences, 8(5), 204–210.
Marquez, J. & Mendez, J. (2021). Neurofeedback training in virtual environments for ADHD treatment. Frontiers in Human Neuroscience, 15, 637218.
Martini, M. Perez-Marcos, D. & Sanchez-Vives, M. V. (2014). What color is my arm? Changes in skin color of an embodied virtual arm modulate pain threshold. Frontiers in Human Neuroscience, 8, 438.
Miller, M. R. Herrera, F. Jun, H. Landay, J. A. & Bailenson, J. N. (2020). Personal space regulation in immersive virtual environments. PLoS One, 15(5), e0233260.
Nijboer, F. & Furdea, A. (2015). The future of BCIs in neurorehabilitation. Frontiers in Neuroengineering, 8, 15.
Noor, M. & Moghavvemi, S. (2020). EEG signals as an indicator of emotional states in VR therapy. IEEE Access, 8, 221073–221084.
Pfurtscheller, G. & Neuper, C. (2001). Motor imagery and direct brain–computer communication. Proceedings of the IEEE, 89(7), 1123–1134.
Ramos-Murguialday, A. & Birbaumer, N. (2015). Brain–machine interfaces in stroke neurorehabilitation. Current Opinion in Neurology, 28(6), 596–603.
Rao, R. P. & Scherer, R. (2010). Brain–computer interfacing: An introduction. Cambridge University Press.
Rebsamen, B. Guan, C. Zhang, H. Wang, C. Teo, C. Ang, M. Burdet, E. (2010). A brain–controlled wheelchair based on P300 and SSVEP. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18(6), 596–606.
Rohani, D. A. & Puthusserypady, S. (2015). BCI-based rehabilitation of stroke patients: Motor imagery combined with virtual reality. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23(6), 1019–1030.
Ros, T. Enriquez-Geppert, S. & Gruzelier, J. (2020). The neurophysiology of VR-based neurofeedback. Neuroscience & Biobehavioral Reviews, 118, 77–85.
Slater, M. & Sanchez-Vives, M. V. (2016). Enhancing our lives with immersive virtual reality. Frontiers in Robotics and AI, 3, 74.
Vourvopoulos, A. & Bermudez i Badia, S. (2016). Motor priming in VR with EEG-based neurofeedback. Journal of Neuroengineering and Rehabilitation, 13(1), 1–11.
Wolpaw, J. R. & Wolpaw, E. W. (Eds.). (2012). Brain–computer interfaces: Principles and practice. Oxford University Press.
Zhang, R. & Wang, C. (2019). A survey on EEG signal processing methods for BCI applications. BioMedical Engineering Online, 18(1), 34.
Zhu, F. & Luo, Y. (2021). Real-time EEG-based attention detection in VR classrooms. Computers & Education, 172, 104264.
Publicado
30/09/2025
Como Citar
MENDES, Bianca Louise C. et al.
Integrating virtual reality and electroencephalography: applications, opportunities and challenges from a patents review. In: SIMPÓSIO DE REALIDADE VIRTUAL E AUMENTADA (SVR), 27. , 2025, Salvador/BA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 576-587.
