nemo.nic.uoregon.edunemo.nic.uoregon.edu/wiki/images/b/bc/SimDataPCAIC… · Web viewRobert Frank,...

Mapping spatial & temporal metrics derived from Simulated DataRobert Frank, Haishan Liu, Gwen Frishkoff, & Dejing Dou

Created: 01/21/2009 by HLLast edit: 06/27/2009 by GF

Dataset: SDv5 (P1N1N3MFNP3-SDv5 datasets)

1. STUDY GOALS & SUMMARY OF METHODS AND RESULTSThe goal of this study was to explore methods for identifying correspondences (“mappings”) between different spatial and temporal metrics that are used to summarize ERP patterns.

The data for this study comprised 80 simulated ERP datasets representing 40 subjects in 2 experimental conditions. Each ERP dataset was generated by the superposition of 5 patterns with unique spatiotemporal characteristics representing distinct neuronal groups: the P100, N100, N3, MFN and P300, Inter-subject and inter-conditional variance were induced by modulating pattern intensity across datasets.

Spatiotemporal components from the temporal PCA (tPCA) and spatial ICA (sICA) decompositions of this simulated data were assessed and labeled using pattern rules that quantified the spatial and temporal characteristics of their respective patterns. The component that most frequently captured each pattern across subjects and conditions was labeled as the modal component for that pattern and subjected to further analysis.

Overview of data processing steps:1. Decomposition -- why we selected tPCA and sICA (vs. sPCA). Relevant question for mapping work:

sensitivity of mapping methods to distribution of measures for spatial vs. temporal metrics. [part since there are many more spatial than temporal metrics, which could bias the method]

2. Selection of modal factors/components for each latent pattern and autolabeling of observations (match vs. mismatch)

3. Generation of alternative sets of spatial and temporal metrics. Note about why onset, offset, and duration were not used for this study.

4. Clustering of instances (matches for modal factors) -- for validation and refinement, to align sets of observations with (latent) pattern labels. Post-processing of clusters to align latent patterns. [explain what this accomplishes that autolabeling by itself does not -- i.d. observations that do not represent simple structure, weed these out... ]

5. Mapping across metrics. Explain advantages of this procedure over simple correlation of means & s.d. across observations within each pattern.

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Table 1. Summary of cluster-to-pattern assignment for 8 simulated ERP datasets.SG01-tPCA

SG02-tPCA

SG01-sICA

SG02-sICA

SG01-tPCA

SG02-tPCA

SG01-sICA

SG02-sICA

Metric 1

Metric 1

Metric 1

Metric 1

Metric 2

Metric 2

Metric 2

Metric 2

P100 2 5+10 2+10 1+7+8 2 6 0+4 0+1+2+15N100 0 1+2+3 0+5+6 3+11 0 2+7 1+8 5+7+12N3 1 0+6 3+7+9 2+4+12 1 0+3 3+5+6 3+6+9+14MFN 3* 4+7 1+8 5+10* 3 1 2 11+13P300 4 9 4+11 0+6+9 4 4+5 7 10+16

*-- signifies that observations in these clusters were assigned to more than one pattern/rule. See Section 2.6 for details.

Note that the assignment of observations to clusters, and the corresponding splitting of observations of a single pattern into two or more clusters on some occasions, is a function of the metrics used to generate the dimensions and axis orientations of the multidimensional attribute space. Observations that are close in L2 norm along one dimension of a spatiotemporal attribute space may be farther away in L2 norm on a separate dimension of an alternate attribute space instantiated by an alternate metric set. This is consistent with mathematical topology, in which mathematical objects that are close in one topology, or one generalized measure of distance, can be far apart in another topology, or an alternate measure of distance. This phenomena is also present in the expert labeling of observations, as changes to expert-defined pattern rules redefine observation to pattern mappings and may reshuffle observation labels.

2. SIMULATED DATASETSTo generate the simulated data for this experiment, we first designed two ERP datasets, each of which comprised 40 ERPs from 20 simulated subjects in 2 experimental conditions: SDv5_SG01 and SDv5_SG02. The ERPs in each dataset are superpositions of 5 simulated ERP patterns as defined in the SDv5 protocol and described in Section 2.1. We then generated 4 new datasets by separately applying sICA and tPCA to the 40 ERPs in each of the two 20-subject groups, Two alternative metric sets, m1 and m2, were applied to these 4 PCA/ICA derived datasets to quantify the spatiotemporal characteristics of their corresponding tPCA factors and sICA components. Thus, the computed metric values summarize the spatiotemporal characteristics of the original subject and condition specific simulated ERPs as now represented by their respective PCA and ICA decompositions. These final 8 sets of extracted spatiotemporal attributes form the input datasets for the subsequent evaluation and labeling procedures. Table 2 gives an overview of the dataset design:

Table 2. 2 x 2 x 2 Factorial Design for 8 Simulated DatasetstPCA sICA

Metric Set 1

Dataset 1

SDv5_SG01_tPCA_18Facm1.xlsSDv5_SG01_sICA_18ICm1.xls

Metric Set 1

Dataset 2


Metric Set 2

Dataset 1


Metric Set 2

Dataset 2


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Datasets 1-2 refer to two independent sets of “observations” from subject group #1 and subject group #2, respectively, each of which contains 20 simulated subjects in 2 experimental conditions; See Section 2.1 for details

o SDv5 refers to “simulated data set, version 5”. The SDv5 subject and condition specific ERP datasets are based upon the superposition of 5 simulated ERP patterns, P100, N100, N3, MFN and P3, with definite and distinct, though fixed, spatial topographies and temporal latencies. For each subject and condition, only pattern intensities are randomly perturbed from their respective baseline values to simulate subject and condition variance.

o SG01 / SG02 refer to subject groups #1 and #2, respectively. Each subject group comprised the ERPs of 20 simulated subjects in 2 experimental conditions, for a total of 40 ERP datasets per subject group.

tPCA / sICA refer to temporal PCA and spatial ICA, respectively. They are distinct methods for transforming continuous spatiotemporal data into discrete, rank-1, spatiotemporal patterns for classification and labeling (see Section 2.2 for details).

Metric Sets 1-2 refer to two alternative sets of spatial and temporal attributes used to summarize the simulated ERP patterns (see Section 2.3 for details)

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2.1 Simulated data design and creation

The simulated ERP datasets for this study were generated using the August 2007 release of Dipole Simulator, which employs a 3-shell spherical head model. We used the application’s default conductivity values for each shell in modeling the propagation of source dipole activity to the spherical scalp surface.

Nine dipoles were carefully located and oriented within a 3-shell spherical model to simulate the topographies of 5 ERP components commonly seen in studies of visual word recognition: the P100, N100, N3, MFN and P300. Each dipole was then assigned an activation profile consistent with the polarity and time course of its respective ERP component. Simulated “scalp-surface” electrode locations were specified with an EGI HC-GSN 129-channel montage. Owing to volume conduction and the overlap of their temporal activity, the dipole activations induce a complex spatial and temporal superposition of the 5 modeled ERP patterns on the volume boundary. This simulated EEG data were recorded by computing the resultant surface intensities of the dipole activations at 129 channel locations for 600 milliseconds, or 150 samples at a 250 Hz sampling rate (Figure 1). The baseline location and orientation of each dipole are listed in Table 3. Table 4 lists corresponding ERP pattern latency (onset, offset, and peak) and surface peak-intensity baseline values, and inter-subject and inter-condition baseline offsets.

Figure 1: Base model (constructed in Dipole Simulator) for generation of simulated ERPs

The first 8 dipoles are grouped into 4 laterally symmetric pairs, representing the P100, N100, N3 and P300 components. The remaining medially-located 9th dipole models the frontal MFN.

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Table 3. Spatial ERP Pattern Dipole Parameters: Region of Interest (ROI), Location (Volume conductor xyz coordinate 3-tuple), Orientation (Direction vector xyz coefficient 3-tuple)

Source # ROI Location(x, y, z)

Orientation(x, y, z)

1) P100 L-Occipital (-0.30, -0.68, +0.00) (-0.27, -0.96, +0.00)2) P100 R-Occipital (+0.30, -0.68, +0.00) (+0.27, -0.96, +0.00)3) N100 L-Occipital (-0.17, -0.68, -0.16) (+0.24, +0.97, +0.09)4) N100 R-Occipital (+0.17, -0.68, -0.16) (-0.24, +0.97, +0.09])5) N3 L-AnteriorTemporal (-0.52, +0.53, +0.36) (+0.20, -0.11, -0.98)6) N3 R-AnteriorTemporal (+0.52, +0.53, +0.36) (-0.20, -0.11, -0.98)7) P300 L-Parietal (-0.47, -0.39, +0.50) (-0.62, -0.43, +0.66)8) P300 R-Parietal (+0.47, -0.39, +0.50) (+0.62, -0.43, +0.66)9) MFN Medial Frontal (+0.00, +0.40, +0.54) (+0.14, -0.54, -0.63)

Table 4. Spatial & Temporal ERP Pattern Parameters: Region of Interest (ROI), Peak Channel Intensity (Baseline), Start/Stop Latency (Baseline), Inter-Subject and Inter-Condition Baseline Offset

Source # ROI Intensity(uv)

Latency(ms)

Subject Offset Condition OffsetIntensity Latency Intensity Latency

1) P100 L-Occipital +3.50 (Ch: 01) 070 : 150 2.0 N/A 0.2 N/A2) P100 R-Occipital +3.50 (Ch: 01) 070 : 150 2.0 N/A 0.2 N/A3) N100 L-Occipital -5.00 (Ch: 01) 150 : 260 2.5 N/A 2 +/- 0.2 N/A4) N100 R-Occipital -5.00 (Ch: 01) 150 : 260 2.5 N/A 2 +/- 0.2 N/A5) N3 L-AnteriorTemporal -3.00 (Ch: F3) 160 : 300 2.5 N/A 1.5 +/- 0.2 N/A6) N3 R-AnteriorTemporal -3.00 (Ch: F3) 160 : 300 2.5 N/A 1.5 +/- 0.2 N/A7) P300 L-Parietal +4.0 (Ch: P3) 380 : 580 2.5 N/A 2 +/- 0.2 N/A8) P300 R-Parietal +4.0 (Ch: P3) 380 : 580 2.5 N/A 2 +/- 0.2 N/A9) MFN Medial Frontal -3.00 (Ch: Fz) 200 : 380 1.5 N/A 1.5 +/- 0.2 N/A

Note: Subject and condition offsets specify the mean (if applicable) and standard deviation of a normal distribution from which perturbations to peak electrode intensity (uv) and latency (ms) baseline values are drawn.

Following Dien, Khoe, and Mangun (2007), we quantified the degree of spatial and temporal correlation of these 4 patterns. Our goal was to generate pattern combinations with different spatiotemporal characteristics to test the efficacy of data decomposition methods (tPCA, sPCA, sICA, Microstate Analysis, …) for separation of ERP patterns in the presence of different degrees of spatial and temporal interaction.

Table 5. Baseline values of spatial and temporal correlation between the 10 distinct pairs of the 5 simulated ERP patterns: <P100, N100>, <P100, N3>, <P100, MFN>, <P100, P3>, <N100, N3>, <N100, MFN>,

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<N100, P3>, <N3, MFN> , <N3, P3> and <MFN, P3>. Spatial correlations compare ERP topographies at respective peak latencies. Temporal correlations compares ERP time courses recorded at central channel 064.

Pair Pattern A Pattern B Spat Correlation Temp Correlation

#1 P100 N100 -0.9678 +0.0000

#2 P100 N3 +0.2737 +0.0000

#3 P100 MFN +0.5813 +0.0000

#4 P100 P3 +0.3031 +0.0000

#5 N100 N3 -0.3006 -0.7749

#6 N100 MFN -0.5757 -0.0913

#7 N100 P3 -0.1960 +0.0000

#8 N3 MFN 0.7733 +0.3914

#9 N3 P3 -0.3455 +0.0000

#10 MFN P3 0.08118 +0.0000

By adding small perturbations in pattern intensity to the baseline ERP pattern parameters, pursuant to the ERP_Dipole_Specs-v14.xls spreadsheet, we simulated two groups of "subject"-specific ERPs (20 subjects/group) under the first experimental "condition", for a total of 40 condition # 1 simulated ERPs. Perturbations of the ERP pattern intensities were drawn from a normal distribution with zero mean and pattern-specific standard deviations, as listed in the Subject Offset column of Table 4.

Perturbing the spatiotemporal properties of the ERP patterns that constitute the 40 "subject"-specific ERPs generated under condition #1 results in a second dataset containing, for each “identical” subject, distinct ERPs generated under experimental condition #2. In accordance with pattern rules, the intensity of the ERP patterns that constitute each subject-specific condition #1 ERP were altered to establish their corresponding condition #2 ERP. Perturbations of the ERP pattern intensities were drawn from normal distributions with pattern-specific mean and standard deviations, as listed in the Condition Offset column of Table 4.

Simulated noise was added to try to give the data some of the properties of background EEG (Dipole Simulator 3.2.0.3, Patrick Berg 2001 – 2009):

The noise is coherent in the sense that there is quite a high correlation between signal amplitudes from electrodes that are close together.

There is some attempt to emulate the frequency characteristics of EEG, with added weighting around 10 Hz, although the 10 Hz signal does not show the characteristic occipital-parietal dominance.

The noise is defined using the following steps: The waveform generated in the program is assumed to consist of 150 time points, sampled at 100 Hz,

representing an interval of 1.5 s. 200 random sources are generated subject to the following restrictions:

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1. eccentricity lies between 0.6 and 0.7 of the head radius2. location is not below 0.5 of the head radius below the sphere center.

3. orientation is random. For each source, its waveform is defined in frequency space:

1. Based on a 256 time point waveform, at each of the 256 frequencies, the phase is selected at random. The amplitude at each frequency is constant.

2. The resulting frequencies are weighted by the function 1/Öf where f is the frequency, defined as a number between 1 and 256, and an additional function which adds a cosine window from 7 to 13 Hz (maximum at 10 Hz), in order to simulate alpha. The cosine window is weighted by a factor between 0 and 1 which determines the alpha level. This factor is adjusted in the program.

3. Using an inverse fft (Fast Fourier Transform), the frequencies are converted to a waveform. The first 150 time points are used.

For each source, the data waveform at each electrode is generated. For each electrode, data waveforms are summed over the contributions from all 200 sources.

Over all waveforms and electrodes, the average referenced data are scaled to unit standard deviation. These noise data are then added in the user-specified proportion to the dipole simulated data.

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2.2 tPCA and sICA decompositions

Temporal PCA (tPCA) and spatial ICA (sICA) were chosen to decompose the simulated ERP data. tPCA and sICA respectively utilize 2nd and higher-order data statistics to separate spatially and temporally overlapping patterns onto discrete rank-1 spatiotemporal components, facilitating their identification and labeling. These particular variants of PCA and ICA were chosen because they are compatible with the high degree of spatial correlation, arising from volume conduction, that is common to many ERP patterns

We implemented tPCA and sICA with Dr. Joseph Dien’s PCAToolbox, v1.092. For tPCA, the 18 lead tPCA eigenvectors from each subject group’s temporal data covariance matrix underwent a subsequent Varimax rotation and Promax relaxation. The Varimax rotation extracts components with compact time courses, while the Promax relaxation removes the spatial orthogonality constraint. 18 eigenvectors were chosen because they accounted for ~ 90% of the data’s variance, consistent with the simulated ERP dataset design in which the simulated patterns accounted for the majority of the variance, with added noise composing the rest. A scree plot of cumulative variance versus retained eigenvectors validated this choice.

The sICA used the Infomax algorithm applied to the temporally concatenated ERPs of each subject group. 18 independent components were retained because they again accounted for the majority (> 90 %) of the data’s variance and because this choice was consistent with the tPCA procedure.

P100 N100 N3 MFN P300

Figure 2. Latent patterns (scalp projections of symmetric dipoles)

P100 (Fac4) N100 (Fac3)* N3 (Fac9) MFN (Fac2) P300 (Fac1)

Figure 3. tPCA-decomposed data ("modal factors" for SG01)

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P100 (Fac5) N100 (Fac3) N3 (Fac2)* MFN (Fac4) P300 (Fac1)

Figure 4. sICA-decomposed data ("modal factors" for SG01)

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2.3 Alternative Sets of Metrics used for Clustering and Mapping of SDv5 Data

Tables 6a-b summarize the alternative sets of pattern attributes used in metric sets 1 and 2.

Table 6a. Metric Set 1SuperClass Metric Set 1 Brief Descriptiontemporal TI-max1 Peak latency (in ms)temporal TI-duration Duration (in ms)spatial IN-LOCC Intensity over LOCC scalp regionspatial IN-ROCC Intensity over ROCC scalp regionspatial IN-LPAR Intensity over LPAR scalp regionspatial IN-RPAR Intensity over RPAR scalp regionspatial IN-LPTEM Intensity over LPTEM scalp regionspatial IN-RPTEM Intensity over RPTEM scalp regionspatial IN-LATEM Intensity over LATEM scalp regionspatial IN-RATEM Intensity over RATEM scalp

regionspatial IN-LORB Intensity over LORB scalp regionspatial IN-RORB Intensity over RORB scalp regionspatial IN-LFRON Intensity over LFRON scalp regionspatial IN-RFRON Intensity over RFRON scalp region

Table 6b. Metric Set 2SuperClass Metric Set 2 Brief Descriptiontemporal TI-max2 Alt. measure of peak latency (in ms)temporal TI-begin Onset of pattern (in ms)temporal TI-end Offset of pattern (in ms)spatial IN-O1 Intensity at left occipital electrode O1spatial IN-O2 Intensity at right occipital electrode O2spatial IN-C3 Intensity at left parietal electrode C3spatial IN-C4 Intensity at right parietal electrode C4spatial IN-T7 Intensity at left posterotemporal electrode T7spatial IN-T8 Intensity at right posterotemporal electrode T8spatial IN-F7 Intensity at left frontotemporal electrode F7spatial IN-F8 Intensity at right frontotemporal electrode F8spatial IN-Fp1 Intensity at left frontopolar electrode FP1spatial IN-Fp2 Intensity at right frontopolar electrode FP2spatial IN-F3 Intensity at left frontocentral electrode F7spatial IN-F4 Intensity at right frontocentral electrode F8

NOTE: because the mapping method assumes a 1-1 mapping between measures in two sets, TI-duration in Metric Set 1 and TI-begin and TI-end in Metric Set 2 were dropped from the analysis.

