摘要
为提取脑信息处理过程中的动态特征参数,提出运用基于相空间重构思想的时间序列分维算法(G-P算法).讨论了G-P算法的3个重要参数(即无标度域、嵌入维数和延时)的确定规则,记录大脑在不同状态下的EEG信号并计算其关联维数.实验结果表明,EEG关联维数能够反映脑信息处理过程中的神经元群活动状态,可作为脑信息处理的非线性特征参数.
To acquire dynamic characteristics in brain information processing, a G-P algorithm based on the idea of restructuring of phase space is adopted. The rule of selecting three important parameters （non-graduation area, embedding dimension and delay） according to G-P algorithm is discussed. EEG signals under different brain condition are recorded and the correlation dimension is calculated. Experiments showed that the correlation dimension of EEG signal can reflect the active conditions of neuronal groups during brain information processing and can be used as the non-linear parameter of brain information processing.
出处
《北京理工大学学报》
EI
CAS
CSCD
北大核心
2006年第3期221-225,共5页
Transactions of Beijing Institute of Technology
基金
教育部博士学科点专项科研基金资助课题(20010007016)
关键词
脑信息处理
脑电图
关联维数
神经元群
brain information processing
EEG
correlation dimension
neuronal group
