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A comparison of univariate, vector, bilinear autoregressive, and band power features for brain–computer interfaces
Selecting suitable feature types is crucial to obtain good overall brain–computer interface performance. Popular feature types include logarithmic band power (logBP), autoregressive (AR) parameters, time-domain parameters, and wavelet-based methods. In this study, we focused on different variants of...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer-Verlag
2011
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3208819/ https://www.ncbi.nlm.nih.gov/pubmed/21947797 http://dx.doi.org/10.1007/s11517-011-0828-x |