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2.4 Five (5) Pattern Rules used for Autolabeling of SDv5 DataThe input to the clustering consists of labeled data (attribute vectors) for 5 patterns: P100, N100, N3, MFN and P3. For these studies, the data (each subject-, condition-, and tPCA/sICA factor-specific observation) were labeled by PCAautolabel pursuant to ERP pattern rules as defined in Simulated_ERP_Rules_2008-05-06 (corrected).doc*. The 5 rules are:

Rule #1 (pattern PT1 = P100-visual “component” of the ERP)Let ROI=occipital (average of left occipital, right occipital)For any n, FAn = PT1 iff

70ms < TI-max (FAn) ≤ 140ms AND temporal criterion #1 |IN-mean(ROI)| ≥ .4 mV AND min variance criterion IN-mean(ROI) > 0 spatial criterion #1

Rule #2 (pattern PT2 = N100-visual “component” of the ERP)Let ROI=occipital (average of left occipital, right occipital)For any n, FAn = PT2 iff

141ms < TI-max (FAn) ≤ 220 ms AND temporal criterion #2 |IN-mean(ROI)| ≥ .4 mV AND min variance criterion IN-mean(ROI) < 0 spatial criterion #2

Rule #3 (pattern PT2 = N3 “component” of the ERP)Let ROI= left and right frontalFor any n, FAn = PT2 iff

221ms < TI-max (FAn) ≤ 260ms AND temporal criterion #2 |IN-mean(ROI)| ≥ .4 mV AND min variance criterion IN-mean(ROI) < 0 spatial criterion #2

Rule #4 (pattern PT3 = MFN “component” of the ERP)Let ROI= left and right frontalFor any n, FAn = PT3 iff

261ms < TI-max (FAn) ≤ 400ms AND temporal criterion #3 |IN-mean(ROI)| ≥ .4 mV AND min variance criterion IN-mean(ROI) < 0 spatial criterion #3

Rule #5 (pattern PT3 = P300 “component” of the ERP)Let ROI= left and right parietalFor any n, FAn = PT3 iff

401ms < TI-max (FAn) ≤ 600ms AND temporal criterion #3 |IN-mean(ROI)| ≥ .4 mV AND min variance criterion IN-mean(ROI) > 0 spatial criterion #3

* Simulated_ERP_Rules_2008-05-05.doc specified initial PCAautolabel ERP pattern rules. Observations were subsequently labeled according to ERP pattern rules as specified in Simulated_ERP_Rules_2008-05-06 (corrected).doc to improve factor / pattern correspondence.

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2.5 Autolabeling results (preprocessing of data prior to clustering)

SG01 SDv5 Dataset#Observations = 80#Subjects = 40 (2 groups of 20)#Conditions = 2

#PCA Factors Retained for Autolabeling (Patt/Fac Matching) = 15Pattern rules as specified in Section 2.4.

Table 7. Summary of autolabeling results (modal factor for each pattern of interest).SG01-tPCA

SG02-tPCA

SG01-sICA SG02-sICA

Metric 1&2

Metric 1&2

Metric 1&2

Metric 1&2

P100 4 3 5 5N100 3 4 3 (+1) 3 (+2)N3 9 (+3,2) 10 (+4,2) 1 (+3) 2 (+3)MFN 2 2 4 4P300 1 1 2 1

Compare Figures 3-4 in Section 2.2.

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Table 8. Autolabeling results for tPCA-SG01 (20 "subjects" * 2 "conditions"). Note these results are based on rules that use metric set 1 attributes.

Factor 4 Factor 3 Factor 9 Factor 2 Factor 1 NObsRule #1P100Cell1

#Nonmatch 2 — — — — 2#Match 18 — — — — 18%Match 90% — — — — 90%

Rule #1P100Cell2


Rule #2N100Cell1

#Nonmatch — 2 — — — 2#Match — 18 — — — 18%Match — 90% — — — 90%

Rule #2N100Cell2


Rule 3(N3)Cell1

#Nonmatch — 11 5 1 — 17#Match — 9 15 19 — 43%Match — 40% 75% 95% — >100% (multiple

matches)Rule 3(N3)Cell2


matches)Rule 4(MFN)Cell1

#Nonmatch — — — 1 — 1#Match — — — 19 — 19%Match — — — 95% — 95%

Rule 4(MFN)Cell2


Rule 5(P3)Cell1

#Nonmatch — — — — 1 1#Match — — — — 19 19%Match — — — — 95% 95%

Rule 5(P3)Cell2


Modal Factor ** Fac4/P1 Fac3/N1 Fac9/N3 Fac2/MFN Fac1/P3 186 matches (93%)Cell1 89 matches (89%)Cell 2 97 matches (97%)

**GF: Note that for Rule 3, there are matches to the pattern rule for observations belonging to more than one factor. This may create a problem for analysis if all pattern matches are included in the clustering. In particular, it violates our (expert) goal of using PCA to find a “simple structure” (pattern-to-factor mapping) in the data. To simplify matters for these case studies, we have therefore selected only pattern matches (i.e, observations) belonging to what we call the “modal factor.” The “modal factor” is typically the factor that has the highest percentage of observations matching the pattern rule.

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Table 9. Autolabeling results for tPCA-SG02 (20 "subjects" * 2 "conditions"). Note these results are based on rules that use metric set 1 attributes.

Factor 3 Factor 4 Factor 10 Factor 2 Factor 1 NObsRule #1P100Cell1


Rule #1P100Cell2


Rule #2N100Cell1


Rule #2N100Cell2


Rule 3(N3)Cell1




matches)Rule 4(MFN)Cell1


Rule 4(MFN)Cell2


Rule 5(P3)Cell1


Rule 5(P3)Cell2


Modal Factor ** Fac3/P1 Fac4/N1 Fac10/N3 Fac2/MFN Fac1/P3 190 matches (95%)Cell1 95 matches (95%)Cell 2 95 matches (95%)

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Table 10. Autolabeling results for sICA-SG01 (20 "subjects" * 2 "conditions"). Note these results are based on rules that use metric set 1 attributes.

IC5 IC3 IC1 IC4 IC2 NObsRule #1P100Cell1


Rule #1P100Cell2


Rule #2N100Cell1

#Nonmatch — 1 18 — — 19#Match — 19 2 — — 21%Match — 95% 10% — — >100% (multiple

matches)Rule #2N100Cell2



#Nonmatch — 19 6 — — 25#Match — 1 16 — — 16%Match — 5% 70% — — 75%

Rule 3(N3)Cell2

#Nonmatch — 19 2 — — 21#Match — 1 18 — — 19%Match — 5% 90% — — 95%

Rule 4(MFN)Cell1


Rule 4(MFN)Cell2


Rule 5(P3)Cell1


Rule 5(P3)Cell2


Modal Factor ** IC5/P1 IC3/N1 IC51/N3 IC4/MFN IC2/P3 186 matches (93%)Cell1 90 matches (90%)Cell 2 96 matches (96%)

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Table 11. Autolabeling results for sICA-SG02 (20 "subjects" * 2 "conditions"). Note these results are based on rules that use metric set 1 attributes.

IC5 IC3 IC2 IC4 IC1 NObsRule #1P100Cell1


Rule #1P100Cell2


Rule #2N100Cell1


matches)Rule #2N100Cell2



#Nonmatch — — 3 — — 3#Match — — 17 — — 17%Match — — 85% — — 85%

Rule 3(N3)Cell2

#Nonmatch — — 2 — — 2#Match — — 18 — — 18%Match — — 90% — — 90%

Rule 4(MFN)Cell1


Rule 4(MFN)Cell2


Rule 5(P3)Cell1


Rule 5(P3)Cell2


Modal Factor ** IC5/P1 IC3/N1 IC52/N3 IC4/MFN IC1/P3 191 matches (95%)Cell1 95 matches (95%)Cell 2 96 matches (96%)

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2.5. Clustering of the 8 simulated ERP datasetsThe number of clusters was not constrained.Only those observations that belonged to the 5 modal factors were used in the clustering. Hence, the total N for each dataset that was input to clustering was ~190–195 (out of 200 total observations).

The following run information gives the settings for clustering of these data in WEKA using the EM algorithm.

=== Run information ===

Scheme: weka.clusterers.EM -I 100 -N -1 -M 1.0E-6 -S 100Relation: SG01_tPCA_m1_mergedInstances: 186Attributes: 42 TI-max IN-LOCC IN-ROCC IN-LPAR IN-RPAR IN-LPTEM IN-RPTEM IN-LATEM IN-RATEM IN-LORB IN-RORB IN-LFRON IN-RFRONIgnored: ExptID SubjID match TheCondition ROI Polarity Window [Start Stop] SP-cor TI-begin TI-end TI-duration IN-max to Baseline IN-min to Baseline IN-max SP-max SP-max ROI IN-min SP-min SP-min ROI CoP-x CoP-y CoP-z

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CoN-x CoN-y CoN-z ERP_Event StimType StimModality PatternTest mode: Classes to clusters evaluation on training data=== Model and evaluation on training set ===

EM==

Number of clusters selected by cross validation: 5

ClusterAttribute 0 1 2 3 4 (0.19) (0.1) (0.19) (0.3) (0.21)=========================================================TI-max mean 216 238.3818 116 258.8006 484 std. dev. 0 14.6722 122.3243 7.4938 122.3243

IN-LOCC mean -5.3812 1.7612 3.0242 1.1646 -0.4292 std. dev. 2.7118 2.5322 1.3368 0.6873 0.1492

IN-ROCC mean -5.3779 1.6191 2.9914 1.2307 -0.4241 std. dev. 2.6872 2.4245 1.3167 0.6281 0.1525

IN-LPAR mean -0.6004 -2.052 -0.0031 -0.426 3.1833 std. dev. 0.5957 0.7665 0.0793 0.3425 1.3182

IN-RPAR mean -0.5618 -2.3356 -0.031 -0.3013 3.1594 std. dev. 0.5667 0.6454 0.089 0.4852 1.2943

IN-LPTEM mean 0.0548 2.2158 0.5477 0.7058 -0.1337 std. dev. 0.7083 0.7849 0.2684 0.3198 0.088

IN-RPTEM mean 0.0516 1.9186 0.486 0.848 -0.1492 std. dev. 0.7206 0.8172 0.2285 0.4964 0.0891

IN-LATEM

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mean 2.8573 2.3311 -1.0504 0.2469 -1.5431 std. dev. 1.0012 1.2583 0.4963 0.3249 0.6538

IN-RATEM mean 2.8216 1.9908 -1.1006 0.4306 -1.5983 std. dev. 1.0918 1.4977 0.5114 0.6028 0.6633

IN-LORB mean 1.6449 -2.3642 -1.4056 -1.2146 -1.5422 std. dev. 1.3803 1.1815 0.636 0.5427 0.6851

IN-RORB mean 3.2897 1.251 -1.4787 -0.325 -1.9669 std. dev. 1.28 1.2889 0.6643 0.3668 0.8534

IN-LFRON mean 0.6284 -4.2409 -1.2911 -1.6457 -0.9716 std. dev. 1.6524 1.6113 0.5867 0.7644 0.4412

IN-RFRON mean 0.5619 -4.8101 -1.3027 -1.4471 -0.9302 std. dev. 1.6799 1.6371 0.5902 0.7196 0.4227

Clustered Instances

0 36 ( 19%)1 18 ( 10%)2 36 ( 19%)3 57 ( 31%)4 39 ( 21%)

Log likelihood: -13.94864

Class attribute: PatternClasses to Clusters:

0 1 2 3 4 <-- assigned to cluster 0 0 36 0 0 | P1 36 2 0 0 0 | N1 0 16 0 18 0 | N3 0 0 0 39 0 | MFN 0 0 0 0 39 | P3

Cluster 0 <-- N1Cluster 1 <-- N3Cluster 2 <-- P1Cluster 3 <-- MFNCluster 4 <-- P3

19

Incorrectly clustered instances : 20.0 10.7527 %

-----------------------


Scheme: weka.clusterers.EM -I 100 -N -1 -M 1.0E-6 -S 100Relation: SG02_tPCA_m1_mergedInstances: 190Attributes: 42 TI-max IN-LOCC IN-ROCC IN-LPAR IN-RPAR IN-LPTEM IN-RPTEM IN-LATEM IN-RATEM IN-LORB IN-RORB IN-LFRON IN-RFRONIgnored: ExptID SubjID match TheCondition ROI Polarity Window [Start Stop] SP-cor TI-begin TI-end TI-duration IN-max to Baseline IN-min to Baseline IN-max SP-max SP-max ROI IN-min SP-min SP-min ROI CoP-x CoP-y CoP-z

20

CoN-x CoN-y CoN-z ERP_Event StimType StimModality PatternTest mode: Classes to clusters evaluation on training data=== Model and evaluation on training set ===

EM==


ClusterAttribute 0 1 2 3 4 5 6 7 8 9 10 (0.02) (0.02) (0.12) (0.07) (0.08) (0.08) (0.07) (0.09) (0.12) (0.21) (0.11)===============================================================================================================TI-max mean 248 212 212 212 260 116 248 260 251.2727 484 116 std. dev. 122.5729 122.5729 0 122.5729 122.5729 122.5729 122.5729 122.5729 5.3443 122.5729 0

IN-LOCC mean 3.8444 -5.9922 -6.5858 -2.6445 1.5939 3.1934 1.8476 1.0271 0.8097 -0.477 1.6189 std. dev. 0.5095 1.0929 0.9862 1.2026 0.2027 0.6198 0.5252 0.1439 0.3625 0.1965 0.4686

IN-ROCC mean 3.8133 -6.1571 -6.562 -2.6841 1.7643 3.1567 1.7635 1.1543 0.76 -0.4674 1.6359 std. dev. 0.5549 1.0907 1.0305 1.1547 0.2096 0.6288 0.4312 0.136 0.3742 0.1671 0.4955

IN-LPAR mean -3.2137 -2.1028 -0.4036 -0.8949 -0.4689 0.0703 -1.5726 -0.3125 -0.7666 3.7167 -0.02 std. dev. 0.2533 0.2591 0.5123 0.413 0.1051 0.1098 0.3327 0.1094 0.4603 1.2046 0.1381

IN-RPAR mean -3.6261 -2.2423 -0.4267 -0.9006 0.0604 -0.0054 -1.8491 0.0165 -0.9077 3.7306 -0.0033 std. dev. 0.3258 0.169 0.4703 0.366 0.0723 0.0996 0.318 0.074 0.6477 1.1975 0.0976

IN-LPTEM mean 3.0073 1.97 -0.1303 0.7351 1.2541 0.6109 1.468 0.805 0.554 -0.1475 0.1946 std. dev. 0.2602 0.1884 0.4257 0.402 0.1768 0.1555 0.4567 0.1356 0.2368 0.1261 0.1226

IN-RPTEM mean 2.7634 1.7197 -0.1076 0.7035 1.7081 0.5402 1.2516 1.1132 0.4023 -0.1414 0.2427 std. dev. 0.2874 0.1255 0.537 0.2514 0.2039 0.132 0.2882 0.1445 0.332 0.1042 0.1233

21

IN-LATEM mean 2.7963 5.5626 3.1375 2.3633 0.3658 -1.1206 1.3703 0.2526 0.6006 -1.8405 -0.6373 std. dev. 0.2458 0.5902 0.9933 0.6531 0.0988 0.2108 0.3289 0.103 0.3909 0.6058 0.2568

IN-RATEM mean 2.347 5.5496 3.2018 2.3384 1.0528 -1.1623 1.069 0.6843 0.3673 -1.8439 -0.5721 std. dev. 0.123 0.6866 0.9586 0.6768 0.15 0.2196 0.2482 0.1298 0.294 0.6038 0.2208

IN-LORB mean -3.7825 0.1108 2.1578 0.1383 -2.2828 -1.5217 -1.742 -1.4665 -0.6252 -1.8542 -0.7158 std. dev. 0.4821 0.3415 0.4436 0.5172 0.2888 0.3043 0.5064 0.1894 0.3255 0.6305 0.2601

IN-RORB mean 1.4954 4.9988 3.7769 2.2386 -0.8595 -1.6248 0.8367 -0.5407 0.4844 -2.3137 -0.7339 std. dev. 0.3608 0.7835 0.764 0.7446 0.1367 0.3361 0.3467 0.1312 0.5793 0.7847 0.2829

IN-LFRON mean -6.4029 -2.6092 1.1318 -1.0778 -2.9977 -1.3705 -2.9996 -1.9481 -1.1483 -1.1897 -0.6568 std. dev. 0.5803 0.2093 0.8297 0.6276 0.3595 0.276 0.6598 0.2316 0.4298 0.4117 0.2195

IN-RFRON mean -7.2954 -2.8757 1.0246 -1.2019 -2.0634 -1.4201 -3.5895 -1.3661 -1.4888 -1.0939 -0.6595 std. dev. 0.6217 0.4172 0.8426 0.6224 0.2351 0.2762 0.7365 0.1748 0.6364 0.3949 0.2352

Clustered Instances

0 4 ( 2%) 1 4 ( 2%) 2 22 ( 12%) 3 14 ( 7%) 4 16 ( 8%) 5 16 ( 8%) 6 14 ( 7%) 7 18 ( 9%) 8 22 ( 12%) 9 40 ( 21%)10 20 ( 11%)



