Semisupervised learning holds significant academic and practical importance in the realm of
EEG-based emotion recognition. Currently, a multitude of research endeavors are dedicated …

Emotion, a fundamental trait of human beings, plays a pivotal role in shaping aspects of our
lives, including our cognitive and perceptual abilities. Hence, emotion recognition also is …

Though Electroencephalogram (EEG) could objectively reflect emotional states of our
human beings, its weak, non-stationary, and low signal-to-noise properties easily cause the …

The use of machine learning (ML) techniques in affective computing applications focuses on
improving the user experience in emotion recognition. The collection of input data (eg …

Electroencephalogram (EEG) monitors the brain's electrical activity and carries useful
information regarding the subject's emotional states. Due to the nonstationary and being …

Since Electroencephalogram (EEG) is resistant to camouflage, it has been a reliable data
source for objective emotion recognition. EEG is naturally multi-rhythm and multi-channel …

Background In the realm of brain-computer interfaces (BCI), identifying emotions from
electroencephalogram (EEG) data is a difficult endeavor because of the volume of data, the …

Studies in the area of neuroscience have revealed the relationship between emotional
patterns and brain functional regions, demonstrating that the dynamic relationship between …

Recently, electroencephalogram (EEG)-based emotion recognition has attracted increasing
interests in research community. The weak, non-stationary, multi-rhythm and multi-channel …

Electroencephalogram (EEG) signals are widely used in the field of emotion recognition
since it is resistant to camouflage and contains abundant physiological information …