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Canonical bicoherence analysis of dynamic EEG data

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Abstract

Bicoherence has been used in quantifying quadratic phase coupling (QPC) in electroencephalography (EEG) signals. However, for high-dimensional EEG signals, the calculations of traditional auto– and cross–bicoherences of signals from multiple electrodes are computationally very expensive. This has been compounded by the recognition of the non-stationary character of EEG signals. This paper introduces a new approach, the time-varying canonical bicoherence (CBC) by short-time weighted Fourier transforms, for analyzing QPC nonlinearities of dynamic EEG signals. This new method shows both computational efficiency and simple interpretation of estimated canonical bicoherences. The canonical bicoherence analysis of EEG records, during a human visual stimulus-driven cognitive process, put into evidence of quadratic phase couplings of Beta waves and Delta waves in the frontal regions.

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Acknowledgements

This study was supported by the Canada Research Chair Program and NSERC Grant RPGIN 250268. We thank Prof. Scott Makeig and Prof. Arnaud Delorme, both with the Swartz center at the University of California San Diego (UCSD), for EEG data and the Matlab toolbox EEGLAB. We thank Maja-Lisa Thomson for proofreading. We thank the reviewers, Prof. Jonathan Victor with Cornell University and Prof. Alain Destexhe with CNRS (France), for their comments to improve this paper.

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  1. Department of Mathematics and Statistics, Queen’s University, Kingston, Ontario, Canada

    Huixia He & David J. Thomson

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  1. Huixia He
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Correspondence to Huixia He.

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Action Editor: Rob Kass

This research was supported by the Canada Research Chair Program and NSERC Grant RPGIN 250268.

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He, H., Thomson, D.J. Canonical bicoherence analysis of dynamic EEG data . J Comput Neurosci 29, 23–34 (2010). https://doi.org/10.1007/s10827-009-0177-z

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