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2-Group T-test Spectral Analysis Report

Description

Performs a spectral analysis and t-test comparison of two groups of sessions of the same task (i.e., the same group performing a task at two different times, such as pre- and post-intervention; can also be used to compare two different groups performing the same task). Performs a spectral analysis of the entire session (a begin/end marker or time can be specified to trim the data before processing). The T-test is performed on the means of each session (between the two groups). Outputs an .html report and .mat file with results.

Input

This pipeline expects two group of files, which can be specified using a path with wildcards in the input_path parameter.

This path is relative to your Neuroscale account storage bucket. It should either point to a folder containing the two files to be processed, or resolve to two files only. Use curly braces {} to act as wild cards and identify metadata that is unique to each file and which designate the factor on which the t-test is to be performed.

Example:

\studies\Study1\{Subject}_{Date}_{Session}.xdf will find all files matching \studies\Study\*_*_*.xdf, and, for each file store the content in the first * as the Subject metadata, the second * as the Date metadata, and the third * as the Session metadata (example: you might have files called s1_10-23-19_pre.xdf and s1_11-15-19_post.xdf for a 'pre' and 'post' session of the same subject, and s2_10-24-19_pre.xdf and s2_10-24-19_post.xdf for another subject, and so on).

Or, you might have your two groups in separate folders, such as\studies\Study1\group1\*.xdf and \studies\Study1\group2\*.xdf, in which case you would specify your input path as \studies\Study1\{Group}\*.xdf and then use Group as the factor in the corresponding param below.

It's important that only two groups of files match this input path.

Other key parameters

factor

The factor used for the t-Test. This must match one of the factors extracted from the input path metadata (specified with {} in the input path). Example: Group will use the metadata captured by {Group} in the input path to determine the factor levels.

factor_levels

The two levels of the factor being tested. These are the two possible values for the factor you specified above. These must be present in the metadata of the files you import. Important: The order in which these are specified is the order in which the t-test will be performed. So if you want to test 'post' vs 'pre' where positive values indicate that 'post' is greater than 'pre', then you should specify: 'post', 'pre'.

trim_markers

A list of 2 markers to which the data will be trimmed. These markers must exist in the data be unique Example: 'begin-task', 'end-task' will only process/analyze the data between those two markers. Note that preprocessing is done on the entire file.

trim_seconds

Similar to trimmarkers, this will trim the data to given time points in the file, _relative to the beginning of the file. Example: 5, 50 would trim the data down to 45 seconds starting 5 seconds into the recording.

bands_measure

The measure for computed power bands which are computed and displayed in the report. This can be one of three possibilities: relativePSD, dB, PSD. (PSD = Power Spectral Density); dB is PSD converted to decibels.

Output

This pipeline generates a report in HTML format that contains the following:

  • Analysis by Channels
  • Power Spectra (line plots)
  • BandPower (bar plots)
  • Bands / Ratios (topoplots)
  • Statistics Tables
  • Analysis by Sources
  • Power Spectra (line plots)
  • BandPower (bar plots)
  • BandPower Ratios (cortex plots)
  • Statistics tables
  • Connectivity Analysis
  • Connectivity diagrams
  • Statistics tables

Also exported is .mat (Matlab format) file with all the computed values and statistics for further analysis if desired.