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| Paper IPM / Cognitive Sciences / 13725 |
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| Abstract: | |||||
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Background:
Despite the variety of effective connectivity measures, few methods can quantify direct nonlinear causal couplings and most of them are not applicable to high-dimensional datasets.
New method:
In this paper, a novel approach (called βmRMR-MLP-GC) is proposed to estimate direct nonlinear effective connectivity of high-dimensional datasets. βmRMR is used to select a suitable subset of candidate regressors for approximating each neural (here EEG) signal. The multilayer perceptron (MLP) is used for multivariate characterization of EEG signals while the optimum MLP structure is selected using an iterative cross-validation scheme. Finally a causality measure is defined based on Granger Causality (GC) concept to quantify the casual relations among EEG channels.
Results:
Applying βmRMR-MLP-GC to high-dimensional simulated datasets with different linear and nonlinear structures yields sensitivity and specificity values higher than 95
Comparison with existing method(s)
βmRMR-MLP-GC is compared with Granger Causality Index, Conditional Granger Causality Index, and Transfer Entropy. It outperforms these methods in terms of sensitivity and specificity in simulated datasets. Also, βmRMR-MLP-GC detects the most number of significant and reproducible Back-to-Front net information flows among the specified brain regions and highlights the posterior brain regions as dominant source of alpha activity propagation.
Conclusions:
βmRMR-MLP-GC provides a novel tool to estimate the direct nonlinear causal networks of high-dimensional datasets.
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