Deep learning models for multimodal expression recognition have reached remarkable
performance in controlled laboratory environments because of their ability to learn …

Emotion recognition based on multimodal physiological signals is attracting more and more
attention. However, how to deal with the consistency and heterogeneity of multimodal …

Accurate sleep staging is crucial for wearable sensor-based sleep monitoring and health
interventions. Polysomnography (PSG) signals, rich in information from multiple …

Sleep staging is crucial for sleep tracking and health assessment. Polysomnography (PSG),
containing multiple modalities such as electroencephalography, electrooculography …

Assessing driver cognitive load (DCL) is essential for enhancing driving safety and
performance. This study introduces a novel method that leverages a pre-trained biosignal …

Sleep stage classification has important clinical significance for the diagnosis of sleep-
related diseases. To pursue more accurate sleep stage classification, multi-channel sleep …

EEG-based emotion recognition is becoming crucial in brain-computer interfaces (BCI).
Currently, most researches focus on improving accuracy, while neglecting further research …

EEG-based emotion recognition (EER) has gained significant attention due to its potential
for understanding and analyzing human emotions. While recent advancements in deep …

Smaller machine learning models, with less complex architectures and sensor inputs, can
benefit wearable sensor-based human activity recognition (HAR) systems in many ways …

Human emotion is a complex phenomenon conveyed and perceived through facial
expressions, vocal tones, body language, and physiological signals. Multimodal emotion …