Dataset Summary

MindBigData 2023 MNIST-2B is a reduced subset of the MindBigData 2023 MNIST-8B https://huggingface.co/datasets/DavidVivancos/MindBigData2023_MNIST-8B (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 256 samples ( a bit more than 1 second), recorded at 250hz (From the Original 8 Billion datapoints dataset, the EEG signals were reduced from 500 samples to 256 samples(a bit more than 1 second))

It consists of 2 main csv data files:

“train.csv” 23.1 GB Header + 120,000 rows 33,559 columns
“test.csv” 3.87 GB Header + 20,000 rows 33,559 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

Dataset Structure

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

Data Fields

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

Citation

@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}
}