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冰毒成瘾者脑电信号的分类研究 被引量:3

Classification of EEG Signals in Methamphetamine Addicts
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摘要 冰毒是新型毒品的一种,它可以引起大脑功能异常甚至致人死亡。目前对冰毒成瘾者的脑电信号的分析主要通过ERP分析,还鲜有对冰毒成瘾者的脑电信号进行分类。该研究对两类受试者的脑电信号进行ERP分析,选定P300范围并提取出每次实验下处于该范围内脑电信号的时域、频域和小波系数作为分类特征来进行模式识别,分类结果显示准确率达到80%以上。此外,分类准确率达到75%以上的5个电极中4个位于额叶区域,这说明相比较于其他脑区,冰毒对额叶的影响最为严重,因此冰毒可能损伤大脑的执行认知功能。C3电极基本映射于初级运动皮层区域,说明冰毒可能对冰毒成瘾者的运动执行功能也产生了一定影响。上述结果为进一步研究冰毒对大脑的神经活动造成的影响提供了研究思路。 Methamphetamine is a new drug that can cause brain disfunction and even death.Most of the current studies only conducted enterprise resource planing(ERP)analysis on the electroencephalogram(EEG)signals of methamphetamine addicts,and rarely classified the EEG signals of methamphetamine addicts.This study conducted an ERP analysis on the EEG signals of two types of subjects,and then extracted to the time domain,frequency domain and wavelet coefficients of the EEG signals within this range for each experiment.The classification accuracy can reach higher than 80%.In addition,4 of the 5 electrodes with a classification accuracy of more than 75%were located in the frontal lobe,indicating that methamphetamine had the most severe impact on the frontal lobe compared to other brain regions.Therefore,the methamphetamine may mainly damage the brain's thinking and cognitive execution functions.The C3 electrode is projected mainly in the primary motor cortex region,methamphetamine may have an impact on the function of exercise execution of the methamphetamine addicts.The result provides some theory to further study on the effects of methamphetamine.
作者 高军峰 张家琦 韦思宏 彭丝雨 周到 GAO Jun-feng;ZHANG Jia-qi;WEI Si-hong;PENG Si-yu;ZHOU Dao(College of Biomedical Engineering,Wuhan 430074;Key Laboratory of Cognitive Science of State Ethnic Affairs Commission,South-Central University for Nationalities,Wuhan 430074)
出处 《电子科技大学学报》 EI CAS CSCD 北大核心 2020年第6期955-960,共6页 Journal of University of Electronic Science and Technology of China
基金 中央高校基本科研业务费(CZZ19004,CZY20039) 国家自然科学基金(GZY17012)。
关键词 脑电信号 特征提取 冰毒 P300 小波变换 EEG feature extraction methamphetamine P300 wavelet transform
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