A primates dementia treatment apparatus according to an embodiment of the present invention includes: a sensing unit including sensors that sense a cerebral state and a nerve conduction state of a cerebral cortex; a stimulation pulse output unit outputting a stimulation treatment pulse suitable for a cerebral cortex varied by a disease; and a controller controlling the stimulation pulse output unit to generate and output a stimulation treatment pulse customized for each user to be suitable for a form of a cerebral cortex varied by a disease in accordance with a result of real-time checking the cerebral state on the basis of sensing data of the sensing unit.

Data regarding typing by a user may be collected and analyzed in order to assess one or more health conditions or functional states of the user. Each health condition that is assessed may be a disease or a symptom of a disease. For instance, based on the typing data, a computer may assess the presence, severity or probability of, or a change in, one or more symptoms such as: mild cognitive impairment; dementia; impairment of fine motor control; impairment of sensory-motor feedback; or behavioral impairment. A computer may calculate keystroke tensors that encode information about the typing. A computer may select or derive features from the keystroke tensors. These features may be fed into one or more machine learning algorithms, which in turn may output an assessment of a health condition or functional state of the user.

Various embodiments relate generally to data science, data analysis, and computer software and systems to apply psychological science and principles to provide an interface, as one of a number of sources of text, for facilitating memory recall, and, more specifically, to a computing and data storage platform that facilitates recall of one or more memories collaboratively and performs in-situ text-based analysis automatically to calculate one or more linguistic measures to predict a cognitive disorder or deficiency. In some examples, a method may include receiving data representing text, classifying words based on a syntactic function to analyze the text, identifying portions of sentences and identifying a source of the text. In some cases, the method includes executing instructions to characterize text and portions of sentences, the characterized data being indicative of a cognitive state or a deficiency thereof.

Described and illustrated herein are methods and systems for the use of physiological data relating to cognitive state of a user, recorded from one or more sensors, for analyzing and reporting assessments of the cognitive state of the user.

A method of testing a subject for impairment includes presenting an object to the subject and measuring gaze positions of the subject. The method further includes generating, using the measured gaze positions, a plurality of values of a gaze metric. The method further includes generating a cognition metric for the subject based on one or more of the group consisting of: a measure of a skewness of a distribution of the plurality of values of the gaze metric; and a measure of kurtosis of the distribution of the plurality of values of the gaze metric. The method further includes determining whether the cognition metric is indicative of cognitive impairment; and generating a report, based at least in part on the determination of whether the cognition metric is indicative of cognitive impairment, indicating the presence or absence of cognitive impairment.

Techniques are described for determining cognitive impairment, an example of which includes accessing a set of epochs of magnetoencephalography (MEG) data of responses of a brain of a test patient to a plurality of auditory stimulus events; processing the set of epochs to identify parameter values one or more of which is based on information from the individual epochs without averaging or otherwise collapsing the epoch data. The parameter values are input into a model that is trained based on the parameters to determine whether the test patient is cognitively impaired.

Example systems, methods, and apparatus, including cognitive platforms, are provided for applying signal detection metrics in computer-implemented adaptive response-deadline procedures to data collected based at least in part on user interaction(s) with computerized tasks and/or interferences. The apparatus can include a response classifier for generating a quantifier of the cognitive abilities of an individual. The apparatus also can be configured to adapt the tasks and/or interferences to enhance the individual's cognitive abilities.

An exemplary hearing device configured to be worn by a user includes a microphone and a processor. The microphone detects an audio signal. The processor is configured to determine that the audio signal includes own voice content representative of a voice of the user and determine that an environmental noise level within the audio signal is below a threshold The processor is further configured to apply, based on the environmental noise level being below the threshold, a biomarker feature analysis heuristic to the own voice content.

An evaluation device includes a display configured to display images; a gaze point detecting unit configured to detect a positional data of a gaze point of a subject; a display controller configured to display, after presenting an instruction information, a specific target object as a answer to the instruction information and comparison target objects on the display; an area setting unit configured to set a specific area for the specific target object and comparison areas for the comparison target objects on the display; a determination unit configured to determine, based on the detected positional data, whether the gaze point is present in the set areas; an arithmetic unit configured to calculate, based on a determination result by the determination unit, a gaze point transition data; and an evaluating unit configured to obtain, based on the gaze point transition data, an evaluation data of the subject.

There is disclosed a method and system for predicting a likelihood that a patient is subject to one or more conditions. Retinal signal data corresponding to the patient may be received. Retinal signal features may be extracted from the retinal signal data. Descriptors may be extracted from the retinal signal features. The descriptors may be applied to a first mathematical model and a second mathematical model. The first mathematical model may correspond to a first condition. The second mathematical model may correspond to second condition. A first predicted probability for the first condition may be generated. A second predicted probability for the second condition may be generated. The first predicted probability and the second predicted probability may be output.