EEGdash›NeMAR›NM000483
Iss. 483 · 1 subjects · 5 recordings · CC-BY-4.0
Dataset Brief · VakilnaSEEG-fingerprint

NM000483: ieeg dataset, 1 subjects#

VakilnaSEEG-fingerprint: sEEG seizure-onset and interictal segments of one polymicrogyria patient with electrode coordinates (Vakilna et al., 2025) (derivative)

Access recordings and metadata through EEGDash.

Citation: Vakilna, Yash Shashank, Atilgan, Deniz, Hampson, Johnson P, Chinara, Chinmay, Medani, Takfarinas, Leahy, Richard, Lacuey Lecumberri, Nuria, Lhatoo, Samden, Pati, Sandipan, Mosher, John, Gavvala, Jay (20). VakilnaSEEG-fingerprint: sEEG seizure-onset and interictal segments of one polymicrogyria patient with electrode coordinates (Vakilna et al., 2025) (derivative). 10.82901/nemar.nm000483

Modality: ieeg Subjects: 1 Recordings: 5 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

1-participant iEEG dataset — VakilnaSEEG-fingerprint: sEEG seizure-onset and interictal segments of one polymicrogyria patient with electrode coordinates (Vakilna et al., 2025) (derivative).

Data & curation Vakilna, Yash Shashank · Atilgan, Deniz · Hampson, Johnson P · Chinara, Chinmay · Medani, Takfarinas · Leahy, Richard · …
Senior author Gavvala, Jay
Year 20 · Distributed via NeMAR
iEEG · 267 (3), 206 (2) ch1000 HzBIDS 1.10.03 tasks
Layer 01Study
What was asked
Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.
Layer 02Signal · BIDS
What was recorded
Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.
Layer 03Training · ML
What you can train on
Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.
§ 01Access · Get started

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000483

dataset = NM000483(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000483(cache_dir="./data", subject="01")

Advanced query

dataset = NM000483(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{nm000483,
  title = {VakilnaSEEG-fingerprint: sEEG seizure-onset and interictal segments of one polymicrogyria patient with electrode coordinates (Vakilna et al., 2025) (derivative)},
  author = {Vakilna, Yash Shashank and Atilgan, Deniz and Hampson, Johnson P and Chinara, Chinmay and Medani, Takfarinas and Leahy, Richard and Lacuey Lecumberri, Nuria and Lhatoo, Samden and Pati, Sandipan and Mosher, John and Gavvala, Jay},
  doi = {10.82901/nemar.nm000483},
  url = {https://doi.org/10.82901/nemar.nm000483},
}
§ 02Study · The README

About This Dataset#

Release: Zenodo 14807262 “Time-Frequency Fingerprint Analysis in SEEG Source-Space to Identify the Epileptogenic Zone” (Vakilna, Yash Shashank, Atilgan, Deniz, Hampson, Johnson P, Chinara, Chinmay, Medani, Takfarinas, Leahy, Richard, Lacuey Lecumberri, Nuria, Lhatoo, Samden, Pati, Sandipan, Mosher, John, Gavvala, Jay; doi:10.5281/zenodo.14807262, CC-BY-4.0), the Brainstorm database

(BST_dataset.zip) behind the case study doi:10.1002/acn3.70115. Zenodo description: “This case study highlights the application of seizure fingerprint analysis in the source-space of SEEG data to accurately localize the epileptogenic zone (EZ) in patients with complex cortical malformations. A 25-year-old female with extensive bilateral perisylvian polymicrogyria presented with intractable focal seizures. The source-level analysis performed in Brainstorm using the sLORETA imaging algorithm subsequently showed EZ fingerprint analysis. The patient underwent MR-guided laser interstitial thermal therapy, targeting the identified EZ, resulting in postoperative seizure freedom with minimal complications. Extending the analysis from the sensor-space to the source-space could further enhance surgical planning and improve outcomes in complex epilepsy cases.”

The release contains three short sensor-space segments of the clinical stereo-EEG (Nihon Kohden EEG-1200A, 2000 Hz) that

the authors imported into Brainstorm, notch-filtered (180-900 Hz harmonics) and resampled to 1000 Hz, and their bipolar (‘bipolar 2’) montage versions. They are written here as BrainVision float32 in microvolts (Brainstorm stores volts), acq-monopolar (267 channels: 228 located sEEG contacts, 20 sEEG contacts without location, scalp EEG, ECG, DC and marker channels, as typed in the Brainstorm channel file) and acq-bipolar (206 bipolar pairs). This is a DERIVATIVE dataset; the full raw recording is not in the release. Brainstorm events (clinical marks, the authors’ onset/wave/spike markers and channel annotations) are in events.tsv; the time axis of each segment (0 = reference event) is kept in the brainstorm_time column.

