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Discrimination and Identification Method of Human Activity Level based on Information Processing of EEG Signals

基于脑电信号信息处理的人体活动水平判别与识别方法

基本信息

批准号：
06650474
负责人：
INOUE Katsuhiro
金额：
$ 1.34万
依托单位：
Kyushu Institute of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1994
资助国家：
日本
起止时间：
1994 至 1996
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-06650474/
关键词：
Electroencephalogram EEG Sleep Stages Human Activity Level Event Related Potential Evoked Response Identification Signal Processing Stochastic System 聴覚誘発電位睡眠ステージニューラルネット

项目摘要

In this research project, we have performed studies on the development of a discrimination and identification method of human activity level (sleep level and arousal level) based on information processing of electroencephalogram (EEG) signals and obtained following research results.1.Automatic determination system of human EEG sleep stagesWe have developed an automatic stage determination system with graphical user interface based on the wave-shape recognition method. By this system, we obtained about 80% accuracy compared with clinician's scoring.2.Feature extraction of sleep stages based on EEG generating modelsThe EEG generating mechanism was modeled by a damped system excited by impulse input process which subjects to Poisson process whose amplitude has a transition property. Furthermore, a Maximum A Posteriori (MAP) method was modified to be applicable to the model. We could then extract useful information directly related to sleep stages by using our method.3.Information processi … More ng of EEG based on artificial neural network (ANN)ANN was applied to EEG signals as the predictor for alpha-waves, high voltage delta-waves and low voltage fast waves. Classification of unknown waves among the above mentioned class were possible by evaluating the infinity norm between ANN output and original signals.4.Investigation of event related potential (ERP) under visual cognitive taskThe measurement system was developed using 2 computers. One is used for visual stimulation, the other is used for data acquisition. Investigating EEG signals under the oddball task (2 kinds of color or 2 kinds of character pattern), it was confirmed that wave patterns were changed by stimulation only at the low arousal level. We also tried to classify the single trial ERPs by using statistical pattern recognition method. This was not easy, because P300 related to vanishing time of stimulation exists in all data (target and non-target stimulus).5.Quantitative evaluation method of stationarity of EEG signals as the features of human activity levelWe have explored the possibilities of the quantitative evaluation based on wavelet transform. It has been confirmed that EEG waves whose duration is 0.5 [sec] could be classified by using wavelet transform and statistical pattern recognition method. These results suggest the possibilities of segmentation for stationary period. Therefore, we will further investigate to find out quantitative evaluation methods related to the segmentations. Less

在本研究项目中，我们开展了基于脑电图（EEG）信号信息处理的人类活动水平（睡眠水平和觉醒水平）的判别和识别方法的开发研究，获得了以下研究成果：人类脑电图睡眠阶段自动判定系统基于波形识别方法，开发了一种具有图形用户界面的睡眠阶段自动判定系统。与临床医生的评分相比，该系统的准确率约为80%。基于脑电信号产生模型的睡眠阶段特征提取脑电信号产生机理建模为脉冲输入过程激励的阻尼系统，该系统服从幅度具有过渡性质的泊松过程。在此基础上，对MAP （Maximum a Posteriori）方法进行了改进，使其适用于该模型。然后，我们可以用我们的方法提取与睡眠阶段直接相关的有用信息。将基于人工神经网络（ANN）的脑电信号信息处理技术应用于脑电信号中，对脑电信号进行α波、高压δ波和低压快波的预测。通过评估人工神经网络输出与原始信号之间的无穷范数，可以对上述类别中的未知波进行分类。视觉认知任务下事件相关电位（ERP）的研究。一个用于视觉刺激，另一个用于数据采集。对古怪任务（2种颜色或2种字符图案）下的脑电图信号进行了研究，证实了只有在低唤醒水平下刺激才会改变波形。我们还尝试使用统计模式识别方法对单次试验erp进行分类。这并不容易，因为与刺激消失时间相关的P300存在于所有数据中（目标和非目标刺激）。作为人类活动水平特征的脑电信号平稳性定量评价方法探讨了基于小波变换的定量评价的可能性。应用小波变换和统计模式识别方法对持续时间为0.5[秒]的脑电波进行了分类。这些结果提示了在平稳期进行分割的可能性。因此，我们将进一步研究寻找与分割相关的定量评价方法。少