P. Oudeyer, J. Gottlieb, and M. Lopes, Intrinsic motivation, curiosity, and learning: Theory and applications in educational technologies, Progress in Brain Research, vol.229, pp.257-284, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01404278

C. Kidd and B. Y. Hayden, The psychology and neuroscience of curiosity, Neuron, vol.88, issue.3, pp.449-460, 2015.

J. Gottlieb and P. Oudeyer, Towards a neuroscience of active sampling and curiosity, Nature Reviews Neuroscience, vol.19, issue.12, pp.758-770, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01965608

P. Oudeyer, F. Kaplan, and V. V. Hafner, Intrinsic motivation systems for autonomous mental development, IEEE Trans. Evol. Comp, 2007.

G. Gordon, C. Breazeal, and S. Engel, Can children catch curiosity from a social robot?, Proc. IEEE ICHRI, pp.91-98, 2015.

R. Tieben, T. Bekker, and B. Schouten, Curiosity and interaction: making people curious through interactive systems, Proc. BCS-HCI, 2011.

E. Law, M. Yin, J. Goh, K. Chen, M. Terry et al., Curiosity killed the cat, but makes crowdwork better. CHI, 2016.

S. Freeman, S. L. Eddy, M. Mcdonough, N. Okoroafor, H. Jordt et al., Active learning increases student performance in science, engineering, and mathematics, Academy of Sciences, vol.111, 2014.

J. Ceha, N. Chhibber, J. Goh, C. Mcdonald, P. Oudeyer et al., Expression of curiosity in social robots: Design, perception, and effects on behaviour, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02371252

M. Clerc, L. Bougrain, and F. Lotte, Brain-Computer Interfaces 1: foundations and methods, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01408991

T. O. Zander and C. Kothe, Towards passive brain-computer interfaces: applying brain-computer interface technology to human-machine systems in general, 2011.

C. Jeunet, B. N'kaoua, and F. Lotte, Towards a cognitive model of MI-BCI user training, International Graz BCI Conference, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01519476

M. Gruber and A. Valji, 16 Curiosity and Learning, 2019.

D. E. Berlyne, A theory of human curiosity, British Journal of Psychology, 1954.

G. Loewenstein, The psychology of curiosity: A review and reinterpretation, Psychological Bulletin, 1994.

P. Mussel, Epistemic curiosity and related constructs: Lacking evidence of discriminant validity, Personality and Individual Differences, 2010.

J. Litman, Encyclopedia of the Sciences of Learning. Encyclopedia of the Sciences of Learning, 2012.

G. Brod and J. Breitwieser, Lighting the wick in the candle of learning: generating a prediction stimulates curiosity. Science of Learning, 2019.

M. Kang, M. Hsu, I. Krajbich, G. Loewenstein, S. M. Mcclure et al., The wick in the candle of learning: Epistemic curiosity activates reward circuitry and enhances memory, Psych. sci, 2009.

R. A. Adcock, A. Thangavel, S. Whitfield-gabrieli, B. Knutson, and J. D. Gabrieli, Reward-Motivated Learning: Mesolimbic Activation Precedes Memory Formation, Neuron, vol.50, issue.3, pp.507-517, 2006.

M. J. Gruber, B. D. Gelman, and C. Ranganath, States of Curiosity Modulate Hippocampus-Dependent Learning via the Dopaminergic Circuit, Neuron, vol.84, issue.2, pp.486-496, 2014.

G. Lima, Curiosity, frontal EEG asymmetry , and learning. 41st Meeting of the CogSci Society, pp.2161-2165, 2019.

Y. Renard, F. Lotte, G. Gibert, M. Congedo, and A. Lécuyer, Open-ViBE: An open-source software platform to design, test, and use braincomputer interfaces in real and virtual environments, Presence, 2010.

J. M. Hektner and M. Csikszentmihalyi, A Longitudinal Exploration of Flow and Intrinsic Motivation in Adolescents, p.31, 1996.

F. Lotte, L. Bougrain, A. Cichocki, M. Clerc, M. Congedo et al., A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces: A 10-year Update, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01846433

K. K. Ang, Z. Y. Chin, C. C. Wang, C. T. Guan, and H. H. Zhang, Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b, Frontiers in Neuroscience, 2012.

A. Appriou, A. Cichocki, and F. Lotte, Modern machine learning algorithms to classify cognitive and affective states from electroencephalography signals, IEEE SMC Magazine, pp.1-8, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02483908

B. Blankertz, R. Tomioka, S. Lemm, M. Kawanabe, and K. Müller, Optimizing spatial filters for robust EEG single-trial analysis, IEEE Sig Proc Magazine, 2008.

H. Peng, F. Long, and C. Ding, Feature Selection Based On Mutual Information: Criteria of Max-Dependency,Max-Relevance, and Min-Redundancy, IEEE Trans Pattern Anal Mach Intell, vol.27, 2005.

F. Yger, M. Berar, and F. Lotte, Riemannian approaches in Brain-Computer Interfaces: a review, IEEE TNSRE, vol.25, issue.10, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01394253

E. Combrisson and K. Jerbi, Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy, J Neur. Meth, 2015.

F. Dehais, A. Lafont, R. Roy, and S. Fairclough, A Neuroergonomics Approach to Mental Workload, Engagement and Human Performance, Frontiers in Neuroscience, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02537107

R. Knapp, J. Kim, and E. Andre, Physiological signals and their use in augmenting emotion recognition for human-machine interaction. Emotion-oriented systems, vol.10, pp.133-159, 2011.