I'm working with deep learning on some EEG data for classification, and I was wondering if there's any systematic/mathematical way to define the architecture of the networks, in order to compare their performance fairly.
Should the comparison be at the level of neurons (e.g. number of neurons in each layer), or at the level of weights (e.g. number of parameters for training in each type of network), or maybe something else?
One idea that emerged was to construct one layer for the MLP for each corresponding convolutional layer, based on the number of neurons after the pooling and dropout layers.
Any ideas? If there's any relative work or paper regarding this problem I would be very grateful to know.
Thank you for your time