Accurate binary classification of electroencephalography (EEG) signals is a challenging task
for the development of motor imagery (MI) brain–computer interface (BCI) systems. In this …

Background Brain–computer interfaces (BCI), initially designed to bypass the peripheral
motor system to externally control movement using brain signals, are additionally being …

Electroencephalography (EEG), as a non-invasive modality, enables the representation of
the underlying neuronal activities as electrical signals with high temporal resolution. In …

Motor imagery (MI)-based brain–computer interfaces (BCI) have shown increased potential
for the rehabilitation of stroke patients; nonetheless, their implementation in clinical practice …

In the electroencephalography (EEG) based cross-subject motor imagery (MI) classification
task, the device and subject problems can cause the time-related data distribution shift …

Deep learning networks have been successfully applied to transfer functions so that the
models can be adapted from the source domain to different target domains. This study uses …

The need for repeated calibration and accounting for intersubject variability is a major
challenge for the practical applications of a brain–computer interface (BCI). The problem …

Electroencephalography (EEG) is the most prevalent signal acquisition technique for brain-
computer interface (BCI). However, the statistical distribution of EEG data varies across …

Electroencephalography (EEG) is a commonly used clinical approach for the diagnosis of
epilepsy which is a life-threatening neurological disorder. Many algorithms have been …

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