Objective. Constructing an efficient human emotion recognition model based on
electroencephalogram (EEG) signals is significant for realizing emotional brain–computer …

Using multimodal fusion method to deal with emotion recognition task has become a trend.
The fusion vector can more comprehensively reflect the subject's emotional change state, so …

The accurate identification of the human emotional status is crucial for an efficient human–
robot interaction (HRI). As such, we have witnessed extensive research efforts made in …

A common drawback of EEG-based emotion recognition is that volume conduction effects of
the human head introduce interchannel dependence and result in highly correlated …

Affect decoding through brain-computer interfacing (BCI) holds great potential to capture
users' feelings and emotional responses via non-invasive electroencephalogram (EEG) …

Emotion detection is an important research issue in electroencephalogram (EEG). Signal
preprocessing and feature selection are parts of feature engineering, which determines 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 …

Electroencephalogram (EEG) signals by virtue of its good time resolution can provide a
more comprehensive way for emotion recognition. To further mine the emotion‐relevant …

Due to the small size of valid samples, multi-source EEG features with high dimensionality
can easily cause problems such as overfitting and poor real-time performance of the …

Background K-complex detection traditionally relied on expert clinicians, which is time-
consuming and onerous. Various automatic k-complex detection-based machine learning …