# EEG peaks do not get identified at the correct frequencies

For my thesis I am re-analyzing a dataset where subjects were visually stimulated with 5.45, 8.57, 12 or 15 Hz. This well-known technique (steady state visual evoked potential) should elicit EEG peaks at similar frequencies. This is exactly what was shown in the original paper (İşcan Z, Nikulin VV (2018) Steady state visual evoked potential (SSVEP) based braincomputer interface (BCI) performance under different perturbations. PLoS ONE 13(1)). Different classifiers were able to differentiate the 4 stimuli with a high accuracy.

The original study used CCA to analyze the data, whereas I am using a relatively new technique called FOOOF (fitting oscillations & one over f). FOOOF decomposes power spectra into an aperiodic component and a number of periodic components. It identifies peaks in a non-canonical fashion. Rather than pre-defining bands (alpha, beta, ...), it lets the data tell you where the peaks are. Supposedly, it should perform at least as well as CCA.

However, when I build classifiers on top of these FOOOF models, I get horrible accuracies (30-40%). From plots, it is clear that frequencies are not identified correctly. Overlaying periodic components of all 4 targets on the same plot shows major overlap, making it impossible for any classifier to distinguish them.

I don't understand what the problem is. I have never analyzed EEG data, so I might be missing something trivial. The only possible clue that I have is the following: the sampling rate is 1000Hz. Plotting power spectra shows high resolutions for higher frequencies (up to 500Hz), but a low resolution for low frequencies (exactly the ones I am interested in). I suspect this is inherent in EEG data?

I tried applying a bandpass filter (1-20; 1-40; or 1-100 Hz) before FFT, but to no avail. Maybe the time signals need to be downsampled (to 200 Hz for example?)? I don't understand the process of downsampling. Information will get lost, but maybe it somehow improves the precision of the 'remaining' data points? I'm quite stuck in this analysis, so any tips are more than welcome!

EEG representations are typically log-transformed to offset $$1/f$$ power scaling, and baseline-normalized, which can drastically change the final output.