MindBigData
The "MNIST" of Brain Digits

The version 1.03 of the open database contains 1,207,293 brain signals of 2 seconds each, captured with the stimulus of seeing  a digit (from 0 to 9) and thinking about it, over the course of almost 2 years between 2014 & 2015, from a single Test Subject David Vivancos. In 2018 we started sharing also a new open dataset "IMAGENET" of The Brain, and in 2021 we started The Visual "MNIST" of Brain Digits. with real individual MNIST digits shown , and don't miss MindBigData2023 MNIST-8B the new 8 billion datapoints multimodal dataset

Update December 2023: Check the new Hugging Face Leaderboard of Models

Update January 2023: Read the Paper "MindBigData 2022 A Large Dataset of Brain Signals" and alternative prepared datasets downloads at Hughing Face

All the signals have been captured using commercial EEGs (not medical grade), NeuroSky MindWave, Emotiv EPOC, Interaxon Muse & Emotiv Insight, covering a total of 19 Brain (10/20) locations.

Four files are available for download:

DataBase File Zip size File size Date Mirror
MindWave MindBigData-MW-v1.0.zip 62,6 MB (65,663,303 bytes) 297 MB (311,994,495 bytes) 09/11/2015
EPOC** MindBigData-EP-v1.0.zip 408 MB (427.958.689 bytes) 2,66 GB (2.859.712.035 bytes) 06/16/2018 US DataHub Mirror
Muse MindBigData-MU-v1.0.zip 62,6 MB (65,663,303 bytes) 297 MB (311,994,495 bytes) 09/11/2015
Insight* MindBigData-IN-v1.06.zip 25,3 MB (26,610,979 bytes) 184 MB (193,010,330 bytes) 12/10/2019

We built our own tools to capture them, but there is no post-processing on our side, so they come raw as they are read from each EEG device, in total 395,072,896 Data Points.

Feel free to test any machine learning, deep learning or whatever algorithm you think it could fit, we only ask for acknowledging the source and please let us know of your performance! 

We choose not to differentiate the signals into training/test/validation  sets at this point so pick the distribution you prefer.

A small portion of the signals were captured without the stimulus of seeing the digits for contrast, all are random actions not related to thinking or seeing digits, you can decide to use them or not in your tests, they use the code -1.


SIGNAL DISTRIBUTION:


This is the distribution of the signals per device and digit:

Device/Digit 0 1 2 3 4 5 6 7 8 9 -1 Total
MindWave (MW) 5,531 5,498 5,517 5,416 5,381 5,568 5,476 5,552 5,545 5,450 12,701 67,635
EPOC (EP) 91,224 88,914 90,930 92,652 88,886 91,994 91,322 88,718 91,728 91,882 2,226 910,476
Muse (MU) 11,904 11,632 11,920 11,832 11,536 12,052 12,368 12,080 12,208 11,988 44,412 163,932
Insight (IN)* 6,305 6,740 6,535 6,605 6,620 6,460 6,425 6,470 6,590 6,500 0 65,250
Total 114,964 112,784 114,902 116,505 112,423 116,074 115,591 112,820 116,071 115,820 59,339 1,207,293

* Insight captures started in September 2015, dataset updated to fix the channel sepparation by comma and use dot for the decimals, instead of commas only , last update 10/12/2019 v1.06

** EPOC dataset updated to fix the channel sepparation by comma and use dot for the decimals, instead of commas only , last update 06/16/2018 v1.01

 

FILE FORMAT:

The data is stored in a very simple text format including:

[id]: a numeric, only for reference purposes.

[event] id, a integer, used to distinguish the same event captured at different brain locations, used only by multichannel devices (all except MW).

[device]: a 2 character string, to identify the device used to capture the signals, "MW" for MindWave, "EP" for Emotive Epoc, "MU" for Interaxon Muse & "IN" for Emotiv Insight.

[channel]: a string, to indentify the 10/20 brain location of the signal, with possible values:
 
MindWave "FP1"
EPOC "AF3, "F7", "F3", "FC5", "T7", "P7", "O1", "O2", "P8", "T8", "FC6", "F4", "F8", "AF4"
Muse "TP9,"FP1","FP2", "TP10"
Insight "AF3,"AF4","T7","T8","PZ" 

[code]: a integer, to indentify the digit been thought/seen, with possible values 0,1,2,3,4,5,6,7,8,9 or -1 for random captured signals not related to any of the digits.

