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Enhancing Classification Performance of Functional Near-Infrared Spectroscopy- Brain–Computer Interface Using Adaptive Estimation of General Linear Model Coefficients
In this paper, a novel methodology for enhanced classification of functional near-infrared spectroscopy (fNIRS) signals utilizable in a two-class [motor imagery (MI) and rest; mental rotation (MR) and rest] brain–computer interface (BCI) is presented. First, fNIRS signals corresponding to MI and MR...
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| Publicat a: | Front Neurorobot |
|---|---|
| Autors principals: | , , , , , |
| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
Frontiers Media S.A.
2017
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| Matèries: | |
| Accés en línia: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5512010/ https://ncbi.nlm.nih.gov/pubmed/28769781 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3389/fnbot.2017.00033 |
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