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A Fast, Open EEG Classification Framework Based on Feature Compression and Channel Ranking
Superior feature extraction, channel selection and classification methods are essential for designing electroencephalography (EEG) classification frameworks. However, the performance of most frameworks is limited by their improper channel selection methods and too specifical design, leading to high...
| Autores principales: | , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Frontiers Media S.A.
2018
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5911500/ https://www.ncbi.nlm.nih.gov/pubmed/29713262 http://dx.doi.org/10.3389/fnins.2018.00217 |