Differences between revisions 30 and 148 (spanning 118 versions)

Tutorial 27: Workflows

Authors: Francois Tadel, Elizabeth Bock, Dimitrios Pantazis, John Mosher, Richard Leahy, Sylvain Baillet

This page provides some general recommendations for your event-related analysis. It is not directly related with the auditory dataset, but provides guidelines you should consider for any MEG/EEG experiment.
We do not provide standard analysis pipelines for resting or steady state recordings yet, but we will add a few examples soon in the section Other analysis scenarios of the tutorials page.

Contents

What is your question?
Common pre-processing pipeline
EEG recordings
MEG recordings
Constrained cortical sources
Unconstrained cortical sources
Regions of interest (scouts)
1. Statistics: Single subject
2. Statistics: Group analysis, within subject
Time-frequency maps

What is your question?

The most appropriate analysis pipeline for your data depends on the question you are trying to answer. Before defining what are the main steps of your analysis, you should be able to state clearly the question you want to answer with your recordings.

What dimension?

MEG/EEG recordings
Cortical sources
- Individual anatomy or template
- Constrained (one value per vertex) or unconstrained (three values per grid point)
- Full cortex or regions of interests
Frequency or time-frequency maps

What kind of experiment?

Single subject: Contrast two experimental conditions across trials, for one single subject.
- Files A: Single trials for condition A.
- Files B: Single trials for condition B.
Group analysis, within subject: Contrast two conditions A and B measured for each subject.
- Files A: Subject-level averages for condition A (all the subjects).
- Files B: Subject-level averages for condition B (all the subjects).
Group analysis, between subjects: Contrast two groups of subjects for one condition.
- Files A: Subject-level averages for group #1 (G1).
- Files B: Subject-level averages for group #2 (G2).

What level of precision?

Difference of averages
Statistically significant differences between conditions or groups

What statistical test?

A = B
- Tests the null hypothesis H0:(A=B) against the alternative hypothesis H1:(A≠B)
- Correct detection: Identify correctly where and when the conditions are different.
- Ambiguous sign: We cannot say which condition is stronger.
Power(A) = Power(B)
- Tests the null hypothesis H0:(Power(A)=Power(B)) against the alternative hypothesis H1:(Power(A)≠Power(B))
- Incorrect detection: Not sensitive to the cases where A and B have opposite signs.
- Meaningful sign: We can identify correctly which condition has a stronger response.
- Power(x) = |x|², where |x| represents the modulus of the values:
  - Absolute value for scalar values (recordings, constrained sources, time-frequency)
  - Norm of the three orientations for unconstrained sources.
Multiple comparisons: FDR is a good choice for correcting p-values for multiple comparisons.

Design considerations

Use within-subject designs whenever possible (i.e. collect two conditions A and B for each subject), then contrast data at the subject level before comparing data between subjects.
Such designs are not only statistically optimal, but also ameliorate the between-subject sign ambiguities as contrasts can be constructed within each subject.

Common pre-processing pipeline

Most event-related studies can start with the pipeline we've introduced in these tutorials.

Import the anatomy of the subject (or use a template for all the subjects).
Access the recordings:
- Link the continuous recordings to the Brainstorm database.
- Prepare the channel file: co-register sensors and MRI, edit type and name of channels.
- Edit the event markers: fix the delays of the triggers, mark additional events.
Pre-process the signals:
- Evaluate the quality of the recordings with a power spectral density plot (PSD).
- Apply frequency filters (low-pass, high-pass, notch).
- Identify bad channels and bad segments.
- Correct for artifacts with SSP or ICA.
Import the recordings in the database: epochs around some markers of interest.

How many trials to include?

Single subject: Include all the good trials (unless you have a very low number of trials).
See the averaging tutorial.
Group analysis: Use a similar numbers of trials for all the subjects (no need to be strictly equal), reject the subjects for which you have much less good trials.

EEG recordings

Average

Average the epochs across acquisition runs: OK.
Average the epochs across subjects: OK.
Electrodes are in the same standard positions for all the subjects (e.g. 10-20).
Never use an absolute value for averaging or contrasting sensor-level data.

Statistics: Single subject

A = B: Parametric or non-parametric t-test, independent, two-tailed.

Statistics: Group analysis, within subject

A = B
- First-level statistic: For each subject, sensor average for conditions A and B.
- Second-level statistic: Parametric or non-parametric t-test, paired, two-tailed.

Statistics: Group analysis, between subjects

G1 = G2
- First-level statistic: For each subject, sensor average for the conditions to test.
- Second-level statistic: Parametric/non-parametric t-test, independent, two-tailed.

MEG recordings

Average

Average the epochs within each acquisition runs: OK.
Average across runs: Not advised because the head of the subject may move between runs.
Average across subjects: Strongly discouraged because the shape of the heads vary but the sensors are fixed. One sensor does not correspond to the same brain region for different subjects.
Tolerance for data exploration: Averaging across runs and subjects can be useful for identifying time points and sensors with interesting effects but should be avoided for formal analysis.
Note for Elekta/MaxFilter users: You can align all acquisition run to a reference run, this will allow direct channel comparisons and averaging across runs. Not recommended across subjects.
Never use an absolute value for averaging or contrasting sensor-level data.

Statistics: Single subject

A = B: Parametric or non-parametric t-test, independent, two-tailed.

Statistics: Group analysis

Not recommended with MEG recordings: do your analysis in source space.

Constrained cortical sources

Average: Single subject

Sensor average: Compute one sensor-level average per acquisition run and per condition.
Sources: Estimate sources for each average (constrained, no normalization).
Source average: Average the source-level run averages to get one subject average.
Compute a weighted average to balance for different numbers of trials across runs.
Low-pass filter your evoked responses (optional).
- If you filter the average before normalizing wrt baseline, it will lead to an underestimation of the baseline variance, and therefore to an overestimation of the Z scores computed in the next step, especially if the baseline is too short (typically less than 200 time points). The filter increases the autocorrelation of the time series, and therefore biases the estimation of the signal variance (Wikipedia).
- You have to take into account the possible edge effects due to the filter. You can either extract a small time window or exclude the beginning of the baseline for the normalization.
Normalize the subject min-norm averages: Z-score wrt baseline (no absolute value).
Justification: The amplitude range of current densities may vary between subjects because of anatomical or experimental differences. This normalization helps bringing the different subjects to the same range of values.
Do not rectify the cortical maps, but display them as absolute values if needed.

