The "MNIST" of Brain Digits; Given the brain signal(s) of 2 seconds each, captured with the stimulus of seeing a digit (from 0 to 9) and thinking about it, determine what the digit is

Note that I changed the data location to its dedicated folder, some of the older notebook may require changing the URL

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

//TODO

//TODO

Data is from a single Test Subject David Vivancos, and can be found here.

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:

[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)

Directory of repositories

3rd-year Cognitive science student at Simon Fraser University. Interested in Machine learning, Computational Data science, Finance, Anomaly detection, and Embedded systems for Automation.

Research Interests: Applications of Qualitative Analytics in Human behavior

...and Agent behavior, however, that’s just sci-fi... for now. This is useful for side-channel attacks on reinforcement learners

VP of Cognitive Science Student Society. Member of the Robot Soccer CluSb, and Finance club.

Data is under the Database Contents License (DbCL) v1.0

Stuff that is mine to License, and License-able has the MIT license.

if for whatever reason this is inconvenient, fell free to open a issue with the reason(s), and i’ll consider changing the License to the Unlicense