[Eeglablist] Rank deficiency and ASR+ICA

Cedric Cannard ccannard at protonmail.com
Thu Sep 26 15:02:40 PDT 2024 

Hi Francisco,

I agree with Makoto it is not ideal solution.But I haven't found any perfect solution personally.

If you do decide to go with the initial approach, here are the general steps. 

Yes, always get the best signal quality possible before ICA (although preserve eye movements as much as possible so they are extracted by ICA and not ASR, which removes relevant signals with the eye artifacts). 

A good way for this approach is to make a copy of your dataset before filtering. Filter copy data >1Hz, remove bad channels, run ASR, interpolate bad channels (after ASR to avoid data rank issues as there is no input like for ICA, to my knowledge), run ICA (input effective data rank). Store bad channels, bad segment latencies, ICA latencies during this process. 

Go back to the original dataset (<1 hz filter), remove bad channels, interpolate, remove bad segments, and transfer ICA weights (based on stored info). Run EEG = eeg_checkset(EEG); to make sure everything is good. Run ICLabel to flag and remove bad components. 

Hope this helps and that I didn't miss something. 

Cedric




On Thursday, September 26th, 2024 at 2:59 PM, Cedric Cannard <ccannard at protonmail.com> wrote:

> Hi Francisco,
> 
> Yes, always get the best signal quality possible before ICA (although preserve eye movements as much as possible so they are extracted by ICA and not ASR, which removes relevant signals with the eye artifacts).
> 
> A good way for this approach is to make a copy of your dataset before filtering. Filter copy data >1Hz, removebad channels, run ASR, interpolate bad channels (after ASR to avoid data rank issues as there is no input like for ICA, to my knowledge), run ICA (input effective data rank). Store bad channels, bad segment latencies, ICA latencies during this process.
> 
> 
> Go back to original dataset (<1 hz filter), remove bad channels, interpolate, remove bad segments, and transfer ICA weights (based on stored info). Run EEG = eeg_checkset(EEG); to make sure everything is good. Run ICLabel to flag and remove bad components.
> 
> Hope this helps and that I didn't miss something.
> 
> Cedric
> 
> 
> 
> 
> 
> 
> On Monday, September 23rd, 2024 at 10:13 AM, Francisco Zambrano via eeglablist eeglablist at sccn.ucsd.edu wrote:
> 
> > 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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