A method of predicting progression of a cognitive state in a subject is disclosed including obtaining a neuroimage of the subject, acquiring a data sample from the neuroimage, selecting a time at which to predict progression of the cognitive state, and performing a calculation on the data sample by a transformation function to determine data associated with multiple cognitive metrics. A method of evaluating a cognitive state of a subject is also disclosed including providing a convolutional neural network, training the convolutional neural network with reference data to construct a transformation function, using the transformation function to predict multiple cognitive metrics from a subject data sample and a selected time, and determining the cognitive state of the subject from the predicted cognitive metrics. A cognitive evaluation system is also disclosed including a memory, a processor, and a cognitive state prediction component configured to program the processor with a transformation function.

A cognitive function evaluation device includes: an obtainment unit configured to obtain speech data indicating speech uttered by a subject; a calculation unit configured to extract a plurality of vowels from the speech data obtained by the obtainment unit, and calculate, for each of the plurality of vowels, a feature value based on a frequency and an amplitude of at least one formant obtained from a spectrum of the vowel; an evaluation unit configured to evaluate a cognitive function of the subject from the feature value calculated by the calculation unit; and an output unit configured to output an evaluation result of the evaluation unit.

In embodiments, devices, methods and systems to analyze the different mediums of brain function in a mathematically uniform manner may be provided. These devices, methods and systems may manifest at several levels and ways relating to brain physiology, including neuronal activity, molecular chirality and frequency oscillations. For example, in an embodiment, a computer-implemented method for determining structure of living neural tissue may comprise receiving at least one signal from at least one read modality, the signal representing at least one physical condition of the living neural tissue, determining action potentials based on the signals received from the read modalities, determining frequency oscillations based on the signals received from the read modalities and the action potentials, and determining neuron network structures based on the signals received from the read modalities, the action potentials, and the frequency oscillations.

Systems and methods are provided for an implantable neurostimulator system comprising an array of microelectrodes, a pair of current drivers, and a dynamic current allocation network (DCAN). Each current driver in the pair of current drivers is configured to deliver a respective portion of an electrical signal to the array of microelectrodes to deliver the desired electrical stimulation through the array of microelectrodes to the subject. The DCAN is coupled to the pair of current drivers and the array of microelectrodes to selectively electrically connect each of the pair of the current drivers to individual microelectrodes in the array of microelectrodes to deliver the electrical signal to selective collections of less than all microelectrodes in the array of microelectrodes to deliver the desired electrical stimulation. The pair of current drivers and the DCAN are arranged in respective or a common housing configured to be implanted into the subject.

In some embodiments, a display system comprising a head-mountable, augmented reality display is configured to perform a neurological analysis and to provide a perception aid based on an environmental trigger associated with the neurological condition. Performing the neurological analysis may include determining a reaction to a stimulus by receiving data from the one or more inwardly-directed sensors; and identifying a neurological condition associated with the reaction. In some embodiments, the perception aid may include a reminder, an alert, or virtual content that changes a property, e.g. a color, of a real object. The augmented reality display may be configured to display virtual content by outputting light with variable wavefront divergence, and to provide an accommodation-vergence mismatch of less than 0.5 diopters, including less than 0.25 diopters.

Systems, methods, and products for language learning that may extract text from various resources having text, using various natural-language processing features, which can be combined with custom-designed learning activities to offer a needs-based, adaptive learning methodology. The system may receive a resource, extract keywords pedagogically valuable to non-native language learning and academic exercises. Metadata describing various aspects of resources from which keywords are extracted may be associated with keywords. Metadata describing various aspects of keywords may also be associated with keywords. Extracted keywords may be stored into a keyword store along with any metadata associated with keywords.

Methods and systems for diagnosing a condition of a central nervous system are provided. A method includes providing a DBSI-MRI data set obtained from the central nervous system of the subject, and transforming the DBSI-MRI data set to obtain at least one DBSI biomarker value. The method further includes comparing each DBSI biomarker value to at least one corresponding threshold value from a diagnostic database to obtain a relation between each DBSI biomarker value and the at least one corresponding threshold value, and diagnosing the condition according to at least one diagnostic rule, wherein each diagnostic rule defines a candidate condition in terms of the relations between the at least one DBSI biomarker value and the at least one corresponding threshold value.

A training apparatus is capable of generating and presenting a suitable training menu for finger exercise to maintain or improve a cognitive function and/or an exercise function of a human and supporting training of the human. The training apparatus includes a measuring apparatus and a terminal device. Analysis evaluating data based on measurement of the finger exercise including finger tapping is obtained, and the analysis evaluating data contains an evaluation value of an index item related to an exercise function of a user. A training menu for the user is generated based on the analysis evaluating data and contains a training item for the finger exercise. The index item includes at least one of an amount of exercise, endurance, rhythmicity, cooperativeness of both sides, or marker trackability. The training item includes an exercise to carry out the finger tapping in accordance with teaching information, stimulation, or a marker.

The present invention is directed to devices and methods for rhythmic therapy. A system streams a patient specific audio file to a patient for therapy or rehabilitation while the patient is performing an exercise, where the audio comprises a musical track selected from a library of musical tracks, and simultaneously playing a second audio file with a motivational message, for example, and further where the tempo of the track, or the selection of the tracks are changed based on data received from sensors and determining if the patient is improving or regressing.

A computer network implemented system for improving the operation of one or more biofeedback computer systems is provided. The system includes an intelligent bio-signal processing system that is operable to: capture bio-signal data and in addition optionally non-bio-signal data; and analyze the bio-signal data and non-bio-signal data, if any, so as to: extract one or more features related to at least one individual interacting with the biofeedback computer system; classify the individual based on the features by establishing one or more brain wave interaction profiles for the individual for improving the interaction of the individual with the one or more biofeedback computer systems, and initiate the storage of the brain waive interaction profiles to a database; and access one or more machine learning components or processes for further improving the interaction of the individual with the one or more biofeedback computer systems by updating automatically the brain wave interaction profiles based on detecting one or more defined interactions between the individual and the one or more of the biofeedback computer systems. A number of additional system and computer implemented method features are also provided.