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首页> 外文会议>International conference on communications and cyber physical engineering >BPSO Based Method for Screening of Alcoholism
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BPSO Based Method for Screening of Alcoholism

机译：基于BPSO的酗酒筛选方法

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Selection of optimal channels for alcoholic detection is a major issue in recent year. Advance knowledge of brain region or EEG channels, most affected by alcohol, will reduce the computational complexity and new EEG recording device can be designed using selected channels. In this paper, we propose complexity and nonlinearity features and ensemble subspace K NN classifier to differentiate alcoholics and nonalcohol's from visually evoked potential (VEP). Binary particle swarm optimization (BPSO) is used to select optimum number of channels that minimize classification errors. A novel fitness function is designed to use in optimization technique. Fitness function evaluated using classification error and selected channels. Experimental results show that optimal channel selected have biological significance associated with alcoholic person. Thus, the outcome of the proposed channel selection methodology can be used for the accurate and rapid classification of normal and alcoholic subjects.
机译：近年来，酒精检测最优渠道的选择是一个主要问题。大脑区域或脑电图渠道的推进知识，受酒精影响最大，将减少计算复杂性，并且可以使用所选通道设计新的EEG记录设备。在本文中，我们提出了复杂性和非线性特征和集合子空间K NN分类器来区分视觉诱发潜力（VEP）的酗酒者和非酒精。二进制粒子群优化（BPSO）用于选择最大限度地减少分类错误的最佳通道数。新颖的健身功能旨在用于优化技术。使用分类误差和所选频道评估健身功能。实验结果表明，选择的最佳通道具有与酒精人员相关的生物学意义。因此，所提出的渠道选择方法的结果可用于正常和酒精受试者的准确和快速分类。

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