No abstract

Various time-frequency methods have been used to study the time-varying properties of non-stationary neurophysiological signals. In the present study, a time-frequency coherence using continuous wavelet transform (CWT) together with its confidence intervals are proposed to evaluate the correlation between two non-stationary processes. 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. Surprisingly and in contrast to the common belief, the coherence estimation based upon CWT does not always supersede STFT. We suggest that a combination of STFT and CWT would be most suitable for analysing non-stationary neural data. In both frequency and time domains, methods to test whether there are two coherent signals presented in recorded data are presented. Our approach is then applied to the electroencephalogram (EEG) and surface electromyogram (EMG) during wrist movements in healthy subjects and the local field potential (LFP) and surface EMG during resting tremor in patients with Parkinson's disease. A software package including all results presented in the current paper is available at

No abstract

No abstract