Detecting time-dependent coherence between non-stationary electrophysiological signals--a combined statistical and time-frequency approach.
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 .
Zhan
Yang
Y
Department of Electronics, University of York, York YO10 5DD, UK.
Halliday
David
D
Jiang
Ping
P
Liu
Xuguang
X
Feng
Jianfeng
J
D000465:Algorithms; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000706:statistics & numerical data; D004576:Electromyography / Q000706:statistics & numerical data; D004594:Electrophysiology / Q000706:statistics & numerical data*; D005583:Fourier Analysis; D006801:Humans; D009068:Movement / Q000502:physiology; D013997:Time Factors; D014202:Tremor / Q000503:physiopathology; D014953:Wrist / Q000502:physiology