Abstract
The spatio-temporal EEG features are extracted by a two-stage ICAs. First, a spatial ICA is performed to extract spatially-distributed sources, and the second ICA is introduced in temporal domain for the coefficients of spatial sources. This 2-stage method provides much better features than spatial ICA only, and is computationally more efficient than single-stage spatio-temporal ICA. Among the extracted spatio-temporal features critical features are selected for the given tasks based on Fisher criterion. The extracted features may be applicable to the classification of single-trial EEG signals.
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Lee, S., Lee, SY. (2008). ICA-Based Spatio-temporal Features for EEG Signals. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4985. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69162-4_95
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DOI: https://doi.org/10.1007/978-3-540-69162-4_95
Publisher Name: Springer, Berlin, Heidelberg
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