the methodology used in the protocols as close as possible to the needs of the target populations. Therefore, these protocols need to be as descriptive and transparent as possible to better understand their application and compare them to allow for evidence-based adaptation of NFB therapy. Indeed, the success of a method actually depends on multiple parameters. The challenge will be to obtain a system that can be widely used while remaining acceptable, accurate, and adaptive to be as efficient as possible. NFB remains an open door for future research.

## AUTHOR CONTRIBUTIONS

SL, GH, and MG: conceptualization, methodology, and writing—original draft and review. SF and LB:

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## FUNDING

We acknowledge the support of the French Agence Nationale de la Recherche (ANR), under grant ANR- 19-CE33-0007 (project GRASP-IT).

## ACKNOWLEDGMENTS

We thank Daniela Freire for the complete proofreading in English of this article.

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Jang, S. H., and Cho, M. K. (2022). Relationship of recovery of contra-lesional ankle weakness with the corticospinal and corticoreticular tracts in stroke patients. *Am. J. Phys. Med. Rehabil.* 101, 659–665. doi: 10.1097/PHM.00000000000001881

Jang, Y. Y., Kim, T. H., and Lee, B. H. (2016). Effects of brain–computer interface-controlled functional electrical stimulation training on shoulder subluxation for patients with stroke: a randomized controlled trial. *Occup. Ther. Int.* 23, 175–185. doi: 10.1002/oti.1422

Jeunet, C., Lotte, F., Batail, J.-M., Philip, P., and Micoulaud Franchi, J.-A. (2018). Using recent BCI literature to deepen our understanding of clinical neurofeedback: a short review. *Neuroscience* 378, 225–233. doi: 10.1016/j.neuroscience.2018.03.013

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Kim, T., Kim, S., and Lee, B. (2016). Effects of action observational training plus brain-computer interface-based functional electrical stimulation on paretic arm motor recovery in patient with stroke: a randomized controlled trial. *Occup. Ther. Int.* 23, 39–47. doi: 10.1002/oti.1403

Kober, S. E., Witte, M., Ninaus, M., Neuper, C., and Wood, G. (2013). Learning to modulate one's own brain activity: the effect of spontaneous mental strategies. *Front. Hum. Neurosci.* 7:695. doi: 10.3389/fnhum.2013.00695

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Faralli, A., Bigoni, M., Mauro, A., Rossi, F., and Carulli, D. (2013). Noninvasive strategies to promote functional recovery after stroke. *Neural Plast* 2013:854597. doi: 10.1155/2013/854597

Favre, I., Zeffiro, T. A., Detante, O., Krainik, A., Hommel, M., and Jaillard, A. (2014). Upper limb recovery after stroke is associated with ipsilesional primary motor cortical activity: a meta-analysis. *Stroke* 45, 1077–1083. doi: 10.1161/STROKEAHA.113.003168

Feigin, V. L., Forouzanfar, M. H., Krishnamurthi, R., Mensah, G. A., Connor, M., Bennett, D. A., et al. (2014). Global and regional burden of stroke during 1990–2010: findings from the Global Burden of Disease Study 2010. *Lancet* 383, 245–254.

Fleury, M., Lioi, G., Barillot, C., and Lécuyer, A. (2020). A Survey on the use of haptic feedback for brain-computer interfaces and neurofeedback. *Front Neurosci.* 14:528. doi: 10.3389/fnins.2020.00528

Flowers, H. L., Skoretz, S. A., Silver, F. L., Rochon, E., Fang, J., Flamand-Roze, C., et al. (2016). Poststroke aphasia frequency, recovery, and outcomes: a systematic review and meta-analysis. *Arch. Phys. Med. Rehabil.* 97, 2188-2201. doi: 10.1016/j.apmr.2016.03.006

Foong, R., Ang, K. K., Quek, C., Guan, C., Phua, K. S., Kuah, C. W. K., et al. (2020). Assessment of the efficacy of EEG-Based MI-BCI with visual feedback and EEG correlates of mental fatigue for upper-limb stroke rehabilitation. *IEEE Trans. Biomed. Eng.* 67, 786–795. doi: 10.1109/TBME.2019.2921198

Gonzalez-Astudillo, J., Cattai, T., Bassignana, G., Corsi, M.-C., and Fallani, F. D. V. (2021). Network-based brain-computer interfaces: principles and applications. *J. Neural Eng.* 18:011001. doi: 10.1088/1741-2552/abc760

Graffigna, G., Barello, S., Wiederhold, B., Bosio, C., and Riva, G. (2013). *Annual Review of Cybertherapy and Telemedicine 2013: Positive Technology and Health Engagement for Healthy Living and Active Ageing*. Amsterdam: IOS Press.

