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Drug Abuse Identification based EEG-P300 Amplitude and Latency with Fuzzy Logic Calssifier

机译:基于模糊逻辑分类器的基于药物滥用识别的EEG-P300振幅和潜伏期

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The difficulty in detecting drug users is one of the hindrances in overcoming drug abuse. The influence of the drugs on a person's nervous system mainly attacks the brain. If the brain is damaged, it will cause permanent disability and is difficult to repair. In this paper, a classification method to identify a drug user is developed. In the experiment, the drug picture is randomly flashed into the subject to stimuli the drug withdrawal. EEG-P300 potentials which quantified by their amplitude and latency is measured to reflects unique cognitive brain functions. The alteration of the brain activities which represented by amplitude and latency according to the given stimuli among of the selected area is used as a feature for classifier to detect a drug abuser. The recorded brain signals of thirty subjects (addictive, methadone treatment (rehabilitation), and control) were carry out. The classification results using fuzzy logic during withdrawal of drug have demonstrated increases in latencies and decreases amplitudes.
机译:检测吸毒者的困难是克服药物滥用的障碍之一。药物对人的神经系统的影响主要攻击大脑。如果大脑受损,将导致永久性残疾,并且难以修复。本文提出了一种识别毒品使用者的分类方法。在实验中,药物图片会随机闪烁到对象中以刺激药物戒断。测量通过振幅和潜伏期量化的EEG-P300电位,以反映独特的认知脑功能。根据所选区域中给定的刺激,以幅度和潜伏期表示的大脑活动的变化被用作分类器检测吸毒者的特征。记录了三十名受试者的大脑信号(上瘾,美沙酮治疗(康复)和对照)。在停药期间使用模糊逻辑进行分类的结果表明,延迟增加了,幅度减小了。

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