---
license: odbl
---
## Dataset Summary

MindBigData 2023 MNIST-8B is the largest, to date (June 1st 2023), brain signals open dataset created for Machine Learning, based on EEG signals from a single subject captured using a custom 128 channels device, replicating the full 70,000 digits from Yaan LeCun et all MNIST dataset. The brain signals were captured while the subject was watching the pixels of the original digits one by one on a screen and listening at the same time to the spoken number 0 to 9 from the real label.

Supporting dataset for paper https://arxiv.org/abs/2306.00455

The dataset contains 140,000 records from 128 EEG channels, each of 2 seconds, recorded at 250hz, in total 17,920,000 brain signals and 8,960,000,000 data points.

It consists of 2 main csv data files:
- “train.csv” 45Gb Header + 120,000 rows 64,791 columns
- “test.csv” 7,52Gb Header + 20,000 rows 64,791 columns

10 audio files at a folder named “audiolabels”: “0.wav”, “1.wav”......“9.wav”

And 1 csv file with 3d coordinates of the EEG electrodes: “3Dcoords.csv” 4,27Kb Header + 130 rows 4 columns


>update July 18th 2023: As requested a reduced 2Billion datapoints is released https://huggingface.co./datasets/DavidVivancos/MindBigData2023_MNIST-2B

## Dataset Structure

review supporting paper https://arxiv.org/abs/2306.00455

## Data Fields

review supporting paper https://arxiv.org/abs/2306.00455

## Citation
```sh
@article{MindBigData_2023_MNIST-8B,
  title={MindBigData 2023 MNIST-8B The 8 billion datapoints Multimodal Dataset of Brain Signals},
  author={David Vivancos},
  journal={arXiv preprint arXiv:2306.00455},
  year={2023}
}
```