Multiclass Brain–Computer Interface Classification by Riemannian Geometry

By Alexandre Barachant, Stéphane Bonnet, Marco Congedo, Christian Jutten. IEEE Transactions on Biomedical Engineering, 2012

838 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/74a65c1dcc01a6af030aeac1d94d03e258e1dc8c

How researchers see this paper

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

In Cellular and Molecular Neuroscience

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

In Cognitive Neuroscience

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

Citations in the corpus

• Transfer Learning for Brain–Computer Interfaces: A Euclidean Space Data Alignment Approach (2018, methodology): “We generated a random integer n0 ∈ [1, 200], reserved the subsequent m trials {n0 + i}i=1 as the online pool,5 and used the remaining 200 − m trials as the test data.”
• Riemannian Approaches in Brain-Computer Interfaces: A Review (2017, positive): “Regarding EEG signal processing, some recent results suggest that a new family of approaches is very promising to address the multiple challenges mentioned above: Riemannian approaches [15]–[17].”
• Classification of covariance matrices using a Riemannian-based kernel for BCI applications (2013, background): “This paper is an extended version of the work presented in Barachant et al. (2012a).”
• Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016 (2020, neutral): “Zanini et al. also proposed improvements to the minimum distance to the Riemannian mean (MDRM) [58] classifier and demonstrated the effectiveness of RA and the improved MDRM in both MI and ERP classifications.”
• Federated Transfer Learning for EEG Signal Classification (2020, methodology): “Furthermore, following the successful Riemannian approach [6], [7], [8], [9], [10], our architecture used signal covariance matrix as an input.”
• Transfer Learning: A Riemannian Geometry Framework With Applications to Brain–Computer Interfaces (2018, methodology): “In [2] the authors used a basic classifier, named Minimum Distance to Mean (MDM), which takes into account distances on the manifold between the observations and some reference points of the classes, known as centers of mass, means, or barycenters.”
• 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 [85, 89].”
• Brain-Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives (2014, neutral): “various pattern classification techniques [88], [121].”
• Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review (2024, methodology): “Under these conditions, all information on the Riemannian manifold at a given point can be invariantly projected onto the tangent space (Euclidean space) using a logarithmic map [154,155].”