Gramfort, A. (2013). MEG and EEG data analysis with MNE-Python. *Front. Neurosci.* 7:267. doi: 10.3389/fnins.2013.00267

Grozea, C., Voinescu, C. D., and Fazli, S. (2011). Bristle-sensors-low-cost flexible passive dry EEG electrodes for neurofeedback and BCI applications. *J. Neural Eng.* 8:025008. doi: 10.1088/1741-2560/8/2/025008

Guillot, A., Collet, C., Nguyen, V. A., Malouin, F., Richards, C., and Doyon, J. (2008). Functional neuroanatomical networks associated with expertise in motor imagery. *NeuroImage* 41, 1471–1483. doi: 10.1016/j.neuroimage.2008.03.042

Gürkök, H., and Nijholt, A. (2012). Brain–Computer Interfaces for Multimodal Interaction: A Survey and Principles. *Int. J. Hum.Comput. Interact.* 28, 292–307. doi: 10.1080/10447318.2011.582022

Halder, S., Agorastos, D., Veit, R., Hammer, E. M., Lee, S., Varkuti, B., et al. (2011). Neural mechanisms of brain-computer interface control. *Neuroimage* 55, 1779–1790. doi: 10.1016/j.neuroimage.2011.01.021

Hara, Y. (2015). Brain plasticity and rehabilitation in stroke patients. *J. Nippon Med Sch.* 82, 4–13. doi: 10.1272/jnms.82.4

Hashimoto, Y., Kakui, T., Ushiba, J., Liu, M., Kamada, K., and Ota, T. (2021). Portable rehabilitation system with brain-computer interface for inpatients with acute and subacute stroke: a feasibility study. *Assist. Technol.* 2021, 1–9. doi: 10.1080/10400435.2020.1836067

Hayashi, M. (2020). Neurofeedback of scalp bi-hemispheric EEG sensorimotor rhythm guides hemispheric activation of sensorimotor cortex in the targeted hemisphere. *NeuroImage* 223:117298. doi: 10.1016/j.neuroimage.2020.117298

Ibáñez, J., Monge-Pereira, E., Molina-Rueda, F., Serrano, J. I., del Castillo, M. D., Cuesta-Gómez, A., et al. (2017). Low latency estimation of motor intentions to assist reaching movements along multiple sessions in chronic stroke patients: a feasibility study. *Front. Neurosci.* 11:126. doi: 10.3389/fnins.2017.00126

ISO 9241-910:2011 (2011). *Ergonomics of Human-System Interaction — Part 910: Framework for Tactile and Haptic Interaction*. Geneva: International Organization for Standardization (ISO).

Jang, S. H., Chang, C. H., Lee, J., Kim, C. S., Seo, J. P., and Yeo, S. S. (2013). Functional role of the corticorectular pathway in chronic stroke patients. *Stroke* 44, 1099–1104. doi: 10.1161/STROKEAHA.111.000269

Jang, S. H., and Cho, M. K. (2022). Relationship of recovery of contra-lesional ankle weakness with the corticospinal and corticoreticular tracts in stroke patients. *Am. J. Phys. Med. Rehabil.* 101, 659–665. doi: 10.1097/PHM.00000000000001881

Jang, Y. Y., Kim, T. H., and Lee, B. H. (2016). Effects of brain–computer interface-controlled functional electrical stimulation training on shoulder subluxation for patients with stroke: a randomized controlled trial. *Occup. Ther. Int.* 23, 175–185. doi: 10.1002/oti.1422

Jeunet, C., Lotte, F., Batail, J.-M., Philip, P., and Micoulaud Franchi, J.-A. (2018). Using recent BCI literature to deepen our understanding of clinical neurofeedback: a short review. *Neuroscience* 378, 225–233. doi: 10.1016/j.neuroscience.2018.03.013

