Camille Jeunet
Camille Jeunet
CNRS Researcher, CLLE Lab (UT2, CNRS), TMBI (Univ. Toulouse)
Bestätigte E-Mail-Adresse bei univ-tlse2.fr - Startseite
Titel
Zitiert von
Zitiert von
Jahr
EEG-based workload estimation across affective contexts
C Mühl, C Jeunet, F Lotte
Frontiers in neuroscience 8, 114, 2014
1252014
Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns
C Jeunet, B N'Kaoua, S Subramanian, M Hachet, F Lotte
PLoS ONE 10 (12), e0143962, 2015
1072015
Why standard brain-computer interface (BCI) training protocols should be changed: an experimental study
C Jeunet, E Jahanpour, F Lotte
Journal of neural engineering 13 (3), 036024, 2016
1042016
Advances in user-training for mental-imagery-based BCI control: Psychological and cognitive factors and their neural correlates
C Jeunet, B N’Kaoua, F Lotte
Progress in brain research 228, 3-35, 2016
772016
Towards improved BCI based on human learning principles
F Lotte, C Jeunet
The 3rd International Winter Conference on Brain-Computer Interface, 1-4, 2015
582015
Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist)
T Ros, S Enriquez-Geppert, V Zotev, KD Young, G Wood, ...
Brain 143 (6), 1674-1685, 2020
512020
Continuous tactile feedback for motor-imagery based brain-computer interaction in a multitasking context
C Jeunet, C Vi, D Spelmezan, B N’Kaoua, F Lotte, S Subramanian
IFIP Conference on Human-Computer Interaction, 488-505, 2015
432015
“Do you feel in control?”: towards novel approaches to characterise, manipulate and measure the sense of agency in virtual environments
C Jeunet, L Albert, F Argelaguet, A Lécuyer
IEEE transactions on visualization and computer graphics 24 (4), 1486-1495, 2018
272018
Using recent BCI literature to deepen our understanding of clinical neurofeedback: a short review
C Jeunet, F Lotte, JM Batail, P Philip, JAM Franchi
Neuroscience 378, 225-233, 2018
262018
Towards BCI-based interfaces for augmented reality: feasibility, design and evaluation
H Si-Mohammed, J Petit, C Jeunet, F Argelaguet, F Spindler, A Evain, ...
IEEE transactions on visualization and computer graphics, 2018
252018
Towards explanatory feedback for user training in brain-computer interfaces
J Schumacher, C Jeunet, F Lotte
2015 IEEE International Conference on Systems, Man, and Cybernetics, 3169-3174, 2015
242015
Defining and quantifying users’ mental imagery-based BCI skills: a first step
F Lotte, C Jeunet
Journal of neural engineering 15 (4), 046030, 2018
232018
How Well Can We Learn With Standard BCI Training Approaches? A Pilot Study.
C Jeunet, A Cellard, S Subramanian, M Hachet, B N'Kaoua, F Lotte
212014
Using EEG-based brain computer interface and neurofeedback targeting sensorimotor rhythms to improve motor skills: Theoretical background, applications and prospects
C Jeunet, B Glize, A McGonigal, JM Batail, JA Micoulaud-Franchi
Neurophysiologie Clinique 49 (2), 125-136, 2019
192019
Towards a cognitive model of MI-BCI user training
C Jeunet, B N'Kaoua, F Lotte
142017
Towards a spatial ability training to improve Mental Imagery based Brain-Computer Interface (MI-BCI) performance: A Pilot study
S Teillet, F Lotte, B N'Kaoua, C Jeunet
2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC …, 2016
122016
Peanut: Personalised emotional agent for neurotechnology user-training
L Pillette, C Jeunet, B Mansencal, R N'Kambou, B N'Kaoua, F Lotte
112017
Mind the traps! design guidelines for rigorous BCI experiments
C Jeunet, S Debener, F Lotte, J Mattout, R Scherer, C Zich
Brain–Computer Interfaces Handbook: Technological and Theoretical Advances, 1-33, 2018
102018
Online classification accuracy is a poor metric to study mental imagery-based bci user learning: an experimental demonstration and new metrics
F Lotte, C Jeunet
102017
Why and how to use intelligent tutoring systems to adapt mi-bci training to each user
C Jeunet, B N'Kaoua, R N'Kambou, F Lotte
102016
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