Proposed Json Schema for Bci Model Cards V0001
Abstract
I wrote a first pass JSON schema for the BCI model cards, building off of Brian’s spike in Nov 2023. I’m putting this up here for others to review. I will be using this schema as part of the Agentic workflow to populate the JSON, starting with the “Pipeline Steps” section.
What you’ll find below are:
- Proposed JSON schema template
- Example of JSON schema that I manually filled in for MindTV.
Proposed JSON schema
Below is the proposed JSON schema, in the code box below and also in the attached JSON file.
I’ve also attached a visualization of the JSON schema via JSON Crack – you can reproduce this visualization by copying the below JSON code into the editor in the link.
{
"BCI Model card version": "0.0.0.1",
"Pipeline Name": null,
"Pipeline Steps": [
{
"Step": "Generic",
"Used": false,
"From": null,
"Function": null,
"Description": null,
"Details": null,
"parameters": {
"param_1": {
"type": null,
"description": null,
"value": null
},
"param_2": {
"type": null,
"description": null,
"value": null
},
"param_N": {
"type": null,
"description": null,
"value": null
}
}
},
{
"Step": "Resampling",
"Used": false,
"From": null,
"Function": null,
"Description": null,
"Details": null,
"parameters": {
"param_1": {
"type": null,
"description": null,
"value": null
},
"param_2": {
"type": null,
"description": null,
"value": null
},
"param_N": {
"type": null,
"description": null,
"value": null
}
}
},
{
"Step": "Preprocessing",
"Used": true,
"From": null,
"Function": null,
"Description": null,
"Details": null,
"parameters": {
"param_1": {
"type": null,
"description": null,
"value": null
},
"param_2": {
"type": null,
"description": null,
"value": null
},
"param_N": {
"type": null,
"description": null,
"value": null
}
}
},
{
"Step": "Feature selection",
"Used": false,
"From": null,
"Function": null,
"Description": null,
"Details": null,
"parameters": {
"param_1": {
"type": null,
"description": null,
"value": null
},
"param_2": {
"type": null,
"description": null,
"value": null
},
"param_N": {
"type": null,
"description": null,
"value": null
}
}
},
{
"Step": "Channel selection",
"Used": false,
"From": null,
"Function": null,
"Description": null,
"Details": null,
"parameters": {
"param_1": {
"type": null,
"description": null,
"value": null
},
"param_2": {
"type": null,
"description": null,
"value": null
},
"param_N": {
"type": null,
"description": null,
"value": null
}
}
},
{
"Step": "Spatial filtering",
"Used": false,
"From": null,
"Function": null,
"Description": null,
"Details": null,
"parameters": {
"param_1": {
"type": null,
"description": null,
"value": null
},
"param_2": {
"type": null,
"description": null,
"value": null
},
"param_N": {
"type": null,
"description": null,
"value": null
}
}
},
{
"Step": "Classification",
"Used": true,
"From": null,
"Function": null,
"Description": null,
"Details": null,
"parameters": {
"Model architecture": {
"param_1": {
"type": null,
"description": null,
"value": null
},
"param_2": {
"type": null,
"description": null,
"value": null
},
"param_N": {
"type": null,
"description": null,
"value": null
}
},
"Hyperparameters": {
"param_1": {
"type": null,
"description": null,
"value": null
},
"param_2": {
"type": null,
"description": null,
"value": null
},
"param_N": {
"type": null,
"description": null,
"value": null
}
},
"Training parameters": {
"param_1": {
"type": null,
"description": null,
"value": null
},
"param_2": {
"type": null,
"description": null,
"value": null
},
"param_N": {
"type": null,
"description": null,
"value": null
}
}
}
}
],
"Individual Fields": {
"name": "Participant Name",
"channels selection": {
"channels rejected": null,
"channels selected": null
},
"classifier performance": {
"accuracy": null,
"AUC": null,
"precision": null,
"recall": null
}
},
"Population Fields": {
"name": "Participant Name",
"Population database": null,
"Population metrics": {
"accuracy": null,
"AUC": null,
"precision": null,
"recall": null
}
}
}
BCI_ModelCard_Template_v0001.json
Example of JSON schema from MindTV
This is a what the JSON schema looks like after I manually filled it in for MindTV as of 2024-04-30. As before, you’ll find the JSON code in the code box and attached file, and a visualization of what it looks like if put into JSONCrack
{
"BCI Model card version": "0.0.0.1",
