EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 1 Independent Component Clustering Why cluster components? EEGLAB studysets: the STUDY structure Creating a STUDY – pop_createstudy() Preparing the clustering measures – pop_preclust() Performing the clustering – pop_clust() Reviewing the results – pop_clustedit() Cluster editing – pop_clustedit() Hierarchical clustering Signal processing cluster activities – cls_envtopo() Clustering example – STUDY of 5 datasets

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 2 Why cluster components? ICA transforms the data from a channel basis (activity recorded at each channel) to a component basis (activity computed at each independent spatially-filtered cortical or non-cortical component process). Normally, EEG researchers assume that electrode, say F7 == F7 == F7... in each subject – and then ‘cluster‘ their data by channel... But this is only roughly correct!

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 3 S. Makeig, 2005 Scalp ICs IC1 IC2 IC3 IC4 Cortex ∑ Example: First Subject Electrode F7

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 4 S. Makeig, 2005 Second Subject Scalp ICs IC1 IC2 IC3 IC4 Cortex ∑ Electrode F7

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 5 S. Makeig, 2005 Scalp ICs IC1 IC2 IC3 IC4 Cortex Electrode F7 ∑ Third Subject

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 6 S. Makeig, 2005 Scalp ICs IC1 IC2 IC3 IC4 Cortex ∑ Fourth Subject Electrode F7

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 7 Largest 30 independent components (single subject )

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 8 The same problems hold for clustering independent components Across Ss, components don’t even have “the same” scalp maps!  Are “the same” components found across subjects? What should define “the same” (i.e., “component equivalence”)? Similar scalp maps? Similar cortical or 3-D equivalent dipole locations? Similar activity power spectra? Similar ERPs? Similar ERSPs? Similar ITCs? OR …, Similar combinations of the above? … So how to cluster components?

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 9 Does the spatial distribution of independent components depend on the task the subject performs? i.e. Do “the same” components (and clusters) appear for every task?

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 10 Equivalent dipole density Onton et al., 2005 Sternberg letter memory task Onton et al., ‘05

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 11 Equivalent dipole density Onton et al., 2005 Letter twoback with feedback Onton et al., ‘05

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 12 Equivalent dipole density Onton et al., 2005 Auditory oddball plus novel sounds Onton et al., ‘05

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 13 Equivalent dipole density Onton et al., 2005 Emotion imagery task Onton et al., ‘05

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 14 Equivalent dipole density Exp I Onton et al., 2005 Word memory (old/new) task Onton et al., ‘05

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 15 Equivalent dipole density Exp II Onton et al., 2005 Visually cued button press task Onton et al., ‘05

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 16 … Some caveats Here, The electrode locations were not individualized. MR images were not available  co-registration crude. Single versus dual-dipole model selection was subjective. Different electrode montages  possible location effects Onton, 2005

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 17 Clustering by spectra (1 subject) Makeig et al., unpublished

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 18 Visual Selective Attention Task 15 subjects

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 19

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 20 Left muRight mu Makeig et al., submitted Clustering ICA components by eye

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 21 Clustered components from 15 Ss using a ‘component distance metric’ incorporating differences between their (weighted) scalp maps, dipole locations, spectra, ERP, ERSP, and ITC patterns. Hand-adjusted clusters to remove outliers. Determined time/frequency regions of significant ERSP and ITC for each component using permutation-based statistics. Used binomial statistics to highlight time/frequency regions significantly active within clusters. Semi-automated clustering

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering Relative Power (dB) Frequency (Hz) Ss 11 Comp 13 Ss 23 Comp 9 Ss 15 Comp 10 Ss 13 Comp 8 Ss 9 Comp 8 Ss 12 Comp 9 Ss 11 Comp 11 Ss 17 Comp + mean - α CP α RP α LP μ RC μ LC Θ FC CMCM LC N1 Component Clusters Makeig et al., Science 2002

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 23

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 24

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 25

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 26

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 27

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 28 Complex event-related dynamics underlie ‘the’ P300

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 29 Distributed theta burst event - brainmovie3d()

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 30 Onton et al., NeuroImage 2005 A FM  cluster during working memory

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 31 Onton et al., NeuroImage 2005 The FM  cluster accounts for all the memory effect

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 32 Naïve realism (a.k.a. “expertise”) “Yes! … because I know one when I see one!” “If it appears where Mu components appear, and acts like Mu components act, then it IS a Mu component!” Convergent evidence (a.k.a., “doublechecking”) Two possible approaches: Cluster on PLACE  Check ACTIVITY consistency (re task) Cluster on ACTIVITY  Check PLACE consistency Absolute truth: More ideal forward and inverse models Invasive multiscale recordings + modeling Are obtained component clusters “real“?

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 33 Not all subjects contribute components to each cluster. Why not? Different numbers of artifact components (~INR) Subject differences!? Is my subject group a Gaussian cloud??  subject space Should all subjects be included in each cluster?

