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EGNN-C+: Interpretable Evolving Granular Neural Network and Application in Classification of Weakly-Supervised EEG Data Streams
As an application, we focus on the classification of emotion-related patterns within electroencephalogram (EEG) signals.
Sequential Dimensionality Reduction for Extracting Localized Features
In particular, nonnegative matrix factorization (NMF) has become very popular as it is able to extract sparse, localized and easily interpretable features by imposing an additive combination of nonnegative basis elements.
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