A package for the automatic detection of evoked responses in SPES/CCEP data
Project description
Evoked Response Detection
A python package and docker application for the automatic detection of evoked responses in SPES/CCEP data
Docker Usage
To launch an instance of the container and analyse data in BIDS format, type:
$ docker run multimodalneuro/n1detect <bids_dir> <output_dir> [--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]
For example, to run an analysis, type:
docker run -ti --rm \
-v /local_bids_data_root/:/data \
-v /local_output_path/:/output \
multimodalneuro/erdetect /data /output --participant_label 01 --skip_bids_validator
Python Usage
- First install ERdetect, in the command-line run:
pip install erdetect
- To run:
- a) From the commandline:
python -m erdetect ~/bids_data ~/output/ [--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]
- b) A subset of the data directly in a python script:
import erdetect
erdetect.process('/bids_data_root/bids_subj/ieeg/data_file', '/output_path/')
Configure detection
From the command-line, a JSON file can be passed using the --config_filepath [JSON_FILEPATH]
parameter to adjust the preprocessing, the evoked response detection and the visualization settings.
An example JSON containing the standard settings looks as follows:
{
"preprocess": {
"high_pass": false,
"line_noise_removal": "off",
"early_re_referencing": {
"enabled": false,
"method": "CAR",
"stim_excl_epoch": [-1.0, 2.0]
}
},
"trials": {
"trial_epoch": [-1.0, 2.0],
"out_of_bounds_handling": "first_last_only",
"baseline_epoch": [-0.5, -0.02],
"baseline_norm": "median",
"concat_bidirectional_pairs": true,
"minimum_stimpair_trials": 5
},
"channels": {
"measured_types": ["ECOG", "SEEG", "DBS"],
"stim_types": ["ECOG", "SEEG", "DBS"]
},
"detection": {
"negative": true,
"positive": false,
"peak_search_epoch": [ 0, 0.5],
"response_search_epoch": [ 0.009, 0.09],
"method": "std_base",
"std_base": {
"baseline_epoch": [-1, -0.1],
"baseline_threshold_factor": 3.4
}
},
"visualization": {
"negative": true,
"positive": false,
"x_axis_epoch": [-0.2, 1],
"blank_stim_epoch": [-0.015, 0.0025],
"generate_electrode_images": true,
"generate_stimpair_images": true,
"generate_matrix_images": true
}
}
Acknowledgements
-
Written by Max van den Boom (Multimodal Neuroimaging Lab, Mayo Clinic, Rochester MN)
-
Local extremum detection method by Dorien van Blooijs & Dora Hermes (2018), with optimized parameters by Jaap van der Aar
-
Dependencies:
- PyMef by Jan Cimbalnik, Matt Stead, Ben Brinkmann, and Dan Crepeau (https://github.com/msel-source/pymef)
- MNE-Python (https://mne.tools/)
- BIDS-validator (https://github.com/bids-standard/bids-validator)
- NumPy
- SciPy
- Pandas
- KiwiSolver
- Matplotlib
- psutil
-
This project was funded by the National Institute Of Mental Health of the National Institutes of Health Award Number R01MH122258 to Dora Hermes
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