0 1 2 3 4 5 6 7 8 9 10 <-- assigned to cluster 0 0 0 0 0 16 0 0 0 0 20 | P1 0 4 22 14 0 0 0 0 0 0 0 | N1 4 0 0 0 0 0 14 0 16 0 0 | N3 0 0 0 0 16 0 0 18 6 0 0 | MFN

22

0 0 0 0 0 0 0 0 0 40 0 | P3

Cluster 0 <-- No classCluster 1 <-- No classCluster 2 <-- N1Cluster 3 <-- No classCluster 4 <-- No classCluster 5 <-- No classCluster 6 <-- No classCluster 7 <-- MFNCluster 8 <-- N3Cluster 9 <-- P3Cluster 10 <-- P1


---------------------------------------------------------------------------------------


Scheme: weka.clusterers.EM -I 100 -N -1 -M 1.0E-6 -S 100Relation: SG01_sICA_m1_mergedInstances: 186Attributes: 42 TI-max IN-LOCC IN-ROCC IN-LPAR IN-RPAR IN-LPTEM IN-RPTEM IN-LATEM IN-RATEM IN-LORB IN-RORB IN-LFRON IN-RFRONIgnored: ExptID SubjID match TheCondition ROI Polarity Window [Start Stop] SP-cor TI-begin TI-end TI-duration IN-max to Baseline

23

IN-min to Baseline IN-max SP-max SP-max ROI IN-min SP-min SP-min ROI CoP-x CoP-y CoP-z CoN-x CoN-y CoN-z ERP_Event StimType StimModality PatternTest mode: Classes to clusters evaluation on training data=== Model and evaluation on training set ===

EM==


ClusterAttribute 0 1 2 3 4 5 6 7 8 9 10 11 (0.08) (0.13) (0.12) (0.06) (0.12) (0.05) (0.08) (0.05) (0.08) (0.06) (0.09) (0.09)========================================================================================================================TI-max mean 208.6027 294.3801 115.2417 244.4159 484.7109 205.0882 207.7353 237.3334 299.4551 240.7271 116 485.22 std. dev. 1.4309 9.1827 1.5678 3.9784 4.6875 2.5488 1.7626 1.8856 4.3558 4.1141 125.5069 3.3692

IN-LOCC mean -25.9997 3.2515 6.5824 1.9359 -0.6184 -7.8283 -13.6427 6.0677 1.5035 3.4334 15.4726 -1.2714 std. dev. 3.21 0.8582 2.2179 0.4553 0.2107 1.7281 2.227 1.3833 0.4544 0.3613 3.0689 0.242

IN-ROCC mean -25.9985 3.5206 6.532 1.9669 -0.6268 -7.8279 -13.6421 6.1649 1.6288 3.4884 15.3544 -1.2886 std. dev. 3.2098 0.9352 2.2009 0.4626 0.2135 1.728 2.2269 1.4054 0.4923 0.3671 3.0454 0.2453

IN-LPAR

24

mean -4.2124 -2.1738 -0.882 -4.5454 4.7834 -1.2683 -2.2104 -14.2464 -0.9825 -8.0613 -2.0733 9.8342 std. dev. 0.5201 0.4792 0.2972 1.0691 1.6297 0.28 0.3608 3.2477 0.297 0.8483 0.4112 1.8721

IN-RPAR mean -4.2307 -1.3529 -0.9402 -4.4673 4.7456 -1.2738 -2.22 -14.0018 -0.6001 -7.9229 -2.21 9.7563 std. dev. 0.5223 0.3142 0.3168 1.0507 1.6168 0.2812 0.3624 3.192 0.1814 0.8337 0.4383 1.8573

IN-LPTEM mean -0.5749 2.2484 0.9875 4.2488 -0.2892 -0.1731 -0.3017 13.3168 1.0194 7.5353 2.3211 -0.5946 std. dev. 0.071 0.5009 0.3327 0.9993 0.0985 0.0382 0.0492 3.0358 0.3081 0.793 0.4604 0.1132

IN-RPTEM mean -0.6049 2.9374 0.9091 4.314 -0.3119 -0.1821 -0.3174 13.5213 1.3404 7.6511 2.1369 -0.6412 std. dev. 0.0747 0.6803 0.3063 1.0147 0.1063 0.0402 0.0518 3.0824 0.4051 0.8051 0.4238 0.1221

IN-LATEM mean 13.8414 1.2267 -2.0137 5.2079 -2.4346 4.1675 7.2629 16.3231 0.536 9.2364 -4.7335 -5.0053 std. dev. 1.7089 0.3292 0.6785 1.2249 0.8295 0.92 1.1856 3.7212 0.162 0.972 0.9389 0.9528

IN-RATEM mean 13.763 2.2788 -2.07 5.3171 -2.4705 4.1439 7.2218 16.6652 1.0261 9.43 -4.8658 -5.0791 std. dev. 1.6992 0.4995 0.6975 1.2506 0.8417 0.9147 1.1789 3.7991 0.3101 0.9923 0.9651 0.9669

IN-LORB mean 10.069 -3.408 -2.3063 -4.3374 -2.2155 3.0317 5.2835 -13.5946 -1.5598 -7.6925 -5.4213 -4.5548 std. dev. 1.2431 0.8088 0.7771 1.0202 0.7548 0.6692 0.8625 3.0992 0.4714 0.8095 1.0753 0.8671

IN-RORB mean 16.5864 -0.6456 -2.6557 3.0265 -2.9601 4.994 8.7033 9.486 -0.3223 5.3677 -6.2426 -6.0855 std. dev. 2.0478 0.4027 0.8948 0.7118 1.0085 1.1024 1.4207 2.1625 0.0974 0.5649 1.2382 1.1585

IN-LFRON mean 5.6622 -4.8985 -2.0491 -8.2855 -1.2741 1.7048 2.9711 -25.969 -2.2277 -14.6946 -4.8167 -2.6194 std. dev. 0.6991 1.1086 0.6904 1.9487 0.4341 0.3763 0.485 5.9201 0.6733 1.5463 0.9554 0.4986

IN-RFRON mean 5.188 -3.4902 -2.1142 -8.4142 -1.1933 1.5621 2.7223 -26.3723 -1.5697 -14.9228 -4.9697 -2.4532 std. dev. 0.6405 0.7651 0.7124 1.979 0.4065 0.3448 0.4444 6.0121 0.4744 1.5704 0.9857 0.467

Clustered Instances

0 14 ( 8%)

25

1 24 ( 13%) 2 22 ( 12%) 3 12 ( 6%) 4 23 ( 12%) 5 9 ( 5%) 6 15 ( 8%) 7 9 ( 5%) 8 15 ( 8%) 9 11 ( 6%)10 16 ( 9%)11 16 ( 9%)



0 1 2 3 4 5 6 7 8 9 10 11 <-- assigned to cluster 0 0 22 0 0 0 0 0 0 0 16 0 | P1 14 0 0 0 0 9 15 0 0 0 0 0 | N1 0 0 0 12 0 0 0 9 0 11 0 0 | N3 0 24 0 0 0 0 0 0 15 0 0 0 | MFN 0 0 0 0 23 0 0 0 0 0 0 16 | P3

Cluster 0 <-- No classCluster 1 <-- MFNCluster 2 <-- P1Cluster 3 <-- N3Cluster 4 <-- P3Cluster 5 <-- No classCluster 6 <-- N1Cluster 7 <-- No classCluster 8 <-- No classCluster 9 <-- No classCluster 10 <-- No classCluster 11 <-- No class


--------------------------------------------------------------------------------


Scheme: weka.clusterers.EM -I 100 -N -1 -M 1.0E-6 -S 100Relation: SG02_sICA_m1_mergedInstances: 191Attributes: 42 TI-max

26

IN-LOCC IN-ROCC IN-LPAR IN-RPAR IN-LPTEM IN-RPTEM IN-LATEM IN-RATEM IN-LORB IN-RORB IN-LFRON IN-RFRONIgnored: ExptID SubjID match TheCondition ROI Polarity Window [Start Stop] SP-cor TI-begin TI-end TI-duration IN-max to Baseline IN-min to Baseline IN-max SP-max SP-max ROI IN-min SP-min SP-min ROI CoP-x CoP-y CoP-z CoN-x CoN-y CoN-z ERP_Event StimType StimModality PatternTest mode: Classes to clusters evaluation on training data=== Model and evaluation on training set ===

EM==


27

ClusterAttribute 0 1 2 3 4 5 6 7 8 9 10 11 12 (0.08) (0.03) (0.1) (0.12) (0.02) (0.1) (0.07) (0.12) (0.04) (0.06) (0.12) (0.09) (0.05)=================================================================================================================================TI-max mean 483.7336 116 244 207.2727 241 293.4001 484.0021 116 116 485.0871 295.826 205.9999 246.6667 std. dev. 6.1042 123.5828 3.6707 1.5428 1.7321 14.0441 5.238 0 0 3.8495 9.9024 2.4037 4.2164

IN-LOCC mean -0.5898 5.1172 3.9064 -21.5857 7.4838 3.7329 -1.0002 8.8639 13.1311 -1.345 2.1464 -10.5423 2.2952 std. dev. 0.1746 1.4126 0.5881 2.8322 0.6237 0.9798 0.0674 1.1854 0.9762 0.1453 0.692 3.1524 0.3888

IN-ROCC mean -0.6038 5.0954 3.9658 -21.624 7.5976 4.0252 -1.024 8.8261 13.0751 -1.3769 2.3182 -10.561 2.3301 std. dev. 0.1787 1.4066 0.5971 2.8372 0.6332 1.0797 0.069 1.1804 0.972 0.1488 0.7505 3.158 0.3947

IN-LPAR mean 4.7549 -1.1225 -9.2042 -5.3414 -17.6333 -2.7831 8.0642 -1.9444 -2.8804 10.8434 -1.506 -2.6087 -5.4079 std. dev. 1.4073 0.3099 1.3857 0.7008 1.4695 0.3806 0.5437 0.26 0.2141 1.1717 0.4198 0.78 0.9161

IN-RPAR mean 4.7289 -1.2063 -8.8823 -5.3903 -17.0167 -1.889 8.02 -2.0896 -3.0955 10.784 -0.9827 -2.6326 -5.2188 std. dev. 1.3996 0.333 1.3373 0.7072 1.4181 0.4162 0.5407 0.2795 0.2301 1.1653 0.2588 0.7872 0.884

IN-LPTEM mean -0.2701 0.7203 8.4758 0.9002 16.2379 2.5924 -0.458 1.2477 1.8484 -0.6159 1.4042 0.4397 4.9799 std. dev. 0.0799 0.1988 1.2761 0.1181 1.3532 0.353 0.0309 0.1669 0.1374 0.0666 0.3922 0.1315 0.8436

IN-RPTEM mean -0.305 0.6583 8.7318 0.8097 16.7284 3.3638 -0.5173 1.1403 1.6892 -0.6956 1.8561 0.3954 5.1304 std. dev. 0.0903 0.1817 1.3146 0.1062 1.3941 0.4915 0.0349 0.1525 0.1256 0.0752 0.5394 0.1182 0.8691

IN-LATEM

28

mean -2.4245 -1.3497 10.3152 13.6965 19.7618 1.6005 -4.1119 -2.3379 -3.4634 -5.5289 0.7908 6.6893 6.0606 std. dev. 0.7176 0.3726 1.553 1.7971 1.6469 0.6221 0.2772 0.3127 0.2575 0.5974 0.21 2.0002 1.0267

IN-RATEM mean -2.4642 -1.4468 10.8088 13.5815 20.7074 2.7546 -4.1792 -2.506 -3.7125 -5.6196 1.4651 6.6331 6.3506 std. dev. 0.7293 0.3994 1.6273 1.782 1.7257 0.4457 0.2818 0.3352 0.276 0.6072 0.3962 1.9834 1.0758

IN-LORB mean -2.2228 -1.4302 -8.6949 7.4094 -16.6576 -3.7131 -3.7698 -2.4774 -3.6701 -5.0691 -2.063 3.6187 -5.1086 std. dev. 0.6579 0.3948 1.309 0.9721 1.3882 0.5897 0.2542 0.3313 0.2728 0.5477 0.6093 1.0821 0.8654

IN-RORB mean -2.9637 -1.7987 5.9503 15.3761 11.3996 -0.5226 -5.0263 -3.1157 -4.6156 -6.7585 -0.3979 7.5096 3.4961 std. dev. 0.8771 0.4965 0.8958 2.0174 0.95 0.8302 0.3389 0.4167 0.3431 0.7303 0.2408 2.2455 0.5922

IN-LFRON mean -1.2858 -1.2441 -16.5186 2.3033 -31.6462 -5.4872 -2.1806 -2.1551 -3.1925 -2.9321 -2.9936 1.1249 -9.7054 std. dev. 0.3805 0.3434 2.4869 0.3022 2.6373 0.7431 0.147 0.2882 0.2373 0.3168 0.8485 0.3364 1.6441

IN-RFRON mean -1.198 -1.384 -16.4431 1.6788 -31.5016 -3.9701 -2.0318 -2.3973 -3.5514 -2.7321 -2.0986 0.8199 -9.661 std. dev. 0.3546 0.3821 2.4756 0.2203 2.6253 0.6988 0.137 0.3206 0.264 0.2952 0.5625 0.2452 1.6365

Clustered Instances

0 15 ( 8%) 1 4 ( 2%) 2 19 ( 10%) 3 22 ( 12%) 4 4 ( 2%) 5 20 ( 10%) 6 14 ( 7%) 7 24 ( 13%) 8 8 ( 4%) 9 11 ( 6%)10 23 ( 12%)11 18 ( 9%)12 9 ( 5%)

29



0 1 2 3 4 5 6 7 8 9 10 11 12 <-- assigned to cluster 0 4 0 0 0 0 0 24 8 0 0 0 0 | P1 0 0 0 22 0 0 0 0 0 0 0 18 0 | N1 0 0 19 0 4 2 0 0 0 0 1 0 9 | N3 0 0 0 0 0 18 0 0 0 0 22 0 0 | MFN 15 0 0 0 0 0 14 0 0 11 0 0 0 | P3

Cluster 0 <-- P3Cluster 1 <-- No classCluster 2 <-- N3Cluster 3 <-- N1Cluster 4 <-- No classCluster 5 <-- No classCluster 6 <-- No classCluster 7 <-- P1Cluster 8 <-- No classCluster 9 <-- No classCluster 10 <-- MFNCluster 11 <-- No classCluster 12 <-- No class


--------------------------------------------------------------------------


Scheme: weka.clusterers.EM -I 100 -N -1 -M 1.0E-6 -S 100Relation: SG01_sICA_m2_mergedInstances: 186Attributes: 43 TI-max2 IN-O1 IN-O2 IN-C3 IN-C4 IN-T7 IN-T8 IN-F7 IN-F8 IN-Fp1 IN-Fp2

30

IN-F3 IN-F4Ignored: ExptID SubjID match TheCondition ROI Polarity Window [Start Stop] TI-max SP-cor TI-end2 TI-begin2 TI-duration IN-max to Baseline2 IN-min to Baseline2 IN-max SP-max SP-max ROI IN-min SP-min SP-min ROI CoP-x CoP-y CoP-z CoN-x CoN-y CoN-z ERP_Event StimType StimModality PatternTest mode: Classes to clusters evaluation on training data=== Model and evaluation on training set ===

EM==


ClusterAttribute 0 1 2 3 4 5 6 7 8 9 (0.09) (0.13) (0.22) (0.06) (0.11) (0.06) (0.04) (0.21) (0.08) (0.01)============================================================================================TI-max2 mean 37.5001 59.1667 55.0244 58.546 36.1999 55.6667 58.2857 55.7949 50.5714 56 std. dev. 1.9365 10.2618 13.0075 1.924 1.9899 4.4593 1.9795 5.5061 2.4411 11.0677

31

IN-O1 mean 26.1821-16.1627 2.526 0.1194 12.0809 0.0647 0.1878 -0.0673-37.1796 0.2923 std. dev. 4.7932 4.75 1.0435 0.0126 2.6441 0.018 0.0203 0.0297 5.043 0.0219

IN-O2 mean 26.0726-16.1639 2.7141 0.1609 12.0303 0.0872 0.2531 -0.0782-37.1824 0.3939 std. dev. 4.7732 4.7503 1.1283 0.0169 2.6331 0.0243 0.0273 0.0345 5.0434 0.0295

IN-C3 mean -4.6064 0.1972 -2.3091-12.3069 -2.1255 -6.6686-19.3545 3.6237 0.4537-30.1278 std. dev. 0.8433 0.058 1.0767 1.2946 0.4652 1.8589 2.0889 1.5966 0.0615 2.2531

IN-C4 mean -4.8174 0.1784 -1.1948-12.0533 -2.2228 -6.5312-18.9556 3.5321 0.4103-29.5069 std. dev. 0.8819 0.0524 0.5362 1.2679 0.4865 1.8206 2.0459 1.5562 0.0557 2.2066

IN-T7 mean -0.8763 2.285 1.5315 8.322 -0.4043 4.5094 13.0877 -1.5515 5.2563 20.3727 std. dev. 0.1604 0.6715 0.7531 0.8754 0.0885 1.257 1.4125 0.6836 0.713 1.5235

IN-T8 mean -1.0798 2.2513 2.2427 8.47 -0.4982 4.5896 13.3204 -1.5948 5.1788 20.735 std. dev. 0.1977 0.6616 1.1007 0.891 0.109 1.2794 1.4377 0.7026 0.7024 1.5506

IN-F7 mean -5.9352 7.4848 0.577 12.3193 -2.7386 6.6753 19.374 -3.9674 17.2174 30.1582 std. dev. 1.0866 2.1997 0.3543 1.2959 0.5994 1.8608 2.091 1.748 2.3353 2.2553

IN-F8 mean -6.0461 7.4438 1.5056 12.5492 -2.7898 6.7999 19.7355 -4.0237 17.1232 30.721 std. dev. 1.1069 2.1876 0.7995 1.32 0.6106 1.8956 2.13 1.7727 2.3226 2.2974