Jeunet, C., N'Kaoua, B., Subramanian, S., Hachet, M., and Lotte, F. (2015a). Predicting mental imagery-based BCI performance from personality, cognitive profile and neurophysiological patterns. *PLoS One* 10:e0143962. doi: 10.1371/journal.pone.0143962

Jeunet, C., Vi, C., Spelmezan, D., N'Kaoua, B., Lotte, F., and Subramanian, S. (2015b). "Continuous tactile feedback for motor-imagery based brain-computer interaction in a multitasking context," in *Human-Computer Interaction – INTERACT 2015*, eds J. Abascal, S. Barbosa, M. Fetter, T. Gross, P. Palanque, and M. Winckler (Cham: Springer International Publishing), 488–505. doi: 10.1007/978-3-319-22701-6_36

Jochumsen, M., Knoche, H., Kidmose, P., Kjær, T. W., and Dinesen, B. I. (2020). Evaluation of EEG headset mounting for brain-computer interface-based stroke rehabilitation by patients, therapists, and relatives. *Front. Hum. Neurosci.* 14:13. doi: 10.3389/fnhum.2020.000013

Jokinen, H., Melkas, S., Ylikoski, R., Pohjasvaara, T., Kaste, M., Erkinjuntti, T., et al. (2015). Post-stroke cognitive impairment is common even after successful clinical recovery. *Eur. J. Neurol.* 22, 1288–1294. doi: 10.1111/ene.12743

Kilintari, M. (2016). *Brain Activation Profiles During Kinesthetic and Visual Imagery: an FMRI Study*. Available online at: http://discovery.ucl.ac.uk/1500198/1/Kilintari_Kilintari_etal_Manuscript.pdf (accessed February 10, 2019).

Kim, T., Kim, S., and Lee, B. (2016). Effects of action observational training plus brain-computer interface-based functional electrical stimulation on paretic arm motor recovery in patient with stroke: a randomized controlled trial. *Occup. Ther. Int.* 23, 39–47. doi: 10.1002/oti.1403

Kober, S. E., Witte, M., Ninaus, M., Neuper, C., and Wood, G. (2013). Learning to modulate one's own brain activity: the effect of spontaneous mental strategies. *Front. Hum. Neurosci.* 7:695. doi: 10.3389/fnhum.2013.00695

Kohl, S. H., Mehler, D. M. A., Lührs, M., Thibault, R. T., Konrad, K., and Sorger, B. (2020). The potential of functional near-infrared spectroscopy-based neurofeedback—a systematic review and recommendations for best practice. *Front. Neurosci.* 14:594. doi: 10.3389/fnins.2020.00594

Kothe, C. A., and Makeig, S. (2013). BCILAB: a platform for brain–computer interface development. *J. Neural Eng.* 10:056014. doi: 10.1088/1741-2560/10/5/056014

Kruse, A., Suica, Z., Taeymans, J., and Schuster-Amft, C. (2020). Effect of brain-computer interface training based on non-invasive electroencephalography using motor imagery on functional recovery after stroke - a systematic review and meta-analysis. *BMC. Neurol.* 20:385. doi: 10.1186/s12883-020-01960-5

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Mattia, D., Pichiorri, F., Colamarino, E., Masciullo, M., Morone, G., Toppi, J., et al. (2020). The Promotoer, a brain-computer interface-assisted intervention to promote upper limb functional motor recovery after stroke: a study protocol for a randomized controlled trial to test early and long-term efficacy and to identify determinants of response. *BMC Neurol.* 20:254. doi: 10.1186/s12883- 020-01826-w

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Mihara, M., Hattori, N., Hatakenaka, M., Yagura, H., Kawano, T., Hino, T., et al. (2013). Near-infrared spectroscopy-mediated neurofeedback enhances efficacy

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Nakayama, H., Jørgensen, H. S., Raaschou, H. O., and Olsen, T. S. (1994). Recovery of upper extremity function in stroke patients: the Copenhagen Stroke Study. *Arch. Phys. Med. Rehabil.* 75, 394–398. doi: 10.1016/0003-9993(94)90161-9

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Nishimoto, A., Kawakami, M., Fujiwara, T., Hiramoto, M., Honaga, K., Abe, K., et al. (2018). Feasibility of task-specific brain-machine interface training for upper-extremity paralysis in patients with chronic hemiparetic stroke. *J. Rehabil. Med.* 50, 52–58. doi: 10.2340/16501977-2275

Olivi, E. (2011). *Coupling of Numerical Methods for the Forward Problem in Magneto- and Electro-Encephalography*. Nice: Université Nice Sophia Antipolis.

