神经网络的脑电图分析

Vuong-Thuy-Ngan Nguyen, Van-Tuan Huynh, Thi-Hong-Hanh Nguyen
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引用次数: 2

摘要

为了从脑电波-脑电图信号中获取人类活动的信息,介绍和研究了涉及数字滤波、小波滤波和神经网络三种主要滤波器的各种算法。通过对脑电图的不同状态进行滤波,对比其他神经网络在人脑简单活动上的表现,对神经网络的性能有了更深入的了解。具体而言,研究的人类活动包括涉及眼睛行为、面部表情和思维信号的状态。利用Emotiv EPOC+对原始脑电信号进行采集,并用Matlab进行分析。并通过均方误差值等参数对算法的效率进行了比较,验证了神经网络用于脑电信号分析的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Electroencephalography Analysis Using Neural Network
To gather information of human activities through brain wave - EEG (Electroencephalogram) signal, various algorithms have been introduced and researched relating to 3 main filters: digital filter, wavelet filter and neural network. By applying filters to identify some different states of EEG, this paper gives an insight of neural network performance, comparing with others, on simple activities of human brain. In details, researched human activities include states involving to eyes behavior, facial expression and thinking signal. The raw EEG signal has been acquired by Emotiv EPOC+and analyzed with Matlab. Furthermore, the comparison of algorithms efficiency has been done with Mean Square Error value and other parameters which demonstrate the possibility of using neural network to analyze EEG signal.
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