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首页> 外文会议>IEEE-EMBS Conference on Biomedical Engineering and Sciences >Towards Detecting Connectivity in EEG: A Comparative Study of Parameters of Effective Connectivity Measures on Simulated Data
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Towards Detecting Connectivity in EEG: A Comparative Study of Parameters of Effective Connectivity Measures on Simulated Data

机译：走向脑电中的连通性检测：模拟数据有效连通性度量参数的比较研究

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摘要

We compare the performance of many effective connectivity measures in detecting statistically significant causal connections between time series drawn from linear and nonlinear coupled systems. Fifteen measures are compared, drawn from two families (information theoretic, and frequency- and time-based multivariate autoregressive models), including common and uncommon measures. Measures were tested on simulated data from three systems: three coupled Hénon maps; a multivariate autoregressive (MVAR) model with and without EEG as an exogenous input; and simulated EEG. Comparisons focus on the effective of parameter choices, e.g. maximum model order or maximum number of lags, for different lengths of data. Performance varies with dataset, and no measure was outstanding for all datasets. Strong performance is obtained where the measure's model and data source match (eg MVAR model, or frequency domain measures with narrowband data). When there is no match, information theoretic measures and Copula Granger causality generally perform best.
机译：我们比较了许多有效的连通性度量在检测线性和非线性耦合系统中的时间序列之间具有统计意义的因果关系时的性能。比较了从两个族（信息理论以及基于频率和时间的多元自回归模型）中得出的15种度量，包括常见度量和非常见度量。在来自三个系统的模拟数据上测试了度量：三个耦合的Hénon映射；带有和不带有EEG作为外部输入的多元自回归（MVAR）模型；和模拟的脑电图。比较集中于参数选择的有效性，例如对于不同长度的数据，最大模型阶数或最大滞后数。性能随数据集而变化，并且没有针对所有数据集的出色度量。在测度的模型和数据源匹配的地方（例如，MVAR模型或具有窄带数据的频域测度），可以获得强大的性能。如果没有匹配项，则信息理论方法和Copula Granger因果关系通常表现最佳。

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