Abstract
In the application of brain-computer interfaces (BCIs), energy features are both physiologically well-founded and empirically effective to describe electroencephalogram (EEG) signals for classifying brain activities. Recently, a linear method named extreme energy ratio (EER) for energy feature extraction of EEG signals in terms of spatial filtering was proposed. This paper gives a nonlinear extension of the linear EER method. Specifically, we use the kernel trick to derive a kernelized version of the original EER feature extractor. The solutions for optimizing the criterion in kernel EER are provided for future use.
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Sun, S. (2008). A Theoretical Derivation of the Kernel Extreme Energy Ratio Method for EEG Feature Extraction. In: Fyfe, C., Kim, D., Lee, SY., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2008. IDEAL 2008. Lecture Notes in Computer Science, vol 5326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88906-9_41
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DOI: https://doi.org/10.1007/978-3-540-88906-9_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88905-2
Online ISBN: 978-3-540-88906-9
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