摘要
提供了两种分析认知事件相关电位(ERP)复杂度动态变化的估计算法———时变Tsallis熵(ETsEn)和时变近似熵(EApEn),并将其应用于分析Stroop任务中ERP的动态复杂度.实验发现:ETsEn比EApEn能更好地反映不同刺激类型的ERP复杂度差异;EApEn比ETsEn能更准确地体现ERP复杂度随时间变化的规律.额区、中央区和顶区的ERP的ETsEn和EApEn在刺激前、刺激处理过程中、刺激处理后均有显著差异,即在刺激前熵较大且无明显变化,刺激处理过程中熵显著减小,刺激处理完成后熵恢复至刺激前状态,其变化的时序与行为数据基本一致.结果证明了时变的Tsallis熵和近似熵对动态复杂度从不同方面度量的有效性,为客观度量ERP的复杂度提供了新方法.
Two estimating algorithms, time-dependent Tsallis entropy （ETsEn） and time-dependent approximate entropy （EApEn）, are provided to analyze the dynamic transformation of cognitive event-related potential complexity in Stroop tasks. It is discovered that ETsEn reflects the differences of ERP complexity between different stimulation types more profitably than EApEn, while EApEn. represents the time-dependent regulation of ERP complexity better than ETsEn. Moreover, the ETsEn and EApEn. of ERP show remarkable differences among three durations （pre-stimulation, within-response, post-response） over frontal, central, and parietal regions, i.e. entropy before stimulation has a superior value and less diversification, while declines significantly within response period and comes back after response. The temporal variation of entropy is consistent with the behavior data. Clearly, ETsEn and EApEn. enable to measure dynamic complexity effectively from different aspects for objective ERP complexity evaluation.
出处
《西安交通大学学报》
EI
CAS
CSCD
北大核心
2007年第2期245-249,共5页
Journal of Xi'an Jiaotong University
基金
国家杰出青年基金资助项目(69925101)
国家自然科学基金资助项目(30070212)
关键词
时变Tsallis熵
时变近似熵
复杂度
事件相关电位
time-dependent Tsallis entropy
time-dependent approximate entropy
complexity
event-related potential
