The brain–computer interface (BCI) is an emerging technology that has the potential to
revolutionize the world, with numerous applications ranging from healthcare to human …

Electroencephalography (EEG) is a non-invasive technique used to record the brain's
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …

Due to the limited perceptual field, convolutional neural networks (CNN) only extract local
temporal features and may fail to capture long-term dependencies for EEG decoding. In this …

Objective. Classification of electroencephalography (EEG)-based motor imagery (MI) is a
crucial non-invasive application in brain–computer interface (BCI) research. This paper …

Motor Imagery (MI) paradigm is critical in neural rehabilitation and gaming. Advances in
brain-computer interface (BCI) technology have facilitated the detection of MI from …

Abstract Electroencephalography (EEG)-based brain-computer interfaces (BCIs) pose a
challenge for decoding due to their low spatial resolution and signal-to-noise ratio. Typically …

Objective. Deep learning (DL) models have been proven to be effective in decoding motor
imagery (MI) signals in Electroencephalogram (EEG) data. However, DL models' success …

Objective: Electroencephalogram (EEG) is one of the most widely used signals in motor
imagery (MI) based brain-computer interfaces (BCIs). Domain adaptation has been …

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 …

Abstract In Machine Learning (ML), a well-known problem is the Dataset Shift problem
where the data in the training and test sets can follow different probability distributions …