Compensating for instantaneous signal mixing in transfer entropy analysis of neurobiological time series

The transfer entropy (TE) has recently emerged as a nonlinear model-free tool, framed in information theory, to detect directed interactions in coupled processes. Unfortunately, when applied to neurobiological time series TE is biased by signal cross-talk due to volume conduction. To compensate for this bias, in this study we introduce a modified TE measure which accounts for possible instantaneous effects between the analyzed time series. The new measure, denoted as compensated TE (cTE), is tested on simulated time series reproducing conditions typical of neuroscience applications, and on real magnetoencephalographic (MEG) multi-trial data measured during a visuo-tactile cognitive experiment. Simulations show that cTE performs similarly to TE in the absence of signal cross-talk, and prevents false positive detection of information transfer in the case of instantaneous mixing of uncoupled signals. When applied to MEG data, cTE detects significant information flow from the visual cortex to the somatosensory area during task execution, suggesting the activation of mechanisms of multisensory integration.