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
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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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container_title	Transactions of Tianjin University
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creator	王学民张翼李向新刘雅婷曹红宝周鹏王晓璐高翔
description	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 improved self-organizing map（ISOM） neuron network was proposed, which successfully identify three different brain status of the subjects： awareness, drowsiness and sleep. Compared with traditional SOM, the experiment results show that the ISOM generates much better classification accuracy, reaching as high as 89.59%.
doi_str_mv	10.1007/s12209-013-2027-3
format	Article
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subjects	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
title	Alertness Staging Based on Improved Self-Organizing Map
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