20 results about "Evoked potential recording" patented technology

Presented herein are techniques for measuring an evoked response to stimulation. One or more electrical stimulation signals are delivered to first ear of a recipient via a first hearing device implanted at the first ear of the recipient. One or more evoked potential evoked by the one or more electrical stimulation signals are measured with a second hearing device implanted at a second ear of the recipient.

Presented herein are techniques for recording an acoustically-evoked response of a recipient. In particular, an acoustic signal is detected, and at least one acoustically-evoked response of a recipient is recorded in response to detecting the acoustic signal to enable recording of the at least one acoustically-evoked response to be desirably initiated.

A neuromodulation device for measuring an evoked response comprising a first electrode; a second electrode, wherein the first and second electrodes are alternately configured as a stimulation electrode; a sensing electrode for sensing an evoked response to a stimulus pulse; and a controller configured to measure an evoked response at the sensing electrode after a stimulus pulse at a first stimulation electrode configuration and after a stimulus pulse at a second alternate stimulation electrode configuration, and to add said pair of measurements.

An illustrative system is configured to perform a process comprising: directing stimulation to be applied to a recipient of a bilateral hearing system that includes a first cochlear implant associated with a first ear of the recipient and a second cochlear implant associated with a second ear of the recipient; and causing the first cochlear implant and the second cochlear implant to simultaneously record, during an acquisition time window, one or more evoked responses within the recipient that are elicited by the stimulation.

Subject measurement systems can generate indications of neurological state, disease, dysfunction, or injury using a machine learning model. The machine learning model can include at least one encoding model and a sequential model pre-trained to perform language processing tasks. The machine learning model can further include a classifier configured to output classifications. The at least one encoding model, sequential model, and at least one decoding model can be jointly trained to predict timeseries output, thereby adapting the pre-trained sequential model for use with neurologically relevant input domains, such as medical images, EEG data, evoked response data, speech data, or the like. The at least one encoding model, sequential model, and classifier can be jointly trained to output indications of neurological state, disease, dysfunction, or injury. A subject measurement system can then generate such indications using patient data and the at least one encoding model, sequential model, and classifier.

Systems and methods for modeling a spatial distribution of evoked responses (ERs) of electrical stimulation and using the model to guide neuromodulation are disclosed. An exemplary system includes at least one multi-electrode lead, an electrical stimulator to provide electrical stimulation to a neural target, a sensing circuit to sense an ER to the electrical stimulation, and a controller circuit. In response to electrical stimulation delivered to the neural target via the stimulation electrodes according to stimulation settings, the controller circuit may collect an ER from each of a set of sensing electrodes located at a respective sensing location and selected from electrodes on the at least one lead, generate a model representing a spatial distribution of ER characteristics of the sensed ER, and providing a suggestion to reposition the lead or adjust the stimulation settings based on the comparison of the model to the acceptance criteria.

Systems and methods for selectively sensing evoked responses (ERs) and using the ERs to guide neuromodulation are disclosed. An exemplary system includes at least one multi-electrode lead, an electrical stimulator to provide electrical stimulation to a neural target, a sensing circuit to sense an ER to the electrical stimulation, and a controller circuit. In response to electrical stimulation delivered to the neural target via the stimulation electrodes according to stimulation settings, the controller circuit may collect ER from each of a set of sensing electrodes selected from electrodes on the lead, but not including all of the electrodes. The selected electrode may be different from the stimulation electrode, or within a specific proximity distance of the stimulation electrode. Based on a comparison of the sensed ER to an acceptance criterion, the controller circuit may provide a suggestion to reposition the lead or adjust the stimulation setting.

Controlling a neural stimulus comprises applying the neural stimulus to a neural pathway in order to give rise to an evoked action potential on the neural pathway, the stimulus being defined by at least one stimulus parameter. A neural compound action potential response evoked by the stimulus is measured, and from the measured evoked response a feedback variable is derived. Loop backoff behaviour is adjusted by applying a non-identity function to the feedback variable to produce a revised feedback variable, and completing a feedback loop by using the revised feedback variable to control the at least one stimulus parameter so as to maintain the feedback variable at a setpoint. Additionally, or alternatively, loop gain can be adjusted in response to a change in the loop setpoint, and / or a logarithm of the stimulus parameter can be controlled.

An illustrative system directs a display screen to display a graphical user interface that includes a selectable option to perform an electrode sweep with respect to a plurality of electrodes disposed on an electrode lead implanted at least partially within a cochlea of a recipient of a cochlear implant, directs, in response to a selection by a user of the option, an acoustic stimulation generator to apply acoustic stimulation having a frequency to the recipient, and directs the cochlear implant to use each electrode included in the plurality of electrodes to record an evoked response measurement in response to the acoustic stimulation.

