Titlebook: Signal Processing in Neuroscience; Xiaoli Li Book 2016 Springer Science+Business Media Singapore 2016 Spike trains.Local filed potential.E

Enthralling

Causality of Spike Trains Based on Entropy,ter presents a method, called permutation conditional mutual information (PCMI), for characterizing the causality between a pair of neurons. The performance of this method is demonstrated with the spike trains generated by the Izhikevich neuronal model, including estimation of the directionality ind

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IVORY

Artifact Removal in EEG Recordings, which is able (1) to remove the artifacts and (2) to avoid loss or disruption of the structural information at the same time; thus the risk of introducing bias to data interpretation may be minimized. In this study, an approach (namely, EEMD-ICA) was proposed to first decompose multivariate neural

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Order Time Series Analysis of Neural Signals,the different absence seizure states in absence seizure epileptic rats. Permutation entropy, forbidden order patterns (FOP), and dissimilarity index are applied to analyze the EEG data from the seizure-free, the pre-seizure, and seizure phases. The results show that the number of FOPs in pre-seizure

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Synchronization Measures in EEG Signals,asures have their own principles to describe the EEG activities, it was difficult to give unified criteria for selecting effective synchronization methods to investigate different brain functions or understand mechanisms of different brain diseases. In this study, we gave an example of using differe

AUGER

Estimating Coupling Direction Between Neuronal Populations,t brain areas. A novel methodology based on permutation analysis and conditional mutual information, called PCMI, is proposed to analyze the coupling direction of bivariate neuronal populations. The coupled neural mass model is used to test the performance of PCMI. Simulations suggest that the metho

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