Average: Group analysis

Subject averages: Compute within-subject averages for all the subjects, as described above.
Rectify the cortical maps (process: Pre-process > Absolute value).
Justification: Cortical maps have ambiguous signs across subjects: reconstructed sources depend heavily on the orientation of true cortical sources. Given the folding patterns of individual cortical anatomies vary considerably, cortical maps have subject-specific amplitude and sign ambiguities. This is true even if a standard anatomy is used for reconstruction.
Project the individual source maps on a template (only when using the individual brains).
For more details, see tutorial: Group analysis: Subject coregistration.
Group average: Compute grand averages of all the subjects.
Do not use a weighted average: all the subjects should have the same weight in this average.
Smooth spatially the source maps (optional).
You can smooth after step #3 for computing non-parametric statistics with the subject averages. For a simple group average, it is equivalent to smooth before of after computing the average.

Difference of averages: Within subject

Sensor average: Compute one sensor-level average per acquisition run and condition.
Sources: Estimate sources for each average (constrained, no normalization).
Source average: Average the source-level session averages to get one subject average.
Subject difference: Compute the difference between conditions for each subject #i: (A_i-B_i)
Low-pass filter the difference (optional)
Normalize the difference: Z-score wrt baseline (no absolute value): Z(A_i-B_i)
Rectify the difference (apply an absolute value): |Z(A_i-B_i)|
Project the individual difference on a template (only when using the individual brains).
Group average: Compute grand averages of all the subjects: avg(|Z(A_i-B_i)|).
Smooth spatially the source maps (optional).

Difference of averages: Between subjects

Grand averages: Compute averages for groups #1 and #2 as in Average:Group analysis.
Difference: Compute the difference between group-level averages: avg(|G₁|)-avg(|G₂|)
Limitations: Because we rectify the source maps before computing the difference, we lose the ability to detect the differences between equal values of opposite signs. And we cannot keep the sign because we are averaging across subjects. Therefore, many effects are not detected correctly.

Statistics: Single subject

A = B: Parametric or non-parametric
- Compute source maps for each trial (constrained, no normalization).
- Parametric or non-parametric two-sample t-test, independent, two-tailed.
  Identifies correctly where and when the conditions are different (sign not meaningful).
- Directionality: Additional step to know which condition has higher values.
  Compute the difference of rectified averages: |avg(A_i)|-|avg(B_i)|
  Combine the significance level (t-test) with the direction (difference): See details.
|mean(A)| = |mean(B)|: Non-parametric
- Compute source maps for each trial (constrained, no normalization).
- Non-parametric independent two-sample "absolute mean test", two-tailed.
  T = (|mean(A)|-|mean(B)|) / sqrt(|var(A)|/N_A + |var(B)|/N_B)
- Interesting alternative that provides at the time a correct estimation of the difference (where and when) and the direction (which condition has higher values).

Statistics: Group analysis, within subject

Power(A-B) = 0: Parametric
- First-level statistic: Rectified difference of normalized averages.
  Proceed as in Difference of averages: Within subjects, but stop before the group average (after step #8). You obtain one measure |A_i-B_i| per subject, test these values against zero.
- Second-level statistic: Parametric one-sample Chi²-test.
  Power = sum(|A_i-B_i|²), i=1..N_subj ~ Chi²(N_subj)
- Identifies where and when the conditions are different (sign not meaningful).
- Warning: Very sensitive test, with lots of false positive (all the brain can be "significant")
|A| = |B|: Parametric or non-parametric
- First-level statistic: Rectified and normalized subject averages.
  Proceed as in Average: Group analysis to obtain two averages per subject: A_i and B_i.
- Second-level statistic: Parametric or non-parametric two-sample t-test, paired, two-tailed.
- This test does not consider the sign difference within a subject, and therefore cannot detect correctly when A and B have opposite signs. Works well and indicates which condition has higher values when A and B have the same sign within a subject.
A = B: Parametric or non-parametric [anatomy template only]
- First-level statistic: Normalized subject averages (not rectified, no projection needed).
  Proceed as in Average: Single subject to obtain two averages per subject: A_i and B_i.
- Second-level statistic: Parametric or non-parametric two-sample t-test, paired, two-tailed.
- Applies only if all the subjects are sharing the same template anatomy.
  Not recommended when using individual anatomies because of the sign issue between subjects (the signs might be opposed between two subjects, and the projection of non-rectified values to a template might be inaccurate).
Power(A) = 0: Parametric
- First-level statistic: Rectified and normalized subject averages.
  Proceed as in Average: Group analysis to obtain one average per subject #i: |A_i|.
- Second-level statistic: Parametric one-sample Chi²-test.
  PowerA = sum(|A_i|²), i=1..N_subj ~ Chi²(N_subj).

Statistics: Group analysis, between subjects

|G1| = |G2|: Non-parametric
- First-level statistic: Rectified and normalized subject averages.
  Proceed as in Average: Group analysis to obtain one average per subject.
- Second-level statistic: Non-parametric two-sample t-test, independent, two-tailed.
Power(G1) = Power(G2): Parametric
- First-level statistic: Rectified and normalized subject averages.
  Proceed as in Average: Group analysis to obtain one average per subject: |Ai|.
- Second-level statistic: Parametric two-sample power F-test.
  PowerG1 = sum(A_i²), i=1..N₁ ~ Chi²(N₁)
  PowerG2 = sum(A_j²), j=1..N₂ ~ Chi²(N₂)
  F(N₁,N₂) = (PowerG1 / N₁) / (PowerG2 / N₂)

Unconstrained cortical sources

Three values for each grid point, corresponding to the three dipoles orientations (X,Y,Z).
We want only one statistic and one p-value per grid point in output.

Averages

Proceed as indicated above for constrained cortical sources.
Just replace the step Rectify with Flatten (process: Sources > Unconstrained to flat map).
The operator |A| has to be interpreted as "norm of the three orientations":
|A| = sqrt(A_x² + A_y² + A_z²)

Statistics: Single subject

|mean(A)| = |mean(B)|: Non-parametric
- Compute source maps for each trial (unconstrained, no normalization).
- Non-parametric independent two-sample "absolute mean test", independent, two-tailed.
  T = (|mean(A)|-|mean(B)|) / sqrt(|var(A)|/N_A + |var(B)|/N_B)
- Provides at the time a correct estimation of the difference (where and when) and the direction (which condition has higher values).

