F
Fabien Lotte
Researcher at L'Abri
Publications - 189
Citations - 11832
Fabien Lotte is an academic researcher from L'Abri. The author has contributed to research in topics: Brain–computer interface & Electroencephalography. The author has an hindex of 42, co-authored 179 publications receiving 9441 citations. Previous affiliations of Fabien Lotte include University of Bordeaux & French Institute for Research in Computer Science and Automation.
Papers
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Journal ArticleDOI
Workshops of the Fifth International Brain-Computer Interface Meeting: Defining the Future
Jane E. Huggins,Christoph Guger,Brendan Z. Allison,Charles W. Anderson,Aaron P. Batista,Anne-Marie A-M Brouwer,Clemens Brunner,Ricardo Chavarriaga,Melanie Fried-Oken,Aysegul Gunduz,Disha Gupta,Andrea Kübler,Robert Leeb,Fabien Lotte,Lee E. Miller,Gernot Müller-Putz,Tomasz M. Rutkowski,Michael Tangermann,David E. Thompson +18 more
TL;DR: The Fifth International Brain-Computer Interface Meeting met June 3-7th, 2013 at the Asilomar Conference Grounds, Pacific Grove, California and included 19 workshops covering topics in brain-computer interface and brain-machine interface research.
Proceedings ArticleDOI
Brain-computer interfaces for 3D games: hype or hope?
TL;DR: This paper elaborates on the suitability of BCI for 3D Video Games (VG), and discusses the limitations of current BCI technology, those being mainly related to usability and performances.
Proceedings ArticleDOI
Averaging covariance matrices for EEG signal classification based on the CSP: An empirical study
TL;DR: The results show that using Riemannian geometry for averaging covariance matrices improves performances for small dimensional problems, but also the limits of this approach when the dimensionality increases.
Proceedings Article
An Efficient P300-based Brain-Computer Interface with Minimal Calibration Time
Fabien Lotte,Cuntai Guan +1 more
TL;DR: This BCI is based on Regularized Canonical Correlation Analysis for feature extraction and Regularized Linear Discriminant Analysis for classification and can reach good P300 detection performances while using much less training examples than current approaches, hence effectively reducing the calibration time.
Journal ArticleDOI
Guest Editorial: Brain/neuronal - Computer game interfaces and interaction
TL;DR: Insight is gained into new biosignal processing algorithms, tested in gaming applications, which exploit BCI and neural signals to enhance gameplay experience and playermotivation, be the players ablebodied or physically impaired.