Classification of covariance matrices using a Riemannian-based kernel for BCI applications

By Alexandre Barachant, Stéphane Bonnet, Marco Congedo, Christian Jutten. Neurocomputing, 2013

493 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/9b143b26d2fe58db6aa11e86b8850f03d78d648c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Fabien Lotte
2. Florian Yger
3. Maxime Bérar
4. Cuntai Guan
5. Dongrui Wu
6. Jae-Hwan Kang
7. Young-Ju Jeon
8. Jang-Han Bae

In Electrical and Electronic Engineering

1. Cuntai Guan
2. Florian Yger
3. Fabien Lotte
4. Maxime Bérar
5. Marco Congedo
6. Yang Liu

In Signal Processing

1. Fabien Lotte
2. Cuntai Guan
3. Christian Jutten
4. Florian Yger
5. Jae-Hwan Kang
6. Young-Ju Jeon
7. Jang-Han Bae
8. Marco Congedo

Citations in the corpus

• Benchmarking functional connectome‐based predictive models for resting‐state fMRI (2019, unknown): “The latter is less frequently used but has solid mathematical foundations and a variety of groups have reported good decoding performances with this framework (Varoquaux et al., 2010a; Barachant et al., 2013; Ng et al., 2014; Dodero et al., 2015; Qiu et al., 2015; Rahim et al., 2017; Wong et al., 20”
• Riemannian Approaches in Brain-Computer Interfaces: A Review (2017, methodology): “MI [15], [16], [19] or respiratory state [48].”
• EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces (2016, methodology): “Project the covariance matrices onto the tangent space using the log-euclidean metric [84,88].”
• Transfer Learning: A Riemannian Geometry Framework With Applications to Brain–Computer Interfaces (2018, background): “In [3], instead, the authors introduce a way to solve the shift problem in a Riemannian framework, for the cross-session situation, however this approach depends”
• Federated Transfer Learning for EEG Signal Classification (2020, methodology): “Barachant [10], [11].”
• Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review (2017, unknown): “In [110] a support vector machine embedded with a Riemannian kernel was used.”
• Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review (2024, methodology): “A linear SVM classifier used these EEG features processed on the kernel and performed better than the common spatial pattern method, the most popular spatial filtering method in BCI applications [154,156].”