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Emotions

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VT-3DCapsNet: Visual tempos 3D-Capsule network for video-based facial expression recognition.

PloS one
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 ...

SFT-SGAT: A semi-supervised fine-tuning self-supervised graph attention network for emotion recognition and consciousness detection.

Neural networks : the official journal of the International Neural Network Society
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...

Automatically Extracting and Utilizing EEG Channel Importance Based on Graph Convolutional Network for Emotion Recognition.

IEEE journal of biomedical and health informatics
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 ...

EEG based depression detection by machine learning: Does inner or overt speech condition provide better biomarkers when using emotion words as experimental cues?

Journal of psychiatric research
BACKGROUND: Objective diagnostic approaches need to be tested to enhance the efficacy of depression detection. Non-invasive EEG-based identification represents a promising area.

Effective Emotion Recognition by Learning Discriminative Graph Topologies in EEG Brain Networks.

IEEE transactions on neural networks and learning systems
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...

Textual emotion classification using MPNet and cascading broad learning.

Neural networks : the official journal of the International Neural Network Society
As one of the most important tasks of natural language processing, textual emotion classification (TEC) aims to recognize and detect all emotions contained in texts. However, most existing methods are implemented using deep learning approaches, which...

How the Degree of Anthropomorphism of Human-like Robots Affects Users' Perceptual and Emotional Processing: Evidence from an EEG Study.

Sensors (Basel, Switzerland)
Anthropomorphized robots are increasingly integrated into human social life, playing vital roles across various fields. This study aimed to elucidate the neural dynamics underlying users' perceptual and emotional responses to robots with varying leve...

Insights from EEG analysis of evoked memory recalls using deep learning for emotion charting.

Scientific reports
Affect recognition in a real-world, less constrained environment is the principal prerequisite of the industrial-level usefulness of this technology. Monitoring the psychological profile using smart, wearable electroencephalogram (EEG) sensors during...

Protocol of the study for predicting empathy during VR sessions using sensor data and machine learning.

PloS one
Virtual reality (VR) technology is often referred to as the 'ultimate empathy machine' due to its capability to immerse users in alternate perspectives and environments beyond their immediate physical reality. In this study, participants will be imme...

Leveraging textual information for social media news categorization and sentiment analysis.

PloS one
The rise of social media has changed how people view connections. Machine Learning (ML)-based sentiment analysis and news categorization help understand emotions and access news. However, most studies focus on complex models requiring heavy resources...