Systems and methods for training a neurome that emulates the brain of a user

A system for training a neurome that emulates a brain of a user, comprising:

• a non-invasive brain interface assembly configured for detecting neural activity of the user in response to analog instances of a plurality of stimuli peripherally input into the brain of the user from at least one source of content;
• memory configured for storing a neurome, wherein the neurome is configured for outputting a plurality of determined brain states of an avatar in response to inputs of the digital instances of the plurality of stimuli, wherein the neurome comprises:
• a feature extraction component configured for extracting single-dimensional vectors of content features from the digital instances of the plurality of stimuli characterizing the at least one source of content;
• and
• a brain state regression model having a first input and a second input, wherein the neurome training processor is configured for modifying the neurome by inputting the single-dimensional vectors of content features into the first input of the brain state regression model, and single-dimensional vectors of brain state features characterizing the determined brain states of the user into the second input of the brain state regression model, such that the brain state regression model outputs single-dimensional vectors of brain state features of the avatar that substantially match the single-dimensional vectors of brain state features of the user;
• and
• a neurome training processor configured for determining a plurality of brain states of the user based on the detected neural activity of the user, and modifying the neurome based on the plurality of determined brain states of the user and the plurality of determined brain states of the avatar.