xDAWN+LDA and XDAWNCov+TS+LR: results on public EEG datasets
xDAWN is a spatial filter, proposed by Rivet, Souloumiac, Attina and Gibert (IEEE Transactions on Biomedical Engineering, 2009), that enhances an evoked response such as the P300 against the ongoing EEG. BCI Report runs two decoders built on it through pyRiemann with fixed settings (4 filters, OAS covariance estimates): xDAWN+LDA, which flattens the filtered windows and classifies them with a shrinkage linear discriminant, and XDAWNCov+TS+LR, which builds xDAWN-augmented covariance matrices (Barachant and Congedo, 2014), maps them to the tangent space and fits a logistic regression. The settings follow the MOABB reference pipelines (xDAWN_LDA.yml with a shrinkage LDA head; XdawnCov_TS_SVM.yml with the SVM replaced by the logistic regression head).
Compare figures within a group only: task, cohort, chance level and electrode layout can differ between groups. One row is above another only where their 95% intervals do not overlap.
MOABB XdawnCov_TS_SVM.yml, with the SVM and its grid replaced by the TS+LR head
pyRiemann 0.12 (wheel SHA-256 74a90b2ef0938c94fe9bf90b6506991fc19da55850aae5fdf992026b3845d43a) and scikit-learn
BSD-3-Clause
xDAWN+LDASupervised fit
4 filters · 50 Hz
MOABB xDAWN_LDA.yml, with shrinkage LDA (lsqr, Ledoit–Wolf)
pyRiemann 0.12 (wheel SHA-256 74a90b2ef0938c94fe9bf90b6506991fc19da55850aae5fdf992026b3845d43a) and scikit-learn
BSD-3-Clause
One fixed configuration per method: library or MOABB-reference settings with the stated deviations. The 4–40 Hz band-pass (or demeaning plus 50 Hz decimation on the ERP protocols) is part of the recipe and is applied to each window on its own.
Sensitivity lines
The same methods with other input filtering, labelled; they are not matrix rows.
XDAWNCov+TS+LR · 4 filters · raw · P300 target ERP: 60.7% (57.7%–63.6%), macro F1 0.602, 21 people
xDAWN+LDA · 4 filters · raw · P300 target ERP: 58.9% (56.3%–61.5%), macro F1 0.586, 21 people
XDAWNCov+TS+LR · 4 filters · raw · Semantic target ERP: 60.2% (57.8%–62.8%), macro F1 0.589, 30 people
xDAWN+LDA · 4 filters · raw · Semantic target ERP: 54.6% (52.9%–56.4%), macro F1 0.542, 30 people
Paired differences
Each difference is the mean over people of each person’s difference in balanced accuracy, in percentage points (pp), on the same windows and people.
Paired participant bootstrap (10,000 draws). The four contrasts against the Spectral ridge cells carry 98.75 % intervals (Bonferroni over four); the other contrasts carry nominal 95 % intervals with no familywise claim. 'Higher' or 'lower' means the interval lies wholly above or below zero; 'unresolved' means it includes zero.
Primary contrasts against the Spectral ridge cell, with the Temporal ridge beside them
Rivet, Souloumiac, Attina and Gibert. xDAWN algorithm to enhance evoked potentials: application to brain–computer interface. IEEE Transactions on Biomedical Engineering 56(8), 2009. doi ↗
Barachant and Congedo. A Plug&Play P300 BCI using information geometry. arXiv:1409.0107, 2014.
Barachant, Bonnet, Congedo and Jutten. Multiclass brain–computer interface classification by Riemannian geometry. IEEE Transactions on Biomedical Engineering 59(4), 2012. doi ↗
Chen, Wiesel, Eldar and Hero. Shrinkage algorithms for MMSE covariance estimation. IEEE Transactions on Signal Processing 58(10), 2010. doi ↗
Jayaram and Barachant. MOABB: trustworthy algorithm benchmarking for BCIs. Journal of Neural Engineering 15(6), 2018. doi ↗
Ledoit and Wolf. A well-conditioned estimator for large-dimensional covariance matrices. Journal of Multivariate Analysis 88(2), 2004. doi ↗
Cite this page
BCI Report (2026). xDAWN+LDA and XDAWNCov+TS+LR: results on public EEG datasets. https://bci.report/methods/xdawn/
BCI Report