bids-conversion

Name: bids-conversion
Author: neuromechanist

37stars7forks

How to install bids-conversion?

neuromechanist/research-skills/bids-conversion

$npx -y skills add neuromechanist/research-skills --skill bids-conversion

Installs into the current project.

Summary

Use this skill for \"convert to BIDS\", \"BIDS format\", \"create BIDS dataset\", \"BIDS sidecar\", \"participants.tsv\", \"events.tsv\", \"EEG BIDS\", \"EMG BIDS\", \"MEG BIDS\", \"fMRI BIDS\", \"BIDS validator\", \"channel locations\", \"electrode positions\", \"BIDS metadata\"

Files · 1

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SKILL.md

1	# BIDS Conversion
2
3	Convert neuroscience datasets to Brain Imaging Data Structure (BIDS) format. Supports EEG, EMG, MEG, fMRI, and behavioral data with proper file naming, JSON sidecars, and metadata.
4
5	## When to Use
6
7	- Converting raw data files to BIDS format
8	- Creating or fixing BIDS metadata (JSON sidecars, TSV files)
9	- Validating BIDS compliance
10	- Setting up a new BIDS dataset from scratch
11	- Converting between data formats (e.g., .set to .edf, .vhdr to .bdf)
12
13	## BIDS Directory Structure
14
15	```
16	dataset/
17	dataset_description.json
18	participants.tsv
19	participants.json
20	README
21	CHANGES
22	sub-01/
23	sub-01_scans.tsv
24	eeg/
25	sub-01_task-rest_eeg.set
26	sub-01_task-rest_eeg.json
27	sub-01_task-rest_channels.tsv
28	sub-01_task-rest_electrodes.tsv
29	sub-01_task-rest_coordsystem.json
30	sub-01_task-rest_events.tsv
31	emg/
32	sub-01_task-grasp_emg.edf
33	sub-01_task-grasp_emg.json
34	sub-01_task-grasp_channels.tsv
35	sub-01_task-grasp_events.tsv
36	anat/
37	sub-01_T1w.nii.gz
38	sub-01_T1w.json
39	derivatives/
40	pipeline-name/
41	sub-01/
42	```
43
44	## File Naming Convention
45
46	```
47	sub-<label>[_ses-<label>]_task-<label>[_acq-<label>][_run-<index>]_<suffix>.<extension>
48	```
49
50	- sub: subject identifier (required, alphanumeric, no special chars)
51	- ses: session (optional, for longitudinal studies)
52	- task: task name (required for functional data)
53	- acq: acquisition parameters (optional)
54	- run: run index (optional, for repeated acquisitions)
55	- suffix: data type (eeg, emg, meg, bold, T1w, events, channels, electrodes)
56
57	## Conversion Workflow
58
59	### Step 1: Inventory Source Data
60
61	Identify:
62	- Data format (BrainVision .vhdr, EEGLAB .set, EDF .edf, BDF .bdf, NIfTI .nii.gz)
63	- Number of subjects and sessions
64	- Task names and conditions
65	- Channel types (EEG, EMG, EOG, ECG, misc)
66	- Events/markers in the data
67	- Coordinate system for electrode positions
68
69	### Step 2: Create Dataset Scaffold
70
71	```python
72	import json
