Adaptive window in the PWVD for the IF estimation of FM signals in additive Gaussian noise
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 03
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Multivariate empirical mode decomposition for quantifying multivariate phase synchronization
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in theory and methods for nonstationary signal analysis
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Discrete-time local polynomial approximation of the instantaneousfrequency
IEEE Transactions on Signal Processing
Modification of the ICI rule-based IF estimator for high noise environments
IEEE Transactions on Signal Processing
Improved instantaneous frequency estimation using an adaptiveshort-time Fourier transform
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
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This paper proposes a new approach to estimate the phase synchrony among nonstationary multivariate signals using the linear relationships between their instantaneous frequency (IF) laws. For cases where nonstationary signals are multi-component, a decomposition method like multi-channel empirical mode decomposition (MEMD) is used to simultaneously decompose the multi-channel signals into their intrinsic mode functions (IMFs). We then apply the Johansen method on the IF laws to assess the phase synchrony within multivariate nonstationary signals. The proposed approach is validated first using multi-channel synthetic signals. The method is then used for quantifying the inter-hemispheric EEG asynchrony during ictal and inter-ictal periods using a newborn EEG seizure/non-seizure database of five subjects. For this application, pair-wise phase synchrony measures may not be able to account for phase interactions between multiple channels. Furthermore, the classical definition of phase synchrony, which is based on the rational relationships between phases, may not reveal the hidden phase interdependencies caused by irrational long-run relationships. We evaluate the performance of the proposed method using the differentiation of unwrapped phase as well as other IF estimation techniques. The results obtained on newborn EEG signals confirm that the generalized phase synchrony within EEG channels increases significantly during ictal periods. A statistically consistent phase coupling is also observed within the non-seizure segments supporting the concept of constant inter-hemispheric connectivity in the newborn brain during inter-ictal periods.