The Potential of Convolutional Neural Networks for Identifying Neural States based on Electrophysiological Signals: Code and Figures
This repository accompanies the manuscript
The potential of convolutional neural networks for identifying neural states based on electrophysiological signals: experiments on synthetic and real patient data
The python (>=3.10) dependencies are listed in requirements_gpu.yml
(a CUDA-capable system is required). We recommend installing them through anaconda or mamba:
conda env create -n <<environment_name>> --file requirements_gpu.yml
conda activate <<environment_name>>
Patient data can be downloaded at https://data.mrc.ox.ac.uk/lfp-et-dbs (DOI: 10.5287/bodleian:ZVNyvrw7R, creating a free account is required). This data should be placed into the patient_data
folder.
The repository is separated into a nbs
folder and a code
folder:
-
The
nbs
folder includes notebooks with examples and the code used to generate all figures, which can be found here. -
The
code
folder implements synthetic data generation as well as model implementation and training. It includes two main files (main_patients.py
andmain_synthetic.py
), which train models for patient data and synthetic data tasks respectively. Running these scripts takes some time (a total of over two weeks on a machine with two NVIDIA RTX-3090 GPUs). The scripts take in command-line arguments:Example:
python main_patients.py --n_jobs 4 --accelerator gpu
To see all options:
python main_synthetic.py --help
The script outputs are csv files containing performance metrics.
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