Coherence estimation between EEG signals using multiple window time-frequency analysis compared to Gaussian kernels

It is believed that neural activity evoked by cognitive tasks is spatially correlated in certain frequency bands. The electroencephalogram (EEG) is highly affected by noise of large amplitude which calls for sophisticated time local coherence estimation methods. In this paper we investigate different approaches to estimate time local coherence between two real valued signals. Our results indicate that the method using two dimensional Gaussian kernels has a slightly better average SNR compared to the multiple window approach. On the other hand, the multiple window approach has a more narrow SNR distribution and seems to perform better in the worst case.