The algorithms of electroencephalography (EEG) decoding are mainly based on machine
learning in current research. One of the main assumptions of machine learning is that …

This paper presents a systematic literature review, providing a comprehensive taxonomy of
Data Augmentation (DA), Transfer Learning (TL), and Self-Supervised Learning (SSL) …

This paper presents a comprehensive review of evolutionary algorithms that learn an
ensemble of predictive models for supervised machine learning (classification and …

In an electroencephalogram-(EEG-) based brain–computer interface (BCI), a subject can
directly communicate with an electronic device using his EEG signals in a safe and …

Transfer learning, as a new machine learning methodology, may solve problems in related
but different domains by using existing knowledge, and it is often applied to transfer training …

The intersubject variability present in electroencephalographic (EEG) signals can affect the
performance of the brain–computer interface (BCI) systems. Despite the significant progress …

Traditional training methods need to collect a large amount of data for every subject to train
a subject-specific classifier, which causes subjects fatigue and training burden. This study …

Classification is the machine learning task of categorizing instances into classes. There are
several algorithms in the literature that perform classification, with varying degrees of …

In the first project, we propose a Bayesian generative model to fit the probability distribution
of multi-trial EEG signals in the BCI system. Existing machine learning methods focus on …

Individual difference of brain signal leads to the P300-based interface needs a large amount
of training data to construct a pattern recognition model for each subject. Lots of training …