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- Title
Enhancing Classification Performance of Functional Near-Infrared Spectroscopy-Brain-Computer Interface Using Adaptive Estimation of General Linear Model Coefficients.
- Authors
Qureshi, Nauman Khalid; Naseer, Noman; Noori, Farzan Majeed; Nazeer, Hammad; Khan, Rayyan Azam; Saleem, Sajid
- Abstract
In this paper, a novel methodology for enhanced classification of functional nearinfrared 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 are acquired from the motor and prefrontal cortex, respectively, afterward, filtered to remove physiological noises. Then, the signals are modeled using the general linear model, the coefficients of which are adaptively estimated using the least squares technique. Subsequently, multiple feature combinations of estimated coefficients were used for classification. The best classification accuracies achieved for five subjects, for MI versus rest are 79.5, 83.7, 82.6, 81.4, and 84.1% whereas those for MR versus rest are 85.5, 85.2, 87.8, 83.7, and 84.8%, respectively, using support vector machine. These results are compared with the best classification accuracies obtained using the conventional hemodynamic response. By means of the proposed methodology, the average classification accuracy obtained was significantly higher (p<0.05). These results serve to demonstrate the feasibility of developing a high-classification-performance fNIRS-BCI.
- Subjects
NEAR infrared spectroscopy; BRAIN-computer interfaces; ADAPTIVE estimation (Statistics)
- Publication
Frontiers in Neurorobotics, 2017, p1
- ISSN
1662-5218
- Publication type
Article
- DOI
10.3389/fnbot.2017.00033