  • sub-01_task-seizureonsetlvfa_acq-monopolar: LVFA_and_wave/data_sEEG_onset__SPS_PLON_trial001_notch_resample.mat (sEEG onset: SPS/PLON (#1) | notch(180Hz 300Hz 420Hz 540Hz 660Hz 780Hz 900Hz)), 267 ch, 1000 Hz, 30 s, time axis -15..15 s, 7 events

  • sub-01_task-seizureonsetlvfa_acq-bipolar: LVFA_and_wave_bipolar_2/data_sEEG_onset__SPS_PLON_trial001_notch_resample_montage.mat (sEEG onset: SPS/PLON (#1) | notch(180Hz 300Hz 420Hz 540Hz 660Hz 780Hz 900Hz) | bipolar 2), 206 ch, 1000 Hz, 30 s, time axis -15..15 s, 7 events

  • sub-01_task-seizureonsetspike_acq-monopolar: ictal_repetitive_spike/data_EEG_onset_trial001_notch_resample.mat (sEEG onset (#1) | notch(180Hz 300Hz 420Hz 540Hz 660Hz 780Hz 900Hz)), 267 ch, 1000 Hz, 30 s, time axis -15..15 s, 6 events

  • sub-01_task-seizureonsetspike_acq-bipolar: ictal_repetitive_spike_bipolar_2/data_EEG_onset_trial001_notch_resample_montage.mat (sEEG onset (#1) | notch(180Hz 300Hz 420Hz 540Hz 660Hz 780Hz 900Hz) | bipolar 2), 206 ch, 1000 Hz, 30 s, time axis -15..15 s, 6 events

  • sub-01_task-interictalspike_acq-monopolar: interictal_spike/data_Spike_trial001_notch_resample.mat (Spike (#1) | notch(180Hz 300Hz 420Hz 540Hz 660Hz 780Hz 900Hz)), 267 ch, 1000 Hz, 10 s, time axis -5..5 s, 3 events

DOI

sEEG seizure-onset and interictal segments of one polymicrogyria patient, with electrode coordinates (Vakilna et al., 2025) - derivative

Localisation

electrodes.tsv carries the Brainstorm channel locations (subject coordinate system SCS, native space, mm) of 228 sEEG contacts of 16 depth electrodes (AH, ALAT, ALON, MC, MPM, MPS, PH, PIN, PLAT, PLON, POP, PSIN, SMA, SPL, SPM, SPS); electrode geometry (contact number, spacing, diameter) is in sourcedata/zenodo-14807262/intra_electrodes.json. The subject’s T1 MRI, head/cortex surfaces and volume atlases of the release are NOT redistributed (the MRI is not defaced); they, and the authors’ source-space results (OpenMEEG head model, sLORETA kernels, time-frequency maps, scout time series), remain in the Zenodo record.

Provenance

Converted by the ieeg-nemar-20261005 campaign (lane o5c-B) with code/o5c_B_conv_lit187.py; round-trip checks (MNE read-back) in code/conversion_report.json. sourcedata holds the converted Brainstorm data/channel/study files verbatim.

