Importance Weighted Extreme Energy Ratio for EEG Classification
- Cite this paper as:
- Tu W., Sun S. (2010) Importance Weighted Extreme Energy Ratio for EEG Classification. In: Wong K.W., Mendis B.S.U., Bouzerdoum A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg
Spatial filtering is important for EEG signal processing since raw scalp EEG potentials have a poor spatial resolution due to the volume conduction effect. Extreme energy ratio (EER) is a recently proposed feature extractor which exhibits good performance. However, the performance of EER will be degraded by some factors such as outliers and the time-variances between the training and test sessions. Unfortunately, these limitations are common in the practical brain-computer interface (BCI) applications. This paper proposes a new feature extraction method called importance-weighted EER (IWEER) by defining two kinds of weight termed intra-trial importance and inter-trial importance. These weights are defined with the density ratio theory and assigned to the data points and trials respectively to improve the estimation of covariance matrices. The spatial filters learned by the IWEER are both robust to the outliers and adaptive to the test samples. Compared to the previous EER, experimental results on nine subjects demonstrate the better classification ability of the IWEER method.
KeywordsBrain-computer interface (BCI) Feature extraction Extreme energy ratio (EER) Density ratio
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