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pretty_name: "BigP3BCI Study F — 6x6 multi-paradigm, 3 sessions (10 healthy subjects)"
license: cc-by-4.0
tags:
- eeg
- neuroscience
- eegdash
- brain-computer-interface
- pytorch
- visual
- attention
- other
size_categories:
- n<1K
task_categories:
- other
---
# BigP3BCI Study F — 6x6 multi-paradigm, 3 sessions (10 healthy subjects)
**Dataset ID:** `nm000191`
_Mainsah2025_BigP3BCI_F_
**Canonical aliases:** `BigP3BCI_StudyF` · `BigP3BCI_F`
> **At a glance:** EEG · Visual attention · other · 10 subjects · 270 recordings · CC-BY-4.0
## Load this dataset
This repo is a **pointer**. The raw EEG data lives at its canonical source
(OpenNeuro / NEMAR); [EEGDash](https://github.com/eegdash/EEGDash) streams it
on demand and returns a PyTorch / braindecode dataset.
```python
# pip install eegdash
from eegdash import EEGDashDataset
ds = EEGDashDataset(dataset="nm000191", cache_dir="./cache")
print(len(ds), "recordings")
```
You can also load it by canonical alias — these are registered classes in `eegdash.dataset`:
```python
from eegdash.dataset import BigP3BCI_StudyF
ds = BigP3BCI_StudyF(cache_dir="./cache")
```
If the dataset has been mirrored to the HF Hub in braindecode's Zarr layout,
you can also pull it directly:
```python
from braindecode.datasets import BaseConcatDataset
ds = BaseConcatDataset.pull_from_hub("EEGDash/nm000191")
```
## Dataset metadata
| | |
|---|---|
| **Subjects** | 10 |
| **Recordings** | 270 |
| **Tasks (count)** | 1 |
| **Channels** | 16 (×270) |
| **Sampling rate (Hz)** | 256 (×270) |
| **Total duration (h)** | 12.8 |
| **Size on disk** | 551.9 MB |
| **Recording type** | EEG |
| **Experimental modality** | Visual |
| **Paradigm type** | Attention |
| **Population** | Other |
| **Source** | nemar |
| **License** | CC-BY-4.0 |
## Links
- **NEMAR:** [nm000191](https://nemar.org/dataexplorer/detail?dataset_id=nm000191)
- **Browse 700+ datasets:** [EEGDash catalog](https://huggingface.co/spaces/EEGDash/catalog)
- **Docs:** <https://eegdash.org>
- **Code:** <https://github.com/eegdash/EEGDash>
---
_Auto-generated from [dataset_summary.csv](https://github.com/eegdash/EEGDash/blob/main/eegdash/dataset/dataset_summary.csv) and the [EEGDash API](https://data.eegdash.org/api/eegdash/datasets/summary/nm000191). Do not edit this file by hand — update the upstream source and re-run `scripts/push_metadata_stubs.py`._
|