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000483-blue)](https://doi.org/10.82901/nemar.nm000483) # sEEG seizure-onset and interictal segments of one polymicrogyria patient, with electrode coordinates (Vakilna et al., 2025) - derivative Release: Zenodo 14807262 “Time-Frequency Fingerprint Analysis in SEEG Source-Space to Identify the Epileptogenic Zone” (Vakilna, Yash Shashank, Atilgan, Deniz, Hampson, Johnson P, Chinara, Chinmay, Medani, Takfarinas, Leahy, Richard, Lacuey Lecumberri, Nuria, Lhatoo, Samden, Pati, Sandipan, Mosher, John, Gavvala, Jay; doi:10.5281/zenodo.14807262, CC-BY-4.0), the Brainstorm database (BST_dataset.zip) behind the case study doi:10.1002/acn3.70115. Zenodo description: “This case study highlights the application of seizure fingerprint analysis in the source-space of SEEG data to accurately localize the epileptogenic zone (EZ) in patients with complex cortical malformations. A 25-year-old female with extensive bilateral perisylvian polymicrogyria presented with intractable focal seizures. The source-level analysis performed in Brainstorm using the sLORETA imaging algorithm subsequently showed EZ fingerprint analysis. The patient underwent MR-guided laser interstitial thermal therapy, targeting the identified EZ, resulting in postoperative seizure freedom with minimal complications. Extending the analysis from the sensor-space to the source-space could further enhance surgical planning and improve outcomes in complex epilepsy cases.” ## Content (1 participant, 5 recordings, 1.2 min of sensor-space data) The release contains three short sensor-space segments of the clinical stereo-EEG (Nihon Kohden EEG-1200A, 2000 Hz) that the authors imported into Brainstorm, notch-filtered (180-900 Hz harmonics) and resampled to 1000 Hz, and their bipolar (‘bipolar 2’) montage versions. They are written here as BrainVision float32 in microvolts (Brainstorm stores volts), acq-monopolar (267 channels: 228 located sEEG contacts, 20 sEEG contacts without location, scalp EEG, ECG, DC and marker channels, as typed in the Brainstorm channel file) and acq-bipolar (206 bipolar pairs). This is a DERIVATIVE dataset; the full raw recording is not in the release. Brainstorm events (clinical marks, the authors’ onset/wave/spike markers and channel annotations) are in events.tsv; the time axis of each segment (0 = reference event) is kept in the brainstorm_time column.

  • sub-01_task-seizureonsetlvfa_acq-monopolar: LVFA_and_wave/data_sEEG_onset__SPS_PLON_trial001_notch_resample.mat (sEEG onset: SPS/PLON (#1) | notch(180Hz 300Hz 420Hz 540Hz 660Hz 780Hz 900Hz)), 267 ch, 1000 Hz, 30 s, time axis -15..15 s, 7 events

  • sub-01_task-seizureonsetlvfa_acq-bipolar: LVFA_and_wave_bipolar_2/data_sEEG_onset__SPS_PLON_trial001_notch_resample_montage.mat (sEEG onset: SPS/PLON (#1) | notch(180Hz 300Hz 420Hz 540Hz 660Hz 780Hz 900Hz) | bipolar 2), 206 ch, 1000 Hz, 30 s, time axis -15..15 s, 7 events

  • sub-01_task-seizureonsetspike_acq-monopolar: ictal_repetitive_spike/data_EEG_onset_trial001_notch_resample.mat (sEEG onset (#1) | notch(180Hz 300Hz 420Hz 540Hz 660Hz 780Hz 900Hz)), 267 ch, 1000 Hz, 30 s, time axis -15..15 s, 6 events

  • sub-01_task-seizureonsetspike_acq-bipolar: ictal_repetitive_spike_bipolar_2/data_EEG_onset_trial001_notch_resample_montage.mat (sEEG onset (#1) | notch(180Hz 300Hz 420Hz 540Hz 660Hz 780Hz 900Hz) | bipolar 2), 206 ch, 1000 Hz, 30 s, time axis -15..15 s, 6 events

  • sub-01_task-interictalspike_acq-monopolar: interictal_spike/data_Spike_trial001_notch_resample.mat (Spike (#1) | notch(180Hz 300Hz 420Hz 540Hz 660Hz 780Hz 900Hz)), 267 ch, 1000 Hz, 10 s, time axis -5..5 s, 3 events

## Localisation electrodes.tsv carries the Brainstorm channel locations (subject coordinate system SCS, native space, mm) of 228 sEEG contacts of 16 depth electrodes (AH, ALAT, ALON, MC, MPM, MPS, PH, PIN, PLAT, PLON, POP, PSIN, SMA, SPL, SPM, SPS); electrode geometry (contact number, spacing, diameter) is in sourcedata/zenodo-14807262/intra_electrodes.json. The subject’s T1 MRI, head/cortex surfaces and volume atlases of the release are NOT redistributed (the MRI is not defaced); they, and the authors’ source-space results (OpenMEEG head model, sLORETA kernels, time-frequency maps, scout time series), remain in the Zenodo record. ## Provenance Converted by the ieeg-nemar-20261005 campaign (lane o5c-B) with code/o5c_B_conv_lit187.py; round-trip checks (MNE read-back) in code/conversion_report.json. sourcedata holds the converted Brainstorm data/channel/study files verbatim.

