Quickstart
==========

The pipeline provides a single high-level function, ``group_pipeline()``, to carry out a full EEG analysis on a group of participants.

Here is a fairly minimal example for a (fictional) N400/P600 experiment with two experimental factors: ``semantics`` (e.g., related versus unrelated words) and emotional ``context`` (e.g., emotionally negative versus neutral).

.. code-block:: python

    from pipeline import group_pipeline

    trials, evokeds, config = group_pipeline(
        raw_files='Results/EEG/raw',
        log_files='Results/RT',
        output_dir='Results/EEG/export',
        besa_files='Results/EEG/cali',
        triggers=[201, 202, 211, 212],
        skip_log_conditions={'semantics': 'filler'},
        components={
            'name': ['N400', 'P600'],
            'tmin': [0.3, 0.5],
            'tmax': [0.5, 0.9],
            'roi': [['C1', 'Cz', 'C2', 'CP1', 'CPz', 'CP2'],
                    ['Fz', 'FC1', 'FC2', 'C1', 'Cz', 'C2']]},
        average_by={
            'related_negative': 'semantics == "related" and context == "negative"',
            'related_neutral': 'semantics == "related" and context == "neutral"',
            'unrelated_negative': 'semantics == "unrelated" and context == "negative"',
            'unrelated_neutral': 'semantics == "unrelated" and context == "neutral"'}})

In this example we have specified:

- ``raw_files``, ``log_files``, ``output_dir``, ``besa_files``: The paths to the raw EEG data, to the behavioral log files, to the desired output directory, and to the BESA files for ocular correction

- ``triggers``: The four different numerical EEG trigger codes corresponding to each of the four cells in the 2 × 2 design

- ``skip_log_conditions``: Our log files may contain additional trials from a "filler" condition without corresponding EEG trials/triggers. These filler trials are marked with the condition label ``'filler'`` in the log file column ``semantics``

- ``components``: The *a priori* defined time windows and regions of interest for the relevant ERP components (N400 and P600)

- ``average_by``: The relevant groupings of trials for which by-participant averaged waveforms should be created. The keys (e.g., ``'related_negative'``) are custom labels of our choice; the values are the corresponding logical conditions that must be met for a trial to be included in the average.

For (way) more options, see :doc:`Pipeline inputs <inputs_py>`.