Provided herein are, for example, systems and methods of diagnosing and treating depression, in which a transcranial magnetic stimulation (TMS) therapy is administered to a subject in need thereof and measuring a TMS evoked response in the subject, are provided. The systems and methods allow tailoring or optimizing of a treatment protocol for maximal individual benefit, thereby providing an individualized and optimized treatment protocol for psychiatric disorders such as depression.

Provided herein are, for example, systems and methods of diagnosing and treating depression, in which a transcranial magnetic stimulation (TMS) therapy is administered to a subject in need thereof and measuring a TMS evoked response in the subject, are provided. The systems and methods allow tailoring or optimizing of a treatment protocol for maximal individual benefit, thereby providing an individualized and optimized treatment protocol for psychiatric disorders such as depression.

Aspects are generally directed to a compact and low-noise electric field detector, methods of operation, and methods of production thereof. In one example, an electric field detector includes a proof mass, a source of concentrated charge coupled to the proof mass, and a substrate having a substrate offset space defined therein, the proof mass being suspended above the substrate offset space. The electric field detector further includes a sense electrode disposed on the substrate within the substrate offset space and proximate the proof mass, the sense electrode being configured to measure a change in capacitance relative to the proof mass from movement of the proof mass in response to a received electric field at the source of concentrated charge. The electric field detector includes a control circuit coupled to the sense electrode and configured to determine a characteristic of the electric field based on the measured change in capacitance.

Aspects are generally directed to a compact and low-noise electric field detector, methods of operation, and methods of production thereof. In one example, an electric field detector includes a proof mass, a source of concentrated charge coupled to the proof mass, and a substrate having a substrate offset space defined therein, the proof mass being suspended above the substrate offset space. The electric field detector further includes a sense electrode disposed on the substrate within the substrate offset space and proximate the proof mass, the sense electrode being configured to measure a change in capacitance relative to the proof mass from movement of the proof mass in response to a received electric field at the source of concentrated charge. The electric field detector includes a control circuit coupled to the sense electrode and configured to determine a characteristic of the electric field based on the measured change in capacitance.

A gravity perception function inspection apparatus based on virtual/augmented reality and multiple bio-signal sensors, includes a head-wearable display device worn on an examinee's head and reproducing an inspection screen in which a target object is displayed in a center of the inspection screen, the bio-signal measuring device including an eye tracker that detects a pupil size of an examinee and a posture measuring device that infers a posture value of the examinee by using a plurality of inertial measurement sensors dispersedly arranged on a body of the examinee and obtains and outputs a position control value corresponding to the posture value of the examinee, a human interface device that obtains and outputs a position control value manually input by the examinee, and a control device that generates and provides the inspection screen.

Systems, methods, and computer program products are provided for determining one or more biomarker and/or health condition of a target patient. In various embodiments, a method is provided where a plurality of health data of the target patient and/or a plurality of first order features determined from the plurality of health data of the target patient are received as input to a pre-trained artificial neural network. The plurality of health data is derived from a plurality of modalities. A plurality of latent variables based on the plurality of health data and plurality of first order features are received from an intermediate layer of the pre-trained artificial neural network. The plurality of latent variables are provided to a pre-trained learning system. The pre-trained learning system is trained to receive as input the plurality of latent variables and output one or more biomarker and/or health condition of the target patient.

An automatic noise signal interval detection method and device are provided, and the method includes receiving input data and generating initial data in a time-frequency domain, calculating power for each epoch for each channel of the initial data, generating a power graph for a specific frequency region of each channel based on the power for each epoch, generating a baseline based on an average of each channel value on the power graph, and determining, as a noise signal interval, an interval exceeding a predetermined threshold based on the baseline on the power graph.

Disclosed are systems and methods for a computerized framework that detects medical conditions within patients and dynamically effectuates a treatment via a recursively trained machine learning (ML) algorithm. The disclosed framework is configured for controlling computerized equipment by analyzing data associated with a patient, determining underlying conditions of the patient, then automatically causing such equipment to output electronic stimuli that can address the medical condition(s) detected. The framework can determine a correlation between a patient's attributes, electronic data of a condition of a patient and a medical disorder, and cause a device (e.g., a neuromodulation device) to treat and monitor improvements of the condition and disorder. The treatments can be dynamically adjusted, controlled and monitored so that electronic stimulus patterns respective to a patient's conditions can be rendered.

A method is implemented by a computing device for helping a particular user use a user interface (UI). Electroencephalography (EEG) data is obtained that indicates brain activity of a particular user during a period in which that user views the UI and/or interprets help information that describes how to use the UI. Based on the EEG data, the computing device selects, from among multiple predefined cognitive states, the one or more cognitive states that characterize the particular user during the period. The computing device assists the particular user to use the UI by customizing the help information for the particular user based on the one or more selected cognitive states. A complementary computing device and computer program product are also disclosed.

A method is implemented by a computing device for helping a particular user use a user interface (UI). Electroencephalography (EEG) data is obtained that indicates brain activity of a particular user during a period in which that user views the UI and/or interprets help information that describes how to use the UI. Based on the EEG data, the computing device selects, from among multiple predefined cognitive states, the one or more cognitive states that characterize the particular user during the period. The computing device assists the particular user to use the UI by customizing the help information for the particular user based on the one or more selected cognitive states. A complementary computing device and computer program product are also disclosed.