License: CC-BY-4.0

Authors:

  • Vakilna, Yash Shashank

  • Atilgan, Deniz

  • Hampson, Johnson P

  • Chinara, Chinmay

  • Medani, Takfarinas

  • … and 6 more

Versions:

Version

DOI

Released

current

10.82901/nemar.nm000483
§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=1, range 25–25 yr, mean 25.0 yr)

25
Female · 1

Sex composition

1
subjects
Female
1

Channel counts (ch)

206267

Sampling frequencies: 1000.0 Hz (n=5 recordings)

Total recording duration: 2 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 267 (3), 206 (2) ch · iEEG · 1000 Hz · 1 subjects, 5 recordings
Live trace viewer — sub-01 · task-seizureonsetlvfa

Showing one representative recording out of 1 subjects and 5 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _ieeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?ieeg=<url>) to inspect it.

Electrode layout — iEEG · 222 sensors — 222 channels

NEMAR Processing Statistics#

The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.

NEMAR pipeline statistics — NM000483
HED event descriptors word cloud HED event descriptors word cloud — NM000483
§ 05Manifest · BIDS tree

Manifest#

File Explorer#

Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.

Recordings—
Files—
Subjects—
Modalities—
Click to load file structure…
Full dataset metadata table

Dataset ID

NM000483

Title

VakilnaSEEG-fingerprint: sEEG seizure-onset and interictal segments of one polymicrogyria patient with electrode coordinates (Vakilna et al., 2025) (derivative)

Author (year)

—

Canonical

—

Importable as

NM000483

Year

20

Authors

Vakilna, Yash Shashank, Atilgan, Deniz, Hampson, Johnson P, Chinara, Chinmay, Medani, Takfarinas, Leahy, Richard, Lacuey Lecumberri, Nuria, Lhatoo, Samden, Pati, Sandipan, Mosher, John, Gavvala, Jay

License

CC-BY-4.0

Citation / DOI

10.82901/nemar.nm000483

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{nm000483,
  title = {VakilnaSEEG-fingerprint: sEEG seizure-onset and interictal segments of one polymicrogyria patient with electrode coordinates (Vakilna et al., 2025) (derivative)},
  author = {Vakilna, Yash Shashank and Atilgan, Deniz and Hampson, Johnson P and Chinara, Chinmay and Medani, Takfarinas and Leahy, Richard and Lacuey Lecumberri, Nuria and Lhatoo, Samden and Pati, Sandipan and Mosher, John and Gavvala, Jay},
  doi = {10.82901/nemar.nm000483},
  url = {https://doi.org/10.82901/nemar.nm000483},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.NM000483(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)—
Canonical—
Importable asNM000483
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.NM000483(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

VakilnaSEEG-fingerprint: sEEG seizure-onset and interictal segments of one polymicrogyria patient with electrode coordinates (Vakilna et al., 2025) (derivative)

Study:

nm000483 (NeMAR)

Author (year):

—

Canonical:

—

Also importable as: NM000483.

Modality: ieeg; Subject type: Unknown. Subjects: 1; recordings: 5; tasks: 3.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir#

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query#

Merged query with the dataset filter applied.

Type:

dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000483 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000483 DOI: https://doi.org/10.82901/nemar.nm000483

Examples

>>> from eegdash.dataset import NM000483
>>> dataset = NM000483(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
save(path: str, overwrite: bool = False, offset: int = 0)[source]#

Save datasets to files by creating one subdirectory for each dataset:

path/
    0/
        0-raw.fif | 0-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)
    1/
        1-raw.fif | 1-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)
Parameters:

  • path (str) –

    Directory in which subdirectories are created to store

    -raw.fif | -epo.fif and .json files to.

  • overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.

  • offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.

Access modesMNE → braindecode → PyTorch → ML
.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne
BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode
DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch
Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr
Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — NM000483.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons
Examples using EEGDashcurated · start here
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.Load one EEG recordingResolve a single record to an MNE Raw with channels and events.EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.

Swap any load_dataset(...) call for nm000483 to reproduce the tutorial on this dataset.

Citation

Vakilna, Yash Shashank, Atilgan, Deniz, Hampson, Johnson P, Chinara, Chinmay, Medani, Takfarinas, … (20). VakilnaSEEG-fingerprint: sEEG seizure-onset and interictal segments of one polymicrogyria patient with electrode coordinates (Vakilna et al., 2025) (derivative). 10.82901/nemar.nm000483

Provenance

¹Contributed to nemar in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.82901/nemar.nm000483.

BIDS
BIDS 1.10.0
Sidecars
events · events.json · channels · eeg.json
Provenance
CC-BY-4.0 · 10.82901/nemar.nm000483
Machine-readable
schema.org/Dataset · Croissant
Mirrors
OpenNeuro · NEMAR · HF org

See Also#