With the joint advancement in areas such as pervasive neural data sensing, neural
computing, neuromodulation and artificial intelligence, neural interface has become a …

Non-invasive brain-computer interfaces (BCIs) have been widely used for neural decoding,
linking neural signals to control devices. Hybrid BCI systems using electroencephalography …

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

Brain–computer interface (BCI) based on motor imagery (MI) can control external
applications by decoding different brain physiological signals, such as …

This study explores the combination of electroencephalogram (EEG) and functional near-
infrared spectroscopy (fNIRS) to enhance the decoding performance of motor imagery (MI) …

In this study, a channel and feature selection methodology is devised for brain‐computer
interface (BCI) applications using functional near‐infrared spectroscopy (fNIRS). A graph …

The proposed study is based on a feature and channel selection strategy that uses
correlation filters for brain–computer interface (BCI) applications using …

In recent decades, the brain–computer interface (BCI) has emerged as a leading area of
research. The feature selection is vital to reduce the dataset's dimensionality, increase the …

Diabetes is a persistent metabolic disorder linked to elevated levels of blood glucose,
commonly referred to as blood sugar. This condition can have detrimental effects on the …

Background and objective: Identifying active features from ultra-high dimensional data is
one of the primary and vital tasks in statistical learning and biological discovery. Methods: In …