xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain–Computer Interface

By Bertrand Rivet, Antoine Souloumiac, Virginie Attina, Guillaume Gibert. IEEE Transactions on Biomedical Engineering, 2009

571 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/247b37f24a21aa842d7ed2158d2af0caaaaac32f

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. Florian Yger
4. Fabien Lotte
5. Andrzej S. Cichocki
6. Marco Congedo
7. Alain Rakotomamonjy
8. Laurent Bougrain

In Cognitive Neuroscience

1. Dongrui Wu
2. He He
3. Florian Yger
4. Fabien Lotte
5. Brent Lance
6. Chou P. Hung
7. Vernon J. Lawhern
8. Stephen M. Gordon

In Signal Processing

1. Dongrui Wu
2. Luis F. Nicolás-Alonso
3. Jaime Gómez Gil
4. He He
5. Andrzej S. Cichocki
6. Florian Yger
7. Alain Rakotomamonjy
8. Fabien Lotte

Citations in the corpus

• Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016 (2020, unknown): “Popular spatial filters include common spatial patterns (CSP) [16], independent component analysis (ICA) [17], blind source separation [18], xDAWN [19], etc.”
• EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces (2016, methodology): “ach is the approach which won the Kaggle BCI Competition (code and documenta9 tion at http://github.com/alexandrebarachant/bci-challenge-ner-2015), which uses a combination of xDAWN Spatial Filtering [83], Riemannian Geometry [84,85], channel subset selection and L 1 feature regularization (referred”
• Transfer Learning for Brain–Computer Interfaces: A Euclidean Space Data Alignment Approach (2018, methodology): “3) xDAWN-SVM, which first performs xDAWN [27], [38] to spatially filter the EEG trials, and then PCA and SVM classification.”
• Brain Computer Interfaces, a Review (2012, background): “ensure that the number of repetitions remain unaffected [130,131].”
• A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update (2018, methodology): “Both types of features benefit from being extracted after spatial filtering [22, 136, 185, 188].”