CPC A61N 1/0534 (2013.01) [A61B 5/374 (2021.01); A61B 5/4812 (2013.01); A61B 5/4836 (2013.01); A61N 1/36025 (2013.01); A61N 1/36078 (2013.01); A61N 1/36092 (2013.01); A61N 1/36139 (2013.01); A61N 1/0531 (2013.01); A61N 1/08 (2013.01)] | 20 Claims |
1. A method comprising:
obtaining a plurality of measurements from a brain of a user via an interface, wherein the plurality of measurements includes indications of slow wave oscillations and sleep spindles;
generating, via a processing device, a plurality of brain state parameters characterizing at least one brain state of the brain of the user by using the plurality of measurements, wherein the plurality of brain state parameters includes an indication of a synchrony pattern between the measured slow wave oscillations and the measured sleep spindles including an amount of temporal deviation between slow wave oscillation peaks and sleep spindle peaks; and
transmitting one or more control signals, the one or more control signals based at least in part on the synchrony pattern and a machine learning model of the brain trained based on the plurality of measurements and the synchrony pattern, from a controller to the interface to apply one or more stimuli to cortical tissue of the brain, wherein the one or more stimuli is configured to enhance synchrony of slow wave oscillations and sleep spindles such that sleep spindle peaks occur closer to slow wave oscillation peaks to reduce the amount of temporal deviation.
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