bingzhaozhu / project-2-bespagne

Class Project 2: Fear-Decoding-in-Rodents

Machine learning (CS-433) class project, in cooperation with the Integrated Neurotechnologies Laboratory (INL) at EPFL

Welcome! This repository contains the code regarding class project 2 of the Machine Learning course (CS-433) at EPFL.

Team members:

Feel free to contact any of us in case you have questions:

• Tomas Brants ([email protected], @tbrants)
• Arthur Deleu ([email protected], @ArthurDeleu)
• Alicia Gomez Soria ([email protected], @aliciasoria)

General remark:

The raw data, obtained from the INL is not in this GitHub repository, since this data is confidential. Hence, all data and labels needed to run the ML-models in Python, are provided as .npy files and a .csv file respectively.

Folder informations:

1. The folder 'Matlab codes' is informative and used to compute the BLA and IL power spectra, along with the PAC power spectra.
2. A visualization of the data (upper 6 figures) and -more importantly- the extracted features (lower 3 figures) can be found in the folder 'Visualization data'.
3. The folder 'Data' contains all the data (.npy-files and .csv-file) to run the Jupyter Notebooks.

• 'Feature arrays' are the input data for the 1-channel CNN.
• 'Labels' contains the .csv file with smoothed bar press data and will be binned in Python to create class labels.
• 'PAC_afterCNN.npy' contains the feature vector after running the 1-channel CNN on the PAC colormaps.
• 'input_MLP' contains the IL and BLA power spectra, which are feeded into the MLP together with 'PAC_afterCNN.npy' data.

4. The folder 'Jupyter notebooks' contains all the notebooks relevant for the machine learning models.

• 'CNN_main.ipynb' is the notebook linked to the 1-channel CNN with input the PAC-features. It also contains the baseline model.
• 'MLP.ipynb' is the notebook linked to the MLP with input the CNN feature vector and the BLA and IL power spectra.
• The other notebooks were used to generate and save the data/features as .npy-files and don't need to be ran.

How to use the code:

1. Install Python version 3.7. and the Python libraries Pytorch, Numpy, sickit-learn and Pandas.
2. Run 'run.py'