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

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

Graph convolutional networks (GCNs) have shown great prowess in learning topological
relationships among electroencephalogram (EEG) channels for EEG-based emotion …

Emotion recognition using multi-modal physiological signals is an emerging field in affective
computing that significantly improves performance compared to unimodal approaches. The …

Proactive failure detection of instances is vitally essential to microservice systems because
an instance failure can propagate to the whole system and degrade the system's …

Multimodal relation extraction is a critical task in information extraction, aiming to predict the
class of relations between head and tail entities from linguistic sequences and related …

Multimodal machine learning is an emerging area of research, which has received a great
deal of scholarly attention in recent years. Up to now, there are few studies on multimodal …

Emotion recognition from physiological signals is a topic of widespread interest, and
researchers continue to develop novel techniques for perceiving emotions. However, the …

Motor Imagery (MI) classification with electroencephalography (EEG) is a critical aspect of
Brain–Computer Interface (BCI) systems, enabling individuals with mobility limitations to …