The Braintrust
Report in doc/reports/report.pdf and presentation in doc/presentation/main.pdf!
- Run brain/__main__.py to configure and execute training on EMG and EEG data.
- breze_RNN.py EMG training implementation
- breze_EEG.py EEG training implementation
- globals.py global static variables
- data preprocessing: data.py (no explicit call necessary)
Overwrite default values calling RNN_EEG/test_RNN method
- n_neurons: number of neurons per hidden layer
- batch_size: number of subsets
- participant: list of experimentees to train on
- series: list of series to train on
- subsample: frequency to subsample EMG data
- imp_weights_skip: number of important weights to skip
- n_layers: number of hidden layers
- data_visualization.py
- eeg_plotter.py for visualization of raw data
- /img contains single training error plots
- /images contains multiple trial training plots
- t-SNE call from: bhtsne.py
- tsne.py
- FNN implementation: fnn.py
- C implementation within /windows subdirectory
- data source: https://ndownloader.figshare.com/files/3229301
- data documentation: http://www.nature.com/articles/sdata201447
- data will be downloaded into /data and extracted to /data/matlab by initial execution
- detailed final report and presentation within /doc subdirectory
- t-SNE examination report within /doc/reports/eeg_curiosities
- __init__.py
- breze_RNN_old.py
- test_getTables.py (function call test)
- matlab_data.py (data structure analysis)
- elman.py (alternative approach, not continued)
- lstm.py (alternative approach, not continued)
- rnnrbm.py (alternative approach, not continued)
- LogReg.py (alternative approach, not continued)