In accordance with one embodiment, a method for determining changes in health of a user is disclosed. The method includes sensing multi-dimensional motion of a body part of a user to generate a first multi-dimensional motion signal at a first time and date; in response to the first multi-dimensional motion signal, generating a first neuro-mechanical fingerprint; generating a first health measure in response to the first NFP and user calibration parameters; sensing multi-dimensional motion of the body part of the user to generate another multi-dimensional motion signal at another time and date; in response to the another multi-dimensional motion signal, generating another neuro-mechanical fingerprint; generating another health measure in response to the another NFP and the user calibration parameters; and comparing the first health measure with the another health measure to determine a difference representing the health degradation of the user.

An electric signal transmission device including an electrode 11, disposed to be opposed to an electrogenic cell, and for sending and receiving electric signals to and from the electrogenic cell via the electrode 11.

A method receives continuous EEG data for a long duration of time from at least one electrode intracranially implanted in a subject. The method determines a current or predicted brain state from the EEG data using an artificial intelligence (AI) model.

The objective of the present invention is to provide a biomagnetism measuring device with which it is possible for a magnetic sensor to be disposed in an optimal position in accordance with an object being measured. A biomagnetism measuring device (1) according to the present invention is provided with: a plurality of magnetic sensors (11) which detect biomagnetism; and a holding portion (12) in which are formed frames (13) which detachably hold the plurality of magnetic sensors (11) in such a way as to face a living body. Further, the biomagnetism measuring device (1) according to the present invention is provided with: a plurality of magnetic sensors (11) which detect biomagnetism; and a holding portion (12) in which are formed rails (16) which movably hold the plurality of magnetic sensors (11) in such a way as to face a living body.

In some embodiments, techniques for using machine learning to enable visible light pupilometry are provided. In some embodiments, a smartphone may be used to create a visible light video recording of a pupillary light reflex (PLR). A machine learning model may be used to detect a size of a pupil in the video recording over time, and the size over time may be presented to a clinician. In some embodiments, a system that includes a smartphone and a box that holds the smartphone in a predetermined relationship to a subject's face is provided. In some embodiments, a sequential convolutional neural network architecture is used. In some embodiments, a fully convolutional neural network architecture is used.

A method for determining a brain age, the method comprising the following: providing a brain age determining convolutional neural network (CNN) (200); training the CNN (200) to determine the brain age based on a plurality of sets of input data comprising magnetic resonance imaging (MRI) scans of a brain, the set comprising at least two types of MRI volumes, wherein the at least one type of brain tissue on the first type of the MRI volume is represented by a different contrast with respect to other tissues than on a second type of the MRI volume; and performing an inference process using the trained CNN (200) to determine the brain age based on the set of input data comprising magnetic resonance imaging (MRI) scans of a brain, the set comprising at least the two types of the MRI volumes as used for the training.

A system and method for detecting speech impairment employing a machine learning model extends the use of a model trained exclusively in a target language to classify input obtained from a speech sample in a source language different from the target language. Features extracted from a transcript of the speech sample in the source language are subject to a mapping to features in the target language, then provided as input to the model. The mapping is determined using a domain adaptation system implementing an algorithm such as an optimal transport algorithm trained using a healthy speech dataset to map probability distributions of the features from the source to the target language.

A method for applying a diffusion-weighting gradient during acquisition of diffusion-weighted imaging signals from a selected portion of a nervous system of a subject. Planar diffusion-weighted spin-echo (DWSE) imaging signals and planar diffusion-weighted stimulated-echo (DWSTE) imaging signals can be obtained to provide a plurality of sets of imaging signals. At least one set of imaging signals includes DWSTE signals that are associated with a high-b-value. A signal difference between DWSE imaging signals and DWSTE imaging signals can be corrected based on respective sets of DWSE imaging signals and DWSTE imaging signals having b-values at or near zero.

A transition microelectrode (108) can include a micro well array (104) having a plurality of microwells. The transition microelectrode (108) can further include a plurality of neuronal soma oriented within the plurality of microwells. A bioerodible probe guide (106) can be oriented over the microwell array (104). An electrode (103) can be electrically connected with the plurality of microwells. A transition microelectrode array (116) can include an electrode array having a plurality of the transition microelectrodes (108).

A method of assessing post SARS infection based neurological injuries using a quantitative, noninvasive, clinical objective, oculomotor, vestibular, reaction time, and cognitive response assessment protocol for evaluating post SARS infection based neurological injuries. The method comprises the steps of: coupling a VOG/VNG system to a subject wherein the VOG/VNG system is configured to present a plurality of Oculomotor, vestibular, reaction time, and cognitive tests to the subject; presenting a plurality of Oculomotor, vestibular, reaction time, and cognitive tests to the subject on the VOG/VNG system; obtaining objective physiologic responses of the patient from the plurality of Oculomotor, vestibular, reaction time, and cognitive tests to the subject via the VOG/VNG system; and using a plurality of the objective physiologic responses of the patient to assess post SARS infection based neurological injuries in the subject.