• Publications
  • Influence
A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update.
TLDR
This paper provides a comprehensive overview of the classification algorithms used in EEG-based BCIs, presents the principles of these methods and guidelines on when and how to use them. Expand
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Geometry-aware principal component analysis for symmetric positive definite matrices
TLDR
We develop a new Riemannian geometry based formulation of PCA for SPD matrices that (1) preserves more data variance by appropriately extending PCA to matrix data, and (2) extends the standard definition from the Euclidean to the Riemmannian geometries. Expand
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Robust Neural Networks using Randomized Adversarial Training
TLDR
In this paper, we introduce Randomized Adversarial Training (RAT), a technique that is efficient both against 2 and ∞ attacks. Expand
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Averaging covariance matrices for EEG signal classification based on the CSP: An empirical study
TLDR
This paper presents an empirical comparison of covariance matrix averaging methods for EEG signal classification using Riemannian geometry and Euclidean geometry. Expand
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Wavelet kernel learning
TLDR
This paper addresses the problem of optimal feature extraction from a wavelet representation. Expand
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Riemannian Approaches in Brain-Computer Interfaces: A Review
Although promising from numerous applications, current brain–computer interfaces (BCIs) still suffer from a number of limitations. In particular, they are sensitive to noise, outliers and theExpand
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Recognizing Art Style Automatically in Painting with Deep Learning
TLDR
The artistic style (or artistic movement) of a painting is a rich descriptor that captures both visual and historical information about the painting. Expand
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Adaptive Canonical Correlation Analysis Based On Matrix Manifolds
TLDR
In this paper, we formulate the Canonical Correlation Analysis (CCA) problem on matrix manifolds. Expand
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A review of kernels on covariance matrices for BCI applications
  • F. Yger
  • Computer Science
  • IEEE International Workshop on Machine Learning…
  • 14 November 2013
TLDR
We review and compare the existing kernels on covariance matrices and explore their use for EEG classification in Brain-Computer Interfaces (BCI). Expand
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Learning with infinitely many features
TLDR
We propose a principled framework for learning with infinitely many features, situations that are usually induced by continuously parametrized feature extraction methods. Expand
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