DeepEEG summary - CAN 2019 - Toronto
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Python 

based on MNE raw and epochs objects

Loads 

	-Muse from eeg-notebooks, -Collab Example
		-Various example data included 

	- simulated from MNE, - Collab Example
		-simulated from real data
		-time or frequency domain options
		-change magnitude of effects and difference
		Change signal to noise
		-eye blinks

	- raw or epoched data from BV and other amplifiers - Collab Example
		-connect collab to google drive 
		-run locally on machine by downloading repo


New Class created out of epochs object - feats

epochs are created with various classic ERP methods custom
	-Gratton eye movement correction  
	-mastoid rereferrence


Feats - time or frequency domain
	-frequency domain - power or power+phase concatenated
		-baseline or raw spectrograms
	-time domain - filters, single trial ERP

	-outputs X and Y to train models (data and labels)
	-automatically shaped for input to model

	-watermark option to test models


CreateModel 
		-Keras/TensorFlow 
		- high level abstracted, object oriented programming 
		- NN, CNN, CNN3D, LSTM, Auto, AutoDeep

TrainTestVal
	-Test and Validation sets left out of training
	-predicts binary class