Concept drift techniques aim at learning patterns from data streams that may change over
time. Although such behavior is not usually expected in controlled environments, real-world …

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 …

At present, concept drift in the nonstationary data stream is showing trends with different
speeds and different degrees of severity, which has brought great challenges to many fields …

The ability to detect online changes in stationarity or time variance in a data stream is a hot
research topic with striking implications. In this paper, we propose a novel probability density …

Due to the non-stationary nature of electroencephalography (EEG) signals, a Brain-
computer Interfacing (BCI) system requires frequent calibration. This leads to inter session …

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 …

Abstract Concept drifts and class imbalance are two primary challenges in supervised data
stream classification, whereas their co-occurrence presents a more complicated learning …

The recent availability of large datasets in bio-medicine has inspired the development of
representation learning methods for multiple healthcare applications. Despite advances in …

Recent advancements in magnetoencephalography (MEG)-based brain-computer interfaces
(BCIs) have shown great potential. However, the performance of current MEG-BCI systems …