DS004642: ieeg dataset, 10 subjects#
Intraoperative recordings of medianus stimulation with low and high impedance ECoG
Citation: Vasileios Dimakopoulos, Marian Neidert, Johannes Sarnthein (20). Intraoperative recordings of medianus stimulation with low and high impedance ECoG. 10.18112/openneuro.ds004642.v1.0.1
10-participant iEEG dataset — Intraoperative recordings of medianus stimulation with low and high impedance ECoG.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004642
dataset = DS004642(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004642(cache_dir="./data", subject="01")
Advanced query
dataset = DS004642(
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{ds004642,
title = {Intraoperative recordings of medianus stimulation with low and high impedance ECoG},
author = {Vasileios Dimakopoulos and Marian Neidert and Johannes Sarnthein},
doi = {10.18112/openneuro.ds004642.v1.0.1},
url = {https://doi.org/10.18112/openneuro.ds004642.v1.0.1},
}
About This Dataset#
This dataset of medianus SEP was first analyzed in publication [1]. There we investigated whether the low impedance ECoG electrode (LoZ) improves fast ripple detection over a standard electrode with high impedance contacts (HiZ).
There are 10 patients (median age 40 y, range 19-56 y, 6 female) who underwent brain tumor resections in the perirolandic region at our institution. The data includes the continuous raw data from the ECoG contacts of both electrodes. We recorded medianus SEP intraoperatively (stimulation rate = 4.7 Hz) from two 4-contacts ECoG strips simultaneously (LoZ: a contacts, HiZ: b contacts) that had different median impedance (LoZ: 3.4 kΩ, HiZ: 6.9 kΩ).
Intraoperative recordings of medianus stimulation with low and high impedance ECoG
Repository structure
Main directory (LoZ HFO)
Contains metadata files in the BIDS standard about the participants and the study. Folders are explained below.
Subfolders
LoZ HFO/sub-/ Contains folders for each subject, named sub- and session information.
LoZ HFO/sub-/ses-01/ieeg/ Contains the raw ieeg data in .edf format for each subject. Each *ieeg.edf file contains continuous iEEG data from one stimulation rate recorded at the hand area Ω from both the electrodes simultaneously . Details about the channels are given in the corresponding .tsv file.
Note from the paper
“The offline data processing used the continuous ECoG that was recorded in parallel to the SEP recordings. Data analysis was performed with custom scripts in Matlab. To detect the SEP stimulation artefact, we first filtered the ECoG (high pass cutoff = 200 Hz) and performed local peak detection (minimum peak prominence between peaks = 30 ms, minimum peak width = 4 ms, samples = 0.2 ms). We used the times of the detected stimulus artifact as triggers to define sweeps with post-stimulus recording sweep length 50 ms. We classified sweeps with amplitude ±100 µV as artefact-ridden and excluded them from further analysis.
We averaged 100 sweeps and filtered the averaged trace (bandpass [30 300] Hz, IIR filter, response roll-off -12 db per octave, forward and reverse filtering to avoid phase distortion). We visually inspected the data and selected one optimal channel with high N20 amplitude (positive or negative) for further analysis. From the averaged N20 trace, we determined the N20 peak latency. To obtain the N20 peak amplitude and the SNR, we inspected the latency of the N20 peak. If the N20 latency was >20 ms, we selected a signal window [20 25] ms. If the N20 latency was ≤ 20 ms, we selected a signal window [17 22] ms. In the same way, we filtered the averaged trace in the [250 500] Hz band to obtain the evoked FR and in the [500 1000] Hz band to obtain the evoked HFO. We doubled the largest deflection in the signal window of the N20 frequency band to define the N20 signal amplitude. In the FR and HFO bands we used the peak-to-peak amplitude.”
BIDS Conversion
bids-starter-kid and custom Matlab scripts were used to convert the dataset into BIDS format.
References
[1] Vasileios Dimakopoulos, Marian C. Neidert, Johannes Sarnthein, Low impedance electrodes improve detection of high frequency oscillations in the intracranial EEG, Clinical Neurophysiology, 2023, ISSN 1388-2457, https://doi.org/10.1016/j.clinph.2023.07.002 If you have any inquiries or questions, contact: * Vasileios Dimakopoulos (vasileios.dimakopoulos@usz.ch) * Johannes Sarnthein (johannes.sarnthein@usz.ch)
Cohort#
Dataset Statistics#
Age distribution by gender (n=10, range 19–56 yr, mean 39.7 yr)
Sex composition
Channel counts (ch)
Sampling frequencies: 20000.0 Hz (n=10 recordings)
Total recording duration: 1 h 4 min
Signal · Electrodes & live trace#
Live trace viewer — sub-08 · ses-01 · task-lozHFO · run-01
Showing one representative recording out of
10 subjects and 10 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.
No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.
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.
HED event descriptors word cloud
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.
Full dataset metadata table
Dataset ID |
|
Title |
Intraoperative recordings of medianus stimulation with low and high impedance ECoG |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Vasileios Dimakopoulos, Marian Neidert, Johannes Sarnthein |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004642,
title = {Intraoperative recordings of medianus stimulation with low and high impedance ECoG},
author = {Vasileios Dimakopoulos and Marian Neidert and Johannes Sarnthein},
doi = {10.18112/openneuro.ds004642.v1.0.1},
url = {https://doi.org/10.18112/openneuro.ds004642.v1.0.1},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004642 · Dimakopoulos2023_Intraoperativeeegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004642(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Intraoperative recordings of medianus stimulation with low and high impedance ECoG
- Study:
ds004642(OpenNeuro)- Author (year):
Dimakopoulos2023_Intraoperative- Canonical:
—
Also importable as:
DS004642,Dimakopoulos2023_Intraoperative.Modality:
ieeg; Experiment type:Other; Subject type:Surgery. Subjects: 10; recordings: 10; tasks: 1.- 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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.References
OpenNeuro dataset: https://openneuro.org/datasets/ds004642 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004642 DOI: https://doi.org/10.18112/openneuro.ds004642.v1.0.1 NEMAR citation count: 2
Examples
>>> from eegdash.dataset import DS004642 >>> dataset = DS004642(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.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds004642").huggingface
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 ds004642 to reproduce the tutorial on this dataset.
Citation
Vasileios Dimakopoulos, Marian Neidert, Johannes Sarnthein (20). Intraoperative recordings of medianus stimulation with low and high impedance ECoG. 10.18112/openneuro.ds004642.v1.0.1
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004642.v1.0.1.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset