README.md

EEG Channel Interpolation Using Deep Encoder-decoder Networks

Sari Saba-Sadiya¹, Taosheng Liu¹, Tuka Alhanai², Mohammad Ghassemi^1
1 Michigan State University ² New York University Abu Dhabi

Code for the paper "EEG Channel Interpolation Using Deep Encoder-decoder Networks", presented in BIBM-DLB2H'2020.

Overview:

The code implemented here can be used to interpolate `poped' EEG channels. Though not restricted to any specific EEG acquisition setup, the encoder-decoder in this particular implementation was trained on data collected using a 500Hz international 10-20 system. The EEG data is first segmented into 16ms and transformed into an 8x8x8 tensor before being piped through the encoder-decoder trained to interpolate missing channels.

Youtube Demo

The recorded presentation contains a theoretical part and a demo that starts at 9:40s.

Transfer learning

The performance of the trained model (available in model) can be further improved using transfer learning on the specific dataset you are using. See transfer/ecr_transfer.py for an example.

Contents:

• train:
  • ecr_cnn.py: The code you need to compile train and run the neural networks
  • ecr_hyper_parameters.npy
  • ecr_loadModel: load the trained model and run it to interpolate on non-training data.
  • run.sh code to run the training
• baselines:
  • ecr_baseline.py: The code to calculate the EDP and EGL baselines.
  • ecr_ssp.py: The code to calculate the spherical splines baseline.
• transfer:
  • ecr_transfer.py: The code for transfer learning
  • run_transfer.sh
• README.txt: This file.

Cite

@INPROCEEDINGS{9312979,
  author={S. {Saba-Sadiya} and T. {Alhanai} and T. {Liu} and M. M. {Ghassemi}},
  booktitle={2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)}, 
  title={EEG Channel Interpolation Using Deep Encoder-decoder Networks}, 
  year={2020},
  volume={},
  number={},
  pages={2432-2439},
  doi={10.1109/BIBM49941.2020.9312979}
}