The dataset has $7$ different BCI target frequencies for each target on screen.

You are required to preprocess digital signal with digital filters and classify each EEG epoch or each EEG trial. For this purpose you are asked to divide the data into two groups train and test data as $66\%$ train and $33\%$ test for each frequency. Afterwards you are required to create and train a machine learning model to predict the unseen test data correctly.

First you can extract $7$ SNR at target frequencies as your features. SNR is measured as the signal at the frequency to the noise energy at the neighboring frequencies.