I’m trying to analyze EEG coherence from raw data acquired using ADInstruments’ LabChart.
I pick 30s epochs of raw data during resting state from two channels and export it to a txt file.
I use the spectral_connectivity function on list of epochs. Each element on this list is an array of 2 by 30000 (sampling freq: 1kHz). It appears to work fine when I’m using multiple epochs but when I take just one it show coh to be 1. over all chosen frequencies-- which means I’m not doing something right.
Here is the code I’m using:
import numpy as np from pathlib import Path from mne.connectivity import spectral_connectivity fmin = (0.5, 4, 8, 12, 30, 60) fmax = (4, 8, 12, 30, 50, 100) sfreq = 1000 indices1 = (np.array(), np.array()) lst =  n = int(input("Enter number of epochs: ")) for i in range(0, n): ele = str(input()) lst.append(ele) data_set = [np.loadtxt(p, usecols=(1, 2)).transpose() for p in lst] coh, freqs, times, n_epochs, n_tapers = spectral_connectivity( data_set, method="coh", mode="fourier", indices=indices1, sfreq=sfreq, fmin=fmin, fmax=fmax, faverage=True, n_jobs=1, )
I looked your coherence example (" Compute coherence in source space using a MNE inverse solution"), which, as far as I understands, deals with event related data. I scammed through the irrelevant parts and got to “Choose channels for coherence estimation” which I think might be related to my problem.
so I guess my question is-
Am I using the correct form of input data? why is not working on 1 epoch and whats stc and inverse operator has to do with it… since I have a basic background in signal processing I was looking for a rather straight answer on the web and couldnt find one.
Hope I was clear enough… Be happy to provide txt files of raw data if needed.