Techniques for non-stationary signal analysis are important in understanding dynamical behaviour of complex systems. Time-frequency coherence is widely used to analyse time-varying characteristics in non-stationary signals. This paper presents wavelet-based methods, using Airy wavelet, to estimate coherence. We incorporate a novel technique for removal of low frequency components due to envelope modulation in non-stationary signals. The technique is demonstrated on synthetic and real neurophysiological data. Results not only provide a clear description of desired features in non-stationary signals, but also suppress low frequency components due to envelope modulation. Our novel technique shows an effectiveness in extracting features hidden within the signals. It may lead to improved results in coherence analysis of medical, biological, physical and geophysical data containing low frequency envelope modulation besides non-stationarities.
|Title of host publication||2020 8th International Electrical Engineering Congress, iEECON 2020|
|Publication status||Published - 27 Apr 2020|
|Event||8th International Electrical Engineering Congress, iEECON 2020 - Chiang Mai, Thailand|
Duration: 4 Mar 2020 → 6 Mar 2020
|Name||2020 8th International Electrical Engineering Congress, iEECON 2020|
|Conference||8th International Electrical Engineering Congress, iEECON 2020|
|Period||4/03/20 → 6/03/20|
Bibliographical notePublisher Copyright:
© 2020 IEEE.
- analytic wavelets
- baseline correction
- non-stationary analysis
- signal processing