基于Tsallis熵和近似熵的认知事件相关电位动态复杂度分析

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：BTsEn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．

Dynamic Complexity Analysis to Cognitive Event-Related Potential Based on Tsallis Entropy and Approximate Entropy

Two estimating algorithms,time-dependent Tsallis entropy(E_(TsEn)) and time-dependent approximate entropy(E_(ApEn)),are provided to analyze the dynamic transformation of cognitive event-related potential complexity in Stroop tasks.It is discovered that E_(TsEn) reflects the differences of ERP complexity between different stimulation types more profitably than E_(ApEn),while E_(ApEn) represents the time-dependent regulation of ERP complexity better than E_(TsEn).Moreover,the E_(TsEn) and E_(ApEn) 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,E_(TsEn) and E_(ApEn) enable to measure dynamic complexity effectively from different aspects for objective ERP complexity evaluation.