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Deep learning for EEG-based Motor Imagery classification: Accuracy-cost trade-off
Electroencephalography (EEG) datasets are often small and high dimensional, owing to cumbersome recording processes. In these conditions, powerful machine learning techniques are essential to deal with the large amount of information and overcome the curse of dimensionality. Artificial Neural Networ...
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| Publicado no: | PLoS One |
|---|---|
| Main Authors: | , , , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Public Library of Science
2020
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7289369/ https://ncbi.nlm.nih.gov/pubmed/32525885 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0234178 |
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