I have confusion about signal processing related with EEG signal. I have done some of my research and that made me more confused about processing and filtering the signal.
Let me jump into the problem -
I have found that people are using FFT for EEG signal processing which I don't understand. Why would you do that? FFT is mainly made for stationary waves and we know that EEG signals are non-stationary waves hence FFT is not so useful for EEG signal processing. Well that can be solved with the SFFT but then again, this can't be done with real-time signal EEG. (I might be wrong, please help)
Wavelet transform is another way to process EEG signals because, first, it preserves time and freq whereas FFT looses time resolution. Also, wavelet can be applied on non-stationary signals. (I might be wrong here too, so please go ahead help me with this too)
As in the figure below - Neurosky shows real-time EEG power bands fluctuations, which I believe is FFT on raw signal and some kind of mathematical operation (maybe frequency averaging) applied on a range of frequencies gives those bands (alpha,beta,gamma,delta,theta), Am I right? if yes, then how can you apply FFT on real time signal? if no, then what is the best way to get those bands?
- I have muse, emotiv, neurosky and openbci hardware which I borrowed from my community friends, I have been playing around with those hardware so I would do something with the raw signal so I started learning about these things but as deep as I go in that rabbit hole, more I get confuse. I was gonna apply ML eventually but I seriously got stuck on my first step.