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Machine learning of dorsal column nuclei surface potentials evoked by tactile- and proprioceptive-dominated mechanical stimuli (rat data set)

Alastair John Loutit d405182f93 Update 'README.md' 4 anos atrás
extracted_features 359cc4d659 upload extracted features for six rats 4 anos atrás
raw_data 23bf650fe0 upload MS09 right side 4 anos atrás
LICENSE 7ee29f0378 Initial commit 4 anos atrás
README.md d405182f93 Update 'README.md' 4 anos atrás
data_arrangement.pdf 77ee36a3bc Uploaded file to explain how data is organised 4 anos atrás

README.md

tactile.proprio-evoked_DCN_potentials

Machine learning of dorsal column nuclei surface potentials evoked by tactile- and proprioceptive-dominated mechanical stimuli (rat data set).

Raw data organisation is schematically described in the pdf document: data_arrangement.pdf

Extracted features from raw data (for machine learning) is described as follows:

  • Each row is 1 feature. There are 22 features x 7 electrodes = 154
  • Below the rows of inputs is the 16 output targets in rows 155-170
  • Each column is a trial
  • labels for each row are found in the first two columns

Data are contained in .mat files. For importing to a Python dictionary, see:

https://github.com/wblakecannon/DataCamp/blob/master/05-importing-data-in-python-(part-1)/2-importing-data-from-other-files-types/the-structure-of-mat-in-python.py

https://towardsdatascience.com/how-to-load-matlab-mat-files-in-python-1f200e1287b5

For importing to Julia, see:

https://github.com/JuliaIO/MAT.jl