Statistics: Group analysis, within subject

Power(A-B) = 0: Parametric
- First-level statistic: Flattened difference of normalized averages.
  Proceed as in Difference of averages: Within subjects, but stop before the group average (after step #8). You obtain one measure |A_i-B_i| per subject, test these values against zero.
- Second-level statistic: Parametric one-sample Chi²-test for unconstrained sources.
  Power = sum(|A_i-B_i|²), i=1..N_subj ~ Chi²(3*N_subj)
- Identifies where and when the conditions are different (sign not meaningful).
- Warning: Very sensitive test, with lots of false positive (all the brain can be "significant")
|A| = |B|: Parametric or non-parametric
- First-level statistic: Flattened and normalized subject averages.
  Proceed as in Average: Group analysis to obtain two averages per subject: A_i and B_i.
- Second-level statistic: Parametric or non-parametric two-sample t-test, paired, two-tailed.
- This test does not consider the sign difference within a subject, and therefore cannot detect correctly when A and B have opposite signs. Works well and indicates which condition has higher values when A and B have the same sign within a subject.
Power(A) = 0: Parametric
- First-level statistic: Flattened and normalized subject averages.
  Proceed as in Average: Group analysis to obtain one average per subject #i: |A_i|.
- Second-level statistic: Parametric one-sample Chi²-test for unconstrained sources.
  PowerA = sum(|A_i|²) = sum(A_ix²+A_iy²+A_iz²), i=1..N_subj ~ Chi²(3*N_subj).

Statistics: Group analysis, between subjects

|G1| = |G2|: Non-parametric
- First-level statistic: Flattened and normalized subject averages.
  Proceed as in Average: Group analysis to obtain one average per subject.
- Second-level statistic: Non-parametric two-sample t-test, independent, two-tailed.
Power(G1) = Power(G2): Parametric
- First-level statistic: Flattened and normalized subject averages.
  Proceed as in Average: Group analysis to obtain one average per subject: |Ai|.
- Second-level statistic: Parametric two-sample power F-test (unconstrained sources).
  PowerG1 = sum(A_ix²+A_iy²+A_iz²), i=1..N₁ ~ Chi²(3*N₁)
  PowerG2 = sum(A_jx²+A_jy²+A_jz²), j=1..N₂ ~ Chi²(3*N₂)
  F = (PowerG1 / N₁) / (PowerG2 / N₂) ~ F(3*N₁,3*N₂)

Regions of interest (scouts)

Statistics: Single subject

Even within-subject cortical maps have sign ambiguities. MEG/EEG source mapping has limited spatial resolution and sources in opposing sulcal/gyral areas are typically reconstructed with inverted signs (constrained orientations only). Averaging activity in cortical regions of interest (scouts) would thus lead to signal cancelation. To mitigate this issue, Brainstorm uses algorithms to change the polarity of individual source time series before averaging across a scout cortical region. Please note however that the polarity of the resulting time series remains ambiguous, as it is determined by statistical considerations, not by physiological information.
We therefore recommend that you perform subject-wise averaging/contrast before averaging scout time series. You may also consider aggregating scout source time series using PCA instead of averaging, across all files (all epochs and conditions) from a subject together, ensuring the same component is applied everywhere and therefore mitigating further some of the ambiguities concerning the polarity of the source time series.

Statistics: Group analysis, within subject

Comparison of scout time series between subjects is tricky because there is no way to avoid sign ambiguity for different subjects. Thus there are no clear recommendations. Rectifying before comparing scout time series between subjects can be a good idea or not depending on different cases.
Having a good understanding of the data (multiple inspections across channels/sources/subjects) can offer hints whether rectifying the scout time series is a good idea. Using unconstrained cortical maps to create the scout time series can ameliorate ambiguity concerns.

Time-frequency maps

Average: Single subject

Single trials: Compute time-frequency maps for each trial (magnitude, no normalization).
It is a more standard analysis to take the square root of power before a t-test.
Subject average: Average the time-frequency maps together, separately for each condition. This can be done automatically when computing the TF decompositions (option "Save averaged time-frequency maps" in the process options).
The values are all strictly positive, there is no sign ambiguity: you can directly subtract the averages of the two conditions and interpret the sign of the difference.
If you average time-frequency maps computed on sensor-level data, the same limitations apply as for averaging sensor level data (see sections about MEG and EEG recordings above).

Average: Group analysis

Subject averages: Compute within-subject averages for all the subjects, as described above.
Normalize the subject averages: ERD/ERS or Z-score wrt baseline.
Group average: Compute grand averages of all the subjects.
Difference of averages: Simply compute the difference of the group averages.

Statistics: Single subject

A = B: Parametric or non-parametric
- Compute time-frequency maps for each trial (magnitude, no normalization).
- Parametric or non-parametric two-sample t-test, independent, two-tailed.

Statistics: Group analysis, within subject

A = B: Parametric or non-parametric [anatomy template only]
- First-level statistic: Normalized subject averages (ERS/ERD or Z-score).
  Proceed as in Average: Group analysis to obtain two averages per subject: A_i and B_i.
- Second-level statistic: Parametric or non-parametric two-sample t-test, paired, two-tailed.

Statistics: Group analysis, between subjects

G1 = G2: Non-parametric
- First-level statistic: Normalized subject averages (ERS/ERD or Z-score).
  Proceed as in Average: Group analysis to obtain one average per subject.
- Second-level statistic: Parametric or non-parametric two-sample t-test, independent, two-tailed.

-  ⇤ ← Revision 30 as of 2016-02-08 23:54:56 → 
  Size: 16167
  Editor: FrancoisTadel
  Comment:
+   ← Revision 148 as of 2023-06-05 18:42:05 → ⇥
  Size: 23558
  Editor: SylvainBaillet
  Comment:
-Deletions are marked like this.
+Additions are marked like this.
 Line 2:
-'''[TUTORIAL UNDER DEVELOPMENT: NOT READY FOR PUBLIC USE] '''

''Authors: Francois Tadel, Elizabeth Bock, Dimitrios Pantazis, Richard Leahy, Sylvain Baillet''

This  page provides some general recommendations for your event-related  analysis. It is not directly related with the auditory dataset, but  provides guidelines you should consider for any MEG/EEG experiment. We  do not provide standard analysis pipelines for resting or steady state  recordings yet, but we will add a few examples soon in the section [[http://neuroimage.usc.edu/brainstorm/Tutorials#Other_analysis_scenarios|Other analysis scenarios]] of the tutorials page.
+''Authors: Francois Tadel, Elizabeth Bock, Dimitrios Pantazis, John Mosher, Richard Leahy, Sylvain Baillet''

This  page provides some general recommendations for your event-related  analysis. It is not directly related with the auditory dataset, but  provides guidelines you should consider for any MEG/EEG experiment. <<BR>>We  do not provide standard analysis pipelines for resting or steady state  recordings yet, but we will add a few examples soon in the section [[http://neuroimage.usc.edu/brainstorm/Tutorials#Other_analysis_scenarios|Other analysis scenarios]] of the tutorials page.
-Line 11:
+Line 9:
-The most appropriate analysis pipeline for your data depends on the question you are trying to answer.

What is the objective you have with your data?

