Alertness Staging Based on Improved Self-Organizing Map  

作　　者：王学民 张翼 李向新 刘雅婷 曹红宝 周鹏 王晓璐 高翔 

机构地区：[1]School of Precision Instruments and Opto-Electronics Engineering,Tianjin University [2]Department of Biomedical Engineering, Tulane University

出　　处：《Transactions of Tianjin University》2013年第6期459-462,共4页天津大学学报（英文版）

基　　金：Supported by National Natural Science Foundation of China(No.51007063)

摘　　要：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%.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%.

关 键 词：electroencephalogram（EEG） improved self-organizing map（ISOM） alertness staging 

分 类 号：Q427[生物学—神经生物学]