ecogtools's Introduction

ecogtools

functions for ecog data analysis

See example notebooks in pearsonlab/ecog_data_analysis_notebooks for best examples of analysis workflow.

See comments in ecogtools.py file for specifics about different functions/class.

Use

Class Data has the majority of the functions for analysis, while subclasses (e.g. ToM_Localizer) have task-specific parameters.

Calling e.g. ToM_Localizer class
- accepts "patient_num" : an integer value assigned to each patient (usually in #2000s)
- searches for analysis.json files with parameters for:
  - Patient
  - Task
- Parameter files contain directory information for patients, event information and locations of relevant files.
Does following automatically:
- Loads physiology data (ecog- fif/edf file)
- Loads behavioral data (task - json file)
- Loads trigger data (task/ecog - csv file)
- Combines behavioral data and triggers
- Defines events for MNE
- Checks directories (for saving image files)
- Sets triggers (task/class-specific) to differentiate conditions
Additional functions within Data Class:
- Remove_irrelevant_channels – helpful for looping through all channels and not having Event, EKG or Stim channels in averages.
  - accepts list of channels to remove or removes default irrelevant channels
- Initialize_epochs_object – used for creating epochs objects (loads data automatically)
  - accepts channels of interest and any kwargs used by MNE Epochs
  - saves epochs object as data.epochs
- create_evoked - used for creating evoked objects
  - accepts "condition" from event_id list (as string)
  - returns evoked object and saves object to data.evoked_list
- compute_power - used to calculate power with tfr_morlet
  - accepts "condition" and all kwargs for tfr_morlet
  - out-dated : better to use tfr_multitaper
- compute_diff_power - used to calculate ratio of two TF objects
  - accepts two TF objects
  - returns combined (one/two) TF object

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