Convolutional neural networks (CNNs) have been successfully applied to motor imagery
(MI)-based brain–computer interface (BCI). Nevertheless, single-scale CNN fail to extract …

Although advances in deep learning technologies have greatly facilitated the brain intention
decoding from electroencephalogram (EEG) in motor imagery brain–computer interfaces (MI …

Electroencephalography (EEG) decoding is a challenging task due to the limited availability
of labelled data. While transfer learning is a promising technique to address this challenge, it …

Individual differences in electroencephalography (EEG) present significant challenges for
subject-independent EEG classification in brain–computer interfaces (BCIs). Existing …

Motor imagery (MI) brain-computer interface (BCI) paradigms have been extensively used in
neurological rehabilitation. However, due to the required long calibration time and non …

The brain-computer interface (BCI) systems based on motor imagery typically rely on a large
number of electrode channels to acquire information. The rational selection of …

Objective. In brain-computer interfaces (BCIs) that utilize motor imagery (MI), minimizing
calibration time has become increasingly critical for real-world applications. Recently …

In cross-subject fall risk classification based on plantar pressure, a challenge is that data
from different subjects have significant individual information. Thus, the models with …

Motor imagery (MI)-based brain-computer interfaces (BCIs) using electroencephalography
(EEG) have found practical applications in external device control. However, the non …

Transfer learning is useful in increasing the generalization ability of a model, for dealing with
variations among different subjects in the brain-computer interface (BCI). Nevertheless, most …