DS007315: eeg dataset, 2 subjects#

tACS for Patients with Post-Stroke Anomia

Access recordings and metadata through EEGDash.

Citation: Maria Martzoukou, Nefeli K. Dimitriou, Binbin Xu, Malo Renaud-d’Ambra, Anastasia Nousia, Alexandre Aksenov, Anne Beuter, Grigorios Nasios (2026). tACS for Patients with Post-Stroke Anomia. 10.18112/openneuro.ds007315.v1.0.1

Modality: eeg Subjects: 2 Recordings: 14 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007315

dataset = DS007315(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS007315(cache_dir="./data", subject="01")

Advanced query

dataset = DS007315(
    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{ds007315,
  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 = {10.18112/openneuro.ds007315.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds007315.v1.0.1},
}

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>)

Dataset Information#

Dataset ID

DS007315

Title

tACS for Patients with Post-Stroke Anomia

Author (year)

Martzoukou2026_tACS_Patients

Canonical

Martzoukou2024_Post_A

Importable as

DS007315, Martzoukou2026_tACS_Patients, Martzoukou2024_Post_A

Year

2026

Authors

Maria Martzoukou, Nefeli K. Dimitriou, Binbin Xu, Malo Renaud-d’Ambra, Anastasia Nousia, Alexandre Aksenov, Anne Beuter, Grigorios Nasios

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds007315.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds007315,
  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 = {10.18112/openneuro.ds007315.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds007315.v1.0.1},
}

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

  • Subjects: 2

  • Recordings: 14

  • Tasks: 1

Channels & sampling rate

  • Channels: 32

  • Sampling rate (Hz): 500.0

  • Duration (hours): 4.888012222222223

Tags

  • Pathology: Other

  • Modality: Visual

  • Type: Clinical/Intervention

Files & format

  • Size on disk: 1.1 GB

  • File count: 14

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds007315.v1.0.1

Provenance

API Reference#

Use the DS007315 class to access this dataset programmatically.

class eegdash.dataset.DS007315(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

tACS for Patients with Post-Stroke Anomia

Study:

ds007315 (OpenNeuro)

Author (year):

Martzoukou2026_tACS_Patients

Canonical:

Martzoukou2024_Post_A

Also importable as: DS007315, Martzoukou2026_tACS_Patients, Martzoukou2024_Post_A.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Other. 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

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/ds007315 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007315 DOI: https://doi.org/10.18112/openneuro.ds007315.v1.0.1

Examples

>>> from eegdash.dataset import DS007315
>>> dataset = DS007315(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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

  • path (str or Path) – Destination file path.

  • overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also#