 * Contrast two experimental conditions across trials, for '''one single subject''' (within subject)
  * Files A: Single trials for condition A.
  * Files B: Single trials for condition B.
  * Use independent tests, or difference of averages.
 * Contrast two groups of subjects for one given experimental condition (between subjects)
  * Files A: Averages for group of subjects #1.
  * Files B: Averages for group of subjects #2.
  * Use '''independent tests''', or difference of averages.
 * Contrast two experimental conditions across multiple subjects (between subjects)
  * Files A: All subjects, average for condition A.
  * Files B: All subjects, average for condition B.
  * Use '''paired tests''' (= dependent tests), or average of differences.

What are the dimensions you want to explore?
+The most appropriate analysis pipeline for your data depends on the question you are trying to answer. Before defining what are the main steps of your analysis, you should be able to state clearly the question you want to answer with your recordings.

==== What dimension? ====
-Line 31:
+Line 13:
- * Cortical sources:
+ * Cortical sources
-Line 35:
+Line 17:
- * Time-frequency dimensions

What level of precisions you want to get?

 * Averages / difference of averages
 * Identify statistically significant differences

'''[TODO: WHEN TO USE WHAT]'''
+ * Frequency or time-frequency maps

==== What kind of experiment? ====
 * '''Single subject''': Contrast two experimental conditions across trials, for one single subject.
  * Files A: Single trials for condition A.
  * Files B: Single trials for condition B.
 * '''Group analysis, within subject''': Contrast two conditions A and B measured for each subject.
  * Files A: Subject-level averages for condition A (all the subjects).
  * Files B: Subject-level averages for condition B (all the subjects).
 * '''Group analysis, between subjects''': Contrast two groups of subjects for one condition.
  * Files A: Subject-level averages for group #1 (G1).
  * Files B: Subject-level averages for group #2 (G2).

==== What level of precision? ====
 * Difference of averages
 * Statistically significant differences between conditions or groups

==== What statistical test? ====
 * '''A = B'''
  * Tests the null hypothesis H0:(A=B) against the alternative hypothesis H1:(A<<HTML(&#8800;)>>B)
  * Correct detection: Identify correctly '''where and when''' the conditions are different.
  * Ambiguous sign: We cannot say which condition is stronger.
 * '''Power(A) = Power(B)'''
  * Tests the  null hypothesis H0:(Power(A)=Power(B)) against the alternative hypothesis H1:(Power(A)<<HTML(&#8800;)>>Power(B))
  * Incorrect detection: Not sensitive to the cases where A and B have opposite signs.
  * Meaningful sign: We can identify correctly which condition has a '''stronger response'''.
  * Power(x) = |x|<<HTML(<SUP>2</SUP>)>>, where |x| represents the modulus of the values: <<BR>> - Absolute value for scalar values (recordings, constrained sources, time-frequency) <<BR>> - Norm of the three orientations for unconstrained sources.
 * '''Multiple comparisons''': FDR is a good choice for correcting p-values for multiple comparisons.

==== Design considerations ====
 * Use  within-subject designs whenever possible (i.e. collect two conditions A  and B for each subject), then contrast data at the subject level before  comparing data between subjects.
 * Such designs are not only  statistically optimal, but also ameliorate the between-subject sign  ambiguities as contrasts can be constructed within each subject.
-Line 45:
+Line 51:
-All the event-related studies can start with the pipeline we've introduced in these beginners' tutorials.
+Most event-related studies can start with the pipeline we've introduced in these tutorials.
-Line 53:
+Line 59:
-  * Evaluate the quality of the recordings with a power spectrum density (PSD).
+  * Evaluate the quality of the recordings with a power spectral density plot (PSD).
-Line 59:
+Line 65:
+==== How many trials to include? ====
 * '''Single subject''': Include all the good trials (unless you have a very low number of trials). <<BR>>See the [[http://neuroimage.usc.edu/brainstorm/Tutorials/Averaging#Number_of_trials|averaging tutorial]].
 * '''Group analysis''':  Use a similar numbers of trials for all the subjects (no need to be  strictly equal), reject the subjects for which you have much less good  trials.
-Line 61:
+Line 71:
- * Average the epochs across sessions and subjects: OK.
+ * Average the epochs across acquisition runs: OK.
 * Average the epochs across subjects: OK.
-Line 63:
+Line 74:
-=== Within subject statistics ===
 * '''A vs'''''' B''':
  * Never use an absolute value for testing recordings.
  * Parametric or non-parametric tests, independent, two-tailed, FDR-corrected.
  * Correct effect size (we identify correctly where and when the conditions are different).
  * Ambiguous sign (we cannot say which condition is stronger).

=== Between subjects statistics ===
 * '''(A-B=0)''': Parametric or non-parametric tests, two-tailed, FDR-corrected.
+ * Never use an absolute value for averaging or contrasting sensor-level data.

=== Statistics: Single subject ===
 * '''A ='''''' B''': Parametric or non-parametric t-test, '''independent''', two-tailed.

=== Statistics: Group analysis, within subject ===
 * '''A ='''''' B'''
  * '''First-level statistic''': For each subject, sensor average for conditions A and B.
  * '''Second-level statistic''': Parametric or non-parametric t-test, '''paired''', two-tailed.

=== Statistics: Group analysis, between subjects ===
 * '''G1 ='''''' G2'''
  * '''First-level statistic''': For each subject, sensor average for the conditions to test.
  * '''Second-level statistic''': Parametric/non-parametric t-test, '''independent''', two-tailed.
-Line 75:
+Line 90:
-==== Average ====
 * Average the epochs within each session: OK.
 * Averaging across sessions: Not advised because the head of the subject may move between runs.
 * Averaging across subjects: Strongly discouraged because the shape of the heads vary but the sensors are fixed.
 * Tolerance for data exploration: averaging across runs and subjects can be useful for identifying time points and sensors with interesting effects but should be avoided for formal analysis.
 * Note for Elekta/MaxFilter users: You can align all sessions to a reference session, this will allow direct channel comparisons within-subject. Not recommended across subjects.

==== Within subject statistics ====
 * '''A = ''''''B'''
  * Never use an absolute value for testing recordings.
  * Parametric or non-parametric tests, independent, two-tailed, FDR-corrected.
  * Correct effect size (we identify correctly where and when the conditions are different).
  * Ambiguous sign (we cannot say which condition is stronger).

