DSCnet: detection of drug and alcohol addiction mechanisms based on multi-angle feature learning from the hybrid representation of EEG

DSCnet: detection of drug and alcohol addiction mechanisms based on multi-angle feature learning from the hybrid representation of EEG

IntroductionDrug and alcohol addiction impair neurotransmitter systems, leading to severe physiological, psychological, and social issues. Electroencephalography (EEG) is commonly used to analyze addiction mechanisms, but traditional feature extraction methods such as time-frequency analysis, Princi...

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Bibliographic Details
Main Authors: Jing Wu, Nan Zhang, Qilei Ye, Xiaorui Zheng, Minmin Shao, Xian Chen, Hui Huang
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-06-01
Series:Frontiers in Neuroscience
Subjects:
electroencephalograms
alcoholism
drug addiction
computer-aided diagnosis
convolutional neural networks
classification
Online Access:https://www.frontiersin.org/articles/10.3389/fnins.2025.1607248/full
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https://www.frontiersin.org/articles/10.3389/fnins.2025.1607248/full

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