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

This is the authors' abstract. We don't add key points for this paper.

In this paper, a novel methodology for enhanced classification of functional near-infrared spectroscopy (fNIRS) signals utilizable in a two-class (motor imagery and rest; mental rotation and rest) brain-computer interface (BCI) is presented. First, fNIRS signals corresponding to motor imagery and mental rotation are acquired from the motor and prefrontal cortex respectively afterwards, filtered to remove physiological noises. Then, the signals are modelled using the general linear model (GLM), 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 motor imagery versus rest are 79.5, 83.7, 82.6, 81.4, and 84.1% whereas those for mental rotation versus rest are 85.5, 85.2, 87.8, 83.7, and 84.8%, respectively, using support vector machine (SVM). 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.