Canonical bicoherence analysis of dynamic EEG data

Authors:
Huixia He;David J. Thomson
Affiliations:
Department of Mathematics and Statistics, Queen's University, Kingston, Canada;Department of Mathematics and Statistics, Queen's University, Kingston, Canada
Venue:
Journal of Computational Neuroscience
Year:
2010

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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.