https://github.com/elsehow/synchronized-brainwave-starter-pack
Starter kit for working with the synchronized brainwave dataset
https://github.com/elsehow/synchronized-brainwave-starter-pack
Last synced: about 2 months ago
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Starter kit for working with the synchronized brainwave dataset
- Host: GitHub
- URL: https://github.com/elsehow/synchronized-brainwave-starter-pack
- Owner: elsehow
- Created: 2015-07-29T22:15:09.000Z (almost 10 years ago)
- Default Branch: master
- Last Pushed: 2015-08-17T21:20:38.000Z (almost 10 years ago)
- Last Synced: 2024-04-15T07:13:53.803Z (about 1 year ago)
- Language: Python
- Size: 120 KB
- Stars: 2
- Watchers: 4
- Forks: 3
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Starter kit for working with the synchronized brainwave dataset
Here's an example, along with a few support libraries, on how to work with data from the [synchronized brainwave dataset](http://biosense.berkeley.edu/indra_mids_5_15_dlpage/).
Download the dataset, name the folder "dataset" and place it in the root directory of this project to run the example.
## Core API
`feature_vector_generator(subject_num, time0, time1)`
Returns a generator of feature vectors for the given subject between time0 and time1.
Optionally, this can take a third argument, `sq`, which defines a threshold signal quality to be eligible in a feature vector. (By default, only readings with perfect signal quality are included).
## Notes
Of particular note is lib/featurevectorgenerator.py, which contains most of logic around building feature vectors. (The rest is from @wazaahhh's [brainlib](https://github.com/wazaahhh/brainlib)).
Running example.py requires scikit-learn and numpy, plus dateutil for parsing dates from the source .csv files. You should be able to `pip install` all of these on your platform.