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Description

Computes eyes-closed vs eyes-open spectral analysis of an individual session with statistics between the two conditions (eyes-closed vs eyes-open). Includes spectra, bandpower (for both channels and sources), and connectivity.

Report sections

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 (ribbons)
    • Statistics tables

PARAMETERS

Category: Input

This pipeline expects one file (or a Path Iterator formatted path name) and its path is relative to your Neuroscale account storage bucket. (The easiest way to run it is to go to your Storage page, select the file, click the Task button which will take you to the Create Task page, and then select this pipeline. The filename will be filed in automatically.)

input path

The path to the file to be processed (see above).

Category: Output

The pipeline will automatically generate a .html report into the same path as the input file using an appropriate tag to indicate the type of report that was run. There are additional optional outputs one can choose to save out.

export pkl

Whether or not to export a .pkl format structure file of session means. These files are needed from individual session analyses if the dataset will be used in a group analysis pipeline.

Category: Preprocessing

This pipeline preprocesses the EEG data including:

  • high and low pass filtering
  • rereferencing
  • removing and reinterpolating bad channels
  • removing bad time windows
  • cleaning artifacts
  • de-jittering timestamps
  • rescaling
  • Parameters for some of these functions are included in this section.

skip preprocessing

Whether or not to skip the preprocessing stage. This should usually be False, unless the data has already been preprocessed (filtered, rereferenced, cleaned, etc.) and the input file is this preprocessed version of the data.

lowpass frequencies

High noise frequencies to be filtered out with a lowpass filter. You can either give the cutoff frequency as a single value, or two frequencies (separated by a comma), to specify the rolloff curve.

highpass frequencies

Low noise frequencies to be filtered out with a highpass filter. You can either give the cutoff frequency as a single value, or two frequencies (separated by a comma), to specify the rolloff curve.

desired sampling rate

Target sampling rate (in Hz). If you want to resample the data, specify the sampling rate here. Leave empty to not performing resampling.

bad channel correlation threshold

Correlation threshold. Higher values (above 0.7) are more stringent and will remove more channels (i.e., moderately bad channels get removed). Values below 0.6 would be considered very lax (i.e., only the worst channels get removed). This threshold is based on the correlation between a channel and what one would expect the channel to be given the other channels. Note that this parameter is only used when channel locations are available as it is based on each channel's positional neighbors.

skip artifact removal

Skip artifact and bad time window removal.

Category: Events

marker conditions

Dictionary of event conditions using marker stream. This is a nested dictionary with the first level being the condition names you want to create. Each condition name needs to have a second dictionary in which the following keys names are used 'begin' and 'end'. The 'begin' value should be the marker name of the start of each segment/condition period. The 'end' value should be the marker name of the end of each segment/condition period.

Category: Style

style figure font

Font to be used throughout the report

Category: Statistics

winsorize

Winsorization is the transformation of statistics by limiting extreme values in the statistical data to reduce the effect of possibly spurious outliers. If true, outlier values will be replaced by the mean when taking means over the individual trials.

trim proportion

Proportion of data instances to trim (per variable) on the lower and upper tails of the data. If only a single number is provided then it is used for both lower and upper. If no non-zero numbers are provided then no trimming is performed (default). For example, [0.05, 0.05] will remove and replace the least and greatest 5% of extreme data values with the mean.

error type

std: standard deviation

sem: standard error of the mean

var: variance

mad: median absolute difference

CI: confidence interval

CI range

Range for confidence interval if 'CI' selected as error_type. This is a proportion (between 0 and 1) such that 0.95 is the 95% confidence interval.

ttest mcc

Whether to correct for multiple comparisons using False Discovery Rate (FDR).

pval threshold

P-value to determine significance level. Values below specified threshold (e.g. 0.05) will be considered significant for statistics.

Category: Spectra

spectra method

Method to use for estimating power spectral density. Options are ['Multitaper', 'Welch', 'Spectrogram']['Multitaper', 'Welch', 'Spectrogram'].

spectra segmentation window

Time range in seconds relative to the target event (typically stimulus or response onset), within which data will be selected to compute spectra. Express in this format: [0, 3] (will select from target event onset to 3 seconds after).

Category: Topoplot

topoplot colormap

Colormap to use for plotting.

Category: 3D Cortex Plot

cortex plot colormap

Colormap to use for plotting.

cortex plot limits

Limits to use for scaling color map. 'absmax': scale map colors to +/- the absolute max after saturation. 'maxmin': scale map colors to the data range after saturation