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Padfield, N., Zabalza, J., Zhao, H., Masero, V., and Ren, J. (2019). EEG-based brain-computer interfaces using motor-imagery: techniques and challenges. *Sensors (Basel).* 19:1423. doi: 10.3390/s19061423

Parikh, R. M., Robinson, R. G., Lipsey, J. R., Starkstein, S. E., Fedoroff, J. P., and Price, T. R. (1990). The impact of poststroke depression on recovery in activities of daily living over a 2-year follow-up. *Arch. Neurol.* 47, 785–789. doi: 10.1001/archneur.1990.00530070083014

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Perronnet, L., Lécuyer, A., Mano, M., Bannier, E., Lotte, F., Clerc, M., et al. (2017). Unimodal versus bimodal EEG-fMRI neurofeedback of a motor imagery task. *Front. Hum. Neurosci.* 11:193. doi: 10.3389/fnhum.2017.00193

Perronnet, L., Lécuyer, A., Mano, M., Fleury, M., Lioi, G., Cury, C., et al. (2018). Learning 2-in-1: Towards Integrated EEG-fMRI-Neurofeedback. *bioRxiv* (Preprint) *Bioengineering* doi: 10.1101/397729

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Li, J., You, S.-J., Xu, Y.-N., Yuan, W., Shen, Y., Huang, J.-Y., et al. (2019). Cognitive impairment and sleep disturbances after minor ischemic stroke. *Sleep Breath* 23, 455–462. doi: 10.1007/s11325-018-1709-4

Lioi, G., Butet, S., Fleury, M., Bannier, E., Lécuyer, A., Bonan, I., et al. (2020). A multi-target motor imagery training using bimodal eeg-fmri neurofeedback: a pilot study in chronic stroke patients. *Front. Hum. Neurosci.* 14:37. doi: 10.3389/fnhum.2020.00037

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Lopez-Calderon, J., and Luck, S. J. (2014). ERPLAB: an open-source toolbox for the analysis of event-related potentials. *Front. Hum. Neurosci.* 8:213. doi: 10.3389/fnhum.2014.00213

López-Larraz, E., Sarasola-Sanz, A., Irastorza-Landa, N., Birbaumer, N., and Ramos-Murguialday, A. (2018). Brain-machine interfaces for rehabilitation in stroke: a review. *NeuroRehabilitation* 43, 77–97. doi: 10.3233/NRE-172394

Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., et al. (2018). A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update. *J. Neural Eng.* 15:031005. doi: 10.1088/1741-2552/aab2f2

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Mane, R., Chouhan, T., and Guan, C. (2020). BCI for stroke rehabilitation: motor and beyond. *J. Neural Eng.* 17, 041001. doi: 10.1088/1741-2552/aba162

Mano, M., and Barillot, C. (2017). How to build a hybrid neurofeedback platform combining EEG and fMRI. *Front. Neurosci.* 11:16. doi: 10.3389/fnins.2017.00140

Mansour, S., Ang, K. K., Nair, K. P. S., Phua, K. S., and Arvaneh, M. (2021). Efficacy of brain–computer interface and the impact of its design characteristics on poststroke upper-limb rehabilitation: a systematic review and meta-analysis of randomized controlled trials. *Clin. EEG. Neurosci.* 53, 78–90. doi: 10.1177/15500594211009065

Marins, T. F., Rodrigues, E. C., Engel, A., Hoefle, S., Basilio, R., Lent, R., et al. (2015). Enhancing motor network activity using real-time functional mri neurofeedback of left premotor cortex. *Front. Behav. Neurosci.* 9:341. doi: 10.3389/fbeh.2015.00341

Marquez-Chin, C., and Popovic, M. R. (2020). Functional electrical stimulation therapy for restoration of motor function after spinal cord injury and stroke: a review. *BioMed. Eng. OnLine* 19:34. doi: 10.1186/s12938-020-00773-4

