Hello everyone!
I’m using:
- MNE version: 1.3.0
- operating system: e.g. macOS 13
I’m new to connectivity analysis (and really any EEG analysis) and I have got a connectivity matrix using this code script:
con_methods = ['wpli']
con_freqs = ['4-30', '4-8', '8-13', '13-20', '20-30', '8-10', '10-13']
freqs_min = [int(x.split('-')[0]) for x in con_freqs]
freqs_max = [int(x.split('-')[1]) for x in con_freqs]
sen_or_sour = 'sensors'
def calculate_conn(data, con_methods, sfreq, freqs_min, freqs_max, node_names, person, output_folder):
for con_method in con_methods:
con = mne_connectivity.spectral_connectivity_epochs(data, method = con_methods,
mode = 'multitaper',
sfreq=sfreq, fmin=freqs_min,
fmax = freqs_max, faverage = True,
mt_adaptive = True, n_jobs = -1)
con = con.get_data(output='dense')
for shp in range(0, con.shape[2]):
output_filename = f'{output_folder}/{person}_{sen_or_sour}_{con_method}.csv'
savefile = pd.DataFrame(np.array(con[:,:,shp]))
savefile.columns = savefile.index = node_names
savefile.to_csv(output_filename)
epochs = epoching(raw, 6., 0.5) #type - numpy array
person = 'man'
calculate_conn(epochs, con_methods, epochs.info['sfreq'], freqs_min, freqs_max, epochs.info['ch_names'], person, output_folder)
Please someone explain me how do I enterprise the results? I have a table now with values 1 and values between 0 and 1. Do I understand correctly there is no certain values or cutoffs that tell brain regions are interconnected significantly more than the others? What is a common practice interpreting the results?
I’m performing analysis on a EEG of epilepsy patient. Is there any way I can tell their brain connectivity do or do not differ from Normal EEG? Should I compare them?
Really any answers or suggestions on analysis are highly appreciated!