A Study on the Assessment of Drug Addiction Level of Bimodal Eeg-Nirs Based on Tsnet
Pages 258 - 264
Abstract
Traditional drug addiction assessment is based on psychological scales, oral statements of drug addicts, and lacks objective physiological indicators. This experiment proposes a new method based on the deep learning framework TSNET, aiming to assess the degree of drug addiction by combining electroencephalography (EEG) and near-infrared spectroscopy (NIRS) techniques. In this experiment, we designed an experimental paradigm to induce drug craving in drug addicts, collected EEG data and NIRS data related to addiction, and fused the bimodal data and objectively evaluated the degree of drug addiction through the deep learning algorithm which called TSNET. An accuracy of 83.7% was achieved using this deep learning algorithm, indicating that the method is an effective means of discriminating the degree of drug addiction and is easy to use and objective.
References
[1]
Adi Alhudhaif. 2021. An effective classification framework for brain-computer interface system design based on combining of fNIRS and EEG signals. PeerJ Computer Science 7, (May 2021), e537.
[2]
Maged S. AL-Quraishi, Irraivan Elamvazuthi, Tong Boon Tang, Muhammad Al-Qurishi, Syed Hasan Adil, and Mansoor Ebrahim. 2021. Bimodal Data Fusion of Simultaneous Measurements of EEG and fNIRS during Lower Limb Movements. Brain Sciences 11, 6 (May 2021), 713.
[3]
Jun Cao, Enara Martin Garro, and Yifan Zhao. 2022. EEG/fNIRS Based Workload Classification Using Functional Brain Connectivity and Machine Learning. Sensors 22, 19 (October 2022), 7623.
[4]
Tianzhen Chen, Hang Su, Na Zhong, Haoye Tan, Xiaotong Li, Yiran Meng, Chunmei Duan, Congbin Zhang, Juwang Bao, Ding Xu, Weidong Song, Jixue Zou, Tao Liu, Qingqing Zhan, Haifeng Jiang, and Min Zhao. 2020. Disrupted brain network dynamics and cognitive functions in methamphetamine use disorder: insights from EEG microstates. BMC Psychiatry 20, 1 (December 2020), 334.
[5]
Wei-Liang Chen, Julie Wagner, Nicholas Heugel, Jeffrey Sugar, Yu-Wen Lee, Lisa Conant, Marsha Malloy, Joseph Heffernan, Brendan Quirk, Anthony Zinos, Scott A. Beardsley, Robert Prost, and Harry T. Whelan. 2020. Functional Near-Infrared Spectroscopy and Its Clinical Application in the Field of Neuroscience: Advances and Future Directions. Front. Neurosci. 14, (July 2020), 724.
[6]
Pietro A. Cicalese, Rihui Li, Mohammad B. Ahmadi, Chushan Wang, Joseph T. Francis, Sudhakar Selvaraj, Paul E. Schulz, and Yingchun Zhang. 2020. An EEG-fNIRS hybridization technique in the four-class classification of alzheimer's disease. Journal of Neuroscience Methods 336, (April 2020), 108618.
[7]
Arnaud Delorme. 2023. EEG is better left alone. Sci Rep 13, 1 (February 2023), 2372.
[8]
Chen Fang, Chunfei Li, Parisa Forouzannezhad, Mercedes Cabrerizo, Rosie E. Curiel, David Loewenstein, Ranjan Duara, and Malek Adjouadi. 2020. Gaussian discriminative component analysis for early detection of Alzheimer's disease: A supervised dimensionality reduction algorithm. Journal of Neuroscience Methods 344, (October 2020), 108856.
[9]
Xuelin Gu, Banghua Yang, Shouwei Gao, Lin Feng Yan, Ding Xu, Wen Wang, School of Mechanical and Electrical Engineering and Automation, Shanghai University, Shanghai 200444, China, Shanghai Drug Rehabilitation Administration Bureau, Shanghai 200080, China, and Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, China. 2021. Application of bi-modal signal in the classification and recognition of drug addiction degree based on machine learning. MBE 18, 5 (2021), 6926–6940.
[10]
Ayşegül Güven, Miray Altınkaynak, Nazan Dolu, Meltem İzzetoğlu, Ferhat Pektaş, Sevgi Özmen, Esra Demirci, and Turgay Batbat. 2020. Combining functional near-infrared spectroscopy and EEG measurements for the diagnosis of attention-deficit hyperactivity disorder. Neural Comput & Applic 32, 12 (June 2020), 8367–8380.
[11]
Mustafa A. H. Hasan, Muhammad U. Khan, and Deepti Mishra. 2020. A Computationally Efficient Method for Hybrid EEG-fNIRS BCI Based on the Pearson Correlation. BioMed Research International 2020, (August 2020), 1–13.
[12]
Du Li, Banghua Yang, Xuelin Gu, Dewen Kong, and Peng Zan. 2020. Drug Addiction Detection Algorithm Based on CNN-BN. In Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition, October 30, 2020. ACM, Xiamen China, 96–100. ***
[13]
Mizuho Nishio, Shunjiro Noguchi, Hidetoshi Matsuo, and Takamichi Murakami. 2020. Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methods. Sci Rep 10, 1 (October 2020), 17532.
[14]
Banghua Yang, Xuelin Gu, Ding Xu, and Chengcheng Fan. 2020. A Study on EEG-NIRS Testing of Drug Users’ Brain Function under Visual Induction. In Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition, October 30, 2020. ACM, Xiamen China, 85–89. ***
Index Terms
- A Study on the Assessment of Drug Addiction Level of Bimodal Eeg-Nirs Based on Tsnet
Recommendations
Drug Addiction Detection Algorithm Based on CNN-BN
ICCPR '20: Proceedings of the 2020 9th International Conference on Computing and Pattern RecognitionThere is currently few related research in the field of drug detoxification to objectively quantify the degree of drug addiction among drug addicts. This paper designs an experimental paradigm, using a high-density near infrared spectroscopy device to ...
A longitudinal study of the impact of parental loneliness on adolescents' online game addiction: The mediating roles of adolescents’ social skill deficits and loneliness
AbstractThis study longitudinally examined the predictors and mediators of adolescents' online game addiction. Apropos this, the questions of whether parental loneliness at T1 predicts adolescents' online game addiction at T3 and whether ...
Highlights- This study investigates the relationship between parental loneliness and adolescents’ online game addiction.
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
May 2024
279 pages
ISBN:9798400717666
DOI:10.1145/3674658
Copyright © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 18 November 2024
Check for updates
Author Tags
Qualifiers
- Research-article
Conference
ICBBT 2024
ICBBT 2024: 2024 16th International Conference on Bioinformatics and Biomedical Technology
May 24 - 26, 2024
Chongqing, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 3Total Downloads
- Downloads (Last 12 months)3
- Downloads (Last 6 weeks)3
Reflects downloads up to 30 Nov 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderFull Text
View this article in Full Text.
Full TextHTML Format
View this article in HTML Format.
HTML Format