Why the study?
Due to methodological challenges, studying the functional value of the bidirectional interplay between the central and autonomous nervous systems required further exploration.
The proposed PSV-SDG computational method successfully estimates bidirectional brain-heart interactions and distinguishes resting states from emotional arousal using EEG and Poincaré plot-derived HRV measures.
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New method extends brain-heart interaction tools for cognitive research; leaves open clinical utility pending validation.
Recent studies on brain-heart interaction suggest a functional involvement of such interaction in cognitive processes. Proposals on brain-heart interaction analysis aim at understanding the information exchange between two dynamic systems. Because of methodological challenges, studying the functional value of the bidirectional interplay between central and autonomous nervous systems still has space for further exploration. Here, I introduce a new computational method to estimate brain-heart interactions, the Poincaré Sympathetic-Vagal Synthetic Data Generation Model (PSV-SDG). The PSV-SDG combines EEG and cardiac sympathetic-vagal dynamics to gather time-varying and bidirectional estimators of mutual interplay. The method is grounded on the Poincaré plot, an acknowledged heart rate variability method to estimate sympathetic-vagal activity, accounting for potential non-linearities. The proposed algorithm represents a new method and computational tool for the functional assessment of the interplay between EEG and cardiac sympathetic-vagal activity. The proposed method is implemented in MATLAB under an open-source license.
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Diego Candia‐Rivera (2023) studied this question.
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