A Fast and Efficient Ensemble Transfer Entropy and Applications in Neural Signals.

ABSTRACT: The ensemble transfer entropy (TEensemble) refers to the transfer entropy estimated from an ensemble of realizations. Due to its time-resolved analysis, it is adapted to analyze the dynamic interaction between brain regions. However, in the traditional TEensemble, multiple sets of surrogate data should be used to construct the null hypothesis distribution, which dramatically increases the computational complexity. To reduce the computational cost, a fast, efficient TEensemble with a simple statistical test method is proposed here, in which just one set of surrogate data is involved. To validate the improved efficiency, the simulated neural signals are used to compare the characteristics of the novel TEensemble with those of the traditional TEensemble. The results show that the time consump

SUBMITTER: Zhu J 

PROVIDER: S-EPMC9407540 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature