alexenge / hu-neuro-pipeline

Single trial EEG pipeline at the Abdel Rahman Lab for Neurocognitive Psychology, Humboldt-Universität zu Berlin

Home Page:https://hu-neuro-pipeline.readthedocs.io

Geek Repo:Geek Repo

Github PK Tool:Github PK Tool

hu-neuro-pipeline

PyPI PyPI - Python Version GitHub

Single trial EEG pipeline at the Abdel Rahman Lab for Neurocognitive Psychology, Humboldt-Universität zu Berlin

Based on Frömer, R., Maier, M., & Abdel Rahman, R. (2018). Group-level EEG-processing pipeline for flexible single trial-based analyses including linear mixed models. Frontiers in Neuroscience, 12, 48. https://doi.org/10.3389/fnins.2018.00048

1. Installation

1.1 For Python users

Install the pipeline via pip from the Python Package Index (PyPI):

pip install hu-neuro-pipeline

Alternatively, you can install the latest development version from GitHub:

pip install git+https://github.com/alexenge/hu-neuro-pipeline.git

1.2 For R users

First install and load reticulate (an R package for accessing Python functionality from within R):

install.packages("reticulate")
library("reticulate")

Check if you already have conda (a scientific Python distribution) installed on your system:

conda_exe()

If this shows you the path to a conda executable, you can skip the next step. If instead it shows you an error, you need to install conda:

install_miniconda()

Then install the pipeline from the Python Package Index (PyPI):

py_install("hu-neuro-pipeline", pip = TRUE)

Alternatively, you can install the latest development version from GitHub:

py_install("git+https://github.com/alexenge/hu-neuro-pipeline.git", pip = TRUE)

2. Usage

2.1 For Python users

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).

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': 'semantics == "related"',
                'unrelated': 'semantics == "unrelated"'})

In this example we have specified:

  • 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

  • Four different EEG triggers corresponding to each of the four cells in the 2 × 2 design

  • The fact that log files contain additional trials from a semantic 'filler' condition (which we want to skip because they don't have corresponding EEG triggers)

  • The a priori defined time windows and regions of interest for the N400 and P600 components

  • The log file columns (average_by) for which we want to obtain by-participant averaged waveforms (i.e., for all main and interaction effects)

2.2 For R users

Here is the same example as above but for using the pipeline from R:

# Import Python module
pipeline <- reticulate::import("pipeline")

# Run the group level pipeline
res <- pipeline$group_pipeline(
    raw_files = "Results/EEG/raw",
    log_files = "Results/RT",
    output_dir = "Results/EEG/export",
    besa_files = "Results/EEG/cali",
    triggers = c(201, 202, 211, 212),
    skip_log_conditions = list("semantics" = "filler"),
    components = list(
        "name" = list("N400", "P600"),
        "tmin" = list(0.3, 0.5),
        "tmax" = list(0.5, 0.9),
        "roi" = list(
            c("C1", "Cz", "C2", "CP1", "CPz", "CP2"),
            c("Fz", "FC1", "FC2", "C1", "Cz", "C2")
        )
    ),
    average_by = list(
        related = "semantics == 'related'",
        unrelated = "semantics == 'unrelated'"
    )
)

# Extract results
trials <- res[[1]]
evokeds <- res[[2]]
config <- res[[3]]

3. Processing details

See the documentation for more details about how to use the pipeline and how it works under the hood.

About

Single trial EEG pipeline at the Abdel Rahman Lab for Neurocognitive Psychology, Humboldt-Universität zu Berlin

https://hu-neuro-pipeline.readthedocs.io

License:MIT License


Languages

Language:Python 100.0%