==== Between subjects statistics ====
+=== Average ===
 * Average the epochs within each acquisition runs: OK.
 * Average across runs: Not advised because the head of the subject may move between runs.
 * Average across subjects: Strongly discouraged because the shape of the heads vary but the sensors are fixed. One sensor does not correspond to the same brain region for different subjects.
 * Tolerance for data exploration: Averaging across runs and subjects can be useful for identifying time points and sensors with interesting effects but should be avoided for formal analysis.
 * Note for Elekta/MaxFilter users: You can align all acquisition run to a reference run, this will allow direct channel comparisons and averaging across runs. Not recommended across subjects.
 * Never use an absolute value for averaging or contrasting sensor-level data.

=== Statistics: Single subject ===
 * '''A ='''''' B''': Parametric or non-parametric t-test, '''independent''', two-tailed.

=== Statistics: Group analysis ===
-Line 93:
+Line 105:
-=== Within-subject average ===
 1. '''Sensor average''': Compute one sensor-level average''' '''per acquisition session and condition (MEG/EEG). <<BR>>Use the '''same number of trials''' for all the averages.
 1. '''Sources''': Estimate sources for each session average (constrained or unconstrained, no normalization).
+=== Average: Single subject ===
 1. '''Sensor average''': Compute one sensor-level average''' '''per acquisition run and per condition.
 1. '''Sources''': Estimate sources for each average (constrained, no normalization).
 1. '''Source average''': Average the source-level run averages to get one subject average.<<BR>>Compute a weighted average to balance for different numbers of trials across runs.
 1. '''Low-pass filter''' your evoked responses (optional).
  * If you filter the average before normalizing wrt baseline, it will lead to an underestimation of the baseline variance, and therefore to an overestimation of the Z scores computed in the next step,  especially if the baseline is too short (typically less than 200 time points). The filter increases the autocorrelation of the time series, and therefore biases the estimation of the signal variance ([[https://en.wikipedia.org/wiki/Unbiased_estimation_of_standard_deviation#Effect_of_autocorrelation_.28serial_correlation.29|Wikipedia]]).
  * You have to take into account the possible [[Tutorials/ArtifactsFilter|edge effects]] due to the filter. You can either extract a small time window or exclude the beginning of the baseline for the normalization.
 1. '''Normalize '''the subject min-norm averages: Z-score wrt baseline (no absolute value).<<BR>>Justification: The amplitude range of current densities may vary between subjects because of anatomical or experimental differences. This normalization helps bringing the different subjects to the same range of values.
 1. '''Do not rectify the cortical maps''', but display them as absolute values if needed.

=== Average: Group analysis ===
 1. '''Subject averages''': Compute within-subject averages for all the subjects, as described above.
 1. '''Rectify''' the cortical maps (process: Pre-process > Absolute value). <<BR>>Justification: Cortical  maps have ambiguous signs across subjects: reconstructed sources depend  heavily on the orientation of true cortical sources. Given the folding  patterns of individual cortical anatomies vary considerably, cortical  maps have subject-specific amplitude and sign ambiguities. This is true even if a standard anatomy is used  for reconstruction.
 1. '''Project '''the individual source maps on a template (only when using the individual brains). <<BR>> For more details, see tutorial: [[Tutorials/CoregisterSubjects|Group analysis: Subject coregistration]].
 1. '''Group average''': Compute grand averages of all the subjects.<<BR>>Do __not__ use a weighted average: all the subjects should have the same weight in this average.

 1. '''Smooth '''spatially the source maps (optional).<<BR>>You can smooth after step #3 for computing non-parametric statistics with the subject averages. For a simple group average, it is equivalent to smooth before of after computing the average.

=== Difference of averages: Within subject ===
 1. '''Sensor average''': Compute one sensor-level average per acquisition run and condition.
 1. '''Sources''': Estimate sources for each average (constrained, no normalization).
-Line 97:
+Line 127:
-. <<HTML(<FONT color="#777777">)>>'''Low-pass filter''' < 40Hz for evoked responses (optional)<<HTML(</FONT>)>>
 1. '''Normalize '''the subject min-norm averages: Z-score vs. baseline (no absolute value).<<BR>>Justification: The amplitude range of current densities may vary between subjects because of anatomical or experimental differences. This normalization helps bringing the different subjects to the same range of values.
 1. '''Do not rectify the cortical maps''', but display them in absolute values.

=== Between-subjects average ===
 1. '''Subject average'''s: Compute the within-subject averages for all the subjects, as described just above.
 1. '''Rectify''' the cortical maps (apply an absolute value). <<BR>>Justification: Cortical  maps have ambiguous signs across subjects: reconstructed sources depend  heavily on the orientation of true cortical sources. Given the folding  patterns of individual cortical anatomies vary considerably, cortical  maps have subject-specific amplitude and sign ambiguities. This is true even if a standard anatomy is used  for reconstruction.
 1. '''Project '''the individual source maps on a template.
 1. '''Smooth '''spatially the sources.<<BR>>Justification: The effects observed with constrained cortical maps may be artifically very focal and not overlapping very well between subjects. Smoothing the cortical maps may help the activated regions overlap between subjects.
+. '''Subject difference''': Compute the difference between conditions for each subject #i: (A,,i,,-B,,i,,)
 1. '''Low-pass filter''' the difference (optional)
 1. '''Normalize '''the difference: Z-score wrt baseline (no absolute value): Z(A,,i,,-B,,i,,)
 1. '''Rectify''' the difference (apply an absolute value): |Z(A,,i,,-B,,i,,)|

 1. '''Project '''the individual difference on a template (only when using the individual brains).
 1. '''Group average''': Compute grand averages of all the subjects: avg(|Z(A,,i,,-B,,i,,)|).

 1. '''Smooth '''spatially the source maps (optional).

=== Difference of averages: Between subjects ===
 1. '''Grand averages''': Compute averages for groups #1 and #2 as in ''Average:Group analysis.''

 1. '''Difference''': Compute the difference between group-level averages: avg(|G,,1,,|)-avg(|G,,2,,|)
 1. '''Limitations''': Because we rectify the source maps before computing the difference, we lose the ability to detect the differences between equal values of opposite signs. And we cannot keep the sign because we are averaging across subjects. Therefore, many effects are not detected correctly.

=== Statistics: Single subject ===
 * '''A = B''': Parametric or non-parametric
  * Compute source maps for each trial (constrained, no normalization).
  * Parametric or non-parametric two-sample t-test, independent, two-tailed.<<BR>>Identifies correctly '''where and when''' the conditions are different (sign not meaningful).
  * '''Directionality''': Additional step to know which condition has higher values.<<BR>>Compute the difference of rectified averages: |avg(A,,i,,)|-|avg(B,,i,,)|<<BR>>Combine the significance level (t-test) with the direction (difference): [[http://neuroimage.usc.edu/brainstorm/Tutorials/Statistics#Directionality:_Difference_of_absolute_values|See details]].
 * '''|mean(A)| = |mean(B)|''': Non-parametric
  * Compute source maps for each trial (constrained, no normalization).
  * Non-parametric independent two-sample "absolute mean test", two-tailed.<<BR>>T = (|mean(A)|-|mean(B)|) / sqrt(|var(A)|/N,,A,, + |var(B)|/N,,B,,)
  * Interesting alternative that provides at the time a correct estimation of the difference (where and when) and the direction (which condition has higher values).

