Wow, I love it when I see very different types of datasets. This following one seems very interesting and is from David Vivancos. It's an Open Dataset of "Brain Digits" , it is at: http://www.mindbigdata.com/opendb . This reminds of a discussion we had a while back about compressive EEG systems. From the page (the page has actual links to the datasets):
The version 1.03 of the open database contains 1,183,368 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.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.......
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 389,519,941 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!
........
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, InsightContact us if you need any more info.Let's decode My Brain!
September 2015
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/
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.
No comments:
Post a Comment