Athletes generally make use of motor imagery (MI) as a part of their routines to visualise the perfect movement to perform and improve their physical performances. However, the lack of feedback during those cognitive trainings makes it difficult to know if they are done efficiently and if they enable an over-time progression. EEG-based neurofeedback (NF) training procedures could be used to fill this gap, but their lack of usability hampers their development outside laboratories [1]. One way to increase this usability, is to determine the most relevant feedback content to provide athletes with, in terms of NF performance (i.e., ability to self-regulate brain patterns), acceptability and cognitive load. Previous studies suggested that evaluative feedback is preferable for experts, whereas explanatory feedback should be used for novices [2, 3, 4, 5]. Still, most feedback used during NF trainings are evaluative. So far, only a few studies have compared these 2 kinds of feedback during NF trainings [6, 7], and none have assessed the relevance of combining them. Yet, this combination brings very important research questions. Indeed, we hypothesise that evaluative feedback will benefit NF performance, especially in participants who are used to practising MI, while explanatory feedback will be more usable by providing richer and meaningful information. Nonetheless, combining both might result in high cognitive load, which could be detrimental for NF performance [9, 10]. Thus, our study aims to assess the relevance of explanatory and evaluative feedback during MI-NF, in terms of NF performance, acceptability and cognitive load, in athletes. We will use a within-subject paradigm with a 2*2 design: Exp2 (explanatory feedback: yes vs. no) and Eval2 (evaluative feedback: yes vs. no). Thereby, each athlete will take part in two-sessions of MI-NF training during which their EEG activity will be recorded. They will also complete acceptability and cognitive-load questionnaires. Sources [1] Teillet, S., Lotte, F., N’Kaoua, B., & Jeunet, C. (2016). Towards a spatial ability training to improve Mental Imagery based Brain-Computer Interface (MI-BCI) performance : A Pilot study. 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 003664‑003669. https://doi.org/10.1109/SMC.2016.7844803 [2] Magill, R. A. (1994). The Influence of Augmented Feedback on Skill Learning Depends on Characteristics of the Skill and the Learner. Quest, 46(3), 314‑327. https://doi.org/10.1080/00336297.1994.10484129 [3] Roc, A., Pillette, L., Mladenović, J., Benaroch, C., N’Kaoua, B., Jeunet, C., & Lotte, F. (2021). A review of user training methods in brain computer interfaces based on mental tasks. Journal of Neural Engineering. https://doi.org/10.1088/1741-2552/abca17 [4] Schumacher, J., Jeunet, C., & Lotte, F. (2015). Towards Explanatory Feedback for User Training in Brain-Computer Interfaces. 2015 IEEE International Conference on Systems, Man, and Cybernetics, 3169‑3174. https://doi.org/10.1109/SMC.2015.550 [5] Shute, V. J. (2008). Focus on Formative Feedback. Review of Educational Research, 78(1), 153‑189. https://doi.org/10.3102/0034654307313795 [6] Sollfrank, T., Ramsay, A., Perdikis, S., Williamson, J., Murray-Smith, R., Leeb, R., Millán, J. d. R., & Kübler, A. (2016). The effect of multimodal and enriched feedback on SMR-BCI performance. Clinical Neurophysiology, 127(1), 490‑498. https://doi.org/10.1016/j.clinph.2015.06.004 [7] Mercier-Ganady, J., Lotte, F., Loup-Escande, E., Marchal, M., & Lecuyer, A. (2014). The Mind-Mirror : See your brain in action in your head using EEG and augmented reality. 2014 IEEE Virtual Reality (VR), 33‑38. https://doi.org/10.1109/VR.2014.6802047 [8] Juliano, J. M., Spicer, R. P., Vourvopoulos, A., Lefebvre, S., Jann, K., Ard, T., Santarnecchi, E., Krum, D. M., & Liew, S.-L. (2020). Embodiment Is Related to Better Performance on a Brain–Computer Interface in Immersive Virtual Reality : A Pilot Study. Sensors, 20(4), Art. 4. https://doi.org/10.3390/s20041204 [9] Jeunet, C. (2016). Understanding & Improving Mental-Imagery Based Brain-Computer Interface (Mi-Bci) User-Training : Towards A New Generation Of Reliable, Efficient & Accessible Brain- Computer Interfaces [Phdthesis, Université de Bordeaux]. https://tel.archives-ouvertes.fr/tel-01417606 [10] Frederiksen, J. G., Sørensen, S. M. D., Konge, L., Svendsen, M. B. S., Nobel-Jørgensen, M., Bjerrum, F., & Andersen, S. A. W. (2020). Cognitive load and performance in immersive virtual reality versus conventional virtual reality simulation training of laparoscopic surgery: a randomized trial. Surgical endoscopy, 34(3), 1244–1252. https://doi.org/10.1007/s00464-019-06887-8