Compared to other modalities, EEG-based emotion recognition can intuitively respond to the
emotional patterns in the human brain and, therefore, has become one of the most …

The progress of EEG-based emotion recognition has received widespread attention from the
fields of human-machine interaction and cognitive science. However, recognizing emotions …

Electroencephalogram (EEG) signals are widely used in the field of emotion recognition
since it is resistant to camouflage and contains abundant physiological information …

Objective Recognizing emotions from electroencephalography (EEG) signals is a
challenging task due to the complex, nonlinear, and nonstationary characteristics of brain …

Over the past decades, the radial basis function network (RBFN) has attracted extensive
attention due to its simple network structure and powerful learning ability. Meanwhile …

Background In the realm of brain-computer interfaces (BCI), identifying emotions from
electroencephalogram (EEG) data is a difficult endeavor because of the volume of data, the …

Due to the problem of a small amount of EEG samples and relatively high dimensionality of
electroencephalogram (EEG) features, feature selection plays an essential role in EEG …

Transfer learning is one of the popular methods to solve the problem of insufficient data in
subject-specific electroencephalogram (EEG) recognition tasks. However, most existing …

Electroencephalogram (EEG) based emotion recognition has become an important topic in
human-computer interaction and affective computing. However, existing advanced methods …

Multi-label feature selection is an indispensable technology in the preprocessing of multi-
label high-dimensional data. Approaches utilizing information theory and sparse models …