- MNE-Python version: 0.24
- MAC OS 11.6.1
I’m encountering a warning when attempting to fit my ica to my raw data. I have a 10-minute EEG recording consisting of 46 channels with sampling rate 250 Hz.
I’ve tried to use the syntax given under the API reference for ICA.
from mne.preprocessing import ICA
raw = mne.io.read_raw_edf('myfile.edf',preload=True)
raw_filt = raw.filter(l_freq=1, h_freq=None)
ica = ICA(method='fastica',n_components=.999999, random_state=6,max_iter="auto")
In the fitting step, I receive the following warning:
> RuntimeWarning: Using n_components=0.999999 (resulting in n_components_=6) may lead to an unstable mixing matrix estimation because the ratio between the largest (46) and smallest (2.2e-05) variances is too large (> 1e6); consider setting n_components=0.999999 or an integer <= 5
I’m not sure how to address this, particularly since I’ve followed the suggestion and set
n_components=.999999 . I observed that setting
n_components=5 did not produce a warning, but I’m not sure why.
Also, do I apply the fit to the original raw data or the filtered version?
I have just encountered the same issue and I am not sure how to fix it. Did you end up finding a solution or an explanation to the problem?
@Giuseppe-1993 what kind of data are you using? MEG, EEG, …? How many channels? Thanks!
thank you for your attention. I am using EEG data, 64 channels, recorded using BioSemi. I have a total of 30 participants and this ICA issue seems to happen only on particular datasets. I am now inspecting the data to see if perhaps the dataset itself is corrupted but it does not look like it.
@Giuseppe-1993 would you mind sharing one of the affected datasets and a minimal working example to reproduce the issue you’re seeing?
You can share a link to the data via email: email@example.com
Unfortunately, I am not able to share either the dataset or the code due to IP agreements. However, after careful inspection, I can conclude that the behaviour was very likely due to the low signal-to-noise ratio. After rejecting manually segments of the data that seemed problematic, the ICA issue seems to stop. I will keep you updated should this happen again.