"Pipeline Name": "MindTV",
"Pipeline Steps": [
{
"Step": "Generic",
"Used": false,
"From": null,
"Function": null,
"Description": null,
"Details": null,
"parameters": {
"param_1": {
"type": null,
"description": null,
"value": null
},
"param_2": {
"type": null,
"description": null,
"value": null
},
"param_N": {
"type": null,
"description": null,
"value": null
}
}
},
{
"Step": "Resampling",
"Used": false,
"From": null,
"Function": null,
"Description": null,
"Details": null,
"parameters": {
"param_1": {
"type": null,
"description": null,
"value": null
},
"param_2": {
"type": null,
"description": null,
"value": null
},
"param_N": {
"type": null,
"description": null,
"value": null
}
}
},
{
"Step": "Preprocessing",
"Used": true,
"From": "bci_essentials.signal_processing",
"Function": "bandpass",
"Description": null,
"Details": null,
"parameters": {
"f_low": {
"type": "float",
"description": "Lower corner frequency",
"value": 1
},
"f_high": {
"type": "float",
"description": "Upper corner frequency",
"value": 40
},
"order": {
"type": "float",
"description": "Order of the filter",
"value": 5
},
"f_sample": {
"type": "float",
"description": "Sampling rate of signal",
"value": 256
}
}
},
{
"Step": "Feature selection",
"Used": false,
"From": null,
"Function": null,
"Description": null,
"Details": null,
"parameters": {
"param_1": {
"type": null,
"description": null,
"value": null
},
"param_2": {
"type": null,
"description": null,
"value": null
},
"param_N": {
"type": null,
"description": null,
"value": null
}
}
},
{
"Step": "Channel selection",
"Used": false,
"From": null,
"Function": null,
"Description": null,
"Details": null,
"parameters": {
"param_1": {
"type": null,
"description": null,
"value": null
},
"param_2": {
"type": null,
"description": null,
"value": null
},
"param_N": {
"type": null,
"description": null,
"value": null
}
}
},
{
"Step": "Spatial filtering",
"Used": false,
"From": null,
"Function": null,
"Description": null,
"Details": null,
"parameters": {
"param_1": {
"type": null,
"description": null,
"value": null
},
"param_2": {
"type": null,
"description": null,
"value": null
},
"param_N": {
"type": null,
"description": null,
"value": null
}
}
},
{
"Step": "Classification",
"Used": true,
"From": "bci_essentials.classification.mi_classifier",
"Function": "MiClassifier",
"Description": "This classifier is used to classify MI data",
"Details": "The type of classifier used is a Tangent space logistic regression classifier",
"parameters": {
"Model architecture": {
"type": {
"type": "string",
"description": "Type of MI classifier used",
"value": "TS",
"details": "Tangent space logistic regression from pyriemann.classification.TSclassifier"
}
},
"Hyperparameters": {
"n_splits": {
"type": "int",
"description": "Number of folds for cross-validation",
"value": 3
},
"covariance_estimator": {
"type": "string",
"description": "Covariance estimator. See pyriemann Covariances.",
"value": "oas"
}
},
"Training parameters": {
"rebuild": {
"type": "boolean",
"description": "Rebuild from scratch with each training",
"value": true
},
"pred_threshold": {
"type": "float",
"description": "Probability threshold for true predictions",
"value": null
},
"remove_flats": {
"type": "boolean",
"description": "Description of parameter `remove_flats`",
"value": false
},
"whitening": {
"type": "boolean",
"description": "Description of parameter `whitening`",
"value": false
},
"artifact_rejection": {
"type": "string",
"description": "Description of parameter `artifact_rejection`",
"value": "none"
},
"channel_selection": {
"type": "string",
"description": "Description of parameter `channel_selection`",
"value": "none"
},
"random_seed": {
"type": "int",
"description": "Random seed",
"value": 35
},
"n_jobs": {
"type": "int",
"description": "The number of threads to dedicate to this calculation",
"value": 1
}
}
}
}
],
"Individual Fields": {
"name": "Participant Name",
"channels selection": {
"channels rejected": null,
"channels selected": null
},
"classifier performance": {
"accuracy": null,
"AUC": null,
"precision": null,
"recall": null
}
},
"Population Fields": {
"name": "Participant Name",
"Population database": null,
"Population metrics": {
"accuracy": null,
"AUC": null,
"precision": null,
"recall": null
}
}
}
BCI_ModelCard_Template_v0001-MindTV_manual_example.json
