Processing time-domain and frequency-domain representations of eeg data

A system comprising one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:

• obtaining a plurality of EEG signal measurements corresponding to respective EEG trials of a user;
• generating a time-domain representation from the plurality of EEG signal measurements, wherein the time-domain representation comprises a plurality of rows, and wherein each row corresponds to a different set of one or more EEG signal measurements;
• applying the time-domain representation as input to a neural network having a plurality of network parameters, final values of the network parameters having been determined by a transfer learning process wherein the neural network is initially trained to perform an image processing task by using a first training data set comprising a plurality of training images to determine initial values for the plurality of network parameters, and the neural network is subsequently trained to perform EEG analysis by using a second training data set to determine the final values for the plurality of network parameters from the initial values of the plurality of network parameters;
• and
• obtaining, from the neural network, a mental health prediction for the user.

One or more non-transitory computer storage media encoded with computer program instructions that when executed by a plurality of computers cause the plurality of computers to perform operations comprising:

• obtaining a plurality of EEG signal measurements corresponding to respective EEG trials of a user;
• generating a time-domain representation from the plurality of EEG signal measurements, wherein the time-domain representation comprises a plurality of rows, and wherein each row corresponds to a different set of one or more EEG signal measurements;
• applying the time-domain representation as input to a neural network having a plurality of network parameters, final values of the network parameters having been determined by a transfer learning process wherein the neural network is initially trained to perform an image processing task by using a first training data set comprising a plurality of training images to determine initial values for the plurality of network parameters, and the neural network is subsequently trained to perform EEG analysis by using a second training data set to determine the final values for the plurality of network parameters from the initial values of the plurality of network parameters;
• and
• obtaining, from the neural network, a mental health prediction for the user.