EEGLAB ببساطة

ICLabel وحذف المكوّنات

المحطة 4 · الدرس 5 · EEGLAB 2026.1

من الواجهة

  1. ToolsClassify components using ICLabelLabel components
  2. Select which icversion of ICLabel to use:Default (recommended)OK
  3. ToolsClassify components using ICLabelFlag components as artifacts
  4. Select range for flagging component for rejectionMinMaxMuscleEye
  5. OK
  6. ToolsRemove components from data
  7. Manual rej.YesYes
  8. List of component(s) to remove from dataOK
  9. Plot single trials
  10. Accept
  11. FileSave current dataset asOverwrite it in memory (set=yes; unset=create a new dataset) D1 main cleanD1_main_clean.set

السكربت

EEG = pop_iclabel(EEG, 'default');
classes = EEG.etc.ic_classification.ICLabel.classes;
probs   = EEG.etc.ic_classification.ICLabel.classifications;
disp(classes);
disp(round(probs(1:10, :) * 100));
thresh = [NaN NaN; 0.9 1; 0.9 1; NaN NaN; NaN NaN; NaN NaN; NaN NaN];
EEG = pop_icflag(EEG, thresh);
artifactComps = find(EEG.reject.gcompreject);
addpath(fullfile(fileparts(which('iclabel')), 'viewprops'));
if ~isempty(artifactComps)
    pop_viewprops(EEG, 0, artifactComps);
end
if ~isempty(artifactComps)
    EEG = pop_subcomp(EEG, artifactComps, 0);
end
EEG.setname = 'D1 main clean';
EEG = pop_saveset(EEG, 'filename', 'D1_main_clean.set', 'filepath', pwd);
EEG = pop_eegfiltnew(EEG, 'hicutoff', 30);
EEG.setname = 'D1 main clean lp30';
EEG = pop_saveset(EEG, 'filename', 'D1_main_clean_lp30.set', 'filepath', pwd);

أي قيمة أختار؟

الإعدادالقيمة
نسخة المصنّفDefault
حد وسم الحذفEye و Muscle فوق 90%
مكوّنات أخرى (Heart، Line Noise، Channel Noise)لا تُعلّم تلقائياً
إصدار ICLabel1.6 أو أحدث

تأكّد

  • قارن الإشارة
  • عُدّ المكوّنات
  • افتح خرائط المكوّنات

لقسم Methods

Components were classified with ICLabel (version 1.7; Pion-Tonachini et al., 2019), and components with a probability above 0.9 for the Eye or Muscle class were reviewed visually and removed ([N] components). Data were then low-pass filtered (passband edge 30 Hz, cutoff frequency [33.64] Hz at −6 dB, filter length [59] points; pop_eegfiltnew, firfilt plugin 2.8).

النسخة الكاملة: https://simplyeeglab.pages.dev/lesson/iclabel/

رخصة المحتوى: CC BY-NC-SA 4.0 · MIT (code)

موقع تعليمي مستقل، غير تابع رسمياً لـ EEGLAB أو SCCN.