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Multiclass EEG signal classification utilizing Rényi min-entropy-based feature selection from wavelet packet transformation
This paper proposes a novel feature selection method utilizing Rényi min-entropy-based algorithm for achieving a highly efficient brain–computer interface (BCI). Usually, wavelet packet transformation (WPT) is extensively used for feature extraction from electro-encephalogram (EEG) signals. For the...
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| 出版年: | Brain Inform |
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| 主要な著者: | , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Springer Berlin Heidelberg
2020
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7297893/ https://ncbi.nlm.nih.gov/pubmed/32548772 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s40708-020-00108-y |
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