Signal Processing Stack Exchange is a question and answer site for practitioners of the art and science of signal, image and video processing. It's 100% free, no registration required.

Sign up
Here's how it works:
  1. Anybody can ask a question
  2. Anybody can answer
  3. The best answers are voted up and rise to the top

I have an EEG signal that I want to extract features to apply multilayer perceptron (MLP). What I should use, Fourier or wavelet coefficients?

share|improve this question
up vote 1 down vote accepted

Fourier analysis doesn't provide you with temporal information. If you need temporal information you have several choices, two of which are the short term Fourier transform STFT, and wavelet transforms.

The STFT provides frequency coefficients in fixed time intervals across your data. There is a trade off between the time and frequency resolution obtained.

Wavelet transforms pick apart features in your data at various time resolutions which may give you better time localization for your coefficients. Additionally, you can choose different wavelet functions to control how features of your signal are extracted. Another way of saying this is that your choice of wavelet function affects what your coefficients represent.

Most of the work on feature extraction that I've seen recently (this is in general, no specific to EEG signals) employ some form of wavelet analysis.

Here is an example paper:

share|improve this answer
Bruce, can you please make sure the link works? It does not open. Thanks – Mohammad Jan 15 '13 at 15:54
It works. Other copies. – Emre Jan 15 '13 at 19:41

Your Answer


By posting your answer, you agree to the privacy policy and terms of service.

Not the answer you're looking for? Browse other questions tagged or ask your own question.