eden-kramer-lab/cnn_spectrogram_algorithm

A method to classify spectrograms from raw EEG data using a convolutional neural network

CNN_Spectrogram_Algorithm

The CNN spike ripple detector: a method to classify spectrograms from EEG data using a convolutional neural network (CNN).

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Usage

See folder Demo-Application for an example applciation of the trained CNN spike ripple detector to simulated EEG data.

See folder Demo-Training for an example of how to train the CNN spike ripple detector using simulated spectrogram images.

Code in folder fastai comes from fastai version 0.7 by Jeremy Howard: https://www.fast.ai/

Data Structure

To run either demonstration, you must have a data folder of the following structure:

data/

├── train/

├── Yes

├── No

├── valid/

├── Yes

├── No

├── test/

For training, the Yes and No subfolders contain positive and negative case images on which we train the model. The test folder contains uncategorized images on which we test the model.

For application, the test folder contains new test data to be evaluated by the pretrained model (full_trained_model.pkl). For the code to run with this library, the Yes and No subfolders of train and valid cannot be empty: fill them with a few images from your test data -- this will not affect the output.

Environment

Below is a step-by-step method to prepare an environment capable of running the notebooks:

0. Ensure you have both conda and pip installed

1. In terminal, load in a virtual environment with conda, give it a name (environment_name):

conda env create -f new_enviro.yml -n environment_name

conda activate environment_name

3. Open the jupyter console to run notebooks:

jupyter notebook

4. When done, use conda deactivate to deactivate your virtual environment. To reload this environment in the future, use conda activate environment_name, skipping step 2.