I am new at EEG preprocessing and classification. I have followed Clemens Brunner's blog https://cbrnr.github.io/2018/01/29/removing-eog-ica/ as a tutorial for MNE.
This is how I have preprocessed and singled out all the individual trials for all subjects in the dataset BCI Competition IV Dataset 2a. The dataset can be found here: http://bnci-horizon-2020.eu/database/data-sets
ch_names = ['Fz', 'FC3', 'FC1', 'FCz', 'FC2', 'FC4', 'C5', 'C3', 'C1', 'Cz', 'C2', 'C4', 'C6', 'CP3', 'CP1', 'CPz', 'CP2', 'CP4', 'P1', 'Pz', 'P2', 'POz'] mat = loadmat('A01T.mat') for j in range(6): timelist = mat['data'][j+3] //this gives starting of each event timelist = list(timelist) timelist.append(np.array(, dtype='int32')) //length of eeg for single channel is added timelist = np.array(timelist) actionlist = mat['data'][j+3] //these are the labels eeg = ((mat["data"][j+3]*10e-6).T)[:22] raw = mne.io.RawArray(eeg, info) raw.set_montage("standard_1020") raw.filter(1, 30) raw_temp = raw.copy() ica = mne.preprocessing.ICA(method='infomax', fit_params=dict(extended=True)) ica = ica.fit(raw_temp, picks=['eeg']) ica.detect_artifacts(raw_temp) raw = ica.apply(raw) trial = raw.get_data() label =  train =  for k in range(48): trial_temp = trial[:, timelist[k]:timelist[k+1]] train.append(trial_temp) label.append(actionlist[k] - 1)
The file is saved in the shape of 22 channels and corresponding measures for the next 7-8 seconds. Frequency is 250Hz.
How do I apply baseline methods such as SVM to this preprocessed data?
Also, how do I calculate the power of the signals using FFT or any other method?