Enhancing Classification Performance of Functional Near-Infrared Spectroscopy- Brain–Computer Interface Using Adaptive Estimation of General Linear Model Coefficients

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...

Full description

Bibliographic Details
Main Authors: Nauman Khalid Qureshi, Noman Naseer, Farzan Majeed Noori, Hammad Nazeer, Rayyan Azam Khan, Sajid Saleem
Format: Article
Language:English
Published: Frontiers Media S.A. 2017-07-01
Series:Frontiers in Neurorobotics
Subjects:
functional near-infrared spectroscopy
brain–computer interface
general linear model
least squares estimation
adaptive estimation
support vector machine
Online Access:http://journal.frontiersin.org/article/10.3389/fnbot.2017.00033/full

Internet

http://journal.frontiersin.org/article/10.3389/fnbot.2017.00033/full

Similar Items