Effectiveness of sparse Bayesian algorithm for MVAR coefficient estimation in MEG/EEG source-space causality analysis

TL;DR

This study evaluates a sparse Bayesian algorithm for estimating MVAR coefficients in MEG/EEG causality analysis, demonstrating its robustness to interference but also its limitations in detecting true causality.

Contribution

The paper introduces and assesses a sparse Bayesian method for MVAR coefficient estimation, comparing its performance to least-squares in interference-heavy conditions.

Findings

Sparse Bayesian method is less affected by interference than least-squares.

Robustness of the sparse Bayesian method reduces true causal detectability.

Permutation-test thresholds are more conservative than surrogate data bootstrap thresholds.

Abstract

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.