DATA ANALYTIC APPROACH TO PERSONALIZED QUESTIONNAIRE DEVELOPMENTS

    公开(公告)号:US20210391039A1

    公开(公告)日:2021-12-16

    申请号:US16902422

    申请日:2020-06-16

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving a plurality of answers to a first set of questions. The actions include generating an adjacency matrix based on the question-answer pairs. The actions include determining a network graph that includes question nodes and edges. The actions include identifying one or more clusters of question nodes by applying a community detection algorithm on the network graph. The actions include determining, for each cluster, i) a cluster centrality and ii) a cluster magnitude. The actions include ranking the clusters based on the cluster centralities and the cluster magnitudes of the one or more clusters. The actions include selecting a second set of questions for the user. And, the actions include causing the questions from the second set of questions to be presented to the user.

    Artifact identification in EEG measurements

    公开(公告)号:US11576601B2

    公开(公告)日:2023-02-14

    申请号:US16388709

    申请日:2019-04-18

    Abstract: Methods, systems, and computer programs encoded on a computer storage medium, for improving EEG measurements by identifying artifacts present in EEG measurements and providing a real-time indication to a user of likely artifacts in EEG measurements are described. EEG measurements of a patient can be obtained by placing a wearable device or EEG cap on a patient's head. Sensors in the cap provide EEG data to a computing device that processes the data to identify one or more artifacts in the EEG data. The artifacts can be identified by conducting one or more operations of determining the signal to noise ratio of the line noise, calculating mutual information between sensor pairs, and applying the p-welch method. Based on the types of artifacts identified, the computing device can output an indicator that provides feedback to the technician performing an EEG test to make adjustments to the test setup.

    ARTIFACT IDENTIFICATION IN EEG MEASUREMENTS
    4.
    发明申请

    公开(公告)号:US20200329990A1

    公开(公告)日:2020-10-22

    申请号:US16388709

    申请日:2019-04-18

    Abstract: Methods, systems, and computer programs encoded on a computer storage medium, for improving EEG measurements by identifying artifacts present in EEG measurements and providing a real-time indication to a user of likely artifacts in EEG measurements are described. EEG measurements of a patient can be obtained by placing a wearable device or EEG cap on a patient's head. Sensors in the cap provide EEG data to a computing device that processes the data to identify one or more artifacts in the EEG data. The artifacts can be identified by conducting one or more operations of determining the signal to noise ratio of the line noise, calculating mutual information between sensor pairs, and applying the p-welch method. Based on the types of artifacts identified, the computing device can output an indicator that provides feedback to the technician performing an EEG test to make adjustments to the test setup.

    RESAMPLING EEG TRIAL DATA
    5.
    发明申请

    公开(公告)号:US20220068476A1

    公开(公告)日:2022-03-03

    申请号:US17007193

    申请日:2020-08-31

    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).

    REAL-TIME ANALYSIS OF INPUT TO MACHINE LEARNING MODELS

    公开(公告)号:US20200205740A1

    公开(公告)日:2020-07-02

    申请号:US16284561

    申请日:2019-02-25

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining feature sets for a first number of diagnostic trials performed with a patient for diagnostic testing, wherein each feature set includes one or more features of electroencephalogram (EEG) signals measured from the patient while the patient is presented with trial content known to stimulate one or more desired human brain systems. Iteratively providing different combinations of the feature sets as input data to a diagnostic machine learning model to obtain model outputs, each model output corresponding to a particular one of the combinations. Determining, based on the model outputs, a consistency metric, the consistency metric indicating whether a quantity of feature sets in the combinations is sufficient to produce accurate output from the diagnostic machine learning model. Selectively ending the diagnostic testing with the patient based on a value of the consistency metric.

    Data analytic approach to personalized questionnaire developments

    公开(公告)号:US11495333B2

    公开(公告)日:2022-11-08

    申请号:US16902422

    申请日:2020-06-16

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving a plurality of answers to a first set of questions. The actions include generating an adjacency matrix based on the question-answer pairs. The actions include determining a network graph that includes question nodes and edges. The actions include identifying one or more clusters of question nodes by applying a community detection algorithm on the network graph. The actions include determining, for each cluster, i) a cluster centrality and ii) a cluster magnitude. The actions include ranking the clusters based on the cluster centralities and the cluster magnitudes of the one or more clusters. The actions include selecting a second set of questions for the user. And, the actions include causing the questions from the second set of questions to be presented to the user.

    PROCESSING TIME-DOMAIN AND FREQUENCY-DOMAIN REPRESENTATIONS OF EEG DATA

    公开(公告)号:US20220101997A1

    公开(公告)日:2022-03-31

    申请号:US17039303

    申请日:2020-09-30

    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

    公开(公告)号:US20220054033A1

    公开(公告)日:2022-02-24

    申请号:US16999636

    申请日:2020-08-21

    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.

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