Abstract: Techniques relating to high likeness high compliance human figure generation are disclosed. A method for high likeness high compliance human figure generation may include selecting training images as a basis for an expert learning model, obtaining embeddings for the training images, the embedding being recorded separately for each expert, computing a geometric property of the embedding space for each expert, embedding a received prompt using the same embedding method as for the training images, and generating an output image using a subset of experts selected for highest compliance (e.g., most similarity) to the received prompt, or for other purposes. A system for implementing this method may include a training data ensemble, an image augmentation pipeline, an LLM and other neural networks, a mood and style ensemble of lightweight expert models built on a foundational model, and a tokenizer.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving brain activity data of a user from a brainwave sensor and user physiological data from a non-brainwave sensor, where the brain activity data represents a brainwave pattern related to a physiological activity of the user and a brainwave pattern related to a mental activity of the user. Identifying a physiological action of the user based on the user physiological data. Identifying, within the brain activity data, a pattern that is representative of the identified physiological action. Filtering the brain activity data to lessen a contribution of the pattern representative of the identified physiological action to the brain activity data, thereby, providing filtered brain activity data.

Abstract: A apparatus for detecting electrooculograph (EOG) signals, comprising: a pair of temple pieces connected to a bridging structure; at least one electrode on each temple piece configured to contact the skin at the temple, and to receive an EOG signal from a proximate orbital socket; a reference electrode displaced from each temple; and a processor configured to process signals from the sensors to detect saccade movements of the eyes. A wavelet-based algorithm permits analysis and coding of the saccade movements.

Abstract: A apparatus for detecting electrooculograph (EOG) signals, comprising: a pair of temple pieces connected to a bridging structure; at least one electrode on each temple piece configured to contact the skin at the temple, and to receive an EOG signal from a proximate orbital socket; a reference electrode displaced from each temple; and a processor configured to process signals from the sensors to detect saccade movements of the eyes. A wavelet-based algorithm permits analysis and coding of the saccade movements.