Emotion recognition through speech is a technique employed in various scenarios of Human-Computer Interaction (HCI). Existing approaches have achieved significant results; however, limitations persist, with the quantity and diversity of data being mo...
Art research has long aimed to unravel the complex associations between specific attributes, such as color, complexity, and emotional expressiveness, and art judgments, including beauty, creativity, and liking. However, the fundamental distinction be...
BACKGROUND: Emotion is an important area in neuroscience. Cross-subject emotion recognition based on electroencephalogram (EEG) data is challenging due to physiological differences between subjects. Domain gap, which refers to the different distribut...
IEEE transactions on neural networks and learning systems
Sep 3, 2024
Inspired by the well-known Papez circuit theory and neuroscience knowledge of reinforcement learning, a double dueling deep Q network (DQN) is built incorporating the electroencephalogram (EEG) signals of the frontal lobe as prior information, which ...
Emotions are integral to human social interactions, with diverse responses elicited by various situational contexts. Particularly, the prevalence of negative emotional states has been correlated with negative outcomes for mental health, necessitating...
Facial expression recognition(FER) is a hot topic in computer vision, especially as deep learning based methods are gaining traction in this field. However, traditional convolutional neural networks (CNN) ignore the relative position relationship of ...
Neural networks : the official journal of the International Neural Network Society
Aug 22, 2024
Emotional recognition is highly important in the field of brain-computer interfaces (BCIs). However, due to the individual variability in electroencephalogram (EEG) signals and the challenges in obtaining accurate emotional labels, traditional method...
IEEE journal of biomedical and health informatics
Aug 6, 2024
Graph convolutional network (GCN) based on the brain network has been widely used for EEG emotion recognition. However, most studies train their models directly without considering network dimensionality reduction beforehand. In fact, some nodes and ...
BACKGROUND: Objective diagnostic approaches need to be tested to enhance the efficacy of depression detection. Non-invasive EEG-based identification represents a promising area.
IEEE transactions on neural networks and learning systems
Aug 5, 2024
Multichannel electroencephalogram (EEG) is an array signal that represents brain neural networks and can be applied to characterize information propagation patterns for different emotional states. To reveal these inherent spatial graph features and i...
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