IN-Fp1 mean -6.5144 7.8656 -1.076 8.0566 -3.0058 4.3655 12.6702 -4.5178 18.0934 19.7228 std. dev. 1.1926 2.3116 0.4671 0.8475 0.6579 1.2169 1.3675 1.9905 2.4542 1.4749

IN-Fp2 mean -6.5797 7.8731 -0.5494 8.1896 -3.036 4.4376 12.8794 -4.5455 18.1107 20.0486 std. dev. 1.2046 2.3138 0.2248 0.8615 0.6645 1.237 1.3901 2.0027 2.4565 1.4993

IN-F3 mean -4.6952 0.8689 -3.9557-24.5502 -2.1664-13.3028 -38.609 -2.3881 1.9988 -60.1 std. dev. 0.8596 0.2554 1.8746 2.5824 0.4742 3.7083 4.167 1.0521 0.2711 4.4945

IN-F4 mean -4.9167 0.8544 -2.6874-24.2358 -2.2687-13.1324-38.1145 -2.47 1.9655-59.3302 std. dev. 0.9001 0.2511 1.2572 2.5494 0.4965 3.6608 4.1137 1.0882 0.2666 4.4369

Clustered Instances

32

0 16 ( 9%) 1 24 ( 13%) 2 41 ( 22%) 3 11 ( 6%) 4 20 ( 11%) 5 12 ( 6%) 6 7 ( 4%) 7 39 ( 21%) 8 14 ( 8%) 9 2 ( 1%)



0 1 2 3 4 5 6 7 8 9 <-- assigned to cluster 16 0 2 0 20 0 0 0 0 0 | P1 0 24 0 0 0 0 0 0 14 0 | N1 0 0 0 11 0 12 7 0 0 2 | N3 0 0 39 0 0 0 0 0 0 0 | MFN 0 0 0 0 0 0 0 39 0 0 | P3

Cluster 0 <-- No classCluster 1 <-- N1Cluster 2 <-- MFNCluster 3 <-- No classCluster 4 <-- P1Cluster 5 <-- N3Cluster 6 <-- No classCluster 7 <-- P3Cluster 8 <-- No classCluster 9 <-- No class


------------------------------------------------------------------


Scheme: weka.clusterers.EM -I 100 -N -1 -M 1.0E-6 -S 100Relation: SG01_tPCA_m2_mergedInstances: 186Attributes: 43 TI-max2 IN-O1 IN-O2

33

IN-C3 IN-C4 IN-T7 IN-T8 IN-F7 IN-F8 IN-Fp1 IN-Fp2 IN-F3 IN-F4Ignored: ExptID SubjID match TheCondition ROI Polarity Window [Start Stop] TI-max SP-cor TI-end2 TI-begin2 TI-duration IN-max to Baseline2 IN-min to Baseline2 IN-max SP-max SP-max ROI IN-min SP-min SP-min ROI CoP-x CoP-y CoP-z CoN-x CoN-y CoN-z ERP_Event StimType StimModality PatternTest mode: Classes to clusters evaluation on training data=== Model and evaluation on training set ===

EM==


34

ClusterAttribute 0 1 2 3 4 5 (0.17) (0.15) (0.19) (0.21) (0.21) (0.07)============================================================TI-max2 mean 52 56 40.053 44 60 54.3611 std. dev. 7.4431 0 0.919 7.4431 7.4431 1.9671

IN-O1 mean -7.5109 2.3636 5.0976 0.8247 -0.065 0.3817 std. dev. 3.727 1.2899 2.2377 0.2988 0.1036 3.7579

IN-O2 mean -7.4976 2.2666 5.0726 0.9274 -0.0562 0.2724 std. dev. 3.703 1.1242 2.2234 0.351 0.0868 3.6836

IN-C3 mean -0.0887 -1.8175 -0.7829 -0.7441 1.6266 -4.1981 std. dev. 1.2235 0.9169 0.3468 0.284 0.6767 1.2728

IN-C4 mean -0.0418 -2.3878 -0.8209 -0.2123 1.5843 -4.4536 std. dev. 1.2448 1.0211 0.3594 0.1037 0.6304 1.3458

IN-T7 mean 1.2177 0.7791 -0.157 0.6011 -0.6599 2.7723 std. dev. 0.5915 0.6521 0.1594 0.2126 0.2646 0.7436

IN-T8 mean 1.1915 0.4965 -0.2376 0.9339 -0.6831 2.5203 std. dev. 0.7033 0.7536 0.1744 0.3656 0.2831 0.7414

IN-F7 mean 3.663 0.9413 -1.2615 0.0992 -1.7359 4.2771 std. dev. 1.2695 0.8385 0.6003 0.1037 0.7549 1.2672

IN-F8 mean 3.6205 0.6446 -1.3136 0.4751 -1.8041 4.0585 std. dev. 1.3675 1.0148 0.6039 0.246 0.7601 1.376

IN-Fp1 mean 3.6881 0.6768 -1.4828 -0.701 -2.0482 2.9788 std. dev. 1.3161 0.6259 0.6793 0.2892 0.9018 1.2085

IN-Fp2 mean 3.6885 0.4648 -1.5068 -0.4832 -2.0827 2.9132 std. dev. 1.3645 0.6205 0.678 0.1974 0.905 1.2411

IN-F3

35

mean -0.5934 -3.8356 -1.3944 -1.7254 -1.2798 -8.9513 std. dev. 2.4678 1.8224 0.6232 0.625 0.5734 2.4878

IN-F4 mean -0.5662 -4.4149 -1.426 -1.1468 -1.3347 -9.216 std. dev. 2.469 1.8859 0.639 0.3779 0.5896 2.6228

Clustered Instances

0 32 ( 17%)1 28 ( 15%)2 36 ( 19%)3 39 ( 21%)4 39 ( 21%)5 12 ( 6%)



0 1 2 3 4 5 <-- assigned to cluster 0 0 36 0 0 0 | P1 32 0 0 0 0 6 | N1 0 28 0 0 0 6 | N3 0 0 0 39 0 0 | MFN 0 0 0 0 39 0 | P3

Cluster 0 <-- N1Cluster 1 <-- N3Cluster 2 <-- P1Cluster 3 <-- MFNCluster 4 <-- P3Cluster 5 <-- No class


----------------------------------------------------------


Scheme: weka.clusterers.EM -I 100 -N -1 -M 1.0E-6 -S 100Relation: SG02_sICA_m2_mergedInstances: 191Attributes: 43 TI-max2 IN-O1 IN-O2

36


EM==


37

ClusterAttribute 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 (0.04) (0.06) (0.03) (0.05) (0.03) (0.06) (0.02) (0.03) (0.03) (0.06) (0.07) (0.09) (0.12) (0.09) (0.05) (0.04) (0.13)====================================================================================================================================================TI-max2 mean 36 36.7273 38.4 57.2 38 56 57 62.6667 40 49.4545 54.1558 57.7778 52.1818 55.5294 57.7778 36 56.6386 std. dev. 10.4896 1.5428 1.9596 3.6 3.0551 5.164 1.7321 4.9889 18.9033 12.4489 4.6045 7.9131 2.8226 9.7867 1.9876 10.4896 4.6216

IN-O1 mean 21.656 11.8827 13.8594 0.131 4.1889-18.5785 0.2852-10.0645 1.0263 0.0804 -0.0164 3.9332 -32.229 2.4676 0.1686 16.8187 -0.0294 std. dev. 1.5899 1.0092 0.3954 0.0077 3.1446 2.8499 0.0238 1.3749 0.5053 0.0201 0.0029 0.5573 4.2283 0.2734 0.0158 0.8723 0.0052

IN-O2 mean 21.597 11.8503 13.8216 0.1824 4.1759-18.5914 0.397-10.0715 1.1007 0.1119 -0.0288 4.215-32.2514 2.6444 0.2348 16.7729 -0.0517 std. dev. 1.5855 1.0064 0.3943 0.0108 3.1381 2.8519 0.0331 1.3759 0.54 0.028 0.0051 0.5972 4.2313 0.293 0.022 0.8699 0.0091

IN-C3 mean -4.1154 -2.2581 -2.6337-12.1477 -0.4486 -1.1687-26.4365 -0.6331 -1.1582 -7.4541 2.7136 -3.5691 -2.0274 -2.2391-15.6343 -3.1961 4.8657 std. dev. 0.3021 0.1918 0.0751 0.7172 1.0944 0.1793 2.2031 0.0865 0.2261 1.8636 0.4839 0.5057 0.266 0.2481 1.4635 0.1658 0.8567

IN-C4 mean -4.5263 -2.4836 -2.8967-11.6179 -0.5341 -1.2324-25.2837 -0.6676 -0.6974 -7.129 2.6669 -1.8373 -2.1379 -1.1527-14.9526 -3.5153 4.7821 std. dev. 0.3323 0.2109 0.0826 0.6859 1.1434 0.1891 2.1071 0.0912 0.2954 1.7823 0.4756 0.2603 0.2805 0.1277 1.3997 0.1823 0.842

IN-T7 mean -0.5742 -0.315 -0.3675 8.2354 -0.2586 3.5321 17.9224 1.9134 0.7616 5.0534 -1.1503 2.3293 6.1273 1.4613 10.5992 -0.4459 -2.0626 std. dev. 0.0422 0.0268 0.0105 0.4862 0.1606 0.5418 1.4936 0.2614 0.1536 1.2634 0.2051 0.33 0.8039 0.1619 0.9922 0.0231 0.3632

IN-T8 mean -0.82 -0.4499 -0.5248 8.5687 -0.3129 3.4644 18.6477 1.8767 1.0677 5.2579 -1.2036 3.4809 6.0098 2.1838 11.0281 -0.6368 -2.1581 std. dev. 0.0602 0.0382 0.015 0.5059 0.1473 0.5314 1.554 0.2564 0.2041 1.3145 0.2146 0.4932 0.7885 0.242 1.0323 0.033 0.38

IN-F7

38

mean -4.3373 -2.3799 -2.7758 11.975 -1.2233 10.0529 26.0607 5.4459 0.4658 7.3481 -2.9952 0.922 17.4392 0.5784 15.4121 -3.3685 -5.3708 std. dev. 0.3184 0.2021 0.0792 0.707 0.3184 1.5421 2.1718 0.744 0.4011 1.8371 0.5341 0.1306 2.288 0.0641 1.4427 0.1747 0.9456

IN-F8 mean -4.6081 -2.5285 -2.9491 12.4836 -1.2815 9.9738 27.1675 5.4031 0.8478 7.6602 -3.0404 2.3525 17.3019 1.4759 16.0667 -3.5788 -5.4517 std. dev. 0.3383 0.2147 0.0841 0.737 0.3353 1.53 2.2641 0.7381 0.2862 1.9151 0.5422 0.3333 2.27 0.1635 1.504 0.1856 0.9599

IN-Fp1 mean -4.6888 -2.5728 -3.0008 7.6363 -1.3463 10.1048 16.6186 5.4741 -0.2754 4.6858 -3.424 -1.6114 17.5293 -1.0109 9.8281 -3.6415 -6.1395 std. dev. 0.3442 0.2185 0.0856 0.4508 0.3514 1.5501 1.3849 0.7478 0.5179 1.1715 0.6106 0.2283 2.2998 0.112 0.92 0.1889 1.081

IN-Fp2 mean -4.8047 -2.6364 -3.0749 7.9145 -1.3713 10.0666 17.2241 5.4533 -0.0636 4.8565 -3.4447 -0.8178 17.4629 -0.5131 10.1862 -3.7315 -6.1767 std. dev. 0.3527 0.2239 0.0877 0.4673 0.3572 1.5442 1.4354 0.745 0.4315 1.2142 0.6143 0.1159 2.2911 0.0568 0.9535 0.1935 1.0875

IN-F3 mean -2.7351 -1.5007 -1.7504-23.9472 -0.762 -1.5847-52.1155 -0.8585 -1.9252-14.6945 -1.8156 -5.6608 -2.7491 -3.5514-30.8207 -2.1241 -3.2556 std. dev. 0.2008 0.1275 0.0499 1.4138 0.1991 0.2431 4.3432 0.1173 0.4777 3.6737 0.3238 0.802 0.3607 0.3935 2.885 0.1102 0.5732

IN-F4 mean -3.1377 -1.7217 -2.0081-23.3075 -0.8472 -1.6904-50.7232 -0.9157 -1.4086 -14.302 -1.8721 -3.7226 -2.9323 -2.3354-29.9973 -2.4368 -3.3568 std. dev. 0.2304 0.1462 0.0573 1.3761 0.2291 0.2593 4.2271 0.1251 0.5894 3.5756 0.3338 0.5274 0.3847 0.2588 2.808 0.1264 0.591

Clustered Instances

0 8 ( 4%) 1 11 ( 6%) 2 5 ( 3%) 3 10 ( 5%) 4 6 ( 3%) 5 12 ( 6%) 6 4 ( 2%) 7 6 ( 3%) 8 6 ( 3%) 9 11 ( 6%)10 13 ( 7%)11 18 ( 9%)12 22 ( 12%)

39

13 17 ( 9%)14 9 ( 5%)15 8 ( 4%)16 25 ( 13%)



0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 <-- assigned to cluster 8 11 5 0 4 0 0 0 0 0 0 0 0 0 0 8 0 | P1 0 0 0 0 0 12 0 6 0 0 0 0 22 0 0 0 0 | N1 0 0 0 10 0 0 4 0 1 11 0 0 0 0 9 0 0 | N3 0 0 0 0 0 0 0 0 5 0 0 18 0 17 0 0 0 | MFN 0 0 0 0 2 0 0 0 0 0 13 0 0 0 0 0 25 | P3

Cluster 0 <-- No classCluster 1 <-- P1Cluster 2 <-- No classCluster 3 <-- No classCluster 4 <-- No classCluster 5 <-- No classCluster 6 <-- No classCluster 7 <-- No classCluster 8 <-- No classCluster 9 <-- N3Cluster 10 <-- No classCluster 11 <-- MFNCluster 12 <-- N1Cluster 13 <-- No classCluster 14 <-- No classCluster 15 <-- No classCluster 16 <-- P3


--------------------------------------------------------


Scheme: weka.clusterers.EM -I 100 -N -1 -M 1.0E-6 -S 100Relation: SG02_tPCA_m2_mergedInstances: 190Attributes: 43 TI-max2 IN-O1 IN-O2

40


EM==


41

ClusterAttribute 0 1 2 3 4 5 6 7 (0.03) (0.21) (0.17) (0.15) (0.09) (0.12) (0.19) (0.04)============================================================================TI-max2 mean 56 44 48 55.9988 60 60 44 48 std. dev. 6.5448 6.5448 0 0.096 6.5448 6.5448 6.5448 6.5448

IN-O1 mean 3.273 1.1401 -6.1285 1.0437 -0.0364 -0.0413 4.3075 -10.486 std. dev. 0.8394 0.4018 2.7468 0.6612 0.1667 0.1508 1.5676 1.3951

IN-O2 mean 3.1458 1.2518 -6.1685 1.0037 -0.0295 -0.0374 4.2819-10.4925 std. dev. 0.9393 0.4302 2.7305 0.651 0.1076 0.1256 1.5544 1.395

IN-C3 mean -5.0163 -0.939 -0.2647 -2.0685 2.4433 1.4601 -0.6204 -1.3077 std. dev. 1.1602 0.3171 1.0431 0.6906 0.3119 0.4391 0.236 1.0758

IN-C4 mean -5.7023 -0.2646 -0.3025 -2.5457 2.4589 1.4856 -0.6819 -1.3615 std. dev. 1.206 0.1035 0.9935 0.8032 0.335 0.3782 0.2768 1.1936

IN-T7 mean 2.5587 0.7689 1.1778 0.8552 -0.9766 -0.6203 -0.1622 2.6942 std. dev. 0.402 0.2832 0.5184 0.4924 0.2309 0.1838 0.1628 0.8629

IN-T8 mean 2.1149 1.2138 1.1594 0.5793 -0.9708 -0.6273 -0.1773 2.6665 std. dev. 0.4684 0.438 0.5115 0.5326 0.1841 0.1923 0.1094 0.6648

IN-F7 mean 3.8078 0.0742 3.3122 1.6928 -2.7309 -1.6195 -1.0809 6.3204 std. dev. 0.9077 0.1168 0.8905 0.6029 0.3843 0.4476 0.4115 0.9952

IN-F8 mean 3.3535 0.6122 3.3313 1.3472 -2.7443 -1.6322 -1.0659 6.3615 std. dev. 0.8403 0.2366 0.9017 0.5492 0.3603 0.4784 0.463 0.9961

IN-Fp1 mean 2.7032 -0.9575 3.2482 1.6011 -3.2305 -1.8948 -1.2677 5.7604 std. dev. 0.7888 0.3886 0.9191 0.6204 0.425 0.5501 0.5524 0.6847

IN-Fp2 mean 2.4933 -0.6615 3.2369 1.4127 -3.2392 -1.9048 -1.2687 5.7691 std. dev. 0.7117 0.2727 0.9425 0.5339 0.4117 0.5683 0.5595 0.6823

IN-F3

42

mean -10.3127 -2.2809 -1.0699 -4.0213 -2.0528 -1.2025 -1.155 -3.6558 std. dev. 1.9778 0.761 2.0185 1.4209 0.2895 0.3557 0.4739 2.4221

IN-F4 mean -10.9725 -1.5381 -1.1013 -4.5473 -2.0809 -1.2055 -1.2024 -3.6736 std. dev. 2.0974 0.4847 2.016 1.5194 0.264 0.3877 0.4977 2.4712

Clustered Instances

0 6 ( 3%)1 40 ( 21%)2 33 ( 17%)3 28 ( 15%)4 16 ( 8%)5 24 ( 13%)6 36 ( 19%)7 7 ( 4%)



