Due to its potential use in evaluating mental health and enhancing patient care, emotion
recognition using physiological signals, such as EEG, ECG, and EMG, has drawn a lot of …

Emotion recognition from electroencephalography (EEG) signals is crucial for human–
computer interaction yet poses significant challenges. While various techniques exist for …

The inefficiency of model inference and the limited sample data are significant issues in
electroencephalogram (EEG) emotion recognition models based on neural networks …

Faces and body parts play a crucial role in human social communication. Numerous studies
emphasize their significance as sociobiological stimuli in daily interactions. Two …

Objective: Emotion recognition through cortical neurovascular measurements poses
significant challenges due to the complex interplay of emotions within cortical activity …

In the field of cross-subject emotion recognition using electroencephalogram (EEG) signals,
significant challenges arise due to substantial inter-individual differences and the …

With the emergence of driverless technology, passenger ride comfort has become an issue
of concern. In recent years, driving fatigue detection and braking sensation evaluation based …

Enhancing the accuracy of emotion recognition models through multimodal learning is a
common approach. However, challenges such as insufficient modal feature learning in …

The use of brain-computer interface (BCI) technology to identify emotional states has gained
significant interest, especially with the rise of virtual reality (VR) applications. However, the …

Emotion recognition, a burgeoning field in recent years, involves computers discerning and
comprehending human emotions, finding widespread applications. While facial expressions …