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