Wavelet

Description

This module implements the Continuous Wavelet Transform (CWT) using the Morlet wavelet. The CWT is a time-frequency analysis tool that decomposes a signal into wavelets, providing localized frequency information over time.

Function

bciflow.modules.tf.wavelet.wavelet(eegdata, levels=5)[source]
Parameters:
  • eegdata (dict) – A dictionary containing the EEG data, where the key ‘X’ holds the raw signal.

  • levels (int) – The number of decomposition levels (scales) for the wavelet transform.

Returns:

The same dictionary passed in parameters, but with the transformed data stored under the key ‘X’.

Return type:

dict

The implementation uses the pywt library to compute the CWT. For more details on wavelet transforms, refer to:
  • Addison, P. S. (2017). The Illustrated Wavelet Transform Handbook. CRC Press.

  • Mallat, S. (2008). A Wavelet Tour of Signal Processing. Academic Press.