Importance-weighted covariance estimation for robust common spatial pattern

• New estimator for covariance matrices based on the importance-weighting of samples.
• Embedded in CSP algorithm it makes CSP more robust to non-stationarity.
• We show the complementarity of a robust feature extraction and a robust classifier.
• Using an importance-weighted classifier, we improve the robustness of the whole BCI.

Non-stationarity is an important issue for practical applications of machine learning methods. This issue particularly affects Brain–Computer Interfaces (BCI) and tends to make their use difficult. In this paper, we show a practical way to make Common Spatial Pattern (CSP), a classical feature extraction that is particularly useful in BCI, robust to non-stationarity. To do so, we did not modify the CSP method itself, but rather make the covariance estimation (used as input by every CSP variant) more robust to non-stationarity. Those robust estimators are derived using a classical importance-weighting scenario. Finally, we highlight the behavior of our robust framework on a toy dataset and show gains of accuracy on a real-life BCI dataset.