Detection of phase coupling in interacting networks of cortical columns

This is a preprint version submitted to review for publication on the IEEE Transactions on Biomedical Engineering (TBME).

In this study we investigated the estimation method of cross-bicoherence to infer quadratic phase coupling (QPC) in interaction networks based on empirical Bayes estimation. Nonlinear interaction, ubiquitous in a wide range of biomedical fields, often makes it challenging to apply conventional parametric approaches. While cross-bicoherence has been recognized as a key statistic of QPC, prior studies have not elucidated how reliable the estimation can be for realistic time series. This work demonstrates that cross-bicoherence estimated by the proposed method effectively detects QPC, with illustrative simulation studies of both typical QPC and neural mass models of cortical columns.