Emotions are a critical aspect of daily life and serve a crucial role in human decision-making,
planning, reasoning, and other mental states. As a result, they are considered a significant …

Affective, emotional, and physiological states (AFFECT) detection and recognition by
capturing human signals is a fast-growing area, which has been applied across numerous …

Electroencephalography (EEG), despite its inherited complexity, is a preferable brain signal
for automatic human emotion recognition (ER), which is a challenging machine learning task …

Electroencephalography (EEG) sensors are flexible and non-invasive sensoring devices for
the measurement of electrical brain activity which is extensively used in some areas of …

Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as
emotion recognition, motor imagery and neurological diseases and disorders. A wide range …

Electroencephalography (EEG) is increasingly important in Brain-Computer Interface (BCI)
systems due to its portability and simplicity. In this paper, we provide a comprehensive …

Emotion is a subjective psychophysiological reaction coming from external stimuli which
impacts every aspect of our daily lives. Due to the continuing development of non-invasive …

Emotion recognition finds broad applications across psychology, computer science, and
artificial intelligence. However, the intricacies of emotional states pose challenges …

In recent years, with the rise of deep learning technologies, EEG-based emotion recognition
has garnered significant attention. However, most existing methods tend to focus on the …

Graph neural network (GNN) models are increasingly being used for the classification of
electroencephalography (EEG) data. However, GNN-based diagnosis of neurological …