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Non-Invasive Brain-Computer Interfaces: a New Perspective on the Assessment and Classification of Individuals with Methamphetamine Addiction

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Abstract

Methamphetamine addiction is a brain disease that causes abnormalities in the structure and function of the brain. EEG, a common signal acquired based on the noninvasive brain-computer interface, can reflect the altered brain activity associated with methamphetamine addiction. EEG-based analysis methods provide a perspective to explore the neural mechanisms of methamphetamine addiction and the effects on brain activity. This paper reports the results of a review of EEG-based assessment and classification of methamphetamine addiction. Current methods commonly used in EEG-based methamphetamine addiction research include traditional resting-state EEG analysis, brain network analysis, and analysis of event-related potentials. A small number of studies have classified methamphetamine addiction and healthy individuals based on resting state EEG features or event-related potentials. EEG is one of the common tools used to examine the effects of methamphetamine on brain function. In follow-up studies, new methods for analyzing resting-state EEG and event-related potentials may help to investigate the neural mechanisms of methamphetamine addiction.

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Funding

This work was supported in part by the National Natural Science Foundation of China, grant no. 61904038 and no. U1913216; National Key R&D Program of China, grant no. 2021YFC0122702 and no. 2018YFC2002300; Shanghai Sailing Program, grant no. 19YF1403600 and no. 22YF1404200; Shanghai Municipal Science and Technology Commission, grant no. 19441907600, no.19441908200, and no. 19511132000; Opening Project of Zhejiang Lab, grant no. 2021MC0AB01; Fudan University-CIOMP Joint Fund, grant no.FC2019-002; Opening Project of Shanghai Robot R&D and Transformation Functional Platform, grant no. KEH2310024; Ji Hua Laboratory, grant no. X190021TB190 and no. X190021TB193; National Natural Integration Project, grant no. 91948302; Shanghai Municipal Science and Technology Major Project, grant no. 2021SHZDZX0103 and no. 2018SHZDZX01.

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Author notes

  1. Gege Zhan and Haolong Su contributed equally to this work.

Authors and Affiliations

  1. Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Academy for Engineering & Technology, Fudan University, Shanghai, China

    Gege Zhan, Haolong Su, Pengchao Wang, Junkongshuai Wang, Lihua Zhang & Xiaoyang Kang

  2. Shanghai Mental Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Haifeng Jiang

  3. CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Sciences, Shanghai, China

    Haifeng Jiang

  4. Ji Hua Laboratory, Foshan, Guangdong Province, China

    Lihua Zhang & Xiaoyang Kang

  5. Yiwu Research Institute of Fudan University, Yiwu City, China

    Xiaoyang Kang

  6. Research Center for Intelligent Sensing, Zhejiang Lab, Hangzhou, China

    Xiaoyang Kang

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  1. Gege Zhan
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Contributions

HJ, LZ, and XK contributed to the study conception and design. GZ, HS, PW, and JW collaborated on literature research and collation. The first draft of the manuscript was written by GZ. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Xiaoyang Kang.

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Zhan, G., Su, H., Wang, P. et al. Non-Invasive Brain-Computer Interfaces: a New Perspective on the Assessment and Classification of Individuals with Methamphetamine Addiction. SN Compr. Clin. Med. 5, 240 (2023). https://doi.org/10.1007/s42399-023-01585-y

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