摘要
运用非线性动力学理论,分析大脑信息处理运动的过程。由混沌动力学吸引子关联维数的物理意义出发,论证了关联维数值与神经元群电发放之间的关系。采用连续加算作业法,同步采集EEG信号,应用分形论中的G-P算法,获取EEG信号的关联维数。实验结果表明:连续加算作业法的下半时EEG信号的关联维数明显小于上半时EEG信号的关联维数。因此,基于G-P算法的关联维数可以作为脑学习机能的评价指数。
The theory of nonlinear dynamics is used to analyze the procedure of brain information processing movement. Based on the physical meaning of attractor correlation dimension in chaos dynamics, the relation between correlation dimension and output activity of neuron- group is demonstrated. Continuous addition work is used and the EEG signals are collected synchronously. G-P arithmetic is applied to get the correlation dimension of EEG signals. The result shows that correlation dimension of the second part of the test is much less than that of the first part of the test,which testifies the evaluation function of correlation dimension on brain study enginery.
出处
《计算机应用与软件》
CSCD
北大核心
2007年第11期28-29,63,共3页
Computer Applications and Software
基金
教育部博士学科点专项科研基金资助项目(20010007016)
关键词
混沌
G—P算法
脑电
关联维数
神经元群
Chaos
G-P arithmetic
EEG
Correlation dimension
Neuron-group
作者简介
陈冬冰，博士生，主研领域：感知与测控技术。
