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    PROCESSING TIME-DOMAIN AND FREQUENCY-DOMAIN REPRESENTATIONS OF EEG DATA
    1.
    发明申请
    PROCESSING TIME-DOMAIN AND FREQUENCY-DOMAIN REPRESENTATIONS OF EEG DATA 有权

    公开(公告)号：US20220101997A1

    公开(公告)日：2022-03-31

    申请号：US17039303

    申请日：2020-09-30

    Applicant: X Development LLC

    Inventor： Asim Iqbal ,  Mustafa Ispir ,  Garrett Raymond Honke ,  Nina Thigpen ,  Vladimir Miskovic ,  Pramod Gupta

    IPC: G16H50/20 ,  G06N3/08

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing representations of EEG measurements. One of the methods includes 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, where the time-domain representation includes a plurality of rows, and where 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 where the neural network is initially trained to perform an image processing task and the neural network is subsequently trained to perform EEG analysis; and obtaining, from the neural network, a mental health prediction for the user.

    EEG SIGNAL REPRESENTATIONS USING AUTO-ENCODERS
    2.
    发明申请
    EEG SIGNAL REPRESENTATIONS USING AUTO-ENCODERS 有权

    公开(公告)号：US20220054033A1

    公开(公告)日：2022-02-24

    申请号：US16999636

    申请日：2020-08-21

    Applicant: X Development LLC

    Inventor： Garrett Raymond Honke ,  Pramod Gupta ,  Mustafa Ispir ,  Nina Thigpen

    IPC: A61B5/048 ,  A61B5/0478 ,  A61B5/00

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining, from one or more electrodes, electroencephalographic (EEG) signals from a user; generating signal vectors from the EEG signals, each signal vector representing one channel of EEG signals. The actions include providing the signal vectors as input data to a variational autoencoder (VAE), wherein the VAE generates a latent representation of the input data, the latent representation having lower dimensionality than the signal vectors, and reconstructs the latent representation into an event related potential (ERP) of the corresponding EEG signal. The actions include providing, for display to a user, a graphical representation of the ERPs.

    PROCESSING EEG DATA WITH TWIN NEURAL NETWORKS
    3.
    发明申请
    PROCESSING EEG DATA WITH TWIN NEURAL NETWORKS 有权

    公开(公告)号：US20220015657A1

    公开(公告)日：2022-01-20

    申请号：US16933219

    申请日：2020-07-20

    Applicant: X Development LLC

    Inventor： Mustafa Ispir ,  Pramod Gupta ,  Garrett Raymond Honke

    IPC: A61B5/04 ,  A61B5/00 ,  G06N3/04 ,  G06N3/08

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating embeddings of EEG measurements. One of the methods includes obtaining a plurality of electroencephalogram (EEG) signal measurements of a user, wherein each EEG signal measurement corresponds to one of a plurality of prompt types of an EEG task; generating, from the plurality of EEG signal measurements, a plurality of network inputs each corresponding to a different prompt type of the plurality of prompt types of the EEG task; processing the network inputs using a twin neural network to generate respective network outputs each corresponding to a different prompt type of the plurality of prompt types of the EEG task; and providing the network outputs to a downstream neural network to generate a mental health prediction for the user.

    RESAMPLING EEG TRIAL DATA
    5.
    发明申请
    RESAMPLING EEG TRIAL DATA 有权

    公开(公告)号：US20220068476A1

    公开(公告)日：2022-03-03

    申请号：US17007193

    申请日：2020-08-31

    Applicant: X Development LLC

    Inventor： Katherine Elise Link ,  Vladimir Miskovic ,  Nina Thigpen ,  Mustafa Ispir ,  Garrett Raymond Honke ,  Pramod Gupta

    IPC: G16H50/20 ,  G06N3/08 ,  G06N7/00

    Abstract: Systems and processes described herein can expand a limited data set of EEG trials into a larger data set by resampling subsets of EEG trial data. Implementations may employ one or more of a variety of different resampling techniques. For example, a subset of the available training data is selected to form a new set of training data. The subset can be selected using replacement (e.g., a sample can be selected more than once, and thus represented multiple times in the new set of training data). Alternatively the subset can be selected without using replacement (e.g., each sample is able to be selected only once, and thus represented a maximum of one time in the new set of training data).

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