| Name | erdetect JSON |
| Version |
2.5.0
JSON |
| download |
| home_page | |
| Summary | A package for the automatic detection of evoked responses in SPES/CCEP data |
| upload_time | 2023-09-25 15:00:40 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.8 |
| license | GPLv3 |
| keywords |
evoked response
detection
ieeg
n1
spes
ccep
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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|
# Evoked Response Detection
A python package and docker application for the automatic detection of evoked responses in SPES/CCEP data
## Python Usage
1. First install ERdetect, in the command-line run:
```
pip install erdetect
```
2. To run:
- a) With a graphical user interface:
```
python -m erdetect ~/bids_data ~/output/ --gui
```
- b) From the commandline:
```
python -m erdetect ~/bids_data ~/output/ [--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]
```
- c) To process a subset directly in a python script:
```
import erdetect
erdetect.process_subset('/bids_data_root/subj-01/ieeg/sub-01_run-06.edf', '/output_path/')
```
## Docker Usage
To launch an instance of the container and analyse data in BIDS format, in the command-line interface/terminal:
```
docker run multimodalneuro/erdetect <bids_dir>:/data <output_dir>:/output [--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
```
## 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:
- IeegPrep (https://github.com/MultimodalNeuroimagingLab/ieegprep)
- BIDS-validator (https://github.com/bids-standard/bids-validator)
- NumPy
- SciPy
- Matplotlib
- 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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