The non-stationary nature of electroencephalography (EEG) signals makes an EEG-based
brain-computer interface (BCI) a dynamic system, thus improving its performance is a …

A common assumption in traditional supervised learning is the similar probability distribution
of data between the training phase and the testing/operating phase. When transitioning from …

Dataset shift is a very common issue wherein the input data distribution shifts over time in
non-stationary environments. A broad range of real-world systems face the challenge of …

A major issue in electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is the
intrinsic nonstationarities in the brain waves, which may degrade the performance of the …

EEG-based deep learning models have trended toward models that are designed to perform
classification on any individual (cross-participant models). However, because EEG varies …

Outliers are unexpected observations, which deviate from the majority of observations.
Outlier detection and prediction are challenging tasks, because outliers are rare by …

A major problem in a brain-computer interface (BCI) based on electroencephalogram (EEG)
recordings is the varying statistical properties of the signals during inter-or intra-session …

Learning with dataset shift is a major challenge in non-stationary environments wherein the
input data distribution may shift over time. Detecting the dataset shift point in the time-series …

A major issue for bringing brain-computer interface (BCI) based on electroencephalogram
(EEG) recordings outside of laboratories is the non-stationarities of EEG signals. Varying …

Learning in the presence of dataset shifts in non-stationary environments is a major
challenge. Dataset shifts in the form of covariate shifts commonly occur in a broad range of …