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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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2025.1607248/full |
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| author | Jing Wu Nan Zhang Qilei Ye Xiaorui Zheng Minmin Shao Xian Chen Hui Huang |
| author_facet | Jing Wu Nan Zhang Qilei Ye Xiaorui Zheng Minmin Shao Xian Chen Hui Huang |
| author_sort | Jing Wu |
| collection | DOAJ |
| description | 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, Principal Component Analysis (PCA), and Independent Component Analysis (ICA) fail to capture complex relationships between variables.MethodsThis paper proposes DSCnet, a novel neural network model for addiction detection. DSCnet combines embedding layers, skip connections, depthwise separable convolution, and our self-designed Directional Adaptive Feature Modulation (DAFM) module. DAFM is a key innovation that adaptively adjusts feature directionality, extracting global features from EEG signals while preserving spatiotemporal information. This enables the model to capture neural activity patterns related to addiction mechanisms. DSCnet uses a multi-angle feature extraction strategy, emphasizing information from various perspectives.ResultsOn the drug addiction dataset, DSCnet achieved 85.11% accuracy, 85.13% precision, 85.12% recall, and 85.12% F1-score. On the UCI alcohol addiction dataset, it achieved 84.56% accuracy, 84.73% precision, 84.56% recall, and 84.63% F1-score.DiscussionThese results outperform existing models and demonstrate a balanced performance across both datasets, highlighting DSCnet's potential in addiction detection. |
| format | Article |
| id | doaj-art-9b77c88b8ce940a2a6b7935bc6772fd3 |
| institution | DOAJ |
| issn | 1662-453X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj-art-9b77c88b8ce940a2a6b7935bc6772fd32025-08-20T03:21:59ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2025-06-011910.3389/fnins.2025.16072481607248DSCnet: detection of drug and alcohol addiction mechanisms based on multi-angle feature learning from the hybrid representation of EEGJing Wu0Nan Zhang1Qilei Ye2Xiaorui Zheng3Minmin Shao4Xian Chen5Hui Huang6College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, ChinaCollege of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, ChinaData Resources Division, Wenzhou Data Bureau, Wenzhou, ChinaDepartment of Drug Rehabilitation and Correction, Wenzhou City Huanglong Compulsory Isolation Drug Rehabilitation Center, Wenzhou, ChinaDepartment of Otolaryngology, Wenzhou Central Hospital, Wenzhou, ChinaInformation Technology Center, Wenzhou Polytechnic, Wenzhou, ChinaCollege of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, ChinaIntroductionDrug 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, Principal Component Analysis (PCA), and Independent Component Analysis (ICA) fail to capture complex relationships between variables.MethodsThis paper proposes DSCnet, a novel neural network model for addiction detection. DSCnet combines embedding layers, skip connections, depthwise separable convolution, and our self-designed Directional Adaptive Feature Modulation (DAFM) module. DAFM is a key innovation that adaptively adjusts feature directionality, extracting global features from EEG signals while preserving spatiotemporal information. This enables the model to capture neural activity patterns related to addiction mechanisms. DSCnet uses a multi-angle feature extraction strategy, emphasizing information from various perspectives.ResultsOn the drug addiction dataset, DSCnet achieved 85.11% accuracy, 85.13% precision, 85.12% recall, and 85.12% F1-score. On the UCI alcohol addiction dataset, it achieved 84.56% accuracy, 84.73% precision, 84.56% recall, and 84.63% F1-score.DiscussionThese results outperform existing models and demonstrate a balanced performance across both datasets, highlighting DSCnet's potential in addiction detection.https://www.frontiersin.org/articles/10.3389/fnins.2025.1607248/fullelectroencephalogramsalcoholismdrug addictioncomputer-aided diagnosisconvolutional neural networksclassification |
| spellingShingle | Jing Wu Nan Zhang Qilei Ye Xiaorui Zheng Minmin Shao Xian Chen Hui Huang DSCnet: detection of drug and alcohol addiction mechanisms based on multi-angle feature learning from the hybrid representation of EEG Frontiers in Neuroscience electroencephalograms alcoholism drug addiction computer-aided diagnosis convolutional neural networks classification |
| title | DSCnet: detection of drug and alcohol addiction mechanisms based on multi-angle feature learning from the hybrid representation of EEG |
| title_full | DSCnet: detection of drug and alcohol addiction mechanisms based on multi-angle feature learning from the hybrid representation of EEG |
| title_fullStr | DSCnet: detection of drug and alcohol addiction mechanisms based on multi-angle feature learning from the hybrid representation of EEG |
| title_full_unstemmed | DSCnet: detection of drug and alcohol addiction mechanisms based on multi-angle feature learning from the hybrid representation of EEG |
| title_short | DSCnet: detection of drug and alcohol addiction mechanisms based on multi-angle feature learning from the hybrid representation of EEG |
| title_sort | dscnet detection of drug and alcohol addiction mechanisms based on multi angle feature learning from the hybrid representation of eeg |
| topic | electroencephalograms alcoholism drug addiction computer-aided diagnosis convolutional neural networks classification |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2025.1607248/full |
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