Abstract: A system includes: an ultrasound array comprising a first subset of transducer elements are configured to emit ultrasound pulses through the subject's skull for performing a neuro-modulation of the subject's brain during use of the system, and a second subset of transducer elements configured to receive ultrasound signals from the subject's skull and brain in response to the ultrasound pulses being emitted from the first subset of transducer elements; and a controller coupled to the ultrasound array, wherein the controller is configured, during use of the system, to: detect a change of a physiological parameter in at least a portion of the subject's brain based on, at least in part, the ultrasound signals received by the second subset of transducer elements, and determine a dose of the neuro-modulation of the subject's brain based on, at least in part, the detected change of the physiological parameter.

Abstract: A system includes: an ultrasound array comprising a plurality of transducer elements, wherein: a first subset of the plurality of transducer elements are configured to emit ultrasound pulses through the subject's skull for performing a neuro-modulation of the subject's brain, and a second subset of the plurality of transducer elements are configured to receive ultrasound signals from the subject's skull and brain in response to the ultrasound pulses being emitted from the first subset of the plurality of transducer elements; and a controller coupled to the ultrasound array, wherein the controller is configured to: generate at least one image depicting at least a portion of the subject's skull and brain based on, at least in part, the ultrasound signals received by the second subset of the plurality of transducer elements, and adapt the neuro-modulation of the subject's brain based on, at least in part, the at least one image.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for tracking object movement within a three-dimensional workspace. A method includes receiving, by a controller, break beam sensor data indicating object detection by a break beam sensor of a plurality of break beam sensors configured to detect objects within the workspace. The method includes receiving plate sensor data indicating object detection by a plate sensor of a plurality of plate sensors configured to detect objects resting on a surface of a plate defining a floor of the workspace. The method includes determining that an object passed through the workspace to rest at a position on the surface; comparing the position of the object to a target position of the object; and in response to determining that the position of the object does not satisfy similarity criteria for matching the target position, performing one or more actions.

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.

Abstract: A system includes ultrasound transducers configured to generate and direct ultrasound beams at a region within a portion of a subject's brain, sensors configured to measure a response from the portion of the subject's brain in response to one or more ultrasound beams, and an electronic controller in communication with the ultrasound transducers configured to generate, based on a measured response from the portion of the subject's brain in response to two or more ultrasound beams generated from two or more different angles, a model of the portion of the subject's brain, wherein the model has a higher resolution than a maximum resolution of a single ultrasound beam, and generate, based on the model of the portion of the subject's brain, stimulation parameters for the ultrasound transducers to generate and direct a stimulation ultrasound beam at the region within the portion of the subject's brain.

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.

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.

Abstract: A method including identifying an activity pattern of a subject's brain, determining, based on the identified activity pattern of the subject's brain and a target parameter, a set of stimulation parameters, generating, by two or more emitters and based on the set of stimulation parameters, a composite stimulation pattern at a portion of the subject's brain, wherein each of the two or more emitters generates a stimulation pattern using a different modality, measuring, by one or more sensors, a response from the portion of the subject's brain in response to the composite stimulation pattern; and dynamically adjusting, for each emitter and based on the measured response from the portion of the subject's brain, a set of stimulation parameters.

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).

Abstract: A machine learning system for aggregating electroencephalographic (EEG) 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.

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.

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 two-dimensional time-frequency electroencephalogram (EEG) representation corresponding to one or more EEG signal measurements of a user; processing the time-frequency EEG representation using a first neural network having a plurality of first network parameters to generate an embedding of the time-frequency EEG representation, wherein the first neural network has been trained using transfer learning; and providing the embedding of the time-frequency EEG representation to a downstream neural network to generate a mental health prediction for the user.

Abstract: A transcranial ultrasonic stimulation headset includes a coupling system, one or more ultrasound transducers configured to generate and direct a first focused ultrasound beam at a region within a portion of a subject's brain, one or more sensors configured to measure a response from the portion of the subject's brain in response to the first focused ultrasound beam, and an electronic controller in communication with the one or more ultrasound transducers configured to dynamically adjust, based on the measured response from the portion of the subject's brain, a particular stimulation parameter for the one or more ultrasound transducers to generate and direct a second focused ultrasound beam at the region within a portion of the subject's brain.

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.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining psychological data, generating a psychopathological analysis data structure (PADS), applying a latent factor analysis algorithm to the PADS to obtain a psychopathological latent factor space (PLFS), generating a latent factor graph, and outputting the latent factor graph.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving physiological data of a patient, obtaining ecological momentary assessment (EMA) data by sending an EMA data prompt, and receiving patient input responsive to the EMA data prompt; and generating, based on the EMA data and the physiological data, a graphical representation of the patient's idiomatic psychopathology symptom network as a symptom network graph.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for synchronizing neuroelectric measurements with diagnostic content presentation by performing actions that include causing a presentation system to present diagnostic content a user, where the diagnostic content includes: a first content frame prompting the user to perform a physical task, a second content frame including electroencephalogram (EEG) synchronization content, the second content frame sequentially following the first content frame, and a third content frame indicating an outcome of the physical task, the third content frame sequentially following the second content frame. The actions include obtaining, from a brainwave sensor, EEG signals of the user during presentation of the diagnostic content. The actions include providing the EEG signals as input features to a machine learning model that is trained to predict a psychological state of the user based on the EEG signals.

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.

Abstract: A mixture of iron powder reinforced with iron oxides, are compressed and sintered to form improved sintered iron pressed pieces.