Mattia, D., Pichiorri, F., Colamarino, E., Masciullo, M., Morone, G., Toppi, J., et al. (2020). The Promotoer, a brain-computer interface-assisted intervention to promote upper limb functional motor recovery after stroke: a study protocol for a randomized controlled trial to test early and long-term efficacy and to identify determinants of response. *BMC Neurol.* 20:254. doi: 10.1186/s12883-020-01826-w

Mehler, D. M. A. (2020). Graded fMRI neurofeedback training of motor imagery in middle cerebral artery stroke patients: a preregistered proof-of-concept study. *Front. Hum. Neurosci.* 14:226. doi: 10.3389/fnhum.2020.00226

Mehler, D. M. A., Williams, A. N., Krause, F., Lührs, M., Wise, R. G., Turner, D. L., et al. (2019). The BOLD response in primary motor cortex and supplementary motor area during kinesthetic motor imagery based graded fMRI neurofeedback. *Neuroimage* 184, 36–44. doi: 10.1016/j.neuroimage.2018.09.007

Metz, S., Duthoit, E., and Pélissier, C. (2012). Processus d'aide en contexte d'apprentissage : une adaptation pour individualiser et personnaliser. *Revue Sticice* 19, 389–406. doi: 10.18463/pelissier.001

Mihara, M., Fujimoto, H., Hattori, N., Otomune, H., Kajiyama, Y., Konaka, K., et al. (2021). Effect of neurofeedback facilitation on poststroke gait and balance recovery. *Neurology* 96:e2587. doi: 10.1212/WNL.0000000000011989

Mihara, M., Hattori, N., Hatakenaka, M., Yagura, H., Kawano, T., Hino, T., et al. (2013). Near-infrared spectroscopy-mediated neurofeedback enhances efficacy

of motor imagery-based training in poststroke victims: a pilot study. *Stroke* 44, 1091–1098. doi: 10.1161/STROKEAHA.111.674507

Mihara, M., and Miyai, I. (2016). Review of functional near-infrared spectroscopy in neurorehabilitation. *Neurophoton* 3:031414. doi: 10.1117/1.NPh.3.3.031414

Monteiro, K. B., Cardoso, M. D. S., Cabral, V. R. D. C., Santos, A. O. B. D., Silva, P. S. D., Castro, J. B. P. D., et al. (2021). Effects of motor imagery as a complementary resource on the rehabilitation of stroke patients: a meta-analysis of randomized trials. *J. Stroke Cerebrovasc. Dis.* 30:105876. doi: 10.1016/j.jstrokecerebrovasdis.2021.105876

Mottaz, A., Corbet, T., Doganci, N., Magnin, C., Nicolo, P., Schnider, A., et al. (2018). Modulating functional connectivity after stroke with neurofeedback: Effect on motor deficits in a controlled cross-over study. *NeuroImage Clin.* 20, 336–346. doi: 10.1016/j.nicl.2018.07.029

Mottaz, A., Solcà, M., Magnin, C., Corbet, T., Schnider, A., and Guggisberg, A. G. (2015). Neurofeedback training of alpha-band coherence enhances motor performance. *Clin. Neurophysiol.* 126, 1754–1760. doi: 10.1016/j.clinph.2014.11.023

Murase, N., Duque, J., Mazzocchio, R., and Cohen, L. G. (2004). Influence of interhemispheric interactions on motor function in chronic stroke. *Ann. Neurol.* 55, 400–409. doi: 10.1002/ana.10848

Nakayama, H., Jørgensen, H. S., Raaschou, H. O., and Olsen, T. S. (1994). Recovery of upper extremity function in stroke patients: the Copenhagen Stroke Study. *Arch. Phys. Med. Rehabil.* 75, 394–398. doi: 10.1016/0003-9993(94)90161-9

Nan, W., Dias, A. P. B., and Rosa, A. C. (2019). Neurofeedback training for cognitive and motor function rehabilitation in chronic stroke: two case reports. *Front. Neurol.* 10:800. doi: 10.3389/fneur.2019.00800

Nishimoto, A., Kawakami, M., Fujiwara, T., Hiramoto, M., Honaga, K., Abe, K., et al. (2018). Feasibility of task-specific brain-machine interface training for upper-extremity paralysis in patients with chronic hemiparetic stroke. *J. Rehabil. Med.* 50, 52–58. doi: 10.2340/16501977-2275

Olivi, E. (2011). *Coupling of Numerical Methods for the Forward Problem in Magneto- and Electro-Encephalography*. Nice: Université Nice Sophia Antipolis.

