Importance-Weighted Covariance Estimation for Robust Common Spatial Pattern (情報論的学習理論と機械学習 情報論的学習理論ワークショップ) Importance-Weighted Covariance Estimation for Robust Common Spatial Pattern
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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 behaviour of our robust framework on a toy dataset and show gains of accuracy on a real-life BCI dataset.
- 電子情報通信学会技術研究報告 = IEICE technical report : 信学技報
電子情報通信学会技術研究報告 = IEICE technical report : 信学技報 114(306), 41-48, 2014-11-17
The Institute of Electronics, Information and Communication Engineers