Configurations are disclosed for a health system to be used in various healthcare applications, e.g., for patient diagnostics, monitoring, and/or therapy. The health system may comprise a light generation module to transmit light or an image to a user, one or more sensors to detect a physiological parameter of the user's body, including their eyes, and processing circuitry to analyze an input received in response to the presented images to determine one or more health conditions or defects.

Configurations are disclosed for a health system to be used in various healthcare applications, e.g., for patient diagnostics, monitoring, and/or therapy. The health system may comprise a light generation module to transmit light or an image to a user, one or more sensors to detect a physiological parameter of the user's body, including their eyes, and processing circuitry to analyze an input received in response to the presented images to determine one or more health conditions or defects.

A method and system for treating schizophrenia and other forms of mental illness, including: given a brain comprising neurons coupled by an axon including an inner core and an outer myelin sheath, and given one or more defects in the outer myelin sheath, repairing the one or more defects in the outer myelin sheath with one or more of a protein and a lipid such that the outer myelin sheath has a substantially constant electrical impedance for the transmission of data energy between the neurons and such that data energy is not undesirably reflected from the direction of a receiving neuron in the direction of a transmitting neuron within the axon.

A brain activity estimation device includes a brain activity estimation portion. The brain activity estimation portion includes a blood-circulation-amount calculating unit and an estimation unit. The blood-circulation-amount calculating unit is configured to calculate time-series blood-circulation-amount data on a facial surface of a human based on RGB data of photographed image data on the facial surface acquired in time series. The RGB data is obtained by conducting RGB processing on the photographed image data. The RGB processing includes decomposing the photographed image data into three color components composed of an R component, a G component and a B component. The estimation unit is configured to estimate brain activity of the human based on a plurality of decomposition components obtained by decomposing the blood-circulation-amount data by singular value decomposition, principal component analysis, or independent component analysis.

The system receives a raw signal carrying information related to slow wave activity; buffers a portion of the raw signal; determines a timing of slow wave events in the buffered portion of the raw signal; filters the raw signal; determines a timing of slow wave events in the filtered raw signal; compares the timing of the slow wave events in the buffered portion of the raw signal to the timing of the slow wave events in the filtered raw signal; determines a first correction factor associated with reducing slow wave activity in the subject and a second correction factor associated with enhancing slow wave activity in the subject; and adjusts a timing of the stimulation provided to the subject during the sleep session based on the first and/or second correction factors.

The system receives a raw signal carrying information related to slow wave activity; buffers a portion of the raw signal; determines a timing of slow wave events in the buffered portion of the raw signal; filters the raw signal; determines a timing of slow wave events in the filtered raw signal; compares the timing of the slow wave events in the buffered portion of the raw signal to the timing of the slow wave events in the filtered raw signal; determines a first correction factor associated with reducing slow wave activity in the subject and a second correction factor associated with enhancing slow wave activity in the subject; and adjusts a timing of the stimulation provided to the subject during the sleep session based on the first and/or second correction factors.

A method of assessing electrical activity of a brain includes, for each of a plurality of electrical-activity records of the brain, applying a machine-learned ESC model to the record to classify the record as one of a seizure record or a non-seizure record, wherein each of record is sensed by a corresponding one of a plurality of sensing channels of an implanted medical device; for each seizure record in a set of seizure records, applying the machine-learned ESC model to the seizure record to classify the seizure record as one of a local-seizure record or a spread-seizure record, wherein the seizure record comprises a first seizure record captured by a first sensing channel and a second seizure record captured by a second sensing channel; and for each spread-seizure record in a set of spread-seizure records, applying a machine-learned SSC model to the spread-seizure record to classify the spread-seizure record as a type of seizure spread pattern.

A biological signal measurement system includes: a biological signal measurer that measures a biological signal including external noise of biological noise and of environmental noise; biological noise measurer that measures a signal including the biological noise; a biological noise estimator that estimates the biological noise from the signal measured by the biological noise measurer; an environmental noise measurer that measures a signal including the environmental noise; an environmental noise estimator that estimates the environmental noise from the signal measured by the environmental noise measurer; and a calculator that calculates the biological signal in consideration of an effect of the external noise using the signal measured by the biological signal measurer, the biological noise estimated by the biological noise estimator and the environmental noise estimated by the environmental noise estimator.

A patient advisory device (“PAD”) and its methods of use. The PAD may be configured to alert the patient about an estimated brain state of the patient. In preferred embodiments, the PAD is adapted to alert the patient of the patient's brain state, which corresponds to the patient's propensity of transitioning into an ictal brain state, e.g., having a seizure. Based on the specific type of alert, the patient will be made aware whether they are highly unlikely to have a seizure for a given period of time, an elevated propensity of having a seizure, a seizure is occurring or imminent, or the patient's brain state is unknown.

Methods and related systems for modulating neural activity by cyclically modulating neural activity in peripheral neural structures are disclosed. Neural activity may be modulated cyclically by stimuli delivered via various types of stimulus sources. In an aspect, activity of a sensory nerve is modulated. Neural modulation may be used, for example, to modulate an undesired sensation, such as pain, or an immune or inflammatory response or process. Delivery of stimuli for modulating neural activity may be controlled in part in response to an input from a user input device.