Single trial EEG pipeline at the Abdel Rahman Lab for Neurocognitive Psychology, Humboldt-Universität zu Berlin
Project description
hu-neuro-pipeline
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.
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