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Importance-weighted covariance estimation for robust common spatial pattern
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DOI：10.1016/j.patrec.2015.09.003.png)
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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 BC! dataset. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
68T10
92C55Biomedical imaging and signal processing
Pattern recognition
Speech recognition
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