A-Generalizable-BCI-using-Machine-Learning-for-Feature-Discovery-masterzip. (229.31 MB)

A Generalizable BCI using Machine Learning for Feature Discovery

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dataset
posted on 15.06.2015 by EWAN NURSE, Pip Karoly
The first variable is data_key_'participant letter', which takes the value 1,2 or 3, depending on if they didn't squeeze their hand, squeezed right or squeezed left during that epoch.

The second is data_epochs_'participant letter'. This is the EEG data corresponding to the data key. The first dimension is all the channels (62 channels for the first two sets) concatenated into a single row, so there's 100 data points for each channel for each epoch.


Funding

This research was supported by the Victorian Life Sciences Computation Initiative (VLSCI), an initiative of the Victorian Government 521 hosted by the University of Melbourne, Australia.

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