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Related Experiment Video

Updated: Jun 28, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

A bicoherence approach to analyze multi-dimensional cross-frequency coupling in EEG/MEG data.

Alessio Basti¹, Guido Nolte², Roberto Guidotti³

¹Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, 66100, Chieti, Italy. alessiobasti@hotmail.it.

Scientific Reports

|April 11, 2024

Summary

We developed Multi-dimensional Antisymmetric Cross-Bicoherence (MACB) to detect quadratic phase interactions in vector time series. MACB outperforms the original ACB method, especially with shorter data or higher dimensions.

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Last Updated: Jun 28, 2025

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Area of Science:

Neuroscience
Signal Processing
Statistical Analysis

Background:

Functional neuroimaging data, such as electro/magnetoencephalographic (EEG/MEG) signals, exhibit complex interactions.
Detecting quadratic lagged phase-interactions in vector time series is crucial for understanding brain activity.
Existing methods like Antisymmetric Cross-Bicoherence (ACB) have limitations in multi-dimensional applications.

Purpose of the Study:

Introduce a blockwise generalization of ACB, termed Multi-dimensional ACB (MACB).
Develop a method to detect quadratic lagged phase-interactions between vector time series in the frequency domain.
Enhance the analysis of complex couplings in neuroimaging data.

Main Methods:

Blockwise generalization of the Antisymmetric Cross-Bicoherence (ACB) statistical method.
Bispectral analysis applied to vector time series.
Development and validation of the Multi-dimensional ACB (MACB) approach.

Main Results:

MACB demonstrates invariance under orthogonal data transformations, ensuring coordinate system independence.
Extensive synthetic experiments show MACB significantly outperforms ACB.
The performance advantage of MACB increases with shorter data lengths and higher data dimensions.

Conclusions:

MACB is a powerful tool for identifying quadratic phase couplings in multi-dimensional time series.
The method offers improved performance over ACB, particularly in challenging data scenarios.
MACB enhances the analysis of functional neuroimaging data, aiding in the understanding of brain dynamics.