Data-Driven Dynamic Graph Convolution Transformer Network Model for EEG Emotion Recognition Under IoMT Environment

Data-Driven Dynamic Graph Convolution Transformer Network Model for EEG Emotion Recognition Under IoMT Environment

With the rapid progress in data-driven approaches, artificial intelligence, and big data analytics technologies, utilizing electroencephalogram (EEG) signals for emotion analysis in the field of the Internet of Medical Things can effectively assist in the diagnosis of specific diseases. While existi...

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Bibliographic Details
Main Authors: Xing Jin, Fa Zhu, Yu Shen, Gwanggil Jeon, David Camacho
Format: Article
Language:English
Published: Tsinghua University Press 2025-05-01
Series:Big Data Mining and Analytics
Subjects:
internet of medical things (iomt) emotion analysis
electroencephalogram (eeg) signals
data-driven big data analytics
graph convolution operation
minimum category confusion (mcc) loss
graph data mining
Online Access:https://www.sciopen.com/article/10.26599/BDMA.2024.9020071
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