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k-Fold Cross-Validation Can Significantly Over-Estimate True Classification Accuracy in Common EEG-Based Passive BCI Experimental Designs: An Empirical Investigation
In passive BCI studies, a common approach is to collect data from mental states of interest during relatively long trials and divide these trials into shorter “epochs” to serve as individual samples in classification. While it is known that using k-fold cross-validation (CV) in this scenario can res...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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MDPI
2023
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10346713/ https://www.ncbi.nlm.nih.gov/pubmed/37447926 http://dx.doi.org/10.3390/s23136077 |