Hello everyone! I am calculating the theta/beta ratio for individual subjects and then comparing them statistically. The issue I am having is statistically comparing amplitudes of different frequency bands. For example, in the theta category, I have a subject that had his frequency range of 2-9 Hz inhibited during neurofeedback. For another subject, his frequency range of 3-6 Hz was inhibited. I cannot compare the average amplitudes of these two subjects because the wider a frequency band is, the larger the amplitude. Basically, for the first subject, what would his/her amplitude be if I restricted the bandwidth from 2-9 Hz to 3-6 Hz? The amplitude for the 2-9 Hz range is 34.3, so how much would the amplitude decrease if I restricted that subject’s frequency range to 3-6 Hz? Let me know if this issue is not clear and I will try to adjust my question. Thanks everybody!
Did you see the previous answer I left for you? Comments? This post is a duplicate.
Thank you for your prompt responses! I posted the question again because the PI of the project was curious whether it was possible to apply some function to the original output. We are capable, however, of rerunning the raw data. Would you mind elaborating as to why we cannot apply a function to the original output? You mentionted that “you have no idea how much energy was in the 2-3 Hz range or 6-9 Hz range until you refilter the raw data.” Once again I am very grateful to you for answering me quickly. Thank you and let me know your thoughts.
Try this “thought experiment”. Imagine an artificial ‘subject’ who had a perfectly flat frequency response between 2 and 9 hz. In other words all FFT bins between 2 and 9 have the same amplitude. In this case, yes you could estimate the combined filter amplitude between 3 and 6 because you know the FFT was flat.
Now imagine a second artificial subject who had ONLY the flat response between 3 and 6, but who had little amplitude in the 2-3 and 6-9 bands.
You can’t make assumptions about a filter output for a different band, without knowing the original raw signal input. The filter LOSES a whole bunch of information when it turns a time series of raw signal values into series of amplitude values.