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 …

Affective brain-computer interface based on electroencephalography (EEG) is an important
branch in the field of affective computing. However, the individual differences in EEG …

Cross-subject EEG emotion recognition suffers a major setback due to high inter-subject
variability in emotional responses. Many prior studies have endeavored to alleviate the inter …

Over the last few years, unsupervised domain adaptation (UDA) based on deep learning
has emerged as a solution to build cross-subject emotion recognition models from …

There is a growing need for sparse representational formats of human affective states that
can be utilized in scenarios with limited computational memory resources. We explore …

Affective brain-computer interfaces (aBCIs) have garnered widespread applications, with
remarkable advancements in utilizing electroencephalogram (EEG) technology for emotion …