Maximum multiple-correlation beamformer for estimating source connectivities in electromagnetic brain activities

Synchrony is a phenomenon of local-scale and long-range integrations within a brain circuit. Synchronous activities manifest themselves in similar temporal structures that can be statistically quantified by temporal correlation. In previous studies, synchronous activities were estimated by calculating the correlation coefficient or coherence between a single reference signal and the activity in a brain region. However, a brain circuit may involve multiple brain regions and these regions may communicate to each other through different temporal patterns. Therefore, temporal correlation to multiple reference signals is effective in quantify the source connectivities in the brain. This paper proposes a novel algorithm to calculate the maximum multiple-correlation for each brain region which has an activity estimated by a beamformer. Furthermore, this algorithm can accommodate various latencies of activities in a circuit. Experimental results demonstrate that the proposed method can accurately detect source activities correlated to the given multiple reference signals, even when unknown latencies exist between the source and references.