EEG Signal Quality
by NeuroPype
Individual
Test Subject 1
File Name
Quick32 Eyes Open Example File.edf
Recording Date
Oct 09, 2025, 16:32:31
Trim Start/End
15 / 303 sec.

Description

This report provides a snapshot of your EEG recording's signal quality, highlighting individual channels using topographic plots. The report measures key metrics, including line noise, high frequency noise, channel artifacts, and channel correlation, and pinpoints the channels, and in some cases time points, where these are detected.

File information

filenameC:\Users\CGX\Desktop\Quick32 Eyes Open Example File.edf
file size in Mb10.7
file modification timeOct 09, 2025, 16:32:31
num channels29
channel labelsAF7, FPz, F7, Fz, T7, FC6, Fp1, F4, C4, Oz, CP6, Cz, PO8, CP5, O2, O1, P3, P4, P7, P8, Pz, PO7, T8, C3, Fp2, F3, F8, FC5, AF8
num timepoints144000
file length in seconds288.0
sampling rate500.0
metadata(none found)

Summary

Good/Bad channel Overview
This metric combines channel correlation and high-frequency noise measurements, to classify the average signal quality captured by a given channel (electrode) as "good" or "bad".

  • Red channels indicate the EEG signal was poor quality.
  • Green channels indicate the EEG signal was good.

Artifacts

Artifact to clean EEG ratio
This measure shows the level of artifacts, computed as the ratio of artifact amplitude to clean EEG amplitude a given channel, averaged across the session.

  • Low (green, smaller disks): Minimal artifacts.
  • High (red, larger disks): Significant artifacts.
  • Percentage of session containing artifacts: 35.7%

Artifacts (over time)

This plot shows differences between the raw EEG data and the clean EEG (after removing artifacts). Spikes in this plot indicate times where there were significant artifacts. If artifacts are present at the beginning and end of the session (i.e., adjusting or removing the headset) and that data will not be used, use the trim settings in the plugin to re-run this report with those portions removed to compute the signal quality of the remaining data.

This is the data on which the Artifact Ratio (as shown in the earlier topoplot) is computed for the session. Note that the differences are trimmed at 20 standard deviations.