0 1 2 3 4 5 6 7 <-- assigned to cluster 0 0 0 0 0 0 36 0 | P1 0 0 33 0 0 0 0 7 | N1 6 0 0 28 0 0 0 0 | N3 0 40 0 0 0 0 0 0 | MFN 0 0 0 0 16 24 0 0 | P3

Cluster 0 <-- No classCluster 1 <-- MFNCluster 2 <-- N1Cluster 3 <-- N3Cluster 4 <-- No classCluster 5 <-- P3Cluster 6 <-- P1Cluster 7 <-- No class


43

2.6 Pattern-to-cluster Tables:

Table 12a. SG01_sICA_m1

assigned0

(N1)1

(MFN)2

(P1)3

(N3)4

(P3)5

(N1)6

(N1)7

(N3)8

(MFN)9

(N3)10

(P1)11

(P3)P1 0 0 22 0 0 0 0 0 0 0 16 0N1 14 0 0 0 0 9 15 0 0 0 0 0N3 0 0 0 12 0 0 0 9 0 11 0 0MFN 0 24 0 0 0 0 0 0 15 0 0 0P3 0 0 0 0 23 0 0 0 0 0 0 16Cell1% 50 29.1 50 58.3 56.5 77.8 33.3 44.4 80 27.3 50 37.5

Table 12b. SG01_sICA_m1Attribute 0 1

2(P1) 3 4 5 6 7 8 9

10(P1) 11

TI-maxmean

208.6027

294.3801

115.2417

244.4159

484.7109

205.0882

207.7353

237.3334

299.4551

240.7271 116

485.22

IN-LOCCmean

-25.9997 3.2515 6.5824 1.9359 -0.6184 -7.8283

-13.6427 6.0677 1.5035 3.4334

15.4726

-1.271

4

IN-ROCC

mean

-25.9985 3.5206 6.532 1.9669 -0.6268 -7.8279

-13.6421 6.1649 1.6288 3.4884

15.3544

-1.288

6

IN-LPARmean -4.2124 -2.1738 -0.882 -4.5454 4.7834 -1.2683 -2.2104

-14.2464 -0.9825 -8.0613

-2.0733

9.8342

IN-RPARmean -4.2307 -1.3529 -0.9402 -4.4673 4.7456 -1.2738 -2.22

-14.0018 -0.6001 -7.9229 -2.21

9.7563

IN-LPTEM

mean -0.5749 2.2484 0.9875 4.2488 -0.2892 -0.1731 -0.3017 13.3168 1.0194 7.5353 2.3211

-0.594

6

IN-RPTEM

mean -0.6049 2.9374 0.9091 4.314 -0.3119 -0.1821 -0.3174 13.5213 1.3404 7.6511 2.1369

-0.641

2

IN-LATEM

mean 13.8414 1.2267 -2.0137 5.2079 -2.4346 4.1675 7.2629 16.3231 0.536 9.2364

-4.7335

-5.005

3

IN-RATEM

mean 13.763 2.2788 -2.07 5.3171 -2.4705 4.1439 7.2218 16.6652 1.0261 9.43

-4.8658

-5.079

1

IN-LORBmean 10.069 -3.408 -2.3063 -4.3374 -2.2155 3.0317 5.2835

-13.5946 -1.5598 -7.6925

-5.4213

-4.554

8

IN-RORB

mean 16.5864 -0.6456 -2.6557 3.0265 -2.9601 4.994 8.7033 9.486 -0.3223 5.3677

-6.2426

-6.085

5IN-LFRON

mean

5.6622 -4.8985 -2.0491 -8.2855 -1.2741 1.7048 2.9711 -25.969 -2.2277 -14.6946

-4.8167

-2.619

44

4

IN-RFRON

mean 5.188 -3.4902 -2.1142 -8.4142 -1.1933 1.5621 2.7223

-26.3723 -1.5697

-14.9228

-4.9697

-2.453

2

P100 = MERGE clusters 2 (115ms) + 10 (116ms)N100 = MERGE clusters 0 (208ms) + 5 (205ms/Cell1) + 6 (208ms/Cell2)N3 = MERGE clusters 3 (244ms) + 7 (237ms) + 9 (241ms/Cell2)MFN = MERGE clusters 1 (294ms/Cell2) + 8 (299ms/Cell1)P300 = MERGE clusters 4 (485ms) + 11 (485ms)

45

Table 13a. SG02_sICA_m1

assigned0

(P3)1

(P1)2

(N3)3

(N1)4

(N3)5

(MFN)6

(P3)7

(P1)8

(P1)9

(P3)10

(MFN)11

(N1)12

(N3)P1 0 4 0 0 0 0 0 24 8 0 0 0 0N1 0 0 0 22 0 0 0 0 0 0 0 18 0N3 0 0 19 0 4 2 0 0 0 0 1 0 9MFN 0 0 0 0 0 18 0 0 0 0 22 0 0P3 15 0 0 0 0 0 14 0 0 11 0 0 0Cell1% 80 50 31.6 50 50 20 42.9 50 50 18.1 78.3 50 77.8

Table 13a. SG02_sICA_m1Attribute 0 1 2 3 4 5 6 7 8 9 10 11 12

TI-maxmean

483.7336 116 244

207.2727 241

293.4001

484.0021 116 116

485.0871

295.826

205.9999

246.6667

IN-LOCC

mean -0.5898

5.1172 3.9064

-21.5857 7.4838 3.7329 -1.0002

8.8639

13.1311 -1.345 2.1464

-10.5423 2.2952

IN-ROCC

mean -0.6038

5.0954 3.9658 -21.624 7.5976 4.0252 -1.024

8.8261

13.0751 -1.3769 2.3182 -10.561 2.3301

IN-LPARmean 4.7549

-1.122

5-

9.2042 -5.3414

-17.633

3 -2.7831 8.0642

-1.944

4-

2.880410.8434 -1.506 -2.6087 -5.4079

IN-RPAR

mean 4.7289

-1.206

3-

8.8823 -5.3903

-17.016

7 -1.889 8.02

-2.089

6-

3.0955 10.784-

0.9827 -2.6326 -5.2188IN-LPTEM

mean -0.2701

0.7203 8.4758 0.9002

16.2379 2.5924 -0.458

1.2477 1.8484 -0.6159 1.4042 0.4397 4.9799

IN-RPTEM

mean -0.305

0.6583 8.7318 0.8097

16.7284 3.3638 -0.5173

1.1403 1.6892 -0.6956 1.8561 0.3954 5.1304

IN-LATEM

mean -2.4245

-1.349

710.315

213.696519.761

8 1.6005 -4.1119

-2.337

9-

3.4634 -5.5289 0.7908 6.6893 6.0606

IN-RATEM

mean -2.4642

-1.446

810.808

813.581520.707

4 2.7546 -4.1792-

2.506-

3.7125 -5.6196 1.4651 6.6331 6.3506

IN-LORB

mean -2.2228

-1.430

2-

8.6949 7.4094

-16.657

6 -3.7131 -3.7698

-2.477

4-

3.6701 -5.0691 -2.063 3.6187 -5.1086

IN-RORB

mean -2.9637

-1.798

7 5.950315.376111.399

6 -0.5226 -5.0263

-3.115

7-

4.6156 -6.7585-

0.3979 7.5096 3.4961

IN-LFRON

mean -1.2858

-1.244

1

-16.518

6 2.3033

-31.646

2 -5.4872 -2.1806

-2.155

1-

3.1925 -2.9321-

2.9936 1.1249 -9.7054

IN-RFRON

mean -1.198

-1.384

-16.443

1 1.6788

-31.501

6 -3.9701 -2.0318

-2.397

3-

3.5514 -2.7321-

2.0986 0.8199 -9.661

46

P100 = MERGE clusters 1 (116ms) + 7 (116ms) + 8 (116ms)N100 = MERGE clusters 3 (207ms) + 11 (206ms)N3 = MERGE clusters 2 (244ms/Cell2) + 4 (241ms) + 12 (247ms/Cell1)MFN = MERGE clusters 5 (293ms/Cell2) + 10 (296ms/Cell1)* (mixture of patterns N3 + MFN)P300 = MERGE clusters 0 (484ms/Cell1) + 6 (484ms) + 9 (485ms/Cell2)

47

Table 14a. SG01_tPCA_m10 1 2 3 4

P1 0 0 36 0 0N1 36 2 0 0 0N3 0 16 0 18 0MFN 0 0 0 39 0P3 0 0 0 0 39Cell1% 50 27.8 50 50.9 48.8

Table 14b. SG01_tPCA_m1Attribute 0 1 2 3 4TI-max mean 216 238.3818 116 258.8006 484IN-LOCC mean -5.3812 1.7612 3.0242 1.1646 -0.4292IN-ROCC mean -5.3779 1.6191 2.9914 1.2307 -0.4241IN-LPAR mean -0.6004 -2.052 -0.0031 -0.426 3.1833IN-RPAR mean -0.5618 -2.3356 -0.031 -0.3013 3.1594IN-LPTEM mean 0.0548 2.2158 0.5477 0.7058 -0.1337IN-RPTEM mean 0.0516 1.9186 0.486 0.848 -0.1492IN-LATEM mean 2.8573 2.3311 -1.0504 0.2469 -1.5431IN-RATEM mean 2.8216 1.9908 -1.1006 0.4306 -1.5983IN-LORB mean 1.6449 -2.3642 -1.4056 -1.2146 -1.5422IN-RORB mean 3.2897 1.251 -1.4787 -0.325 -1.9669IN-LFRON mean 0.6284 -4.2409 -1.2911 -1.6457 -0.9716IN-RFRON mean 0.5619 -4.8101 -1.3027 -1.4471 -0.9302

P100 = cluster 2 N100 = cluster 0N3 = cluster 1MFN = cluster 3* (mixture of patterns N3 + MFN)P300 = cluster 4

48

Table 15a. SG02_tPCA_m1

assigned0

(N3)1

(N1)2

(N1)3

(N1) 45

(P1)6

(N3) 7 8 910

(P1)P1 0 0 0 0 0 16 0 0 0 0 20N1 0 4 22 14 0 0 0 0 0 0 0N3 4 0 0 0 0 0 14 0 16 0 0MFN 0 0 0 0 16 0 0 18 6 0 0P3 0 0 0 0 0 0 0 0 0 40 0Cell1% 50 50 50 50 18.8 50 35.7 61.1 72.7 50 50

Table 15b. SG02_tPCA_m1Attribute 0 1 2 3 4 5 6 7 8 9 10TI-max mean 248 212 212 212 260 116 248 260 251.2727 484 116IN-LOCC mean 3.8444-5.9922 -6.5858 -2.6445 1.5939 3.1934 1.8476 1.0271 0.8097 -0.477 1.6189IN-ROCC mean 3.8133-6.1571 -6.562 -2.6841 1.7643 3.1567 1.7635 1.1543 0.76 -0.4674 1.6359IN-LPAR mean -3.2137 -2.1028 -0.4036 -0.8949 -0.4689 0.0703 -1.5726 -0.3125 -0.7666 3.7167 -0.02IN-RPAR mean -3.6261 -2.2423 -0.4267 -0.9006 0.0604 -0.0054 -1.8491 0.0165 -0.9077 3.7306 -0.0033IN-LPTEM mean 3.0073 1.97 -0.1303 0.7351 1.2541 0.6109 1.468 0.805 0.554 -0.1475 0.1946IN-RPTEM mean 2.7634 1.7197-0.1076 0.7035 1.7081 0.5402 1.2516 1.1132 0.4023-0.1414 0.2427IN-LATEM mean 2.7963 5.5626 3.1375 2.3633 0.3658 -1.1206 1.3703 0.2526 0.6006-1.8405 -0.6373IN-RATEM mean 2.347 5.5496 3.2018 2.3384 1.0528 -1.1623 1.069 0.6843 0.3673-1.8439 -0.5721IN-LORB mean -3.7825 0.1108 2.1578 0.1383 -2.2828 -1.5217 -1.742 -1.4665 -0.6252 -1.8542 -0.7158IN-RORB mean 1.4954 4.9988 3.7769 2.2386 -0.8595 -1.6248 0.8367 -0.5407 0.4844-2.3137 -0.7339IN-LFRON mean -6.4029 -2.6092 1.1318 -1.0778 -2.9977 -1.3705 -2.9996 -1.9481 -1.1483 -1.1897 -0.6568IN-RFRON mean -7.2954 -2.8757 1.0246 -1.2019 -2.0634 -1.4201 -3.5895 -1.3661 -1.4888 -1.0939 -0.6595

P100 = MERGE clusters 5 + 10 N100 = MERGE clusters 1 + 2 + 3N3 = MERGE clusters 0 + 6MFN = MERGE 4 + 7P300 = cluster 9

49

Table 16a. SG01_sICA_m2assigned 0 1 2 3 4 5 6 7 8 9

P100 N100 MFN N3 P100 N3 N3 P300 N100 N3P1 16 0 2 0 20 0 0 0 0 0N1 0 24 0 0 0 0 0 0 14 0N3 0 0 0 11 0 12 7 0 0 2MFN 0 0 39 0 0 0 0 0 0 0P3 0 0 0 0 0 0 0 39 0 0Cell1% 50 50 38.8 27.2 50 58.3 42.9 48.7 50 50

Table 16a. SG01_sICA_m2Attribute 0 1 2 3 4 5 6 7 8 9TI-max2 mean37.5001 59.166755.0244 58.54636.1999 55.6667 58.285755.7949 50.5714 56IN-O1 mean26.1821-16.1627 2.526 0.119412.0809 0.0647 0.1878 -0.0673-37.1796 0.2923IN-O2 mean26.0726-16.1639 2.7141 0.160912.0303 0.0872 0.2531 -0.0782-37.1824 0.3939IN-C3 mean -4.6064 0.1972 -2.3091-12.3069 -2.1255 -6.6686-19.3545 3.6237 0.4537-30.1278IN-C4 mean -4.8174 0.1784 -1.1948-12.0533 -2.2228 -6.5312-18.9556 3.5321 0.4103-29.5069IN-T7 mean -0.8763 2.285 1.5315 8.322 -0.4043 4.5094 13.0877 -1.5515 5.2563 20.3727IN-T8 mean -1.0798 2.2513 2.2427 8.47 -0.4982 4.5896 13.3204 -1.5948 5.1788 20.735IN-F7 mean -5.9352 7.4848 0.577 12.3193 -2.7386 6.6753 19.374 -3.9674 17.2174 30.1582IN-F8 mean -6.0461 7.4438 1.5056 12.5492 -2.7898 6.7999 19.7355 -4.0237 17.1232 30.721IN-Fp1 mean -6.5144 7.8656 -1.076 8.0566 -3.0058 4.3655 12.6702 -4.5178 18.0934 19.7228IN-Fp2 mean -6.5797 7.8731 -0.5494 8.1896 -3.036 4.4376 12.8794 -4.5455 18.1107 20.0486IN-F3 mean -4.6952 0.8689 -3.9557-24.5502 -2.1664-13.3028 -38.609 -2.3881 1.9988 -60.1IN-F4 mean -4.9167 0.8544 -2.6874-24.2358 -2.2687-13.1324-38.1145 -2.47 1.9655-59.3302

P100 = MERGE clusters 0 + 4N100 = MERGE clusters 1 + 8N3 = MERGE clusters 3 + 5 + 6MFN = cluster 2P300 = cluster 7

50

Table 17a. SG01_tPCA_m2assigned 0 1 2 3 4 5

N100 N3 P100 MFN P3 miscP1 0 0 36 0 0 0N1 32 0 0 0 0 6N3 0 28 0 0 0 6MFN 0 0 0 39 0 0P3 0 0 0 0 39 0Cell1% 50 46.4 50 48.7 48.7 33.3

Table 17b. SG01_tPCA_m2Attribute 0 1 2 3 4 5TI-max2 mean 52 56 40.053 44 60 54.3611IN-O1 mean -7.5109 2.3636 5.0976 0.8247 -0.065 0.3817IN-O2 mean -7.4976 2.2666 5.0726 0.9274 -0.0562 0.2724IN-C3 mean -0.0887 -1.8175 -0.7829 -0.7441 1.6266 -4.1981IN-C4 mean -0.0418 -2.3878 -0.8209 -0.2123 1.5843 -4.4536IN-T7 mean 1.2177 0.7791 -0.157 0.6011 -0.6599 2.7723IN-T8 mean 1.1915 0.4965 -0.2376 0.9339 -0.6831 2.5203IN-F7 mean 3.663 0.9413 -1.2615 0.0992 -1.7359 4.2771IN-F8 mean 3.6205 0.6446 -1.3136 0.4751 -1.8041 4.0585IN-Fp1 mean 3.6881 0.6768 -1.4828 -0.701 -2.0482 2.9788IN-Fp2 mean 3.6885 0.4648 -1.5068 -0.4832 -2.0827 2.9132IN-F3 mean -0.5934 -3.8356 -1.3944 -1.7254 -1.2798 -8.9513IN-F4 mean -0.5662 -4.4149 -1.426 -1.1468 -1.3347 -9.216

P100 = cluster 2N100 = cluster 0N3 = cluster 1MFN = cluster 3P300 = cluster 4

51

Table 18a. SG02_sICA_m2assigned 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

P1 P1 P1 N3 misc N1 N3 N1 misc N3 P3 MFN N1 MFN N3 P1 P3P1 8 11 5 0 4 0 0 0 0 0 0 0 0 0 0 8 0N1 0 0 0 0 0 12 0 6 0 0 0 0 22 0 0 0 0N3 0 0 0 10 0 0 4 0 1 11 0 0 0 0 9 0 0MFN 0 0 0 0 0 0 0 0 5 0 0 18 0 17 0 0 0P3 0 0 0 0 2 0 0 0 0 0 13 0 0 0 0 0 25Cell1% 50 45.5 60 50 66.7 33.3 50 83.3 100 72.7 76.9 16.7 50 70.6 11.1 50 32

