Emotion recognition technology through analyzing the EEG signal is currently an essential
concept in Artificial Intelligence and holds great potential in emotional health care, human …

Sleep stage classification is essential for sleep assessment and disease diagnosis.
Although previous attempts to classify sleep stages have achieved high classification …

EEG signals have been reported to be informative and reliable for emotion recognition in
recent years. However, the inter-subject variability of emotion-related EEG signals still poses …

Emotion recognition based on electroencephalography (EEG) signals has emerged as a
prominent research field, facilitating objective evaluation of diseases like depression and …

EEG-based emotion recognition has become an important task in affective computing and
intelligent interaction. However, how to effectively combine the spatial, spectral, and …

Current research suggests that there exist certain limitations in EEG emotion recognition,
including redundant and meaningless time-frames and channels, as well as inter-and intra …

The dynamic uncertain relationship among each brain region is a necessary factor that limits
EEG-based emotion recognition. It is a thought-provoking problem to availably employ time …

Since electroencephalogram (EEG) signals can truly reflect human emotional state, emotion
recognition based on EEG has turned into a critical branch in the field of artificial …

Using ECG signals captured by wearable devices for emotion recognition is a feasible
solution. We propose a deep convolutional neural network incorporating attentional …

Sleep staging is fundamental for sleep assessment and disease diagnosis. Although
previous attempts to classify sleep stages have achieved high classification performance …