[Eeglablist] Rank deficiency and ASR+ICA

[Francisco Zambrano]

Hello again,

Thanks a lot for your insights, the results from Cedric's reply gave me an
answer that sounds more in line with what I've read.

I've come with another question regarding ICA. If I'm trying to use ICA's
trick of applying the >1 Hz data's unmixing matrix  to the <1 Hz data, but
I don't know if It's better to do the whole processing pipeline(removing
bad channels, interpolating, removing bad trials,...) first and then
applying it back to the <1Hz data that is also been processed with the same
steps; or if I'm supposed to do it in another way. Could you please help me
with this?

Thanks in advance.
Francisco.


El mié, 7 ago 2024 a la(s) 3:37 p.m., Makoto Miyakoshi via eeglablist (
eeglablist at sccn.ucsd.edu) escribió:

> Hi Pancho,
>
> > rank(EEG.data(:,:)) over the original data It says the rank is 11
>
> Try rank(double(EEG.data(:,:)) and you'll see more realistic values at
> least. It is counterintuitive if you see it naively that single or double
> makes such a difference here.
>
> For the complete answer, see Cedric's reply.
>
> Makoto
>
>
> On Mon, Aug 5, 2024 at 9:40 PM Pancho Zambrano via eeglablist <
> eeglablist at sccn.ucsd.edu> wrote:
>
> > Hi!,
> >
> > I'm currently working on a script for my thesis on an EEG channel
> > reconstruction protocol for Universidad de Concepción, and I've found
> > myself in some trouble regarding rank deficiency. The script it's about
> > quantification of the positive yzing impact of ASR algorithm from
> > cleanrawdata() on the data by analyzing the increase of  the amount of
> > dipolar sources present before and after using this default toolbox
> > function. This is accomplished by comparing the residual variance from
> the
> > dipfit model of the clean and original data. The problem is I use the
> > matcorr function to find the best match between the original and clean
> ICA
> > mixing matrices, but they have different number of columns: the first one
> > is 64x64 and the clean one is 64x55. Since I've re-referenced the data
> > after ASR, I thought this was the problem but I also noticed when I use
> > rank(EEG.data(:,:)) over the original data It says the rank is 11, so I'm
> > lost here, please help. I got this method from the article:
> >
> > Evaluation of Artifact Subspace Reconstruction for Automatic Artifact
> > Components Removal in Multi-Channel EEG Recordings
> >
> > from:
> >
> >
> https://urldefense.com/v3/__https://ieeexplore.ieee.org/abstract/document/8768041?casa_token=IsR0oujDOEAAAAAA:T1NPgCRqT15sUYjG8KLtR8a9yW6PqQP7iaKzg5057R93Oef1yCmxDKt6ZtLM4IZk2t_xF4qWJ0I__;!!Mih3wA!ClbUhbvVFcY2DhNRP-wwMym8wBcZTXz2jeCl8yebysZiEmG01Z-cZm3OgV4T4R1GXwiDsWNEwyIFZcOglDZZkbBiLjHP0sFV$
> > This is my code:
> > % Initialize EEGLAB
> > eeglab;
> >
> > % Load the dataset
> > EEG = pop_loadset('filename', 'C110_filt_butt_zap2.set');
> >
> > % Perform ICA on the original data
> > EEG = pop_runica(EEG, 'extended', 1);
> >
> > % Save the original ICA results
> > ica_weights_orig = EEG.icaweights;
> > ica_sphere_orig = EEG.icasphere;
> > ica_act_orig = EEG.icaact;
> > ica_mix_matrix_orig = pinv(ica_weights_orig * ica_sphere_orig);
> >
> > % ASR dataset
> > EEG_clean = pop_loadset('filename', 'prueba_1hz_ica.set');
> >
> > % % Perform ICA on the ASR cleaned data
> > % EEG_clean = pop_runica(EEG_clean, 'extended', 1);
> >
> > % Save the ASR ICA results
> > ica_weights_clean = EEG_clean.icaweights;
> > ica_sphere_clean = EEG_clean.icasphere;
> > ica_act_clean = EEG_clean.icaact;
> > ica_mix_matrix_clean = pinv(ica_weights_clean * ica_sphere_clean);
> >
> > % Calculate the correlations between the components
> > % n_components_orig = size(ica_mix_matrix_orig, 2);
> > % n_components_clean = size(ica_mix_matrix_clean, 2);
> > %
> > % corr_matrix = zeros(n_components_orig, n_components_clean);
> > %
> > % for i = 1:n_components_orig
