تشغيل ICA

من الواجهة

1. Datasets
2. Tools › Filter the data › Basic FIR filter (new, default)
3. Lower edge of the frequency pass band (Hz)OK ← 1
4. Overwrite it in memory (set=yes; unset=create a new dataset) ← D1 ica1hz
5. Tools › Decompose data by ICA
6. ICA algorithm to use (click to select)Extended Infomax (runica.m; default) ← 'extended', 1
7. Reorder components by variance (if that's not already the case)Use only channel type(s) or indicesOK
8. Proposed rank:OK
9. Plot › Component maps › In 2-D
10. Datasets
11. Edit › Dataset info
12. ICA weights array or text/binary file (if any):from other datasetOK ← 2ALLEEG(2).icaweightsALLEEG(2).icasphereALLEEG(2).icachansind
13. OK
14. File › Save current dataset as

السكربت

EEG_main = EEG;
EEG_ica = pop_eegfiltnew(EEG_main, 'locutoff', 1);
EEG_ica.setname = 'D1 ica1hz';
expectedRank = EEG_ica.nbchan - numel(badChans) - 1;
fprintf('Expected data rank: %d\n', expectedRank);
EEG_ica = pop_runica(EEG_ica, 'icatype', 'runica', 'extended', 1);
EEG_main.icaweights  = EEG_ica.icaweights;
EEG_main.icasphere   = EEG_ica.icasphere;
EEG_main.icachansind = EEG_ica.icachansind;
EEG_main.icawinv     = [];   % recomputed by eeg_checkset
EEG_main.icaact      = [];
EEG = eeg_checkset(EEG_main);
pop_topoplot(EEG, 0, 1:20);
EEG = pop_saveset(EEG, 'filename', 'D1_main_ica.set', 'filepath', pwd);

أي قيمة أختار؟

تأكّد

• في نافذة EEGLAB الرئيسية
• في نافذة الأوامر
• افتح خرائط المكوّنات
• افتح الملف المحفوظ من جديد
• الرتبة مطابقة

لقسم Methods

Independent component analysis (extended Infomax; Lee et al., 1999) was computed with runica ([31] components after PCA reduction to the data rank) on a copy of the data high-pass filtered at 1 Hz, and the resulting weights were applied to the 0.1 Hz high-pass filtered data.