Table 18b. SG02_sICA_m2Attribute 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16TI-max2

mean 36

36.7273 38.4 57.2 38 56 57

62.6667 40

49.4545

54.1558

57.7778

52.1818

55.5294

57.7778 36

56.6386

IN-O1mean

21.656

11.8827

13.8594 0.131

4.1889

-18.57

850.285

2

-10.06

451.02

630.080

4

-0.016

43.933

2

-32.22

92.467

60.168

616.81

87

-0.029

4

IN-O2mean

21.597

11.8503

13.8216

0.1824

4.1759

-18.59

14 0.397

-10.07

151.10

070.111

9

-0.028

8 4.215

-32.25

142.644

40.234

816.77

29

-0.051

7

IN-C3mean

-4.11

54

-2.258

1

-2.633

7

-12.14

77

-0.44

86

-1.168

7

-26.43

65

-0.633

1

-1.15

82

-7.454

12.713

6

-3.569

1

-2.027

4

-2.239

1

-15.63

43

-3.196

14.865

7

IN-C4mean

-4.52

63

-2.483

6

-2.896

7

-11.61

79

-0.53

41

-1.232

4

-25.28

37

-0.667

6

-0.69

74-

7.1292.666

9

-1.837

3

-2.137

9

-1.152

7

-14.95

26

-3.515

34.782

1

IN-T7mean

-0.57

42-

0.315

-0.367

58.235

4

-0.25

863.532

117.92

241.913

40.76

165.053

4

-1.150

32.329

36.127

31.461

310.59

92

-0.445

9

-2.062

6

IN-T8mean

-0.82

-0.449

9

-0.524

88.568

7

-0.31

293.464

418.64

771.876

71.06

775.257

9

-1.203

63.480

96.009

82.183

811.02

81

-0.636

8

-2.158

1

IN-F7mean

-4.33

73

-2.379

9

-2.775

811.97

5

-1.22

3310.05

2926.06

075.445

90.46

587.348

1

-2.995

2 0.92217.43

920.578

415.41

21

-3.368

5

-5.370

8

IN-F8mean

-4.60

81

-2.528

5

-2.949

112.48

36

-1.28

159.973

827.16

755.403

10.84

787.660

2

-3.040

42.352

517.30

191.475

916.06

67

-3.578

8

-5.451

7

IN-Fp1

mean

-4.68

88

-2.572

8

-3.000

87.636

3

-1.34

6310.10

4816.61

865.474

1

-0.27

544.685

8-

3.424

-1.611

417.52

93

-1.010

99.828

1

-3.641

5

-6.139

5

IN-Fp2

mean

-4.80

47

-2.636

4

-3.074

97.914

5

-1.37

1310.06

6617.22

415.453

3

-0.06

364.856

5

-3.444

7

-0.817

817.46

29

-0.513

110.18

62

-3.731

5

-6.176

7IN-F3 me

an-

2.73-

1.500-

1.750-

23.94-

0.76-

1.584-

52.11-

0.858-

1.92-

14.69-

1.815-

5.660-

2.749-

3.551-

30.82-

2.124-

3.255

52

51 7 4 72 2 7 55 5 52 45 6 8 1 4 07 1 6

IN-F4mean

-3.13

77

-1.721

7

-2.008

1

-23.30

75

-0.84

72

-1.690

4

-50.72

32

-0.915

7

-1.40

86

-14.30

2

-1.872

1

-3.722

6

-2.932

3

-2.335

4

-29.99

73

-2.436

8

-3.356

8

P100 = MERGE clusters 0 + 1 + 2 + 15N100 = MERGE clusters 5 + 7 + 12N3 = MERGE clusters 3 + 6 + 9 + 14MFN = MERGE clusters 11 + 13P300 = cluster 10 + 16

53

Table 19a. SG02_tPCA_m2assigned 0 1 2 3 4 5 6 7

N3 MFN N1 N3 P3 P3 P1 N1P1 0 0 0 0 0 0 36 0N1 0 0 33 0 0 0 0 7N3 6 0 0 28 0 0 0 0MFN 0 40 0 0 0 0 0 0P3 0 0 0 0 16 24 0 0Cell1% 33.3 50 54.5 53.5 31.2 62.5 50 28.6

Table 19a. SG02_tPCA_m2Attribute 0 1 2 3 4 5 6 7TI-max2 mean 56 44 48 55.9988 60 60 44 48IN-O1 mean 3.273 1.1401 -6.1285 1.0437 -0.0364 -0.0413 4.3075 -10.486IN-O2 mean 3.1458 1.2518 -6.1685 1.0037 -0.0295 -0.0374 4.2819-10.4925IN-C3 mean -5.0163 -0.939 -0.2647 -2.0685 2.4433 1.4601 -0.6204 -1.3077IN-C4 mean -5.7023 -0.2646 -0.3025 -2.5457 2.4589 1.4856 -0.6819 -1.3615IN-T7 mean 2.5587 0.7689 1.1778 0.8552 -0.9766 -0.6203 -0.1622 2.6942IN-T8 mean 2.1149 1.2138 1.1594 0.5793 -0.9708 -0.6273 -0.1773 2.6665IN-F7 mean 3.8078 0.0742 3.3122 1.6928 -2.7309 -1.6195 -1.0809 6.3204IN-F8 mean 3.3535 0.6122 3.3313 1.3472 -2.7443 -1.6322 -1.0659 6.3615IN-Fp1 mean 2.7032 -0.9575 3.2482 1.6011 -3.2305 -1.8948 -1.2677 5.7604IN-Fp2 mean 2.4933 -0.6615 3.2369 1.4127 -3.2392 -1.9048 -1.2687 5.7691IN-F3 mean -10.3127 -2.2809 -1.0699 -4.0213 -2.0528 -1.2025 -1.155 -3.6558IN-F4 mean -10.9725 -1.5381 -1.1013 -4.5473 -2.0809 -1.2055 -1.2024 -3.6736

P100 = cluster 6N100 = MERGE clusters 2 + 7N3 = MERGE clusters 0 + 3MFN = cluster 1P300 = MERGE clusters 4 + 5

Note that the assignment of observations to clusters, and the corresponding splitting of observations of a single pattern into two or more clusters on some occasions, is a function of the metrics used to generate the dimensions and axis orientations of the multidimensional attribute space. Observations that are close in L2 norm along one dimension of a spatiotemporal attribute space may be farther away in L2 norm on a separate dimension of an alternate attribute space instantiated by an alternate metric set. This is consistent with mathematical topology, in which mathematical objects that are close in one topology, or one generalized measure of distance, can be far apart in another topology, or an alternate measure of distance. This phenomena is also present in the expert labeling of observations, as changes to expert-defined pattern rules redefine observation to pattern mappings and may reshuffle observation labels.

54

2.7 Mapping Result5 sampling of random ordering is performed on each cross subject group, metric set mapping study. The mapping result is represented by the selected columns. E.g. the golden standard mapping should be found at the following column indices: 1 2 3 4 5 6 7 8 9 10 11 12 13, indicating the cells at locations (1,1), (2,2), (3,3) …

1. sICA_SG01m1_SG02m2 – sample 1Selected column: 1 2 3 4 5 6 7 8 10 9 11 12 13

4.070045

4.154391

12.92163

8.520473

17.96254

19.29046

14.95145

13.38921

19.67588

22.96902

13.86592

14.77786

10.90138

5.12967

5.810064

10.02239

12.87744

13.51314

17.1766

14.23925

17.12257

13.54964

23.0875

9.899105

19.45215

12.36661

10.07585

7.90358

9.100851

8.283967

14.10237

15.8653

15.5886

13.03815

17.6836

10.41991

9.947328

16.03691

15.09054

8.256769

11.99874

7.059906

6.721359

11.69801

17.05829

18.86003

19.38561

19.44092

15.05833

14.54779

16.72309

14.81214

15.5061

25.52558

27.7702

32.84124

6.930038

6.676377

7.8736

9.816149

16.38231

12.66334

41.60422

30.91454

15.37909

14.07506

15.62792

27.20761

22.50434

6.54516

4.670513

11.31469

9.385753

12.00143

15.23479

38.41391

35.18973

23.09431

23.34464

16.43296

14.36066

22.94513

3.779981

4.617883

3.249631

2.95351

6.947963

4.518756

31.18446

21.02078

20.38081

16.72832

15.8954

15.86894

21.27644

3.727282

2.947679

3.737981

3.650336

7.001593

5.789755

22.66351

25.9365

28.9153

13.71279

11.17835

7.677453

9.509264

14.13152

15.14735

8.407764

13.35411

14.10832

8.224895

14.31425

16.58851

20.45475

17.14578

21.63377

12.70354

15.62111

6.703138

5.651723

2.921751

2.533441

3.852356

3.687031

27.23651

17.04218

21.17092

10.54918

10.95489

5.699902

6.301146

21.91357

27.22052

24.84889

21.71947

14.71817

18.15137

6.101334

6.049079

20.43041

15.74009

16.2249

6.353362

5.535188

28.72205

19.99901

18.52381

19.47475

22.38422

18.04407

5.283927

7.32507

22.88615

6.847423

12.39283

21.08632

11.39239

26.97149

18.19093

24.56667

24.19969

23.29951

16.16445

15.74885

22.39316

7.843167


2.8949

2.8855

7.0226

7.0031

12.3328

12.1891

12.1724

12.0504

12.1389

12.0194

9.8022 9.861

6.6428

2.9242.913

17.015

16.992

512.38

7712.24

2412.21

9412.09

6812.18

1412.06

039.764

69.821

36.653

76.003

36.023

55.103

35.149

910.97

5510.89

4110.43

3510.36

4710.11

7110.06

25 9.5419.654

19.329

75.991

86.010

65.083

55.114

110.99

7810.90

8710.45

9910.38

5110.14

7910.08

889.507

79.614

6 9.34313.42

0413.45

5217.18

1717.20

024.698

84.708

27.597

57.418

79.375

49.467

821.39

7221.49

0215.14

6913.28 13.32 17.04 17.05 4.582 4.563 7.488 7.296 9.271 9.355 21.24 21.33 15.05

55

77 15 11 26 2 2 7 2 5 3 37 25 5211.87

3111.90

0313.43

2513.46

542.729

92.794

62.169

82.039

33.494

93.535

617.21

0917.30

814.56

9911.76

0611.78

6813.34

3413.36

762.721

32.738

42.203

22.026

13.532

53.551

817.11

3117.20

5314.48

988.765

48.779

76.171

96.237

610.01

239.969

48.356

28.363

17.334

97.254

49.615

4 9.72912.75

0111.97

4612.00

0212.54

1512.58

764.289

84.365

32.325

52.364

32.428

52.473

916.17

116.27

4315.00

68

8.9798.969

53.247

63.311

915.22

715.14

8713.59

8413.59

8112.49

9512.37

224.113

74.236

412.48

46

9.1799.166

83.393

63.418

115.64

1615.55

4714.01

4714.00

8512.91

6312.78

253.712

93.814

812.63

736.279

76.305

910.86

1810.95

5514.04

6913.99

9714.73

8814.65

2815.17

5715.14

7914.18

4614.30

944.353

2

3. sICA_SG01m1_SG02m2 – sample 3 Selected column: 1 2 3 4 5 6 7 8 10 9 11 12 13 4.181

6693.221

6687.999

71710.72

3624.07

18822.58

20524.32

85117.26

90517.29

12323.20

78214.00

2415.85

8712.95

9795.047

0264.471

00610.44

1549.573

66615.26

80522.33

65315.43

58822.81

47420.76

79913.47

36118.33

58713.23

9938.284

36411.06

6149.777

5346.924

2195.472

64417.63

7712.19

54413.91

86418.33

57919.46

67219.90

81812.79

6314.86

80815.77

5339.491

1758.115

1927.272

1339.644

10515.91

92311.02

95518.25

99213.51

7120.20

51819.63

07510.59

54615.17

6616.37

41717.84

24221.78

32920.98

92133.71

0085.439

0875.827

60714.90

1678.774

22910.19

45613.06

53442.52

28337.22

06316.28

20116.35

36914.92

50926.58

80517.63

025.720

7376.009

3878.531

0859.581

10115.35

9611.92

66430.25

78339.86

80125.76

31316.49

15920.58

01722.96

27916.40

9784.491

4313.774

8413.940

8692.612

355.885

6096.651

427.51

36318.59

63823.70

54819.97

67722.68

721.09

11519.51

6925.375

0333.085

0063.567

83.721

5953.875

4666.055

15920.11

79619.40

19419.78

815.96

1313.61

8689.488

2647.006

46316.78

71510.30

76316.17

8688.871

4088.214

619.168

63913.79

33611.80

9318.51

38812.45

92312.33

06124.53

61919.09

647.159

0118.563

3933.393

4193.419

0053.677

0614.078

87326.54

67117.09

20224.36

27111.12

66210.51

2983.401

7914.940

79825.87

39918.22

17721.85

2622.15

44722.45

04823.67

4917.623

0935.029

67720.48

33818.26

10517.55

8974.148

6556.140

12728.21

76615.97

62217.61

80519.78

90213.15

72213.06

3856.034

2385.472

2224.54

14110.31

0487.906

15118.65

72911.89

2518.95

79820.62

2720.42

6919.88

75729.97

29626.45

20825.64

39418.98

1177.812

165

4. sICA_SG01m1_SG02m2 – sample 4 Selected column: 1 2 3 4 5 6 7 8 10 9 11 12 13 4.179 4.474 9.902 9.367 19.55 17.64 14.86 19.66 18.10 12.30 10.21 19.16 10.51

56

62 778 548 525 171 79 73 046 91 195 257 703 4365.235

4254.317

84810.72

55712.55

11518.44

89818.99

95513.19

67516.94

32313.55

66313.00

81818.68

48310.65

08310.00

80810.80

89410.85

3910.12

179.070

04816.26

39919.44

09814.97

37611.91

15819.54

77315.14

2218.49

02118.71

312.49

30211.48

1019.325

7729.928

8645.173

74411.39

73516.92

52919.96

6120.71

818.43

89513.30

63110.82

35215.66

43217.20

78717.80

13920.93

72133.16

37329.99

5467.566

945.584

97714.53

3678.067

07616.32

2216.62

70729.97

32936.13

85521.16

00820.14

60214.31

86828.53

45130.40

2834.800

257.070

23310.24

4558.498

9411.55

79317.97

68741.13

99237.42

64122.89

84119.90

22612.75

28718.28

71215.15

9454.685

9655.398

4633.220

0293.322

1186.499

2064.036

08424.08

60524.24

09615.25

43715.96

55421.37

75717.37

91115.22

1784.458

1613.658

1023.307

0753.457

8736.435

3424.210

41920.54

09831.22

07919.95

1616.03

62916.43

2457.135

9649.386

39811.80

76614.71

02712.26

3910.77

3078.303

9710.68

71914.68

55417.75

66117.28

12714.77

89821.20

17223.75

71214.72

6156.147

5485.178

9742.805

0144.171

6583.855

9813.695

86720.59

46731.78

0221.90

21614.83

59215.20

5935.919

2214.602

4818.57

49215.42

46519.72

83215.80

42617.27

08914.91

9768.173

0657.335

84417.61

10717.75

88716.41

2784.810

6115.360

70818.44

05928.97

96817.42

00723.93

37113.44

80414.90

0056.762

7446.744

54924.35

597.908

6610.43

0413.16

94311.03

93926.89

87719.64

41722.35

01815.14

39130.32

1327.21

62722.75

60624.00

4896.105

666

5. sICA_SG01m1_SG02m2 – sample 5 Selected column: 1 2 3 4 5 6 7 8 10 9 11 12 13 4.538

