Abstract
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 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.
Highlights
A brain–computer interface (BCI) system bypasses the peripheral nervous system and provides means of communication for patients suffering from motor disabilities or in a persistent vegetative state using devices, such as robotic arms or other prostheses (Wolpaw et al, 2002)
In order to ensure that the data are normally distributed Kolmogorov–Smirnov method was applied, the significant value was found to be greater than 0.05 which shows normal distribution of the data. These high classification accuracies of the proposed method relative to the conventional method were statistically verified by a statistical significance test: the p-values obtained by performing t-test on the subjectwise accuracy scores was less than 0.05, which confirmed the statistical significance of the proposed methodology’s superior performance for both tasks. Researchers have focused their efforts on enhancing the classification performance of multiple mental tasks in order to generate commands effective for control of external devices or for communication with patients suffering from amyotrophic lateral sclerosis, locked in syndrome, or other physical disabilities
A novel methodology that proceeds by adaptive estimation of general linear model (GLM) coefficients and extraction of the classification performances of motor imagery (MI) versus rest and mental rotation (MR) versus rest task were developed and evaluated
Summary
A brain–computer interface (BCI) system bypasses the peripheral nervous system and provides means of communication for patients suffering from motor disabilities or in a persistent vegetative state using devices, such as robotic arms or other prostheses (Wolpaw et al, 2002). Non-invasive techniques include electroencephalography (EEG) (Wolpaw et al, 2002; Pfurtscheller et al, 2003; Salvaris and Sepulveda, 2010; Cong et al, 2011, 2015; Jin et al, 2011, 2014, 2015; Choi, 2013; Chen et al, 2015), fNIRS-BCI using GLM Coefficients functional magnetic resonance imaging (fMRI) (Enzinger et al, 2008; Sorger et al, 2009), and functional near-infrared spectroscopy (fNIRS) (Ferrari et al, 1985; Kato et al, 1993; Coyle et al, 2004, 2007; Naito et al, 2007; Naseer and Hong, 2013; Naseer et al, 2014; Noori et al, 2017). We propose that features be extracted from the estimated coefficients of the general linear model (GLM)
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