ON007315: eeg dataset, 2 subjects#
tACS for Patients with Post-Stroke Anomia
Citation: Maria Martzoukou, Nefeli K. Dimitriou, Binbin Xu, Malo Renaud-d’Ambra, Anastasia Nousia, Alexandre Aksenov, Anne Beuter, Grigorios Nasios (—). tACS for Patients with Post-Stroke Anomia. 10.82901/nemar.on007315
2-participant EEG dataset — tACS for Patients with Post-Stroke Anomia.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import ON007315
dataset = ON007315(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = ON007315(cache_dir="./data", subject="01")
Advanced query
dataset = ON007315(
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{on007315,
title = {tACS for Patients with Post-Stroke Anomia},
author = {Maria Martzoukou and Nefeli K. Dimitriou and Binbin Xu and Malo Renaud-d’Ambra and Anastasia Nousia and Alexandre Aksenov and Anne Beuter and Grigorios Nasios},
doi = {10.82901/nemar.on007315},
url = {https://doi.org/10.82901/nemar.on007315},
}
About This Dataset#
Title: Transcranial Alternating Current Stimulation (tACS) for Patients with Post-Stroke Anomia: Preliminary Data on Picture Naming Performance
Dataset Description:This dataset includes EEG recordings from two post-stroke patients with chronic anomia who participated in an 8-week individualized neuromodulation intervention using transcranial alternating current stimulation (tACS). The intervention alternated between stimulation and non-stimulation phases every two weeks and was designed to enhance naming abilities via cortical entrainment, guided by individual EEG profiles. Data Overview: - Participants: 2 individuals with post-stroke anomia (1 left-hemisphere lesion, 1 right-hemisphere lesion) - Sessions: EEG recorded every two weeks during the intervention (W1, W2, W4, W6, W8), and at follow-ups (W12, W20) - Stimulation: tACS applied during alternating weeks; frequency and montage were personalized based on initial EEG - Tasks: Picture naming task using a standardized set of stimuli; EEG recorded during task execution - Modality: EEG (recorded using Starstim-32), processed in EEGLAB and prepared for BIDS
Experimental Design:
A single-case experimental design (SCED, ABAB type) was employed. Behavioral and EEG data were collected across 24 naming sessions and 6 EEG recording sessions per participant. The data includes tACS and no-tACS conditions.
Purpose:To investigate whether tACS improves naming accuracy and latency in chronic aphasia and whether those effects are sustained after intervention.
Data Notes:
- EEG recordings are organized in BIDS format, with sessions labeled by week (e.g., week-01, week-12)
- Session and run numbers reflect weeks of intervention
Ethics:All participants provided written informed consent. The study was approved by the Ethics Committee of the Medical School of Ioannina (approval nr. 49625) and conducted in accordance with the Declaration of Helsinki. Contact:For questions about the dataset, contact Maria Martzoukou (<m.martzoukou@uoi.gr>)
Cohort#
Dataset Statistics#
Age distribution by gender (n=2, range 38–54 yr, mean 46.0 yr)
Sex composition
Channel counts: 32 ch (n=14 recordings)
Sampling frequencies: 500.0 Hz (n=14 recordings)
Total recording duration: 4 h 53 min
Signal · Electrodes & live trace#
Live trace viewer — sub-G01 · ses-1 · task-PictureNaming · run-1
Showing one representative recording out of
2 subjects and 14 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _eeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?eeg=<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 |
tACS for Patients with Post-Stroke Anomia |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Maria Martzoukou, Nefeli K. Dimitriou, Binbin Xu, Malo Renaud-d’Ambra, Anastasia Nousia, Alexandre Aksenov, Anne Beuter, Grigorios Nasios |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{on007315,
title = {tACS for Patients with Post-Stroke Anomia},
author = {Maria Martzoukou and Nefeli K. Dimitriou and Binbin Xu and Malo Renaud-d’Ambra and Anastasia Nousia and Alexandre Aksenov and Anne Beuter and Grigorios Nasios},
doi = {10.82901/nemar.on007315},
url = {https://doi.org/10.82901/nemar.on007315},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.ON007315(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
tACS for Patients with Post-Stroke Anomia
- Study:
on007315(NeMAR)- Author (year):
nan- Canonical:
—
Also importable as:
ON007315,nan.Modality:
eeg. Subjects: 2; recordings: 14; 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/on007315 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on007315 DOI: https://doi.org/10.82901/nemar.on007315
Examples
>>> from eegdash.dataset import ON007315 >>> dataset = ON007315(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.pytorch
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 on007315 to reproduce the tutorial on this dataset.
Citation
Maria Martzoukou, Nefeli K. Dimitriou, Binbin Xu, Malo Renaud-d’Ambra, Anastasia Nousia, … (n.d.). tACS for Patients with Post-Stroke Anomia. 10.82901/nemar.on007315
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.on007315.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset