Sir, Can you make a video on discrete stock well transform ,its bit confusing if the analysis is performed in frequency domain only not in time domain.
Hi er. I don't have a video on the Stockwell transform per se, but it is really similar to Morlet wavelet convolution, and I have lots of videos on that. If you understand Morlet wavelets, it's just a tiny step from there to Stockwell. Hope that helps!
Thank you very much for well organized information. Is it also okay to consider baseline while calculatuing PSD? In addition, how to handle offset difference in PSD within subject with different conditions?
Yes, you can still normalize by a baseline, as percent change or dB for example. Any normalization will help with individual differences. Even without a baseline, you can divide the spectrum by the average power (across the spectrum), which will normalize for raw power values.
Sir, Can you make a video on discrete stock well transform ,its bit confusing if the analysis is performed in frequency domain only not in time domain.
Hi er. I don't have a video on the Stockwell transform per se, but it is really similar to Morlet wavelet convolution, and I have lots of videos on that. If you understand Morlet wavelets, it's just a tiny step from there to Stockwell. Hope that helps!
so basically you are whitening, or linearizing the frequency response, correct?
Dear Professor Cohen, what happens when we want to analyze a wide band of spectrum (1-600 Hz) but we don't have a basal to correct the 1/f noise ?
You don't necessarily *need* to normalize for the 1/f. You can compare two spectra, or qualitatively inspect the spectrum (e.g., looking for peaks).
Thank you very much for well organized information. Is it also okay to consider baseline while calculatuing PSD? In addition, how to handle offset difference in PSD within subject with different conditions?
Yes, you can still normalize by a baseline, as percent change or dB for example. Any normalization will help with individual differences. Even without a baseline, you can divide the spectrum by the average power (across the spectrum), which will normalize for raw power values.
@@mikexcohen1 Thanks for your suggestions!👍