=== Statistics: Group analysis, within subject ===
 * '''Power(A-B) = 0''': Parametric
  * '''First-level statistic''': Rectified difference of normalized averages. <<BR>>Proceed as in ''Difference of averages: Within subjects'', but stop before the group average (after step #8). You obtain one measure '''|A,,i,,-B,,i,,|''' per subject, test these values against zero.
  * '''Second-level statistic''': Parametric one-sample Chi^2^-test. <<BR>>Power = sum(|A,,i,,-B,,i,,|^2^), i=1..N,,subj,, ~ Chi^2^(N,,subj,,)
  * Identifies '''where and when''' the conditions are different (sign not meaningful).
  * Warning: Very sensitive test, with lots of false positive (all the brain can be "significant")

 * '''|A| = |B|''': Parametric or non-parametric
  * '''First-level statistic''': Rectified and normalized subject averages. <<BR>>Proceed as in ''Average: Group analysis'' to obtain two averages per subject: A,,i,, and B,,i,,.
  * '''Second-level statistic''': Parametric or non-parametric two-sample t-test, paired, two-tailed.
  * This test does not consider the sign difference within a subject, and therefore cannot detect correctly when A and B have opposite signs. Works well and indicates '''which condition has higher values''' when A and B have the same sign within a subject.

 * '''A = B''': Parametric or non-parametric [anatomy template only]
  * '''First-level statistic''': Normalized subject averages (not rectified, no projection needed). <<BR>>Proceed as in ''Average: Single subject'' to obtain two averages per subject: A,,i,, and B,,i,,.
  * '''Second-level statistic''': Parametric or non-parametric two-sample t-test, paired, two-tailed.
  * Applies only if all the subjects are sharing the same template anatomy. <<BR>>Not recommended when using individual anatomies because of the sign issue between subjects (the signs might be opposed between two subjects, and the projection of non-rectified values to a template might be inaccurate).
 * '''Power(A) = 0''': Parametric
  * '''First-level statistic''': Rectified and normalized subject averages. <<BR>>Proceed as in ''Average: Group analysis'' to obtain one average per subject #i: |A,,i,,|.
  * '''Second-level statistic''': Parametric one-sample Chi^2^-test. <<BR>>PowerA = sum(|A,,i,,|^2^), i=1..N,,subj,, ~ Chi^2^(N,,subj,,).

=== Statistics: Group analysis, between subjects ===
 * '''|G1| = |G2|''': Non-parametric
  * '''First-level statistic''': Rectified and normalized subject averages. <<BR>>Proceed as in ''Average: Group analysis'' to obtain one average per subject.
  * '''Second-level statistic''':  Non-parametric two-sample t-test, '''independent''', two-tailed.

 * '''Power(G1) = Power(G2)''': Parametric
  * '''First-level statistic''': Rectified and normalized subject averages. <<BR>>Proceed as in ''Average: Group analysis'' to obtain one average per subject: |Ai|.
  * '''Second-level statistic''': Parametric two-sample power F-test. <<BR>>PowerG1 = sum(A,,i,,^2^), i=1..N,,1,, ~ Chi^2^(N,,1,,)<<BR>>PowerG2 = sum(A,,j,,^2^), j=1..N,,2,, ~ Chi^2^(N,,2,,)<<BR>>F(N,,1,,,N,,2,,) = (PowerG1 / N,,1,,) / (PowerG2 / N,,2,,)

== Unconstrained cortical sources ==
Three values for each grid point, corresponding to the three dipoles orientations (X,Y,Z). <<BR>>We want only one statistic and one p-value per grid point in output.

=== Averages ===
 * Proceed as indicated above for constrained cortical sources.
 * Just replace the step '''Rectify''' with '''Flatten''' (process: Sources > [[https://neuroimage.usc.edu/brainstorm/Tutorials/PCA#Unconstrained_source_flattening_with_PCA|Unconstrained to flat map]]).
 * The operator |A| has to be interpreted as "norm of the three orientations":<<BR>> |A| = sqrt(A,,x,,^2^ + A,,y,,^2^ + A,,z,,^2^)

=== Statistics: Single subject ===
 * '''|mean(A)| = |mean(B)|''': Non-parametric
  * Compute source maps for each trial (unconstrained, no normalization).
  * Non-parametric independent two-sample "absolute mean test", independent, two-tailed.<<BR>>T = (|mean(A)|-|mean(B)|) / sqrt(|var(A)|/N,,A,, + |var(B)|/N,,B,,)
  * Provides at the time a correct estimation of the  difference (where and when) and the direction (which condition has  higher values).

=== Statistics: Group analysis, within subject ===
 * '''Power(A-B) = 0''': Parametric
  * '''First-level statistic''': Flattened difference of normalized averages. <<BR>>Proceed as in ''Difference of averages: Within subjects'', but stop before the group average (after step #8). You obtain one measure '''|A,,i,,-B,,i,,|''' per subject, test these values against zero.
  * '''Second-level statistic''': Parametric one-sample Chi^2^-test for unconstrained sources. <<BR>>Power = sum(|A,,i,,-B,,i,,|^2^), i=1..N,,subj,, ~ Chi^2^(3*N,,subj,,)
  * Identifies '''where and when''' the conditions are different (sign not meaningful).
  * Warning: Very sensitive test, with lots of false positive (all the brain can be "significant")

 * '''|A| = |B|''': Parametric or non-parametric
  * '''First-level statistic''': Flattened and normalized subject averages. <<BR>>Proceed as in ''Average: Group analysis'' to obtain two averages per subject: A,,i,, and B,,i,,.
  * '''Second-level statistic''': Parametric or non-parametric two-sample t-test, paired, two-tailed.
  * This  test does not consider the sign difference within a subject, and  therefore cannot detect correctly when A and B have opposite signs.  Works well and indicates '''which condition has higher values''' when A and B have the same sign within a subject.
 * '''Power(A) = 0''': Parametric
  * '''First-level statistic''': Flattened and normalized subject averages. <<BR>>Proceed as in ''Average: Group analysis'' to obtain one average per subject #i: |A,,i,,|.
  * '''Second-level statistic''': Parametric one-sample Chi^2^-test for unconstrained sources. <<BR>>PowerA = sum(|A,,i,,|^2^) = sum(A,,ix,,^2^+A,,iy,,^2^+A,,iz,,^2^), i=1..N,,subj,, ~ Chi^2^(3*N,,subj,,).

