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

Ensemble classifiers have been proven to result in better classification accuracy than that of
a single strong learner in many machine learning studies. Although many studies on …

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 …

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 …

Hybrid brain–computer interfaces (BCI) utilizing the high temporal resolution of
electroencephalography (EEG) and the high spatial resolution of functional near-infrared …

The brain–computer interface (BCI) provides an alternate means of communication between
the brain and external devices by recognizing the brain activities and translating them into …

Functional near-infrared spectroscopy (fNIRS) is increasingly used to investigate different
mental tasks for brain-computer interface (BCI) control due to its excellent environmental …

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

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have
potentially complementary characteristics that reflect the electrical and hemodynamic …

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 …