US 2022/0044106 A1 · ATTENTION ENCODING STACK IN AGGREGATION OF DATA

Abstract

A machine learning system for aggregating electroencephalographic (EEG) data, as well as external data, in preparation for downstream analysis via further machine learning models. Machine learning models can be used to assist in diagnosis of various mental health conditions, brain-computer interface, mood detection systems, or other biometric functions. Implementations of the present disclosure, employ a portion of the transformer network (the attention encoder stack) to aggregate EEG trials or EEG data segments, in a data-driven way, by ensuring the important content of each trial is not lost. Each EEG trial to be aggregated is converted into an input embedding, or a vector which numerically represents the data in the trial.

Claims20

Dependent claims

A computer-implemented method executed by one or more processors and comprising:

identifying a plurality of trial embeddings, wherein a trial embedding is a vector of length n representing an electroencephalogram (EEG) trial of an individual;
identifying at least one external embedding, wherein an external embedding is a vector of length n representing data associated with the individual that is not from an EEG trial, and wherein the at least one external embeddings and the plurality of trial embeddings form a plurality of input embeddings;
encoding the plurality of input embeddings using an attention encoder stack network to generate an output embedding that represents an aggregation of the plurality of input embeddings, wherein the output embedding is a vector of fixed length k;
and
providing the output embedding to be used as input in a neural network to determine a mental health status of the individual.

2 more elements

A system for aggregating data, comprising:

one or more processors;
one or more tangible, non-transitory media operably connectable to the one or more processors and storing instructions that, when executed, cause the one or more processors to perform operations comprising:
identifying a plurality of trial embeddings, wherein a trial embedding is a vector of length n representing an electroencephalogram (EEG) trial of an individual;
identifying at least one external embedding, wherein an external embedding is a vector of length n representing data associated with the individual that is not from an EEG trial, and wherein the at least one external embeddings and the plurality of trial embeddings form a plurality of input embeddings;
encoding the plurality of input embeddings using an attention encoder stack network to generate an output embedding that represents an aggregation of the plurality of input embeddings, wherein the output embedding is a vector of fixed length k;
and
providing the output embedding to be used as input in a neural network to determine a mental health status of the individual.

4 more elements

A non-transitory computer readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:

identifying a plurality of trial embeddings, wherein a trial embedding is a vector of length n representing an electroencephalogram (EEG) trial of an individual;
identifying at least one external embedding, wherein an external embedding is a vector of length n representing data associated with the individual that is not from an EEG trial, and wherein the at least one external embeddings and the plurality of trial embeddings form a plurality of input embeddings;
encoding the plurality of input embeddings using an attention encoder stack network to generate an output embedding that represents an aggregation of the plurality of input embeddings, wherein the output embedding is a vector of fixed length k;
and
providing the output embedding to be used as input in a neural network to determine a mental health status of the individual.

2 more elements

Dependencies are inferred from the claim text.

Attention encoding stack in aggregation of data

Life of this patent

Attention encoding stack in aggregation of data

Life of this patent

Abstract

Claims20