=== Statistics: Group analysis, between subjects ===
 * '''|G1| = |G2|''': Non-parametric
  * '''First-level statistic''': Flattened and normalized subject averages. <<BR>>Proceed as in ''Average: Group analysis'' to obtain one average per subject.
  * '''Second-level statistic''':  Non-parametric two-sample t-test, '''independent''', two-tailed.

 * '''Power(G1) = Power(G2)''': Parametric
  * '''First-level statistic''': Flattened and normalized subject averages. <<BR>>Proceed as in ''Average: Group analysis'' to obtain one average per subject: |Ai|.
  * '''Second-level statistic''': Parametric two-sample power F-test (unconstrained sources). <<BR>>PowerG1 = sum(A,,ix,,^2^+A,,iy,,^2^+A,,iz,,^2^), i=1..N,,1,, ~ Chi^2^(3*N,,1,,)<<BR>>PowerG2 = sum(A,,jx,,^2^+A,,jy,,^2^+A,,jz,,^2^), j=1..N,,2,, ~ Chi^2^(3*N,,2,,)<<BR>>F = (PowerG1 / N,,1,,) / (PowerG2 / N,,2,,) ~ F(3*N,,1,,,3*N,,2,,)

== Regions of interest (scouts) ==
=== Statistics: Single subject ===
 * Even  within-subject cortical maps have sign ambiguities. MEG/EEG source mapping has limited  spatial resolution and sources in opposing sulcal/gyral areas are typically reconstructed with inverted signs (constrained orientations only).  Averaging activity in cortical regions of interest (scouts) would thus  lead to signal cancelation. To mitigate this issue, Brainstorm uses algorithms to  change the polarity of individual source time series before averaging across a scout cortical region. Please note however that the polarity of the resulting time series remains ambiguous, as it is determined by statistical considerations, not by physiological information.
 * We therefore recommend that you '''perform subject-wise averaging/contrast before averaging scout time series'''. You may also consider aggregating scout source time series using [[https://neuroimage.usc.edu/brainstorm/Tutorials/PCA|PCA]] instead of averaging, '''across all files''' (all epochs and conditions) from a subject together, ensuring the same component is applied everywhere and therefore mitigating further some of the ambiguities concerning the polarity of the source time series.

=== Statistics: Group analysis, within subject ===
 * Comparison   of scout time series between subjects is tricky because there   is no   way to avoid sign ambiguity for different subjects. Thus  there  are no   clear recommendations. Rectifying before comparing scout  time  series   between  subjects can be a good idea or not depending on  different   cases.
 * Having  a good understanding of the data (multiple  inspections   across  channels/sources/subjects) can offer hints whether  rectifying   the scout  time series is a good idea. Using unconstrained  cortical    maps to create the scout time series can ameliorate ambiguity  concerns.

== Time-frequency maps ==
=== Average: Single subject ===
 * '''Single trials''': Compute time-frequency maps for each trial ('''magnitude''', no normalization).<<BR>>It is a more standard analysis to take the square root of power before a t-test.
 * '''Subject average''': Average the time-frequency maps together, separately for each condition. This can be done automatically when computing the TF decompositions (option "Save averaged time-frequency maps" in the process options).
 * The values are all strictly positive, there is no sign ambiguity: you can directly subtract the averages of the two conditions and interpret the sign of the difference.
 * If you average time-frequency  maps computed on sensor-level data, the same limitations apply as for  averaging sensor level data (see sections about MEG and EEG recordings  above).

=== Average: Group analysis ===
 1. '''Subject averages''': Compute within-subject averages for all the subjects, as described above.
 1. '''Normalize''' the subject averages: ERD/ERS or Z-score wrt baseline.
-Line 108:
+Line 242:
-=== Between-subject difference of average ===
 1. '''Subject averages''': For each subject, compute the within-subject averages for conditions A and B, as described just above.
 1. '''Subject difference''': Compute the difference between conditions for each subject (A-B).
 1. '''Rectify''' the difference of source maps (apply an absolute value).
 1. '''Project '''the individual difference on a template.
 1. '''Smooth '''spatially the sources.
 1. '''Group average''': Compute grand averages of all the subjects.

=== Within-subject statistics ===
 1. '''Sources''': Compute source maps for each trial (constrained or unconstrained, no normalization)
 1. '''Statistics''': Compare all the trials of condition A vs all the trials of condition B.<<BR>>Use as many trials as possible for A and B: No need to have an equal number of trials.

 1. '''A = B'''
  * '''Parametric''' or '''non-parametric''' tests, independent, two-tailed, FDR-corrected.
  * Correct effect size: We identify correctly where and when the conditions are different.
  * Ambiguous sign: We cannot say which condition has the stronger response.
 1. '''|A| = |B|'''
  * '''Non-parametric''' tests only, independent, two-tailed, FDR-corrected.
  * Incorrect effect size: Doesn't detect correctly the effects when A and B have opposite signs.
  * Correct sign: We can identify correctly which condition has a stronger response.

=== Between-subject statistics ===
 1. '''Sources''': Compute source maps for each trial (constrained or unconstrained, no normalization)
 1. '''First-level statistic''': Compute a t-statistic for the source maps of all the trials A vs. all the trials B.
  * Process2: "Test > Parametric test: Independent": t-test with equal variance
  * Use as many trials as possible for A and B: No need to have an equal number of trials.
  * With a relatively high number of trials, we can consider the t-values follow a Z-distribution.
 1. '''Second-level statistic''': Compute a one-sampled power test based on the subject t-statistic.
  * Process1: "Test > Parametric test against zero": One-sampled Chi-square test
  * This tests for '''|A-B|'''=0 using a power test:  X = sum(|ti|^2) ~ Chi-square distribution
  * Correct effect size, no sign (cannot detect which condition has the strongest response).

=== Design considerations ===
 * Use within-subject designs whenever possible (i.e. collect two conditions A and B for each subject), then contrast data within subject before comparing data between subjects. Such designs are not only statistically optimal, but also ameliorate the between-subject sign ambiguities as contrasts can be constructed within each subject.

== Unconstrained cortical sources ==
== Regions of interest (scouts) ==
 * Even  within-subject cortical maps have sign ambiguities. MEG has limited  spatial resolution and sources in opposing sulcal/gyral areas are  reconstructed with inverted signs (constrained orientations only).  Averaging activity in cortical regions of interest (scouts) would thus  lead to signal cancelation. To avoid this brainstorm uses algorithms to  manipulate the sign of individual sources before averaging within a  cortical region. Unfortunately, this introduces an amplitude and sign  ambiguity in the time course when summarizing scout activity.
 * As a result, '''perform any interesting within-subject average/contrast before computing an average scout time series'''.

