Abstract
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 http://www.dcs.warwick.ac.ukffeng/software/COD and http://www.neurospec.org. (c) 2006 Elsevier B.V. All rights reserved.
| Original language | English |
|---|---|
| Pages (from-to) | 322-332 |
| Number of pages | 11 |
| Journal | Journal of Neuroscience Methods |
| Volume | 156 |
| Issue number | 1-2 |
| DOIs | |
| Publication status | Published - 30 Sept 2006 |
Keywords
- wavelet
- Fourier
- coherence
- confidence intervals
- EEGs
- EMGs
- LFPs
- time discrimination
- frequency discrimination
- WAVELET ANALYSIS
- HUMANS
- EMG
- VARIANCE
- CORTEX