A Study via Feature Selection on the Separability of Approximate Entropy for Brain-Computer Interfaces
作者： Bashar Awwad Shiekh Hasan , John Q Gan , Matthew Dyson , Tugce Balli
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摘要: The linear and nonlinear separability of approximate entropy feature for EEG-based brain-computer interfaces (BCI) are tested compared to that two alternative features, band power reflection coefficients. Separability is analyzed using hybrid sequential forward floating search, in which a classifier: discriminate analysis (LDA) classifier or support vector machine (SVM), index: Davies-Bouldin index (DBI) mutual information (MI) based index, jointly utilized evaluate selected subsets. Results on BCI data demonstrate the be comparable coefficients, although each has advantages different situations.
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