Embodiments of the present systems and methods may provide a non-invasive system to measure and integrate behavioral and cognitive features enabling early detection and progression tracking of degenerative disease. For example, a method of detecting neurodegenerative disease may comprise measuring functioning of at least one of the motor system, cognitive function, and brain activity of a subject during everyday life and analyzing the gathered at least one motor system data, cognitive function data, and brain activity data of the subject.

Embodiments may provide multimodal diagnostic systems and methods for detecting neurological disorders, such as Alzheimer's disease (AD), Parkinson's disease (PD), depression, PTSD, schizophrenia, dementia and many others. For example, a system for monitoring brain activity may comprise a plurality of sensors, each adapted to monitor a physical or physiological parameter and output a signal representing the monitored physical or physiological parameter, wherein the plurality of sensors includes at least one sensor configured to monitor a brain activity parameter, a data collection device adapted to receive the plurality of signals from the plurality of sensors and to process the signals to form digital data representing the monitored physical or physiological parameters, and a data processing device adapted to process digital data representing the monitored physical or physiological parameters to determine presence of a neurological disorder or condition.

A system and method for testing a subject for cognitive or oculomotor impairment includes presenting the subject with a display of an object, while presenting the display to the subject, and the subject's head moves horizontally or vertically within a predefined range of movement rates, measuring the subject's right eye positions and/or the subject's left eye positions. The system generates a metric by statistical analysis of the measurements of subject's right eye positions or the subject's left eye positions, and generates a report based on the metric. In some embodiments, the system is configured to train the subject to improve their performance, and thereby remediate or reduce cognitive and oculomotor impairment.

A wearable electronic device, a system and methods of monitoring with a wearable electronic device. The device includes a hybrid wireless communication module with wireless communication sub-modules to selectively acquire location data from both indoor and outdoor sources, as well as a wireless communication sub-module to selectively transmit an LPWAN signal to provide location information based on the acquired data. The device may also include one or more sensors to collect one or more of environmental data, activity data and physiological data. The device may transmit some or all of its acquired data to a larger system, including a cloud-based server to, in addition to providing location-based data, be used as a part of a predictive health care protocol to correlate changes in acquired data to salient indicators of the health of a wearer of the device. In one form, the predictive health care protocol uses a machine learning model.

The invention is a method for automatic detection of neurocognitive impairment, comprising, generating, in a segmentation and labelling step (11), a labelled segment series (26) from a speech sample (22) using a speech recognition unit (24); and generating from the labelled segment series (26), in an acoustic parameter calculation step (12), acoustic parameters (30) characterizing the speech sample (22).

The method is characterised by determining, in a probability analysis step (14), in a particular temporal division of the speech sample (22), respective probability values (38) corresponding to silent pauses, filled pauses and any types of pauses for respective temporal intervals thereof; calculating, in an additional parameter calculating step (15), a histogram by generating an additional histogram data set (42) from the determined probability values (38) by dividing a probability domain into subdomains and aggregating durations of the temporal intervals corresponding to the probability values falling into the respective subdomains; and generating, in an evaluation step (13), decision information (34) by feeding the acoustic parameters (30) and the additional histogram data set (42) into an evaluation unit (32), the evaluation unit (32) using a machine learning algorithm.

The invention is furthermore data processing system, a computer program product and a computer-readable storage medium for carrying out the method.

This disclosure provides systems and methods for mapping and/or measuring a mechanical property of a medium. The mechanical property can be measured by Brillouin spectroscopy. The systems and methods can include a three-dimensional imaging modality that is co-registered with a Brillouin probe beam of a Brillouin spectrometer. The three-dimensional imaging modality can be optical coherence tomography or Scheimpflug camera imaging.

The present invention relates to a neural implant comprising a biomaterial having an outer surface with a stochastic nanoroughness (Rq), and the application of said stochastic nanoroughness in the diagnosis and/or treatment of a neurological disorder, such as, for example, Parkinson's disease, Alzheimer's disease, glioblastoma and/or for disrupting and/or preventing glial scars in the context of mammalian mechanosensing ion channels selected from the family of PIEZO-1 and PIEZO-2 ion channels.

Embodiments of the present disclosure provide a system and method for using facial recognition and mimicry workflows to train cognitive and emotional empathy. The system and methods comprise the use of physiological measurements (e.g., EEG, etc.) in combinations with facial recognition to detect user affect and modify one or more cognitive screening instruments to further promote a user affect. One or more specific emotions associated with one or more specific negative cognitive bias are targeted to influence affect. One or more specific psychopathology may be treated using an emotional recognition training component within a computerized cognitive-bias modification regimen. The regimen may comprise the identification, targeting, and modification of emotions and may utilize one or more facial inputs, cognitive tasks, facial recognition and facial mimicry protocols to adaptively modify one or more attributes of one or more computerized stimuli or interactions within a computerized platform or platform-product.

Methods and system for assessment of a state of the central nervous system (CNS) of a subject and/or differentiation between states of the CNS at a specific time point based on electrophysiological recordings of signals from at least two anatomical structures, wherein the signals are recorded from recording sites located in the anatomical structures, wherein the electrophysiological recordings comprise spatiotemporal fluctuations in the recorded extracellular potential, wherein the electrophysiological recordings are action potentials and/or local field potentials (LFPs) in the anatomical structures and represent the state of the CNS, a s well as the use of the method for assessment of a state of the CNS and/or differentiation of at least two states of the CNS, as well as to the use of the method for evaluating the effect of a treatment of a condition or disease, wherein the condition or disease is neurological and/or psychiatric.

A system for evaluating the degree of brain dysfunction of a subject, such as a decline in cognitive function, calculates a difference in movement functions between both hands in a coordination movement. The system includes a storage device for storing time-series data on a finger movement task of each of both hands of a test subject acquired by a movement sensor; a data processing device for analyzing the time-series data stored in the storage device; and a display device for displaying an analysis result analyzed by the data processing device. The data processing device includes a movement waveform generation unit for generating a movement waveform corresponding to the time-series data stored in the storage device; and a difference-between-hands feature quantity generation unit for generating a difference-between-hands feature quantity which represents a difference in respective finger movement tasks between both hands, based on the respective movement waveforms of both hands.