6864.692

2229.899

4598.504

90915.50

2121.57

0522.32

04418.60

57423.81

32813.84

76511.89

73918.47

9969.826

835.402

2173.020

2579.930

6799.029

29818.41

14718.46

53714.29

19616.10

73516.97

11823.52

59411.70

43714.98

11212.84

2626.274

5679.053

476.905

3869.790

56914.56

11112.88

57918.36

31510.90

10812.57

21611.05

81212.72

13512.52

7015.194

7897.618

9879.661

0449.878

8678.895

90416.20

6817.29

68814.39

77610.86

5811.97

79318.06

78215.51

42913.77

9810.06

86222.70

82314.98

88824.94

79830.51

0479.044

5529.577

3589.562

65911.06

12913.75

93112.18

66729.25

01239.40

87820.90

69113.08

78619.81

40717.82

63431.44

7097.316

1647.832

95711.22

55915.39

95810.15

8559.954

50725.17

5631.42

14614.40

64619.81

24311.75

25118.67

76914.93

6146.601

465.460

7394.266

8575.285

452.786

1774.224

44218.51

47521.81

9319.776

4912.97

55419.95

96324.11

74816.33

0184.356

0467.214

8543.684

842.849

9774.695

7495.118

54525.26

20428.04

41912.88

25716.64

37914.04

0635.263

8439.076

42812.57

66511.03

93715.76

7459.829

18614.99

6528.745

6778.344

49413.07

4710.55

59512.57

84716.37

7414.02

99516.84

48710.37

40910.17

7725.478

7053.914

4945.301

3132.989

99320.10

23224.03

47412.33

82414.02 16.15 7.760 9.283 20.42 32.07 19.56 16.54 21.88 16.37 4.404 2.676 17.29

57

572 213 028 006 163 054 179 241 943 173 151 785 63916.69

26711.73

2136.365

7399.219

42525.03

21118.35

94130.31

78215.85

69124.75

29216.20

0632.982

7433.772

84412.92

05714.50

71720.73

80817.41

8816.41

95119.24

52619.31

52620.72

30514.98

91314.98

13617.83

14925.00

16727.57

4749.907

424


4.496095

3.490179

9.537455

14.56207

13.92134

12.7057

17.25533

15.37751

16.02782

18.69061

10.52615

14.81969

13.34972

3.335282

4.356062

14.13454

14.55683

17.87512

19.13827

22.3365

23.82567

19.73882

18.10634

10.54016

12.62794

14.45283

11.447

9.860628

9.266105

8.286422

18.16221

10.4647

13.40063

16.87963

11.41552

13.16018

13.93305

10.32597

6.44395

8.38207

9.26898

9.685922

10.27909

16.5828

11.06302

20.12999

19.17872

15.69795

11.51748

16.42614

11.52918

5.479465

17.59852

15.90121

20.13099

19.69953

5.840505

9.682535

15.63401

10.02509

9.181838

14.76993

27.92665

36.98506

14.93581

15.09629

14.96988

30.72428

27.0985

5.317849

5.680983

14.54037

9.168453

13.39758

13.1495

24.90018

27.10563

13.67709

17.69739

13.79888

18.74968

22.51677

7.468383

6.764732

5.20559

4.184471

2.780507

4.913718

28.35993

29.78683

10.3323

18.95183

21.03342

22.62391

24.53149

5.07627

5.586705

3.737787

3.623079

3.145366

3.646818

27.84347

18.34948

7.255134

12.07577

16.19895

7.211958

7.691652

15.1948

17.63989

15.69934

13.35242

13.79722

14.0361

10.24277

9.51187

9.749144

14.66248

15.83294

13.94439

18.28872

6.250713

6.948308

5.059481

3.737252

3.612421

3.027137

16.02969

24.4983

10.19248

18.12347

18.79335

5.329884

5.369835

24.29599

25.44837

27.84616

21.57155

27.66522

25.61494

4.329193

4.735654

10.07908

15.73987

16.73751

5.865656

8.230688

30.58195

24.32112

19.95154

25.87326

24.15356

29.05518

3.130964

4.808556

19.75076

20.82379

13.61972

11.80438

14.107

19.63967

19.38142

22.87632

15.36653

17.59911

13.47845

22.09592

21.0311

8.544994

7. sICA_SG02m1_SG01m2 – sample 2 Selected column: 1 2 3 4 5 6 7 8 10 9 11 12 13

3.710021

4.933383

12.13297

9.334674

21.26981

13.65941

20.15989

17.08151

19.22061

18.0088

10.9666

11.67553

9.864396

5.746794

5.689554

12.78443

16.31139

20.19189

24.4107

17.12338

24.99031

23.23181

26.05779

11.46761

18.77111

15.06346

10.48472

7.216003

8.918516

5.698041

12.60917

18.13709

17.79943

13.94443

14.44259

15.17911

12.9481

18.10102

6.22398

10.29373

6.610767

5.741322

5.542687

19.11555

10.97826

16.91218

11.41426

16.88747

10.87816

10.63629

12.84143

6.394166

22.20415

14.68043

25.07312

17.40518

6.921065

8.141543

12.44514

15.40883

7.95775

12.71502

23.79725

32.31815

17.55841

58

13.53389

13.16559

27.81385

16.6421

8.72331

8.657169

15.29878

10.15272

10.69771

10.04117

32.24871

23.90434

12.05872

12.94002

14.33542

15.39176

14.77079

6.39434

4.709902

3.954147

3.423083

5.300455

3.274925

21.0164

21.59132

7.997401

11.72536

20.96981

13.69956

13.84015

5.889476

5.577779

4.159914

4.730938

3.348218

3.643055

28.13731

22.1223

7.911998

16.43402

15.19904

8.178604

7.981097

12.54266

13.00415

13.68297

14.1027

15.36136

8.935172

14.52597

13.38757

8.840663

20.91027

19.06666

15.68941

19.73034

6.161144

8.774581

4.940617

3.407841

3.977819

4.421108

26.71063

25.90271

12.27841

14.51063

12.28915

9.050622

5.234819

24.42379

28.10403

20.07961

21.37235

27.39969

23.40755

4.719683

3.239975

12.64483

15.72376

16.80051

6.291961

6.647866

29.8942

21.73202

20.64868

29.47672

25.9071

26.21936

3.421722

3.818271

15.61122

14.36042

19.53076

16.68278

14.89079

21.38323

20.84396

14.47664

19.46489

17.73895

17.95991

17.86748

29.81207

13.38112


5.296639

4.072037

11.86818

16.61165

14.27942

12.62684

13.46073

23.3513

22.04219

19.5138

13.22114

16.62148

12.23826

3.570274

3.023452

13.73716

14.44589

13.31855

22.31559

24.94199

18.46332

23.64619

17.34956

19.74041

18.79545

11.71757

10.6377

7.087713

6.863874

9.241935

16.8706

17.17909

14.80799

19.41906

19.75119

10.74282

15.46989

14.70825

9.342396

11.18628

10.40711

6.400498

7.923825

18.84423

15.79726

10.32593

12.42566

17.84513

11.06198

10.9928

18.70259

6.998486

19.6341

17.49473

22.79773

17.78146

6.708041

9.0159

14.4707

12.85982

9.844169

11.7298

27.97897

20.32345

19.56152

13.99159

14.78054

23.37158

18.14506

8.475805

9.041491

12.95846

10.49671

11.92572

12.6333

22.90524

29.10323

15.83595

12.24675

22.0339

14.53551

15.2405

4.79587

5.676764

4.45857

3.35286

3.137935

3.042962

20.78214

23.85433

12.32313

17.56233

12.74277

14.07627

12.39612

5.77788

5.07489

4.595019

4.144572

5.412706

3.224573

26.65156

20.06604

11.97368

12.85121

13.12438

7.144312

9.613648

13.70359

14.67631

9.982863

15.83051

14.56599

14.73859

11.36416

10.51289

5.783049

14.54206

12.75392

17.66908

14.25817

9.059102

5.59583

3.613137

5.514898

2.890246

4.264372

26.11679

15.2955

12.66121

18.13159

9.789659

5.715055

6.203707

32.54517

18.57714

23.13291

16.4437

24.14095

24.90448

4.166063

2.769921

16.36376

13.84451

14.86822

8.148105

7.865765

23.21099

23.34153

25.13693

15.43142

23.9142

15.8473

4.514784

4.851214

13.9355

21.91629

16.7474

20.50058

16.20346

16.8984

12.87256

14.65147

21.95958

17.03736

11.77996

21.91821

16.40953

8.342504


59

4.453099

4.85996

10.34855

12.71331

14.94038

16.94457

14.16228

14.34609

20.57447

18.23314

14.66265

11.86883

13.73975

4.037793

5.604295

15.38628

15.70963

15.47642

17.05752

16.39939

23.34169

20.15759

23.72492

13.17815

10.92781

15.24954

8.196359

11.66374

8.038534

6.35351

11.18838

13.83574

19.67945

16.69598

18.51815

10.67178

17.26279

18.62102

8.652742

12.09067

9.259378

6.1283

9.78586

18.62061

15.03622

19.31801

16.78926

16.98371

18.08483

11.44301

14.26834

8.864446

24.8498

19.9013

22.24986

25.86895

5.706973

6.737204

10.20674

9.52536

14.99742

9.143468

27.48086

32.90717

18.45231

22.67783

21.31803

21.35978

25.90898

8.68879

7.504983

12.98472

15.35126

9.723112

13.71251

19.87552

27.434

14.66154

12.90842

14.83375

20.88546

15.13537

6.845881

7.654039

3.346997

5.23303

5.386999

3.545698

21.97288

19.64816

6.899441

16.74267

20.5066

12.40467

22.3734

6.538681

5.648613

2.853613

4.145274

3.360898

5.029772

17.24661

17.96772

8.757291

15.9978

10.42923

9.376713

8.557873

11.06537

15.4872

9.897591

16.92184

12.34654

11.41812

8.747003

14.86774

8.414921

11.98927

21.89674

18.29462

13.52143

6.520421

8.059786

3.372481

5.44886

4.448522

3.684793

26.75353

22.4407

11.21683

11.493

17.87383

6.908653

7.546369

21.6428

29.24267

25.81124

22.92448

20.2548

14.91297

2.85128

4.542528

19.17631

11.01124

15.50378

9.251367

10.13446

22.17217

22.14071

24.36924

16.01661

15.71954

25.67095

2.586766

2.495312

20.25803

20.58427

11.79924

19.35309

19.43426

13.13487

17.0093

18.4852

20.05113

21.52943

20.72888

17.81331

17.5291

15.43093


8.960683

9.913542

8.588776

11.62891

14.42652

19.4912

12.16179

17.92504

14.99338

14.08395

6.731552

7.59396

28.01798

8.150169

9.285307

8.367931

7.950753

19.97134

15.96075

15.83987

22.98453

18.3309

15.70868

10.34967

8.577401

17.11916

12.4085

15.28928

12.42383

12.52917

15.21308

9.76186

15.07885

15.45529

14.51425

14.7108

11.96191

12.57419

14.42595

16.88691

15.09032

10.47037

11.12365

16.72556

11.06717

8.901964

13.66696

9.8564

14.10793

11.98414

14.52755

14.58442

12.71211

17.50106

17.78495

11.84306

7.046121

7.689772

5.751849

8.346581

8.817734

6.220866

20.31647

13.7103

13.51961

17.57087

18.09225

15.4425

15.99489

4.062719

8.138469

4.982984

8.34552

8.73453

6.365359

15.44214

17.61396

13.89428

13.55821

20.59509

10.26682

13.27154

4.927642

6.387746

6.210234

4.64369

7.116696

7.259407

17.19965

19.42055

15.31093

19.06075

13.84481

11.71147

12.48993

5.950407

6.144181

5.564503

5.678053

6.180035

4.597851

10.36782

14.2785

14.85297

14.80342

9.053634

8.777643

13.9946

9.099978

7.726679

9.242493

8.907477

6.687159

6.432974

13.32701

14.16879

22.32416

60

11.57686

12.44132

16.85956

11.66916

4.582466

5.231458

4.967857

4.994827

5.495718

7.474798

12.96886

17.94638

21.09567

8.67595

7.494204

8.335625

8.389507

11.98283

11.32896

13.76309

11.61712

14.44117

12.58311

5.609273

6.121112

14.47588

10.2118

7.925455

7.34133

4.799821

13.11251

15.77834

10.25095

14.43301

11.5752

17.17923

3.364487

4.240213

23.49404

12.62838

12.28532

10.72287

16.22224

12.17873

21.66188

18.81428

19.92006

14.99054

23.96237

16.94796

13.79908

14.25783

11. tPCA_SG01m1_SG02m2 – sample 1Selected column: 1 2 3 4 5 6 9 8 10 7 11 12 13

7.185818

6.860642

7.331778

12.20286

14.84306

14.0744

14.14734

17.59643

14.43111

15.77125

9.013159

5.992188

28.83986

9.972098

6.003061

8.801943

11.16068

14.08706

21.75512

13.35208

22.896

15.8377

22.33483

8.614288

6.056721

21.20895

12.75879

13.92348

9.124684

14.55616

14.79339

10.6488

10.26668

11.4172

10.29512

10.36632

9.413917

16.96987

11.07776

11.59668

8.972614

9.086181

12.45367

13.75289

10.65282

10.24994

17.71492

16.9899

11.64372

12.19078

12.66784

18.25202

12.95272

19.14335

18.60083

15.25702

5.872373

6.605602

6.655365

7.192344

10.30075

7.104292

21.19867

18.24309

20.76501

17.24283

9.435516

15.8684

13.38739

7.095743

7.105313

5.392921

5.031847

7.463934

5.376922

11.59591

13.56228

14.24111

19.73999

14.09198

13.10609

11.31242

4.187729

6.908205

5.088067

5.334525

6.239698

7.494472

16.87849

11.77257

19.57896

14.31724

17.88009

17.78135

16.56758

4.316631

6.19288

6.218244

7.005605

6.909032

6.436586

15.14448

13.97441

24.24048

11.93559

17.26708

12.29995

10.62485

9.532193

7.387669

10.40417

6.75553

9.184258

7.633916

7.854616

10.91248

17.13932

10.99169

11.60113

11.23983

11.70303

7.625386

5.467573

6.996023

3.989566

4.397854

7.197342

13.01315

10.87768

16.37708

13.41095

9.66273

8.263128

6.772979

9.235681

14.37572

9.376594

12.27986

16.15931

9.572811

5.879634

7.796802

17.37535

7.055209

13.1596

4.928349

4.758086

13.48145

13.51736

17.64441

19.01416

12.52803

14.88889

3.419896

6.04206

15.12003

17.28982

20.26149

15.7864

17.81128

19.56754

18.04033

16.35303

23.30788

14.52235

12.59137

21.19405

18.04284

8.698825

12. tPCA_SG01m1_SG02m2 – sample 2 Selected column: 1 2 3 4 5 6 9 8 10 7 11 12 13

8.506772

7.910123

12.52522

6.706976

16.09618

17.44345

20.2191

16.4634

13.10372

13.50464

7.321549

8.165016

20.64821

6.353313

9.968116

8.518746

11.75202

11.3769

15.65003

22.26717

19.48779

14.74567

12.15235

5.771449

6.364045

17.86815

10.58947

11.83072

10.47394

14.71591

14.20697

13.05992

8.946144

12.85379

11.75359

13.57254

12.78455

12.52624

10.5038

61

16.60572

13.74067

9.404861

12.69831

12.46594

8.962727

13.19013

12.54363

9.386334

16.51116

7.594134

11.22567

11.62056

11.19922

11.51166

13.83951

16.96068

5.698697

8.185146

5.724009

7.883141

9.462699

10.12799

21.3938

19.46295

21.08078

13.56675

15.48173

13.73131

18.00416

4.736843

7.760406

6.781638

6.679436

9.637214

5.539577

13.262

19.2396

18.78048

16.99887

11.99732

10.64687

18.84633

6.537108

8.311994

7.38242

5.536268

7.406075

7.276456

15.47081

10.77868

20.86328

11.60099

16.52969

17.89957

17.02085

4.192513

6.777531

6.261061

6.481671

4.72465

5.849574

11.3624

11.97024

15.08329

13.0856

9.64096

7.715428

10.42807

5.871719

10.85806

7.988476

6.759322

8.537948

6.337121

8.394051

10.1771

23.03784

20.19488

20.91835

13.78304

20.63911

4.844669

5.607372

4.430911

5.969203

6.117181

6.053836

10.1864

17.43736

15.76525

13.13074

13.31749

7.419807

7.593919

8.845545

15.85285

9.519052

16.94245

14.05336

12.00248

3.93869

7.891659

20.04537

12.07459

11.97605

6.759133

4.245495

10.37945

16.64774

13.97331

10.5349

10.68693

10.45712

4.174573

3.554492

17.12955

21.2899

17.41129

12.60788

15.9641

14.90976

19.03177

23.4258

18.08079

23.86903

18.04328

14.17345

13.51072

9.584557


6.678397

6.752969

13.26359

9.227997

20.29256

21.76494

23.38732

18.16584

12.46263

16.01088

6.530895

6.758142

22.0539

6.127367

8.20943

9.95799

7.271651

15.20931

17.20359

19.1791

21.81682

12.22604

17.57666

7.694274

8.596818

17.28509

17.93512 17.26

9.927644

12.01855

14.16531

10.46278

12.51291

14.24613

9.849182

17.40797

12.9881

10.67082

17.92876

9.052162

14.19022

10.45868

12.09715

9.456695

15.11454

15.81658

17.34663

12.63999

15.03325

10.01317

11.77897

11.53722

19.87681

19.15363

21.24013

19.53794

4.658291

6.805176

6.385791

9.738028

11.19729

8.384712

17.2109

20.68284

16.99877

11.66211

17.00462

15.04137

18.4846

5.766572

5.62845

5.961576

9.69114

6.950129

5.769557

10.84827

19.60088

19.28384

13.31708

14.70294

12.0561

20.8608

5.434437

5.408858

6.839082

5.681363

5.57215

6.734312

11.03237

18.98817

17.10723

14.41409

18.43103

14.80791

12.88215

3.606999

5.150987

4.239085

4.683809

5.147851

6.720677

12.60459

20.18985

16.24394

13.25942

16.61935

13.48561

12.64955

7.654833

5.909562

9.707116

9.742768

6.350532

8.484954

7.376121

12.19674

21.62742

18.11421

13.93092

12.17299

11.99364

5.291041

7.187975

7.408105

4.786659

5.932216

5.675572

14.02036

17.59935

23.4618

7.32456

11.96905

5.957521

8.974681

13.57731

13.14076

17.13663

12.87012

12.86646

15.81882

6.423234

4.448456

15.69436

12.20105

10.19488

6.464642

7.88494

16.38831

12.12626

10.16646

12.51299

10.58646

12.30168

3.936795

3.999938

20.5781

20.66 14.84 16.20 13.60 20.72 14.48 17.21 23.60 23.31 16.22 19.91 20.08 13.63

62

744 295 833 475 153 78 476 674 535 53 928 195 123


8.969286

5.162945

13.0011

8.73392

21.04154

18.38462

17.51218

12.56868

12.04183

16.44175

7.034347

10.28124

16.60869

10.08169

7.150359

8.14374

6.650857

12.03108

11.2442

23.73947

16.9582

17.04781

13.77055

8.157809

5.959009

30.17304

16.28166

16.25328

9.514718

7.492437

10.40397

9.951059

11.99393

14.51508

17.98609

10.80161

13.94579

11.27101

17.73798

16.44834

14.65657

10.10232

8.185898

13.71932

13.54239

9.43523

13.53713

16.65425

9.436322

14.58438

12.50515

18.75651

16.46151

15.68109

20.31469

14.24823

6.636611

9.844366

8.332528

6.276884

7.828443

8.64006

20.65911

21.39337

14.19002

17.31316

11.27006

12.16826

20.20505

5.817136

6.965845

9.431722

9.075908

7.830381

8.223612

11.65877

14.67641

17.91917

16.56712

19.93417

18.29284

18.61902

5.346034

4.858638

6.488968

7.832895

5.035544

7.010123

18.88276

19.24548

20.5208

16.4665

11.70564

17.73995

15.38447

4.39637

4.567165

5.349356

5.034107

7.118892

5.141466

12.77821

17.2705

15.93981

15.08147

9.590624

9.357137

11.16699

10.24008

10.6849

9.77321

11.44503

10.58898

7.860516

10.25205

12.47572

19.21245

19.55887

16.53421

20.01744

12.60116

6.093727

7.813499

5.027891

5.616919

4.526177

6.438004

18.73292

13.82969

