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Camille Jeunet
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2020 – today
- 2022
- [j4]Anton Nijholt
, José Luis Contreras-Vidal, Camille Jeunet
Editorial: Brain-Computer Interfaces for Non-clinical (Home, Sports, Art, Entertainment, Education, Well-Being) Applications. Frontiers Comput. Sci. 4: 860619 (2022) - [i2]Xiaoxi Wei, A. Aldo Faisal, Moritz Grosse-Wentrup, Alexandre Gramfort, Sylvain Chevallier, Vinay Jayaram, Camille Jeunet, Stylianos Bakas, Siegfried Ludwig, Konstantinos Barmpas, Mehdi Bahri, Yannis Panagakis, Nikolaos A. Laskaris, Dimitrios A. Adamos, Stefanos Zafeiriou, William C. Duong, Stephen M. Gordon, Vernon J. Lawhern, Maciej Sliwowski, Vincent Rouanne, Piotr Tempczyk:
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets. CoRR abs/2202.12950 (2022) - 2021
- [c15]Maria-Isabel Casso
Heading for motor imagery brain-computer interfaces (MI-BCIs) usable out-of-the-lab: Impact of dry electrode setup on classification accuracy. NER 2021: 690-693 - [c14]Xiaoxi Wei
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets. NeurIPS (Competition and Demos) 2021: 205-219 - 2020
- [j3]Léa Pillette
A physical learning companion for Mental-Imagery BCI User Training. Int. J. Hum. Comput. Stud. 136: 102380 (2020) - [j2]Hakim Si-Mohammed
Towards BCI-Based Interfaces for Augmented Reality: Feasibility, Design and Evaluation. IEEE Trans. Vis. Comput. Graph. 26(3): 1608-1621 (2020) - [c13]André Tricot
Working Memory Resource Depletion Effect in Academic Learning: Steps to an Integrated Approach. H-WORKLOAD 2020: 13-26 - [c12]Eidan Tzdaka, Camille Benaroch, Camille Jeunet
Assessing The Relevance Of Neurophysiological Patterns To Predict Motor Imagery-based BCI Users' Performance. SMC 2020: 2490-2495 - [c11]Hakim Si-Mohammed, Catarina Lopes Dias, Maria Duarte, Ferran Argelaguet, Camille Jeunet
Detecting System Errors in Virtual Reality Using EEG Through Error-Related Potentials. VR 2020: 653-661
2010 – 2019
- 2019
- [c10]Camille Benaroch, Fabien Lotte, Camille Jeunet:
Are users' traits informative enough to predict/explain their mental-imagery based BCI performances? GBCIC 2019 - [i1]Léa Pillette
Towards Artificial Learning Companions for Mental Imagery-based Brain-Computer Interfaces. CoRR abs/1905.09658 (2019) - 2018
- [j1]Camille Jeunet
"Do You Feel in Control?": Towards Novel Approaches to Characterise, Manipulate and Measure the Sense of Agency in Virtual Environments. IEEE Trans. Vis. Comput. Graph. 24(4): 1486-1495 (2018) - 2017
- [c9]Jérémy Frey, Jelena Mladenovic
When HCI Meets Neurotechnologies: What You Should Know about Brain-Computer Interfaces. CHI Extended Abstracts 2017: 1253-1256 - [c8]Camille Jeunet, Bernard N'Kaoua, Fabien Lotte:
Towards a Cognitive Model of MI-BCI User Training. GBCIC 2017 - [c7]Fabien Lotte, Camille Jeunet:
Online Classification accuracy is a Poor Metric to Study Mental imagery-based BCI User Learning: an Experimental Demonstration and New Metrics. GBCIC 2017 - [c6]Léa Pillette, Camille Jeunet, Boris Mansencal
Peanut: Personalised Emotional Agent for Neurotechnology User-Training. GBCIC 2017 - 2016
- [c5]Suzy Teillet, Fabien Lotte
Towards a spatial ability training to improve Mental Imagery based Brain-Computer Interface (MI-BCI) performance: A Pilot study. SMC 2016: 3664-3669 - 2015
- [c4]Fabien Lotte
Towards improved BCI based on human learning principles. BCI 2015: 1-4 - [c3]Camille Jeunet
Continuous Tactile Feedback for Motor-Imagery Based Brain-Computer Interaction in a Multitasking Context. INTERACT (1) 2015: 488-505 - [c2]Julia Schumacher
Towards Explanatory Feedback for User Training in Brain-Computer Interfaces. SMC 2015: 3169-3174 - 2014
- [c1]Camille Jeunet
Design and Validation of a Mental and Social Stress Induction Protocol - Towards Load-invariant Physiology-based Stress Detection. PhyCS 2014: 98-106
Coauthor Index
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