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 34 Known Uncertainty

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 35 Cognitive Events Individual Subjects The goal of cognitive neuroscience Phys. Data Model Value Anticipation Re-evaluation Imagination Genetics Culture Age / Gender Personality Multiple Modes Perturbations Modulations Distributed Dynamics

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 36 EEGLAB clustering procedure 1.Identify a set of datasets as an EEGLAB study or ‘studyset’. 2.Specify the subject group, subject code, condition and session of each dataset in the study. 3.Identify components to cluster in each study dataset. 4.Decide on component measures to use in clustering the study and/or to evaluate the obtained component clusters. 5.Compute the component measures for each study dataset. 6.Cluster the components on these component measures. 7.Review the obtained clusters (e.g., their scalp maps, dipoles, and activity measures). 8.Edit the clusters (manually remove/shift components, make sub- clusters, merge clusters, re-cluster). 9.Perform signal processing within or between selected clusters.

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 37 Clustering strategy 1.Cluster on multiple measures (dipole locations, scalp maps, spectra, ERPs, ITCs, ERSPs) in one or more conditions. 2.Reduce the dimension of each measure to a principal component subspace. 3.Compose a PCA-reduced position vector for each component. 4.Cluster the composed component vectors using k-means or other. 5.Use the computed component measures (not PCA-reduced) to visualize the activities and spatial properties of the clustered components. 6.Compute and visualize the cluster-mean measures. 7.Use the clustered study set data as input into std_ functions.

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 38 Cluster ERP contributions - clust_envtopo() clust_envtopo(STUDY, ALLEEG, 'clusters', [], 'subclus',[ ], 'env_erp', 'all', ‘vert', -1100, 'baseline', [-200 0], 'diff', [2 1], 'limits', [ ], 'only_precomp', 'on', 'clustnums', - 4, 'limcontrib', [ ]);

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 39 Component clustering research issues Alternative clustering methods Clustering goodness-of-fit Cluster plotting details Add new pre-clustering measures Study subject differences!? EEGLAB study design issues Vector of conditions  more general exp. designs More complete study statistics

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 40

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 41 Create a STUDY - pop_study() >> [ STUDY ALLEEG] = pop_study(ALLEEG);

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 42 Compute cluster measures - pop_preclust() >> [ALLEEG, STUDY] = pop_preclust(ALLEEG, STUDY);

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 43 Perform clustering - pop_clust() >> [STUDY] = pop_clust(ALLEEG, STUDY);

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 44 Plot cluster-mean scalp maps - pop_culstedit() >> [STUDY] = pop_clustedit(ALLEEG, STUDY, [2:22]);

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 45 Plot the properties of a mu rhythm cluster

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 46 Rename a cluster

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 47 Merge clusters

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 48 Remove components (  outliers cluster #2)

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 49 Reassign components to clusters

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 50 Split a cluster - pop_preclust() >> [ALLEEG, STUDY] = pop_preclust(ALLEEG, STUDY, 14 );

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 51 Sub-cluster - pop_clust() >> [STUDY] = pop_clust(ALLEEG, STUDY);

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 52 Inspect sub-clusters - pop_clustedit() >> [STUDY] = pop_clustedit(ALLEEG, STUDY, [23:24]);

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 53 Hierarchical clustering – mu cluster separation

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 54 function [STUDY,grand_erpdiff,clusnval] = std_ERPdiff(STUDY,ALLEEG, clust, cond1, cond2) % computes the difference ERP of cond1 - cond2 if ~isfield(STUDY.cluster(clust).centroid, 'erp') STUDY = std_centroid(STUDY,ALLEEG, clust, 'erp'); end % compute cluster grand mean (= centroid) ERPs % Compute the grand mean difference ERP grand_erpdiff = STUDY.cluster(clust).centroid.erp{cond1} - STUDY.cluster(clust).centroid.erp{cond2}; try % collect the single-component ERPs for cond1 clusnval1 = std_clusread(STUDY, ALLEEG, clust,'erp',cond1); catch, disp([ 'ERPdiff: Some ERP information is missing, aborting.'] ); return; end try % collect the single-component ERPs for cond2 clusnval2 = std_clusread(STUDY, ALLEEG, clust,'erp',cond2); catch, disp([ 'ERPdiff: Some ERP information is missing, aborting.'] ); return; end % Compute the difference ERPs for the single components clusnval.erp = clusnval1.erp - clusnval2.erp; return Using cluster info – std_ERPdiff()

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 55 Condition difference ERPs

EEGLAB Workshop IV, June 26-29, 2007, Aspet, France: Scott Makeig – Component Clustering 56 Exercise – pop_clustedit() Go to the STUDY directory >> cd [study] Load STUDY of 5 sample datasets: >> load [sample.study] -mat –This study is already clustered Call: >> [STUDY] = pop_clustedit(ALLEEG, STUDY); Test clustedit options…