Single trial EEG pipeline at the Neurocognitive Psychology Lab, Humboldt-Universität zu Berlin
Project description
hu-neuro-pipeline
Single trial EEG pipeline at the Neurocognitive Psychology lab, 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):
python3 -m pip install hu-neuro-pipeline
1.2 For R users
First install reticulate and Miniconda for being able to import Python packages into R:
install.packages("reticulate")
reticulate::install_miniconda()
Then install the pipeline via pip
from the Python Package Index (PyPI):
reticulate::py_install("hu-neuro-pipeline", pip = TRUE, python_version = "3.8")
2. Usage
3. Examples
3.1 For Python users
This is a minimal example for a (fictional) N400/P600 experiment with two experimental factors: Semantics
("related"
vs. "unrelated"
) and emotional Context
("negative"
vs. "neutral"
).
from pipeline import group_pipeline
trials, evokeds = group_pipeline(
vhdr_files='Results/EEG/raw',
log_files='Results/RT',
export_dir='Results/EEG/export',
ocular_correction='Results/EEG/cali',
triggers={'related/negative': 201,
'related/neutral': 202,
'unrelated/negative': 211,
'unrelated/neutral': 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=['Semantics', 'Context', 'Semantics/Context'])
3.2 For R users
Here's the same example as above:
# Import Python module
pipeline <- reticulate::import("pipeline")
# Run the group level pipeline
res <- pipeline$group_pipeline(
vhdr_files = "Results/EEG/raw",
log_files = "Results/RT",
export_dir = "Results/EEG/export",
ocular_correction = "Results/EEG/cali",
triggers = list(
"related/negative" = 201,
"related/neutral" = 202,
"unrelated/negative" = 211,
"unrelated/neutral" = 212
),
skip_log_conditions = list("Semantics" = "filler"),
components = list(
"name" = c("N400", "P600"),
"tmin" = c(0.3, 0.5),
"tmax" = c(0.5, 0.9),
"roi" = list(
c("C1", "Cz", "C2", "CP1", "CPz", "CP2"),
c("Fz", "FC1", "FC2", "C1", "Cz", "C2")
)
),
average_by = c("Semantics", "Context", "Semantics/Context")
)
# Extract results
trials <- res[[1]]
evokeds <- res[[2]]
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