A bicoherence approach to analyze multi-dimensional cross-frequency coupling in EEG/MEG data
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Authors
Basti, Alessio
Nolte, Guido
Guidotti, Roberto
Ilmoniemi, Risto J.
Romani, Gian Luca
Pizzella, Vittorio
Marzetti, Laura
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en
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12
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Scientific Reports, Volume 14, issue 1
Abstract
We introduce a blockwise generalisation of the Antisymmetric Cross-Bicoherence (ACB), a statistical method based on bispectral analysis. The Multi-dimensional ACB (MACB) is an approach that aims at detecting quadratic lagged phase-interactions between vector time series in the frequency domain. Such a coupling can be empirically observed in functional neuroimaging data, e.g., in electro/magnetoencephalographic signals. MACB is invariant under orthogonal trasformations of the data, which makes it independent, e.g., on the choice of the physical coordinate system in the neuro-electromagnetic inverse procedure. In extensive synthetic experiments, we prove that MACB performance is significantly better than that obtained by ACB. Specifically, the shorter the data length, or the higher the dimension of the single data space, the larger the difference between the two methods.Description
Publisher Copyright: © The Author(s) 2024. | openaire: EC/H2020/810377/EU//ConnectToBrain
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Basti, A, Nolte, G, Guidotti, R, Ilmoniemi, R J, Romani, G L, Pizzella, V & Marzetti, L 2024, 'A bicoherence approach to analyze multi-dimensional cross-frequency coupling in EEG/MEG data', Scientific Reports, vol. 14, no. 1, 8461. https://doi.org/10.1038/s41598-024-57014-0