Brain Network Manifold Learned by Cognition-Inspired Graph Embedding Model for Emotion Recognition
Electroencephalogram (EEG) brain network embodies the brain's coordination and
interaction mechanism, and the transformations of emotional states are usually …
interaction mechanism, and the transformations of emotional states are usually …
Multi-view brain functional connectivity and hierarchical fusion for EEG-based emotion recognition
B Fu, X Yu, F Wu, Y Liu - Measurement, 2025 - Elsevier
Emotional states evolve gradually from their onset to full manifestation, reflected in changes
in brain functional connectivity. Existing research often focuses only on the final emotional …
in brain functional connectivity. Existing research often focuses only on the final emotional …
WSEL: EEG feature selection with weighted self-expression learning for incomplete multi-dimensional emotion recognition
X Xu, L Zhuo, J Lu, X Wu - Proceedings of the 32nd ACM International …, 2024 - dl.acm.org
Due to the small size of valid samples, multi-source EEG features with high dimensionality
can easily cause problems such as overfitting and poor real-time performance of the …
can easily cause problems such as overfitting and poor real-time performance of the …
Empirical Study of Feature Selection Methods in Regression for Large-Scale Healthcare Data: A Case Study on Estimating Dental Expenditures
The complexity and high dimensionality of healthcare data present substantial challenges in
building machine learning (ML) models, given the large number of variables such as patient …
building machine learning (ML) models, given the large number of variables such as patient …
Negative-Sample-Free Contrastive Self-Supervised Learning for Electroencephalogram-Based Motor Imagery Classification
Motor imagery-based brain-computer interface (MI-BCI) systems convert user intentions into
computer commands, aiding the communication and rehabilitation of individuals with motor …
computer commands, aiding the communication and rehabilitation of individuals with motor …
Automatically Extracting and Utilizing EEG Channel Importance Based on Graph Convolutional Network for Emotion Recognition
K Yang, Z Yao, K Zhang, J Xu, L Zhu… - IEEE Journal of …, 2024 - ieeexplore.ieee.org
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 …
EEG emotion recognition. However, most studies train their models directly without …
An improved empirical mode decomposition method with ensemble classifiers for analysis of multichannel EEG in BCI emotion recognition
Emotion recognition using EEG is a difficult study because the signals' unstable behavior,
which is brought on by the brain's complex neuronal activity, makes it difficult to extract the …
which is brought on by the brain's complex neuronal activity, makes it difficult to extract the …
Multimodal Emotion Recognition based on Convolutional Neural Networks and Long Short-Term Memory Networks
Y Liu, D Geng, X Wu, Y Liu - 2024 2nd International Conference …, 2024 - ieeexplore.ieee.org
Due to the rapid advancements in big data and high-performance computing technologies,
the application of deep learning models in the fields of computer vision and signal image …
the application of deep learning models in the fields of computer vision and signal image …