Alertness Staging Based on Improved Self-Organizing Map

In order to classify the alertness status, 19 channels of electroencephalogram（EEG） signals from 5 subjects were acquired during daytime nap. Ten different types of features（including time domain features, frequency domain features and nonlinear features） were extracted from EEG signals, and an impr...

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Veröffentlicht in:	Transactions of Tianjin University 2013-12, Vol.19 (6), p.459-462
1. Verfasser:	王学民 张翼 李向新 刘雅婷 曹红宝 周鹏 王晓璐 高翔
Format:	Artikel
Sprache:	eng
Schlagworte:	Engineering Humanities and Social Sciences Mechanical Engineering multidisciplinary Science 分类精度 异构化装置 时域特性 神经网络 脑电信号 自组织映射 非线性特征 频域特性
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