> > %     for j = 1:n_components_clean
> > %         corr_matrix(i, j) = corr(ica_mix_matrix_orig(:, i),
> > ica_mix_matrix_clean(:, j));
> > %     end
> > % end
> >
> > % Use the matcorr function to find the best matching
> > rmmean = true; % Adjust as needed (remove mean or not)
> > method = 0; % Correlation method
> > weighting = []; % No specific weighting
> >
> > [corr, indx, indy, corrs] = matcorr(ica_mix_matrix_orig,
> > ica_mix_matrix_clean, rmmean, method, weighting);
> >
> > % Display the matched correlations
> > best_matches = corrs(sub2ind(size(corrs), indx, indy));
> >
> > disp('Correlations between the best matched components:');
> > disp(best_matches);
> >
> > % Apply the spatial filter obtained from the ICA of the original data to
> > the cleaned data
> > W_orig = ica_weights_orig * ica_sphere_orig;
> > Y_clean = W_orig * reshape(EEG_clean.data, size(EEG_clean.data, 1), []);
> >
> > % Calculate the activities of the ICs
> > Y_clean = reshape(Y_clean, size(EEG_clean.icaact));
> >
> > % Calculate the mean power reduction for the IC activities
> > Y_orig = W_orig * reshape(EEG.data, size(EEG.data, 1), []);
> > Y_orig = reshape(Y_orig, size(EEG.icaact));
> >
> > power_reduction = mean(var(Y_orig, 0, 2)) - mean(var(Y_clean, 0, 2));
> >
> > disp('Mean power reduction:');
> > disp(power_reduction);
> >
> > % Classify the ICs using iclabel
> > EEG = iclabel(EEG, 'default');
> > EEG_clean = iclabel(EEG_clean, 'default');
> >
> > % Get the IC classification labels
> > ic_labels_orig = EEG.etc.ic_classification.ICLabel.classifications;
> > ic_labels_clean =
> EEG_clean.etc.ic_classification.ICLabel.classifications;
> >
> > % Set up DIPFIT for the original data
> > EEG = pop_dipfit_settings(EEG, 'hdmfile',
> 'standard_BEM/standard_vol.mat',
> > ...
> >     'coordformat', 'MNI', 'mrifile', 'standard_mri.mat', ...
> >     'chanfile', 'standard_1005.elc', 'coord_transform', [0 0 0 0 0 0 1 1
> > 1], ...
> >     'chansel', 1:size(EEG.data, 1));
> >
> > % Fit dipoles to the original ICA components
> > EEG = pop_multifit(EEG, 1:size(EEG.icaweights, 1), 'threshold', 100);
> >
> > % Get the residual variance of the fitted dipoles in the original data
> > rv_orig = [EEG.dipfit.model.rv];
> >
> > % Set up DIPFIT for the ASR cleaned data
> > EEG_clean = pop_dipfit_settings(EEG_clean, 'hdmfile',
> > 'standard_BEM/standard_vol.mat', ...
> >     'coordformat', 'MNI', 'mrifile', 'standard_mri.mat', ...
> >     'chanfile', 'standard_1005.elc', 'coord_transform', [0 0 0 0 0 0 1 1
> > 1], ...
> >     'chansel', 1:size(EEG_clean.data, 1));
> >
> > % Fit dipoles to the ASR cleaned ICA components
> > EEG_clean = pop_multifit(EEG_clean, 1:size(EEG_clean.icaweights, 1),
> > 'threshold', 100);
> >
> > % Get the residual variance of the fitted dipoles in the ASR cleaned data
> > rv_clean = [EEG_clean.dipfit.model.rv];
> >
> > % Identify dipolar ICs (residual variance < 5%)
> > dipolar_ICs_orig = find(rv_orig < 0.05);
> > dipolar_ICs_clean = find(rv_clean < 0.05);
> >
> > % Compare the number of dipolar ICs before and after ASR
> > num_dipolar_ICs_orig = length(dipolar_ICs_orig);
> > num_dipolar_ICs_clean = length(dipolar_ICs_clean);
> >
> > % Display dipolar IC results
> > fprintf('Number of dipolar ICs in original data: %d\n',
> > num_dipolar_ICs_orig);
> > fprintf('Number of dipolar ICs in ASR cleaned data: %d\n',
> > num_dipolar_ICs_clean);
> >
> > % Save all results
> > save('ica_evaluation_results.mat', 'ica_weights_orig',
> 'ica_weights_clean',
> > 'ica_sphere_orig', 'ica_sphere_clean', 'ica_act_orig', 'ica_act_clean',
> > 'indx', 'indy', 'best_matches', 'Y_clean', 'power_reduction',
> > 'ic_labels_orig', 'ic_labels_clean', 'rv_orig', 'rv_clean',
> > 'dipolar_ICs_orig', 'dipolar_ICs_clean');
> >
> > Please, help.
> >
> > Francisco Alonso Zambrano Salamanca
> > Biomedical Engineering
> > Universidad de Concepción
> > Chile
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