Abstract Background and Objective Brain-computer interfaces (BCIs) enable people to
control an external device by analyzing the brain's neural activity. Functional near-infrared …

In this paper, a novel technique for determination of the optimal feature combinations and,
thereby, acquisition of the maximum classification performance for a functional near-infrared …

Abstract Functional Near Infrared Spectroscopy (fNIRS) based Brain Computer Interface
(BCI) systems have grown in popularity in the last years, and has shown itself as a useful …

The feasibility of the random subspace ensemble learning method was explored to improve
the performance of functional near-infrared spectroscopy-based brain-computer interfaces …

A state-of-the-art brain–computer interface (BCI) system includes brain signal acquisition,
noise removal, channel selection, feature extraction, classification, and an application …

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 …

Objective. In this paper, a novel methodology for feature extraction to enhance classification
accuracy of functional near-infrared spectroscopy (fNIRS)-based two-class and three-class …

Functional near-infrared spectroscopy (fNIRS) has attracted increasing attention in the field
of brain–computer interfaces (BCIs) owing to their advantages such as non-invasiveness …

Functional near-infrared spectroscopy (fNIRS) is an optical imaging method that can be
used for a brain-computer interface (BCI). In the present study, we concurrently measure and …

In this study, we determine the optimal feature-combination for classification of functional
near-infrared spectroscopy (fNIRS) signals with the best accuracies for development of a two …