[Question] remove EOG / ECG artifacts from EEG signal

Hi there,

I tried to run the tutorial Compute ICA on MEG data and remove artifacts with my own data.

I have several problems with it:

• ica.fit() Adding eeg=40e-6 to reject dict leads to “No clean segment found” Are my data wrong?
• create_ecg_epochs Produces a ValueError: Unable to generate artificial ECG channel What can I do to find the ECG channel?
• find_bads_eog returns [] [-0.94519416 -0.00985051 0.15899545 0.27077895 -0.00273687] So this means that no eog information was found? Is “AF3” a good pick as ch_name? I assume it should be a sensor near the eye.

Any tips or hints will be appreciated - tanks in advance, paul

This is how I load my own data:

def createInfo(filePath):
    with open(filePath, 'rb') as f:
        ch_names = f.readline().strip().split(",")
    ch_types = ["eeg"] * len(ch_names)
    sfreq = 128
    montage = mne.channels.read_montage("standard_1020")
    info = mne.create_info(ch_names, sfreq, ch_types, montage)
    return info

def createRawObject(filePath):
    info = createInfo(filePath)
    data = np.swapaxes(np.delete(np.genfromtxt(filePath, dtype=float, delimiter=","), 0, 0),0,1)
    return mne.io.RawArray(data, info)

filePath = "test_data.txt"
raw = createRawObject(filePath)
raw.filter(1, 45, n_jobs=1, l_trans_bandwidth=0.5, h_trans_bandwidth=0.5,
           filter_length='10s', phase='zero-double')

test_data.txt

[ppasler]

Thanks guys, your answer cleared many things 😃 The images helped and I would appreciate any further information (especially made for noobs). To exclude #3755 I could install the bugfix branch and try it again?

I’ll have a look at the interactive mode and record some “clear blinks” to have a reference (Maybe also some with ear-blinks…). Good hint with the bad channels, I’ll check that first.

After getting my stuff to work, I will suggest some things that were unclear for me in the tutorial.

[mmagnuski]

@jona-sassenhagen not really bored now 😁 (although being back to Poland the lack of sun is even more painful 🌨️ ☁️ ). I’ll try to take a closer look into it in not so distant future (along with other mne todos that I have gathered lately).