== Within-subject statistics ==
For one unique subject, test for significant differences  between two experimental conditions:

 * Compare the '''single  trials''' corresponding to each condition.
 * In most cases, you '''do not need to normalize''' the data.
 * For help with the implications of testing the '''relative or absolute values''', see: [[Tutorials/Difference|Difference]].

==== Unconstrained source maps ====
 * Three values per vertex.
 * Use the non-normalized minimum norm maps for all the trials (current density maps, no Z-score).
 * We need to test the '''norm '''of the three orientations instead of testing the orientations separately.
 * '''Norm(A) vs. Norm(B)''':
  * Null hypothesis H0: (|A|=|B|).
  * '''Non-parametric''' tests only, independent, two-tailed, FDR-corrected.
  * Incorrect effect size, meaningful sign.

==== Regions of interest (scouts) ====
 * Average/constrast cortical maps before summarizing scout activity.
 * Then consider as constrained or unconstrained source maps.

==== Time-frequency maps ====
 * Test the non-normalized time-frequency maps for all the trials (no Z-score or ERS/ERD).
 * The values tested are power or magnitudes, all positive, so (A=B) and (|A|=|B|) are equivalent.
 * '''|A| vs |B|''':
  * Null hypothesis H0: (|A|=|B|)
  * '''Non-parametric''' tests only, independent, two-tailed, FDR-corrected.
  * Correct effect size, meaningful sign.

== Between-subject statistics [TODO] ==
==== Subject averages ====
You need first to process the data separately for each subject:


 1. Sources computed on individual brains: '''Project '''the individual source maps on a template (see the [[Tutorials/CoregisterSubjects|coregistration tutorial]]). Not needed if the sources were estimated directly on the template anatomy. <<BR>>Note:   We evaluated the alternative order (project the sources and then   normalize): it doesn't seem to be making a significant difference. It's   more practical then to normalize at the subject level before projecting   the sources on the template, so that we have normalized maps to look  at  for each subject in the database.

 1. Constrained sources: '''Smooth '''spatially the sources, to make sure the brain responses are aligned. '''Problem''':   This is only possible after applying an absolute value, smoothing in   relative values do not make sense, as the positive and negative signals   and the two sides of a sulcus would cancel out. [TODO]

==== Paired tests ====
 * '''Constrained source maps''' (one value per vertex):
  * '''(A-B=0)''':  Parametric or non-parametric tests, two-tailed, FDR-corrected ('''sign issue?''').
  * '''(|A|-|B|=0)''': Non-parametric tests, two-tailed, FDR-corrected.
  * '''(|A-B| = 0)''': ???

 * '''Unconstrained source maps''' (three values per vertex):
  * '''(Norm(A-B)=0)''': Non-parametric tests, __'''one-tailed'''__ (non-negative statistic), FDR-corrected.
  * '''(Norm(A)-Norm(B)=0)''': Non-parametric tests, two-tailed, FDR-corrected.
 * '''Time-frequency maps''':
  * '''(|A|-|B|=0)''': Non-parametric tests, two-tailed, FDR-corrected.
 * '''Regions of interest''' (scouts):
  * Comparison  of scout time series between subjects is tricky because there   is no  way to avoid sign ambiguity for different subjects. Thus  there  are no  clear recommendations. Rectifying before comparing scout  time  series  between  subjects can be a good idea or not depending on  different  cases. Having  a good understanding of the data (multiple  inspections  across  channels/sources/subjects) can offer hints whether  rectifying  the scout  time series is a good idea. Using unconstrained  cortical   maps to create the scout time series can ameliorate ambiguity  concerns.

 * For help with '''relative/absolute options''', read the previous tutorial: [[Tutorials/Difference|Difference]].

==== Averages ====
 * In order to compute grand averages (across subjects), you should '''rectify'''  your source maps before averaging. Averaging the absolute values of the  subject-level averages will help avoiding possible cancellation effects  due to anatomical  differences between subjects.
 * If you  have two conditions A and B to contrast, first compute the difference  within-subject (A-B), then average the rectified differences:  average_subjects(|Ai-Bi|).

<<TAG(Advanced)>>

== Workflow: Current problems [TODO] ==
The   following inconsistencies are still present in the documentation. We   are actively working on these issues and will update this tutorial as   soon as we found solutions.

 * [Group analysis] Unconstrained sources: How to compute a Z-score?
  * Zscore(A): Normalizes each orientation separately, which doesn't make much sense.
  * Zscore(Norm(A)): Gets rid of the signs, forbids the option of a signed test H0:(Norm(A-B)=0)
  * See also the tutorial: [[http://neuroimage.usc.edu/brainstorm/Tutorials/SourceEstimation#Z-score|Source estimation]]
  * We would need a way to normalize across the three orientations are the same time.
 * [Group analysis] Constrained sources: How do we smooth?
  * Group analysis benefits a lot from smoothing the source maps before computing statistics.
  * However this requires to apply an absolute value first. How do we do?
 * [Single subject] Unconstrained sources: How do compare two conditions with multiple trials?
  * Norm(A)-Norm(B): Cannot detect correctly the differences
  * (A-B): We test individually each orientation, which     doesn't make much sense.
  * We would need a test for the three orientations at once.
 * [Group analysis] Rectify source maps?
  * Recommended in Dimitrios' guidelines, which is incoherent with the rest of the page.
+. '''Difference of averages''': Simply compute the difference of the group averages.

=== Statistics: Single subject ===
 * '''A = B''': Parametric or non-parametric
  * Compute time-frequency maps for each trial ('''magnitude''', no normalization).
  * Parametric or non-parametric two-sample t-test, independent, two-tailed.

=== Statistics: Group analysis, within subject ===
 * '''A = B''': Parametric or non-parametric [anatomy template only]
  * '''First-level statistic''': Normalized subject averages (ERS/ERD or Z-score). <<BR>> Proceed as in ''Average: Group analysis'' to obtain two averages per subject: A,,i,, and B,,i,,.
  * '''Second-level statistic''': Parametric or non-parametric two-sample t-test, paired, two-tailed.

=== Statistics: Group analysis, between subjects ===
 * '''G1 = G2''': Non-parametric
  * '''First-level statistic''': Normalized subject averages (ERS/ERD or Z-score). <<BR>> Proceed as in ''Average: Group analysis'' to obtain one average per subject.
  * '''Second-level statistic''':  Parametric or non-parametric two-sample t-test, independent, two-tailed.

Feedback: Comments, bug reports, suggestions, questions

Email address (if you expect an answer):