Detecting time-dependent coherence between non-stationary electrophysiological signals: A combined statistical and time-frequency approach

Yang Zhan, David Halliday, Ping Jiang, Xuguang Liu, Jianfeng Feng

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Various time-frequency methods have been used to study time-varying properties of non-stationary neurophysiological signals. In the present study, a time-frequency coherence estimate using continuous wavelet transform (CWT) together with its confidence intervals are proposed to evaluate the correlation between two non-stationary processes. The approach is based on averaging over repeat trials. A systematic comparison between approaches using CWT and short-time Fourier transform (STFT) is carried out. Simulated data are generated to test the performance of these methods when estimating time-frequency based coherence. In contrast to some recent studies, we find that CWT based coherence estimates do not supersede STFT based estimates. We suggest that a combination of STFT and CWT would be most suitable for analysing non-stationary neural data. Tests are presented to investigate the time and frequency discrimination capabilities of the two approaches. The methods are applied to two experimental data sets: electroencephalogram (EEG) and surface electromyogram (EMG) during wrist movements in a healthy subject, and local field potential (LFP) and surface EMG recordings during resting tremor in a Parkinsonian patient. Supporting software is available at and (c) 2006 Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)322-332
Number of pages11
JournalJournal of Neuroscience Methods
Issue number1-2
Publication statusPublished - 30 Sept 2006


  • wavelet
  • Fourier
  • coherence
  • confidence intervals
  • EEGs
  • EMGs
  • LFPs
  • time discrimination
  • frequency discrimination
  • EMG

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