Brain-computer interfaces (BCIs) aim to directly bridge the human brain and the outside
world through acquiring and processing the brain signals in real time. In recent two decades …

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 …

Despite the excellent capabilities of machine learning algorithms, their performance
deteriorates when the distribution of test data differs from the distribution of training data. In …

Due to its convenience and safety, electroencephalography (EEG) data is one of the most
widely used signals in motor imagery (MI) brain–computer interfaces (BCIs). In recent years …

The brain–computer interface (BCI) based on motor imagery electroencephalography (EEG)
is widely used because of its convenience and safety. However, due to the distributional …

Due to the individual difference, EEG signals from other subjects (source) can hardly be
used to decode the mental intentions of the target subject. Although transfer learning …

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

Recently, more and more studies have begun to use deep learning to decode and classify
EEG signals. The use of deep learning has led to an increase in the classification accuracy …

Non-stationarity of EEG signals leads to high variability between subjects, making it
challenging to directly use data from other subjects (source domain) for the classifier in the …

Objective. The recent breakthrough of wearable sleep monitoring devices has resulted in
large amounts of sleep data. However, as limited labels are available, interpreting these …