A method or system for generating a clinical effects map for electrical stimulation includes receiving stimulation parameters and at least one clinical response for each of a plurality of stimulation instances; for each of the stimulation instances, determining a radius of a stimulation field according to the stimulation parameters for the stimulation instance; generating the clinical effects map using the at least one clinical response and the stimulation parameters for each of the stimulation instances, wherein, for each of the stimulation instances, the at least one clinical response for the stimulation instance is assigned to the radius of the stimulation field determined for the stimulation instance; and displaying the clinical effects map.

A method for determining an intensity index for electrical stimulation includes receiving stimulation information; determining a stimulation field from the stimulation information; determining at least one stimulation field function using the stimulation field; and analyzing the determined at least one stimulation field function to determine the intensity index. The intensity index corresponds to a stimulation target and indexes at least one dosing reference for electrical stimulation for that stimulation target.

A method for determining an intensity index for electrical stimulation includes receiving stimulation information; determining a stimulation field from the stimulation information; determining at least one stimulation field function using the stimulation field; and analyzing the determined at least one stimulation field function to determine the intensity index. The intensity index corresponds to a stimulation target and indexes at least one dosing reference for electrical stimulation for that stimulation target.

A system includes memory and processing circuitry coupled to the memory and configured to determine a plurality of local field potential (LFP) measurements of an LFP, wherein the LFP is intrinsically generated by a signal source within a brain of a patient, determine one or more electrodes for delivering a therapeutic electrical stimulation signal based on the LFP measurements, control stimulation generation circuitry to deliver a plurality of electrical stimulation signals via the determined one or more electrodes, wherein the plurality of electrical stimulation signals each comprise at least one different therapy parameter, for respective ones of the plurality of electrical stimulation signals, determine respective evoked signals, wherein the respective evoked signals are evoked by delivery of the respective plurality of electrical stimulation signals, and determine at least one parameter for the therapeutic electrical stimulation signal based on the respective evoked signals.

Provided are systems, methods, and devices for measurement, identification, and generation of sleep state models. Systems include a plurality of electrodes configured to be coupled to a brain of a user and configured to obtain a plurality of measurements from the brain of the user, and an interface configured to obtain the plurality of measurements from the plurality of electrodes. Systems include a processing device comprising one or more processors configured to generate a sleep state model associated with the user, the sleep state model identifying characteristics of a plurality of sleep stages, and further identifying characteristics of transitions between the plurality of sleep stages. Systems include a controller comprising one or more processors configured to generate a control signal based on the sleep state model and the plurality of measurements.

A biological information measurement system includes: a time measurement apparatus configured to transmit time information; a first measurement apparatus configured to measure brain neural activity of a subject, based on a biological signal detected from the subject; a first recording apparatus configured to record first data indicating a temporal change in the brain neural activity measured by the first measurement apparatus, in association with the time information received from the time measurement apparatus; an image capturing apparatus configured to capture an image of the subject; and a second recording apparatus configured to record second data indicating a temporal change in a posture of the subject, the posture identified based on the image captured by the image capturing apparatus, in association with the time information received from the time measurement apparatus.

A system for electrical brain stimulation including a plurality of electrodes arranged around the patient's brain (either directly or indirectly through layers of dura, skull or skin) such that axes connecting each electrode pair intersect at a predetermined focal point, and a ground-independent switching circuit configured to selectively activate and deactivate electrodes via a plurality of ground-independent switches. Electrodes are sequentially activated and deactivated.

A neural analysis and treatment system includes a computing device with a memory for storing an application that is executable on a processor to receive amplitude-integrated electroencephalography (aEEG) and range-EEG (rEEG) measurements associated with a patient. The systems determine a spectral edge frequency (SEF) measurement from the received EEG measurements, and determine one or more neural characteristics of the patient according to the determined SEF, aEEG, and rEEG measurements. These neural characteristics may then be used to identify and implement an appropriate therapeutic treatment.

The problem to be solved is to provide a system and the like for augmenting supervisory data while maintaining the relationship among a plurality supervisory data used for machine learning. The present disclosure provides a system for augmenting supervisory data used for machine learning, the system including an obtaining means that obtains a plurality of supervisory data, a first processing means that derives a covariance matrix from the plurality of supervisory data, a second processing means that decomposes the covariance matrix, and a third processing means that applies a random number to the decomposed matrix.

Systems and methods are provided for evaluating a pain disorder. A stimulus is applied to a subject and an evoked potential is obtained from at least one electrogram of the subject. A set of features is extracted from the evoked potential including features from at least two of a set of features representing connectivity between regions of the brain, a set of morphology features, a set of features representing time and frequency, a set of signal decomposition features, and a set of features representing entropy. A clinical parameter relating to a pain disorder is assigned to the subject from the extracted set of features with a machine learning model.