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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Detalles Bibliográficos
Autores principales: Qureshi, Nauman Khalid, Naseer, Noman, Noori, Farzan Majeed, Nazeer, Hammad, Khan, Rayyan Azam, Saleem, Sajid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5512010/
https://www.ncbi.nlm.nih.gov/pubmed/28769781
http://dx.doi.org/10.3389/fnbot.2017.00033

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5512010/
https://www.ncbi.nlm.nih.gov/pubmed/28769781
http://dx.doi.org/10.3389/fnbot.2017.00033

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