Oostenveld, R., Fries, P., Maris, E., and Schoffelen, J.-M. (2011). FieldTrip: open source software for advanced analysis of meg, eeg, and invasive electrophysiological data. *Comput. Intell. Neurosci.* 2011:156869. doi: 10.1155/2011/156869

Opheim, A., Danielsson, A., Alt Murphy, M., Persson, H. C., and Sunnerhagen, K. S. (2015). Early prediction of long-term upper limb spasticity after stroke: part of the SALGOT study. *Neurology* 85, 873–880. doi: 10.1212/WNL.000000000001908

Padfield, N., Zabalza, J., Zhao, H., Masero, V., and Ren, J. (2019). EEG-based brain-computer interfaces using motor-imagery: techniques and challenges. *Sensors (Basel).* 19:1423. doi: 10.3390/s19061423

Parikh, R. M., Robinson, R. G., Lipsey, J. R., Starkstein, S. E., Fedoroff, J. P., and Price, T. R. (1990). The impact of poststroke depression on recovery in activities of daily living over a 2-year follow-up. *Arch. Neurol.* 47, 785–789. doi: 10.1001/archneur.1990.00530070083014

Pei, G., Wu, J., Chen, D., Guo, G., Liu, S., Hong, M., et al. (2018). Effects of an integrated neurofeedback system with dry electrodes: EEG acquisition and cognition assessment. *Sensors (Basel).* 18:E3396. doi: 10.3390/s18103396

Perronnet, L., Lécuyer, A., Mano, M., Bannier, E., Lotte, F., Clerc, M., et al. (2017). Unimodal versus bimodal EEG-fMRI neurofeedback of a motor imagery task. *Front. Hum. Neurosci.* 11:193. doi: 10.3389/fnhum.2017.00193

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Sreedharan, S., Sitaram, R., Paul, J. S., and Kesavadas, C. (2013). Brain-Computer Interfaces for Neurorehabilitation. *Crit. Rev. Biomed. Eng.* 41, 269-279. doi: 10.1615/CritRevBiomedEng.2014010697

Stinear, C. M. (2017). Prediction of motor recovery after stroke: advances in biomarkers. *Lancet Neurol.* 16, 826-836. doi: 10.1016/S1474-4422(17)30283-1

Strehl, U. (2014). What learning theories can teach us in designing neurofeedback treatments. *Front. Hum. Neurosci.* 8:894. doi: 10.3389/fnhum.2014.00894

Toppi, J., Mattia, D., Anzolin, A., Risetti, M., Petti, M., Cincotti, F., et al. (2014). "Time varying effective connectivity for describing brain network changes induced by a memory rehabilitation treatment," in *Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Presented at the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society*, (Chicago, IL), 6786-6789. doi: 10.1109/EMBC.2014.6945186

Touvykine, B., Mansoori, B. K., Jean-Charles, L., Deffeyes, J., Quessy, S., and Dancause, N. (2016). The Effect of Lesion Size on the Organization of the Ipsilesional and Contralesional Motor Cortex. *Neurorehabil. Neural Repair* 30, 280-292. doi: 10.1177/1545968315585356

Várkuti, B., Guan, C., Pan, Y., Phua, K. S., Ang, K. K., Kuah, C. W. K., et al. (2013). Resting state changes in functional connectivity correlate with movement recovery for BCI and robot-assisted upper-extremity training after stroke. *Neurorehabil. Neural Repair* 27, 53-62. doi: 10.1177/154596831244 5910

Volz, L. J. (2017). Time-dependent functional role of the contralesional motor cortex after stroke. *Neuroimage Clin.* 16, 165-174. doi: 10.1016/j.nicl.2017.07.024

