Estimation of functional brain connectivity from electrocorticograms using an artificial network model

This paper proposes a novel network model for estimation of interaction intensity among partially observed signals. The network model can acquire connectivity weights among the signals as a forward model through iterative learning using past and future signals. To evaluate accuracy of the estimation, the model was applied on artificial and physiological data. In case of artificial signals, when all signals were used, the network was able to estimate directional interactions. On the other hand, the network failed to estimate directional interactions when only parts of the signals were used. However, the network was able to estimate whether interactions exist, and signals were successfully grouped into each of its sources using the obtained connectivity. Furthermore, for physiological signals, we obtained connectivity weights that cluster the recording electrode sites into physiologically plausible brain areas. These results suggest that the proposed network model can be used to estimate the clustered interactions from the partially observed signals.