Effectiveness of sparse Bayesian algorithm for MVAR coefficient estimation in MEG/EEG source-space causality analysis
For researchers in MEG/EEG causality analysis, this work provides a comparative evaluation of methods, but the findings are incremental and confirm known trade-offs.
The paper evaluates a sparse Bayesian algorithm for estimating multivariate autoregressive coefficients in MEG/EEG source-space causality analysis, finding it robust to interference but with reduced detectability of true causal relationships compared to least-squares, which suffers from false positives.
This paper examines the effectiveness of a sparse Bayesian algorithm to estimate multivariate autoregressive coefficients when a large amount of background interference exists. This paper employs computer experiments to compare two methods in the source-space causality analysis: the conventional least-squares method and a sparse Bayesian method. Results of our computer experiments show that the interference affects the least-squares method in a very severe manner. It produces large false-positive results, unless the signal-to-interference ratio is very high. On the other hand, the sparse Bayesian method is relatively insensitive to the existence of interference. However, this robustness of the sparse Bayesian method is attained on the scarifies of the detectability of true causal relationship. Our experiments also show that the surrogate data bootstrapping method tends to give a statistical threshold that are too low for the sparse method. The permutation-test-based method gives a higher (more conservative) threshold and it should be used with the sparse Bayesian method whenever the control period is available.