A comparative study of the detection of direct causal influence with bivariate and multivariate measures for EEG

© 2014 IEEE. In this paper, we perform the first comparison of a large variety of connectivity measures in detecting causal effects among observed interacting systems based on their statistical significance. Well-known measures estimating direction and strength interdependence between time series are compared: information theoretic measures, model- based multivariate measures in the frequency domain, and the time domain, and phase-based measures. At the same time the phase locking index is used to consider phase relationship between signals, where the phase locking value implies that the response is delayed with respect to drive at some frequency. The performance of measures is tested on simulated data from three systems: three coupled Hénon maps; a multivariate autoregressive (MVAR) model with and without EEG as an exogenous input; and simulated EEG. No measure was consistently superior. Measures that model the data as MVAR perform well when the data are drawn from that model. Frequency domain measures perform well when the data have a clearly defined band of interest. When neither of these is true, information theoretic measures perform well.