73	from pathlib import Path
74
75	def create_bids_scaffold(root: str, subjects: list[str], tasks: list[str], modality: str = "eeg"):
76	root = Path(root)
77	root.mkdir(exist_ok=True)
78
79	# dataset_description.json
80	desc = {
81	"Name": "Dataset Name",
82	"BIDSVersion": "1.9.0",
83	"DatasetType": "raw",
84	"License": "CC0",
85	"Authors": ["Last, First"],
86	"DatasetDOI": "",
87	"GeneratedBy": [{"Name": "Manual conversion"}]
88	}
89	(root / "dataset_description.json").write_text(json.dumps(desc, indent=2))
90
91	# participants.tsv
92	with open(root / "participants.tsv", "w") as f:
93	f.write("participant_id\tage\tsex\thand\n")
94	for sub in subjects:
95	f.write(f"sub-{sub}\tn/a\tn/a\tn/a\n")
96
97	# Create subject directories
98	for sub in subjects:
99	for task in tasks:
100	(root / f"sub-{sub}" / modality).mkdir(parents=True, exist_ok=True)
101	```
102
103	### Step 3: Convert Data Files
104
105	#### EEG (EEGLAB .set)
106
107	EEGLAB .set files are BIDS-compatible as-is. Copy and rename:
108	```bash
109	cp source.set sub-01/eeg/sub-01_task-rest_eeg.set
110	cp source.fdt sub-01/eeg/sub-01_task-rest_eeg.fdt # if separate .fdt file
111	```
112
113	#### EEG (BrainVision .vhdr)
114
115	BrainVision files come in triplets (.vhdr, .vmrk, .eeg). All three must be renamed consistently:
116	```bash
117	cp source.vhdr sub-01/eeg/sub-01_task-rest_eeg.vhdr
118	cp source.vmrk sub-01/eeg/sub-01_task-rest_eeg.vmrk
119	cp source.eeg sub-01/eeg/sub-01_task-rest_eeg.eeg
120	```
121	Update internal references in .vhdr and .vmrk to point to renamed files.
122
123	#### EEG (EDF/BDF)
124
125	Copy and rename:
126	```bash
127	cp source.edf sub-01/eeg/sub-01_task-rest_eeg.edf
128	```
129
130	#### EMG
131
132	EMG follows the same pattern but uses the `emg` directory and suffix:
133	```bash
134	cp source.edf sub-01/emg/sub-01_task-grasp_emg.edf
135	```
136
137	### Step 4: Create JSON Sidecars
138
139	#### EEG sidecar (required fields)
140
141	```json
142	{
143	"TaskName": "rest",
144	"TaskDescription": "Eyes-open resting state recording",
145	"InstitutionName": "University Name",
146	"InstitutionAddress": "Address",
147	"Manufacturer": "BioSemi",
148	"ManufacturersModelName": "ActiveTwo",
149	"SamplingFrequency": 512,
150	"EEGChannelCount": 64,
151	"EOGChannelCount": 2,
152	"EMGChannelCount": 0,
153	"ECGChannelCount": 0,
154	"MiscChannelCount": 0,
155	"TriggerChannelCount": 1,
156	"PowerLineFrequency": 60,
157	"EEGPlacementScheme": "10-20",
158	"EEGReference": "CMS/DRL",
159	"EEGGround": "n/a",
160	"SoftwareFilters": "n/a",
161	"HardwareFilters": {"Highpass": {"HalfAmplitudeCutoffHz": 0.01}},
162	"RecordingType": "continuous",
163	"RecordingDuration": 300
164	}
165	```