Volz, L. J., Rehme, A. K., Michely, J., Nettekoven, C., Eickhoff, S. B., Fink, G. R., et al. (2016). Shaping early reorganization of neural networks promotes motor function after stroke. *Cereb. Cortex* 26, 2882-2894. doi: 10.1093/cercor/bhw034

Vourvopoulos, A., Pardo, O. M., Lefebvre, S., Neureither, M., Saldana, D., Jahng, E., et al. (2019). Effects of a Brain-Computer interface with virtual reality (VR) Neurofeedback: a pilot study in chronic stroke patients. *Front. Hum. Neurosci.* 13:210. doi: 10.3389/fnhum.2019.00210

Vuckovic, A., and Osuagwu, B. A. (2013). Using a motor imagery questionnaire to estimate the performance of a Brain-Computer Interface based on object oriented motor imagery. *Clin. Neurophysiol.* 124, 1586-1595. doi: 10.1016/j.clinph.2013.02.016

Wen, D., Fan, Y., Hsu, S.-H., Xu, J., Zhou, Y., Tao, J., et al. (2021). Combining brain-computer interface and virtual reality for rehabilitation in neurological diseases: a narrative review. *Ann. Phys. Rehabil. Med.* 64:101404. doi: 10.1016/j.rehab.2020.03.015

Wessel, M. J., and Hummel, F. C. (2018). Non-invasive cerebellar stimulation: a promising approach for stroke recovery? *Cerebellum* 17, 359-371. doi: 10.1007/s12311-017-0906-1

Wolpaw, J. R., Millán, J., del, R., and Ramsey, N. F. (2020). "Brain-computer interfaces: definitions and principles," in *Handbook of Clinical Neurology*, eds M. Aminoff, F. Boller, and D. Swaab (Amsterdam: Elsevier), 15-23. doi: 10.1016/B978-0-444-63934-9.00002-0

Wu, Q., Yue, Z., Ge, Y., Ma, D., Yin, H., Zhao, H., et al. (2020). Brain functional networks study of subacute stroke patients with upper limb dysfunction after comprehensive rehabilitation including bci training. *Front. Neurol.* 10, doi: 10.3389/fneur.2019.01419

Yoo, I. G. (2021). Electroencephalogram-based neurofeedback training in persons with stroke: a scoping review in occupational therapy. *NeuroRehabilitation* 48, 9-18. doi: 10.3233/NRE-201579

Young, K. D., Zotev, V., Phillips, R., Misaki, M., Yuan, H., Drevets, W. C., et al. (2014). Real-Time fMRI neurofeedback training of amygdala activity in patients with major depressive disorder. *PLoS One* 9:e88785. doi: 10.1371/journal.pone.0088785

Zich, C., Debener, S., Thoene, A.-K., Chen, L.-C., and Kranczioch, C. (2017). Simultaneous EEG-fNIRS reveals how age and feedback affect motor imagery signatures. *Neurobiol. Aging* 49, 183-197. doi: 10.1016/j.neurobiolaging.2016.10.011

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

**Publisher's Note:** All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Copyright © 2022 Le Franc, Herrera Altamira, Guillen, Butet, Fleck, Lécuyer, Bougrain and Bonan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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Soulard, J., Huber, C., Baillieul, S., Thuriot, A., Renard, F., Aubert Broche, B., et al. (2020). Motor tract integrity predicts walking recovery: a diffusion MRI study in subacute stroke. *Neurology* 94, e583–e593. doi: 10.1212/WNL. 0000000000008755

Sreedharan, S., Sitaram, R., Paul, J. S., and Kesavadas, C. (2013). Brain-Computer Interfaces for Neurorehabilitation. *Crit. Rev. Biomed. Eng.* 41, 269–279. doi: 10.1615/CritRevBiomedEng.2014010697

Stinear, C. M. (2017). Prediction of motor recovery after stroke: advances in biomarkers. *Lancet Neurol.* 16, 826–836. doi: 10.1016/S1474-4422(17)30283-1

Strehl, U. (2014). What learning theories can teach us in designing neurofeedback treatments. *Front. Hum. Neurosci.* 8:894. doi: 10.3389/fnhum.2014.00894

Toppi, J., Mattia, D., Anzolin, A., Risetti, M., Petti, M., Cincotti, F., et al. (2014). "Time varying effective connectivity for describing brain network changes induced by a memory rehabilitation treatment," in *Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Presented at the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society*, (Chicago, IL), 6786–6789. doi: 10.1109/EMBC.2014.6945186

