Spectrum inference for replicated spatial locally time-harmonizable time series

In this paper, we develop tools for statistical inference on replicated realizations of spatiotemporal processes that are locally time-harmonizable. Our method estimates both the rescaled spatial time-varying Loève-spectrum and the spatial time-varying dual-frequency coherence function under realistic modeling assumptions. We construct confidence intervals for these parameters of interest using the Circular Block Bootstrap method and prove its consistency. We illustrate the application of our methodology on a dataset arising from an experiment in neuropsychology. From EEG recordings, our method allows studying the dynamic functional connectivity within the brain associated to visual working memory performance.