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Deep Learning with Tensor Flow for EEG MNE Epoch Objects
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README.md

DeepEEG

MNE/Keras/Tensorflow library for classification of EEG data

DeepEEG Image

DeepEEG is a Keras/Tensorflow deep learning library that processes EEG trials or raw files from the MNE toolbox as input and predicts binary trial category as output (could scale to multiclass?).

CAN 2019 Poster presentation on DeepEEG - https://docs.google.com/presentation/d/1hO9wKwBVvfXDtUCz7kVRc0A6BsSwX-oVBsDMgrFwLlg/edit?usp=sharing

Collab notebooks for cloud compution

Colab Notebook Example with simulated data: https://colab.research.google.com/github/kylemath/DeepEEG/blob/master/notebooks/DeepEEG_Sim.ipynb

Colab Notebook Example with data from Brain Vision Recorder in google drive: https://colab.research.google.com/github/kylemath/DeepEEG/blob/master/notebooks/Deep_EEG_BV.ipynb

Colab Notebook Example with muse data from NeurotechX eeg-notebooks: https://colab.research.google.com/github/kylemath/DeepEEG/blob/master/notebooks/Deep_EEG_Muse.ipynb

Getting Started Locally:

DeepEEG is tested on macOS 10.14 with Python3. Prepare your environment the first time:

# using virtualenv
 python3 -m venv deepeeg
 source deepeeg/bin/activate
# using conda
#conda create -n deepeeg python=3
#source activate deepeeg
git clone https://github.com/kylemath/DeepEEG/
cd DeepEEG
./install.sh
git clone https://github.com/kylemath/eeg-notebooks

You are now ready to run DeepEEG.

For example, type python and use the following: This loads in some example data from eeg-notebooks

from utils import *
data_dir = 'visual/cueing'
subs = [101,102]
nsesh = 2
event_id = {'LeftCue': 1,'RightCue': 2}

Load muse data, preprocess into trials,prepare for model, create model, and train and test model

#Load Data
raw = LoadMuseData(subs,nsesh,data_dir)
#Pre-Process EEG Data
epochs = PreProcess(raw,event_id)
#Engineer Features for Model
feats = FeatureEngineer(epochs)
#Create Model
model,_ = CreateModel(feats)
#Train with validation, then Test
TrainTestVal(model,feats)

Tests

You can run the unittests with the following command:

python -m unittest tests

Strategy

  • Load in Brain Products or Interaxon Muse files with mne as mne.raw,
  • PreProcess(mne.raw) - normal ERP preprocessing to get trials by time by electrode mne.epochs
  • FeatureEngineer(mne.epochs) - Either time domain or frequency domain feature extraction in DeepEEG.Feats class
  • CreateModel(DeepEEG.Feats) - Customizes DeepEEG.Model for input data, pick from NN, CNN, LSTM, or AutoEncoders, splits data
  • TrainTestVal(DeepEEG.Feats,DeepEEG.Model) - Train the model, validate it during training, and test it once complete, Plot loss during learning and at test

Dataset example

API

Preprocessing

LearningModels

  • First try basic Neural Network (NN)
  • Then try Convolution Neural Net (CNN)
  • New is a 3D convolutional NN (CNN3D) in the frequency domain
  • Then try Long-Short Term Memory Recurrant Neural Net (LSTM)
  • Can also try using (AUTO) or (AUTODeep) to clean eeg data, or create features for other models

DataModels

  • Try subject specific models
  • Then pool data over all subjects
  • Then try multilevel models (in the works)

Benchmarks

Code References

Resources

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