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Type

bids-conversion

37stars7forks

View on GitHub

How to install bids-conversion?

neuromechanist/research-skills/bids-conversion

$npx -y skills add neuromechanist/research-skills --skill bids-conversion

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# BIDS Conversion
2
3	Convert neuroscience datasets to Brain Imaging Data Structure (BIDS) format. Supports EEG, EMG, MEG, fMRI, and behavioral data with proper file naming, JSON sidecars, and metadata.
4
5	## When to Use
6
7	- Converting raw data files to BIDS format
8	- Creating or fixing BIDS metadata (JSON sidecars, TSV files)
9	- Validating BIDS compliance
10	- Setting up a new BIDS dataset from scratch
11	- Converting between data formats (e.g., .set to .edf, .vhdr to .bdf)
12
13	## BIDS Directory Structure
14
15	```
16	dataset/
17	dataset_description.json
18	participants.tsv
19	participants.json
20	README
21	CHANGES
22	sub-01/
23	sub-01_scans.tsv
24	eeg/
25	sub-01_task-rest_eeg.set
26	sub-01_task-rest_eeg.json
27	sub-01_task-rest_channels.tsv
28	sub-01_task-rest_electrodes.tsv
29	sub-01_task-rest_coordsystem.json
30	sub-01_task-rest_events.tsv
31	emg/
32	sub-01_task-grasp_emg.edf
33	sub-01_task-grasp_emg.json
34	sub-01_task-grasp_channels.tsv
35	sub-01_task-grasp_events.tsv
36	anat/
37	sub-01_T1w.nii.gz
38	sub-01_T1w.json
39	derivatives/
40	pipeline-name/
41	sub-01/
42	```
43
44	## File Naming Convention
45
46	```
47	sub-<label>[_ses-<label>]_task-<label>[_acq-<label>][_run-<index>]_<suffix>.<extension>
48	```
49
50	- sub: subject identifier (required, alphanumeric, no special chars)
51	- ses: session (optional, for longitudinal studies)
52	- task: task name (required for functional data)
53	- acq: acquisition parameters (optional)
54	- run: run index (optional, for repeated acquisitions)
55	- suffix: data type (eeg, emg, meg, bold, T1w, events, channels, electrodes)
56
57	## Conversion Workflow
58
59	### Step 1: Inventory Source Data
60
61	Identify:
62	- Data format (BrainVision .vhdr, EEGLAB .set, EDF .edf, BDF .bdf, NIfTI .nii.gz)
63	- Number of subjects and sessions
64	- Task names and conditions
65	- Channel types (EEG, EMG, EOG, ECG, misc)
66	- Events/markers in the data
67	- Coordinate system for electrode positions
68
69	### Step 2: Create Dataset Scaffold
70
71	```python
72	import json
73	from pathlib import Path
74
75	def create_bids_scaffold(root: str, subjects: list[str], tasks: list[str], modality: str = "eeg"):
76	root = Path(root)
77	root.mkdir(exist_ok=True)
78
79	# dataset_description.json
80	desc = {
81	"Name": "Dataset Name",
82	"BIDSVersion": "1.9.0",
83	"DatasetType": "raw",
84	"License": "CC0",
85	"Authors": ["Last, First"],
86	"DatasetDOI": "",
87	"GeneratedBy": [{"Name": "Manual conversion"}]
88	}
89	(root / "dataset_description.json").write_text(json.dumps(desc, indent=2))
90
91	# participants.tsv
92	with open(root / "participants.tsv", "w") as f:
93	f.write("participant_id\tage\tsex\thand\n")
94	for sub in subjects:
95	f.write(f"sub-{sub}\tn/a\tn/a\tn/a\n")
96
97	# Create subject directories
98	for sub in subjects:
99	for task in tasks:
100	(root / f"sub-{sub}" / modality).mkdir(parents=True, exist_ok=True)
101	```
102
103	### Step 3: Convert Data Files
104
105	#### EEG (EEGLAB .set)
106
107	EEGLAB .set files are BIDS-compatible as-is. Copy and rename:
108	```bash
109	cp source.set sub-01/eeg/sub-01_task-rest_eeg.set
110	cp source.fdt sub-01/eeg/sub-01_task-rest_eeg.fdt # if separate .fdt file
111	```
112
113	#### EEG (BrainVision .vhdr)
114
115	BrainVision files come in triplets (.vhdr, .vmrk, .eeg). All three must be renamed consistently:
116	```bash
117	cp source.vhdr sub-01/eeg/sub-01_task-rest_eeg.vhdr
118	cp source.vmrk sub-01/eeg/sub-01_task-rest_eeg.vmrk
119	cp source.eeg sub-01/eeg/sub-01_task-rest_eeg.eeg
120	```
121	Update internal references in .vhdr and .vmrk to point to renamed files.
122
123	#### EEG (EDF/BDF)
124
125	Copy and rename:
126	```bash
127	cp source.edf sub-01/eeg/sub-01_task-rest_eeg.edf
128	```
129
130	#### EMG
131
132	EMG follows the same pattern but uses the `emg` directory and suffix:
133	```bash
134	cp source.edf sub-01/emg/sub-01_task-grasp_emg.edf
135	```
136
137	### Step 4: Create JSON Sidecars
138
139	#### EEG sidecar (required fields)
140
141	```json
142	{
143	"TaskName": "rest",
144	"TaskDescription": "Eyes-open resting state recording",
145	"InstitutionName": "University Name",
146	"InstitutionAddress": "Address",
147	"Manufacturer": "BioSemi",
148	"ManufacturersModelName": "ActiveTwo",
149	"SamplingFrequency": 512,
150	"EEGChannelCount": 64,
151	"EOGChannelCount": 2,
152	"EMGChannelCount": 0,
153	"ECGChannelCount": 0,
154	"MiscChannelCount": 0,
155	"TriggerChannelCount": 1,
156	"PowerLineFrequency": 60,
157	"EEGPlacementScheme": "10-20",
158	"EEGReference": "CMS/DRL",
159	"EEGGround": "n/a",
160	"SoftwareFilters": "n/a",
161	"HardwareFilters": {"Highpass": {"HalfAmplitudeCutoffHz": 0.01}},
162	"RecordingType": "continuous",
163	"RecordingDuration": 300
164	}
165	```

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Type