Artifact Removal from EEG Data using ICA

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I understand but I need to preprocess this dataset and as I understand for an effective ICA, I need those topomaps. What would you suggest me to do? BTW, I have successfully plotted the topomap with the following code:


# %%
import mne
import matplotlib.pyplot as plt

data_path = "/datapath/software/dataset/chb01/chb01_03.edf"

# Load the EEG data
eeg = mne.io.read_raw_edf(data_path, preload=True)

# Define a mapping that avoids duplicate target names by only keeping unique 10-20 equivalents
ch_mapping = {
    'FP1-F7': 'Fp1', 'F7-T7': 'F7', 'T7-P7': 'T7', 'P7-O1': 'P7', 
    'F3-C3': 'F3', 'C3-P3': 'C3', 'P3-O1': 'P3', 
    'FP2-F4': 'Fp2', 'F4-C4': 'F4', 'C4-P4': 'C4', 'P4-O2': 'P4', 
    'FZ-CZ': 'Fz', 'CZ-PZ': 'Cz', 'FP2-F8': 'P5', 'F8-T8': 'F8', 
    'T8-P8-0': 'T8', 'P8-O2': 'P8', 'FP1-F3': 'P2', 'P7-T7': 'P9',
    'T7-FT9': 'T9', 'FT9-FT10': 'FT9', 'FT10-T8': 'FT10', 'T8-P8-1': 'TP8'
}

# Rename the channels in the raw data
eeg.rename_channels(ch_mapping)

# Apply the standard 10-20 montage
eeg.set_montage("standard_1020", on_missing="ignore")

# Now continue with your ICA setup
eeg.filter(l_freq=1, h_freq=None)

ica = mne.preprocessing.ICA(
    method="picard",
    fit_params={"extended": True, "ortho": False},
    random_state=1
)
ica.fit(eeg)

# Plot ICA components to identify artifacts
ica.plot_components(inst=eeg, picks=range(ica.n_components_))
plt.show()

and here is the output:

image
image986×1209 147 KB

As an expert can you please tell me if this is okay?