[size]: a integer, to identify the size in number of values captured in the 2 seconds of this signal, since the Hz of each device varies, in "theory" the value is close to 512Hz for MW, 128Hz for EP, 220Hz for MU & 128Hz for IN, for each of the 2 seconds.

[data]: a coma separated set of numbers, with the time-series amplitude of the signal, each device uses a different precision to identify the electrical potential captured from the brain: integers in the case of MW & MU or real numbers in the case of EP & IN.

There is no headers in the files,  every line is  a signal, and the fields are separated by a tab

For example one line of each device could be (without the headers)

[id] [event] [device] [channel] [code] [size] [data]
27 27 MW FP1 5 952 18,12,13,12,5,3,11,23,37,36,26,24,35,42……
67650 67636 EP F7 7 260 4482.564102,4477.435897,4484.102564…….
978210 132693 MU TP10 1 476 506,508,509,501,497,494,497,490,490,493……
1142043 173652 IN AF3 0 256 4259.487179,4237.948717,4247.179487,4242.051282……

BRAIN LOCATIONS:

Each EEG device capture the signals via different sensors, located in these areas of my brain, the color represents the device:    MindWave, EPOC, Muse, Insight

David Vivancos Brain 10/20 Locations

RELATED RESEARCH, CITATIONS & RESULTS by 3rd parties:

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- Contribution aux décompositions rapides des matrices et tenseurs , Viet-Dung NGUYEN THÈSE UNIVERSITÉ D’ORLÉANS  Nov-16th-2016

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- A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction, Jordan J. Bird , Diego R. Faria, Luis J. Manso, Anikó Ekárt, and Christopher D. Buckingham, School of Engineering and Applied Science, Aston University, Birmingham, UK   Mar-2019

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- HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography,Dashan Gao,Ce Ju,Xiguang Wei, Yang Liu,Tianjian Chen and Qiang Yan, Hong Kong University of Science and Technology, 2AI Lab, WeBank Co. Ltd.   Sep-2019

- Universal EEG Encoder for Learning Diverse Intelligent Tasks,Baani Leen Kaur Jolly, Palash Aggrawal, Surabhi S Nath, Viresh Gupta, Manraj Singh Grover, Rajiv Ratn Shah, MIDAS Lab, IIIT-Delhi   Nov-2019

- Deep Learning based Recognition of Visual Digit Reading Using Frequency Band of EEG,Jaesik Kim , Jeongryeol Seo , and Kyungah Son, Ajou University Republic of Korea. 2019

- Stanford CS230 - Group Project Final Report,Roman Pinchuk and Will Ross 2020

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- A Review on EEG Data Classification Methods for Brain–Computer Interface,Vaibhav Jadhav, Namita Tiwari and Meenu Chawla, Maulana Azad National Institute of Technology, Bhopal, India   Sep-2022

- A survey of electroencephalography open datasets and their applications in deep learning,Alberto Nogales, Álvaro García-Tejedor, Universidad Francisco de Vitoria   Sep-2022

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- EEG-based classification of imagined digits using a recurrent neural network,Nrushingh Charan Mahapatra and Prachet Bhuyan   Apr-2023

- Emotions Classification from EEG Waves Using Deep Learning,Vrachnaki Ioanna University Of Wesrtern Attica  2023

- RECOGNITION OF HUMAN EMOTIONS BASED ON EEG BRAINWAVE SIGNALS USING MACHINE LEARNING TECHNIQUES-A COMPARATIVE STUDY,Saba Tahseen, Ajit Dantii,, Christ University, Bengaluru, India,   2023

- Emotion Detection Using Deep Normalized Attention-Based Neural Network and Modified-Random Forest Shtwai Alsubai, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Saudi Arabia   2023

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- Salient Arithmetic Data Extraction from Brain Activity via an Improved Deep Network,Nastaran Khaleghi, Shaghayegh Hashemi, Sevda Zafarmandi Ardabili, Sobhan Sheykhivand and Sebelan Danishvar, Dec-2023

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Contact us if you need any more info.

Let's decode My Brain!
July 8th 2024
David Vivancos
vivancos@vivancos.com

This MindBigData The "MNIST" of Brain Digits is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/