18.62784

9.653968

7.300346

7.745195

6.790587

16.60955

14.78475

12.30555

10.95474

12.50209

10.8973

5.986833

4.908787

15.69198

9.459165

7.243242

5.958311

7.294407

11.83416

10.99324

13.91625

10.80661

11.4565

12.47162

5.421936

5.552593

24.65609

18.66714

21.21759

9.602226

11.71656

20.46269

12.99618

21.97701

12.09692

15.6063

19.23695

15.55281

22.71356

14.83358


5.230044

9.173944

13.49328

7.492967

21.5383

17.85488

14.97972

16.27536

14.84273

21.03103

8.746054

9.013994

18.04988

8.673722

8.000758

13.06898

7.049699

18.43676

21.01978

17.81781

20.10398

16.71799

17.85589

7.113171

9.228168

29.64705

15.33104

12.16188

10.86277

8.676411

15.19772

8.805982

11.35369

11.42458

17.29854

10.14888

15.05066

10.99853

14.67169

8.753443

13.60424

7.753461

11.73798

10.70706

13.93493

17.04674

10.61834

17.26923

9.024757

10.5172

9.230604

17.53925

17.07431

18.34629

19.57008

16.73676

5.434781

7.1489

7.091371

9.048865

9.99001

8.14988

20.31056

14.24647

20.69758

18.44679

12.08528

17.60233

16.81466

5.179084

7.929368

8.221612

9.08431

10.45424

9.616491

11.26768

17.19219

21.94119

13.21779

18.97167

17.60738

13.05912

4.349963

4.643731

5.838575

6.920088

6.069535

3.96049

12.38758

13.87338

13.18865

63

20.6768

21.48323

15.93901

19.52142

4.677186

6.936539

6.402105

5.046487

6.490509

4.091663

11.91962

11.15409

18.57979

9.405523

10.00942

9.871339

11.88051

10.04379

7.541492

6.068236

6.106429

9.174301

6.376661

10.24181

9.728969

17.78805

13.45586

12.68518

14.61901

20.14337

4.039836

6.583298

7.424788

6.668358

6.296646

5.99014

12.8245

19.99579

19.11405

9.563504

10.90527

8.201147

7.618941

13.41341

10.90264

17.05234

13.33295

8.905805

12.93277

4.85317

5.355017

13.45787

6.905499

10.64611

7.191571

6.887632

16.95003

14.20331

10.8188

9.966742

11.9125

17.05939

3.148555

6.996395

24.78904

17.30127

21.57717

9.203903

17.59249

17.95384

12.27804

14.0598

22.60697

16.31479

18.02121

13.18885

17.82501

9.337495

16. tPCA_SG02m1_SG01m2 – sample 1Selected column: 1 2 3 4 6 5 7 8 10 9 11 12 13

5.424458

4.811587

13.00595

15.79765

15.65521

13.75173

19.10655

17.56897

17.52464

17.61336

14.91812

12.84447

25.23983

4.398413

3.387915

14.99519

14.05067

15.84281

20.73825

20.44117

12.46509

14.93791

18.90022

11.33034

15.00621

15.91542

14.78091

16.47319

4.321577

8.742226

15.46492

13.7546

14.14508

16.96194

12.98499

9.844078

8.087732

6.939008

10.3231

12.941

13.52397

5.422781

4.488467

17.79516

11.32969

14.4279

9.339123

11.14322

16.62842

7.00496

7.145763

16.16218

10.73266

12.22492

14.79116

11.85118

8.601917

6.815655 10.82

10.11956

7.92178

10.64801

13.55002

14.20125

16.45048

10.20395

9.882667

12.45687

9.37925

4.694004

6.047822

7.519087

6.027362

10.96988

8.566958

11.00765

6.561972

12.55629

17.38902

13.74274

16.23832

9.357152

3.695398

2.821595

4.4614

2.544036

3.014436

4.68874

10.22177

7.977053

13.19448

15.38125

12.54171

14.07558

17.85298

4.745641

2.901011

3.611884

3.837817

3.584763

3.120768

10.74527

11.77746

22.30903

16.17531

18.12073

8.778651

8.512044

7.984424

4.900754

6.022315

6.265884

4.973989

5.683288

6.403048

8.291482

20.56943

15.28735

14.23132

14.44473

9.90431

3.971608

5.849798

2.344853

2.19155

2.565998

3.975133

8.94686

11.58449

12.32078

10.64181

13.24035

7.849609

4.959186

8.497783

12.37379

11.93373

8.197348

10.36217

7.79029

4.709282

4.923749

18.92547

8.409135

11.27117

8.227462

4.062974

9.68827

12.00759

9.962996

12.19038

9.449235

9.283294

5.335858

5.285334

16.23295

22.21336

15.17085

12.6355

12.54107

13.28435

22.09169

23.29707

23.06059

19.29556

21.37792

11.3844

10.64495

12.3828


3.592046

5.404575

13.91778

12.47612

13.52869

11.77966

13.55249

23.01509

15.02362

11.79869

11.9569

14.54463

17.01813

64

5.070753

3.847694

15.86511

12.72469

15.18477

19.64414

21.52166

14.99867

18.81139

21.39398

11.56923

13.71804

16.46142

15.16897

10.85126

8.198583

5.463104

14.09906

15.79734

14.64859

18.04453

12.13213

16.58397

8.247931

10.6973

18.46903

8.834626

14.48359

4.968282

7.568663

11.84233

17.2799

9.726869

9.869705

10.31434

10.91775

5.647333

5.77072

10.5277

14.20015

16.15145

15.48967

11.16389

9.842232

5.861102

10.18583

8.588253

7.95449

6.689593

8.978858

10.69874

16.08058

8.031991

10.32727

13.11783

14.51046

5.556986

4.849953

8.981534

8.128322

7.538956

8.391158

7.883475

11.09286

11.95492

21.12559

20.49579

12.99412

9.645196

4.58075

4.496522

4.082488

3.015974

2.996858

4.335489

9.061759

13.01205

18.27007

13.41651

20.0693

14.88549

17.60026

4.370435

4.715755

3.773165

2.089333

3.102269

3.388456

8.979965

13.00178

19.66597

17.04744

11.09844

8.065473

9.407344

5.213195

8.190201

5.701156

4.621847

3.714056

5.165803

6.755732

7.862419

13.36924

20.45041

14.81743

16.4767

12.37371

4.222834

4.845065

3.72061

3.149373

2.45953

3.846955

13.20699

11.3177

11.449

13.34108

10.18644

4.209346

6.845155

6.953323

12.39988

6.961262

7.956145

9.440664

13.07388

5.307739

4.872298

11.56257

11.90806

10.89474

7.445201

5.251302

13.34793

15.40457

13.05807

13.79118

8.871544

11.16097

4.915755

5.648719

14.99614

12.23988

19.91691

8.080714

10.11138

13.57767

19.39533

19.56293

13.87251

12.24804

13.74375

15.83086

13.80793

14.1874


3.375219

5.219252

14.31624

9.050521

18.93169

13.38717

21.06337

21.35008

19.32863

16.5892

13.20385

10.15836

20.04981

4.292306

4.147802

11.01116

12.01319

18.09243

20.12388

20.69867

21.23166

22.34053

17.85358

10.63579

13.09662

17.13725

9.690886

16.42269

7.42528

5.757552

11.47921

12.13746

10.41196

17.45945

10.50058

11.23133

6.523933

9.070553

11.71479

14.81147

11.9066

6.087123

5.755684

15.99417

12.80683

12.07248

10.7301

13.15595

12.1823

7.377422

6.941759

10.56569

17.05123

15.30306

16.54293

12.76352

6.103776

5.8893

11.05403

7.675261

8.94923

6.059968

13.23638

12.14609

20.84362

7.946453

8.84365

10.96989

11.08099

7.939311

5.939856

7.017204

8.272496

6.153057

8.00219

10.17064

10.61564

22.43751

14.68049

13.27418

14.58217

13.70622

5.341835

3.93151

3.597676

2.107087

4.552751

3.858132

12.44094

13.83467

17.20568

20.82388

22.26264

15.22561

17.77575

4.628765

4.257338

3.894736

3.00455

5.054737

4.499189

10.68052

13.39546

22.49457

9.734108

10.57312

9.348746

7.529456

5.183067

4.768048

4.75073

6.513425

3.661868

4.732931

7.109706

5.886283

11.12034

13.26322

16.20658

14.48416

14.69706

3.841493

4.908886

2.973518

3.256619

2.841914

2.724467

12.44028

10.06956

11.82444

65

9.459755

10.216

4.270701

4.879727

11.23847

8.422001

9.98946

10.32667

12.40118

8.629548

4.311147

5.175985

18.56712

8.154708

8.201664

5.372759

4.907595

12.75521

11.58122

8.487277

12.62819

10.15123

13.21022

3.48171

4.731004

18.03088

20.4969

18.03266

12.30997

9.917161

15.79901

13.74762

21.86056

16.92216

22.13221

20.90532

9.738838

14.34869

11.00546


3.049883

3.053281

13.28598

10.47406

11.9487

11.20343

14.46482

14.27338

19.71589

13.20009

11.7427

12.84738

18.40796

5.169739

5.2292

9.907067

9.273449

17.73459

16.48919

13.59272

19.78309

18.59548

15.17294

14.50837

10.07712

25.69065

13.95507

13.88062

6.68048

5.904709

10.60515

11.87388

13.57684

15.26723

9.00906

12.7833

6.114424

6.882851

13.96822

15.67004

14.69547

4.070129

4.445555

17.41001

15.83174

12.34701

13.48739

9.938334

11.15553

5.406725

8.058443

14.54158

16.29315

17.75828

9.731058

18.31798

5.93671

7.978702

5.891166

7.309692

9.222357

6.983534

12.84013

8.762967

20.52048

15.47297

8.695435

11.64543

14.84109

4.99176

3.973413

8.700448

8.008715

9.009306

7.242873

7.511131

11.36963

19.02847

11.26606

15.01942

14.67176

14.04016

5.005681

3.83032

3.111678

3.867737

2.794366

4.612809

7.940712

8.785655

15.96427

15.00752

12.54804

10.74504

9.499252

3.432839

4.866704

3.333336

3.490368

4.834302

3.089305

10.29296

14.66086

14.60473

17.87204

10.78136

7.497682

8.264414

5.318417

8.769926

3.927645

5.610393

4.606895

5.276644

6.87254

8.740085

12.57531

14.21621

13.60245

10.49489

15.45044

6.641099

6.492415

2.535115

3.917889

3.431551

4.090847

9.775138

12.71822

20.88942

7.41008

7.068839

4.177381

5.561604

12.41756

10.81659

11.71255

10.20491

10.44198

12.90299

3.043357

4.056316

10.14806

9.603151

9.346874

6.662555

4.975775

11.51611

15.16363

12.70786

11.63562

14.98791

14.64127

4.694354

5.990468

15.70993

22.4824

20.52437

12.92134

10.33993

23.13088

21.17198

17.3641

14.13069

12.33542

13.57565

13.31744

14.88268

13.65408


3.249547

4.672263

11.11226

13.14088

12.60554

11.91219

19.10702

18.99064

19.24419

18.73553

11.51191

13.59746

19.01812

2.815355

4.203403

13.7187

10.95686

13.1524

11.36645

21.78706

21.28215

13.02931

13.31015

14.1244

9.021721

19.57395

16.19009

11.63649

7.262622

10.08686

17.79567

10.65749

9.319564

16.83823

15.12704

10.38788

7.494591

8.113641

15.37459

13.60706

14.49235

4.584411

7.155255

10.53414

10.77933

16.53655

10.3662

15.5176

12.23678

5.895356

6.998665

14.96063

10.42 13.80 14.46 14.64 6.219 8.212 8.595 6.874 9.010 5.739 14.55 11.57 13.8066

068 152 709 998 748 701 637 742 258 532 112 63 02511.47

5887.960

91411.98

09911.31

7698.291

1097.133

6519.742

9966.167

1789.900

1277.011

078.271

189.887

32211.74

17716.65

5415.80

09815.47

19711.91

2695.361

213.805

7453.656

6413.635

1124.278

6844.526

3288.972

7099.061

32417.96

16220.40

21918.77

5559.790

78817.32

4045.603

6094.087

0722.844

4823.918

7263.004

124.110

37510.35

6017.913

72612.36

60715.38

49912.21

48211.95

338.857

2017.919

3865.613

8634.631

3844.167

6476.602

765.498

528.633

2637.744

38912.05

95517.49

58520.22

1819.649

1516.11

2714.917

4064.621

9053.114

6792.938

4133.022

1464.076

70210.39

0049.919

0920.75

17110.84

9877.742

4286.895

7445.263

3387.577

83211.41

2877.350

88312.11

42211.14

9369.225

6282.806

5795.188

04217.88

2996.517

0968.246

3746.509

2595.942

9877.313

93914.57

058.069

0628.591

87815.35

95610.12

544.150

8165.306

38315.59

35322.87

58520.16

23512.95

2779.989

25521.60

15713.68

28216.19

35523.66

64317.39

12115.65

31117.48

05617.16

36612.48

69

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3. MAPPING

After clustering of instances according to their pattern labels, the instances are then aligned across datasets using the pattern labels, which results in a subsequence reordering. As illustrated in the right-hand graphs in Figure 2, the point-sequence curves for metrics IN-O1 and IN-LOCC are manifestly more similar after reordering subsequences in the two curves by aligning instances that belong to the same (or similar) patterns.

Figure 2: Left, IN-LOCC and IN-O1 point-sequence curves prior to grouping and reordering. Right, Labeled point-sequence curves for metrics IN-O1 and IN-LOCC after grouping and reordering points with the same labels.

After ontology-based alignment of the subsequences, we carried out three pre-processing steps: (1) Normalization, i.e., scaling all the sequence values to unit range; (2) Smoothing using a moving average method; and (3) Interpolation of curves if the number of points in two point-sequence curves (for two value vectors) is different. Figure 3 illustrates the results of normalization, smoothing and interpolation to the point-sequence curves of IN-O1 and IN-LOCC in Figure 3.

Figure 3: After normalization, smoothing, and interpolation of point-sequence curves showed in Figure. 2.

The following heuristic assumptions are adopted in our sequence matching procedure.

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First, we assume that the two datasets from which these alternative metrics are extracted contain the same or similar ERP patterns. This assumption is critical, since we seek to align ERP patterns (clusters) across the two point-sequence curves.

Second, we assume there exists a 1-to-1 mapping between pairs of metrics from the alternative sets of metrics. In other words, there must be no cells selected within the same column.

Table 1. Violation of 1-1 mapping assumption and the solution.

For example, Table 1(a) illustrates a scenario where the 1-to-1 mapping assumption is violated: if we select cells with minimum distance value in each row, we end up with two cells within the same column being selected, which in the present case would suggest that both IN-LOCC and IN-ROCC are mapped to IN-O2. Table 1(b) illustrates the solution: cells are selected using the 1-to-1 mapping heuristic coupled with the global minimum heuristic.

Finally, we assume a global minimum heuristic: we select those cells whose Euclidean distance values sum up to a minimum value.

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Table 3. Cross-spatial join of data from SG01-m1 and SG02-m2 (tPCA).

Table 2. Solution to A using global minimum heuristic

For example, Table 2 shows two alternative selections of cells without violating to the 1-to-1 mapping heuristic. The global minimum heuristic requires us to favor 2(b) because 4.08+3.57 < 3.74+4.01. The selection of cells that achieves the global minimum suggests the most stable mapping result. The global minimum heuristic requires a non-greedy implementation which should take into consideration all possible selections. When the number of metrics is large, this implementation becomes more computationally challenging.

The experiment is conducted on the simulated datasets described in Section 3.1. The test cases for the matching discovery experiment are derived as follows: each test case contains a source and target dataset, which are pulled respectively from one subject group characterized with one metric set, and from the other

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Table 3. Cross-spatial join of data from SG01-m1 and SG02-m2 (tPCA).

Figure 4. Ensemble result from the 20 subsequence mapping case studies.

subject group with the alternative metric set; both the source and target are formulated under the same decomposition method. This yields four test cases, each of which includes two different datasets and two alternative metric sets.

In order to test the robustness of the proposed methods, we replicate the datasets for each test case into five copies with different random ordering of the instances, thus resulting a total of 20 enriched test cases. We test our method on each of these test cases. Table 3, for example, shows a distance table calculated by cross-spatial join of tPCA-derived data from SG01-m1 and SG02-m2. The highlighted cells indicate similarity pairs between two point-sequence curves representing two metrics (row header and column header which meet at this cell) and are selected by using the 1-to-1 mapping and global minimum heuristics described in Section 3.5. A similarity pair represents a potential mapping discovered by our methods.

For example, from this table we derive the following mappings: IN-O1↔ IN-LOCC, IN-O2↔IN-ROCC, IN-C3↔IN-LPAR, etc. Note that the row header and column header are arranged in the particular order such that the golden standard mapping falls along the diagonal cells. Therefore we can easily conclude that the precision of mapping in this test case is 9/13=69.2% since 4 out of 13 highlighted cells are shifted off from the diagonal.

The performance of our methods among the 20 test cases was quite good. Table 3 summarizes the precision for each test case.

Table 3. Precision results for 20 test cases

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Figure 4. Ensemble result from the 20 subsequence mapping case studies.

The table consists of four quadrants, each of which illustratess precision mesure for the datasets generated by five samples of replication to the original four test schemes with random instance ordering. Since the precision of mapping by making a random guess in each test case is almost zero, the precision of our method appears markedly robust.

Combining the mapping results in the 20 test cases into an ensemble model by a majority vote of each individual mapping result, we obtained the ensemble mapping result shown in Figure 4. The overall result is 11/13=84.6%

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