Touvykine, B., Mansoori, B. K., Jean-Charles, L., Deffeyes, J., Quessy, S., and Dancause, N. (2016). The Effect of Lesion Size on the Organization of the Ipsilesional and Contralesional Motor Cortex. *Neurorehabil. Neural Repair* 30, 280-292. doi: 10.1177/1545968315585356

Várkuti, B., Guan, C., Pan, Y., Phua, K. S., Ang, K. K., Kuah, C. W. K., et al. (2013). Resting state changes in functional connectivity correlate with movement recovery for BCI and robot-assisted upper-extremity training after stroke. *Neurorehabil. Neural Repair* 27, 53–62. doi: 10.1177/154596831244 5910

Volz, L. J. (2017). Time-dependent functional role of the contralesional motor cortex after stroke. *Neuroimage Clin.* 16, 165–174. doi: 10.1016/j.nicl.2017.07.024

Volz, L. J., Rehme, A. K., Michely, J., Nettekoven, C., Eickhoff, S. B., Fink, G. R., et al. (2016). Shaping early reorganization of neural networks promotes motor function after stroke. *Cereb. Cortex* 26, 2882–2894. doi: 10.1093/cercor/bhw034

Vourvopoulos, A., Pardo, O. M., Lefebvre, S., Neureither, M., Saldana, D., Jahng, E., et al. (2019). Effects of a Brain-Computer interface with virtual reality (VR) Neurofeedback: a pilot study in chronic stroke patients. *Front. Hum. Neurosci.* 13:210. doi: 10.3389/fnhum.2019.00210

Vuckovic, A., and Osuagwu, B. A. (2013). Using a motor imagery questionnaire to estimate the performance of a Brain-Computer Interface based on object oriented motor imagery. *Clin. Neurophysiol.* 124, 1586–1595. doi: 10.1016/j.clinph.2013.02.016

Wen, D., Fan, Y., Hsu, S.-H., Xu, J., Zhou, Y., Tao, J., et al. (2021). Combining brain-computer interface and virtual reality for rehabilitation in neurological diseases: a narrative review. *Ann. Phys. Rehabil. Med.* 64:101404. doi: 10.1016/j.rehab.2020.03.015

Wessel, M. J., and Hummel, F. C. (2018). Non-invasive cerebellar stimulation: a promising approach for stroke recovery? *Cerebellum* 17, 359–371. doi: 10.1007/s12311-017-0906-1

Wolpaw, J. R., Millán, J., del, R., and Ramsey, N. F. (2020). "Brain-computer interfaces: definitions and principles," in *Handbook of Clinical Neurology*, eds M. Aminoff, F. Boller, and D. Swaab (Amsterdam: Elsevier), 15–23. doi: 10.1016/B978-0-444-63934-9.00002-0

Wu, Q., Yue, Z., Ge, Y., Ma, D., Yin, H., Zhao, H., et al. (2020). Brain functional networks study of subacute stroke patients with upper limb dysfunction after comprehensive rehabilitation including bci training. *Front. Neurol.* 10, doi: 10.3389/fneur.2019.01419

Yoo, I. G. (2021). Electroencephalogram-based neurofeedback training in persons with stroke: a scoping review in occupational therapy. *NeuroRehabilitation* 48, 9–18. doi: 10.3233/NRE-201579

Young, K. D., Zotev, V., Phillips, R., Misaki, M., Yuan, H., Drevets, W. C., et al. (2014). Real-Time fMRI neurofeedback training of amygdala activity in patients with major depressive disorder. *PLoS One* 9:e88785. doi: 10.1371/journal.pone.0088785

Zich, C., Debener, S., Thoene, A.-K., Chen, L.-C., and Kranczioch, C. (2017). Simultaneous EEG-fNIRS reveals how age and feedback affect motor imagery signatures. *Neurobiol. Aging* 49, 183–197. doi: 10.1016/j.neurobiolaging.2016.10.011

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

**Publisher's Note:** All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Copyright © 2022 Le Franc, Herrera Altamira, Guillen, Butet, Fleck, Lécuyer, Bougrain and Bonan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.