A neuromodulation device comprises stimulus electrodes and sense electrodes. A stimulus source provides stimuli to evoke a neural response on a neural pathway. A signal sensed at the sense electrodes includes an evoked neural response. A control unit controls the stimulus source to provide the neural stimulus according to a stimulus intensity parameter; measures an intensity of the evoked response; and completes a feedback loop using the measured intensity and controller parameters to control the stimulus intensity parameter to maintain the measured intensity at a target value. Optimal values of the controller parameters are determined from a representative value of a characteristic of the feedback loop, the representative value having been derived from data of previously programmed patients, and / or the processor is configured to determine optimal values of the one or more controller parameters from a predetermined value of an amplification parameter of the feedback loop.

An illustrative diagnostic system may perform a process comprising: performing, during a lead insertion procedure during which an electrode lead is inserted into a cochlea of a recipient of a cochlear implant, a first evoked response-based measurement test that generates first result data; determining that the first result data satisfies a first condition; performing, based on the first result data satisfying the first condition and during the lead insertion procedure, a second evoked response-based measurement test that generates second result data, the second evoked response-based measurement test being of a different type than the first evoked response-based measurement test; and performing, based on the second result data, an operation associated with the lead insertion procedure.

Methods are disclosed for discovery of knowledge, interest and experience of individuals indicated by measured psychophysiological response (e.g., electroencephalograph) to stimuli (e.g., images). Stimuli are selected to explore targeted topics described in various levels of detail and sub-categories organized as hierarchal trees. The degree of psychophysiological response to stimuli associated with sub-categories of topics in the hierarchal tree guides selection of additional stimuli to systematically explore hierarchal tree nodes, levels and branches to discover the extent of knowledge, interest or experience related to the targeted topic. A system enabling these methods provides archival and search of hierarchal trees associated with topics of interest, target and non-target stimuli; stimulus dataset generation, editing and presentation / application; psychophysiological data collection and processing via local and remote resources; evoked response detection, characterization and scoring; and association of indications of knowledge, interest or experience with regards to facets of the targeted topic.

Systems and methods for providing evoked response guided multi-site deep brain stimulation (DSB) at various brain targets are disclosed. An exemplary system includes at least one multi-electrode lead, an electrical stimulator to provide electrical stimulation to a neural target, a sensing circuit to sense an evoked response (ER) to the electrical stimulation, and a controller circuit. In response to electrical stimulation of the first neural target in accordance with the first stimulation setting, the controller circuit collects the ER from the first sensing location and determines or adjusts a second stimulation setting for stimulating the second neural target based on the sensed ER at the first neural target. The second neural target may be stimulated according to the second stimulation setting to modulate the ER of the electrical stimulation at the first neural target and produce a desired therapeutic outcome in the patient.

An example diagnostic system is configured to: direct an acoustic stimulus generator to apply acoustic stimuli having a plurality of stimulus frequencies to a recipient of a cochlear implant during an insertion procedure in which an electrode lead communicatively coupled to the cochlear implant is inserted into a cochlea of the recipient; direct the cochlear implant to record, using electrodes disposed on the electrode lead, a plurality of evoked response signals during the insertion procedure, each evoked response signal included in the plurality of evoked response signals corresponding to a different stimulus frequency included in the plurality of stimulus frequencies; and determine an insertion status of the electrode lead within the cochlea of the recipient based on an amplitude and a phase of each of one or more evoked response signals included in the plurality of evoked response signals.

A diagnostic system is disclosed configured to direct an acoustic stimulus generator to apply an acoustic stimulus having a stimulus frequency to a recipient of a cochlear implant during an insertion procedure in which an electrode lead of the cochlear implant is inserted into a cochlea of the recipient, direct the cochlear implant to record, using an electrode disposed on the electrode lead, an evoked response signal during the insertion procedure, the evoked response signal representative of amplitudes of a plurality of evoked responses occurring within the recipient in response to the acoustic stimulus applied to the recipient, and incrementally step the stimulus frequency through a sequence of decreasingly lower values as the electrode lead is inserted into the cochlea, the sequence of decreasingly lower values beginning with an initial value and ending with a final value lower than the initial value.

A method includes accessing EEG data obtained from each of a first plurality of subjects and from each of a second plurality of subjects, and training a machine learning model to identify a brain state associated with mild traumatic brain injury (mTBI) using the EEG data for each of the first plurality of subjects. The first plurality of subjects are known to have mTBI, and the second plurality of subjects are known to be mTBI unafflicted. The EEG data for each subject includes EEG data for the subject at rest and sensory evoked response EEG data for the subject. A system for identifying a brain state of a subject includes a portable odorant generation and EEG recording system, and at least one processor configured to use a trained machine learning model to analyze EEG data obtained by the EEG recording system to identify the brain state of the subject.

An illustrative diagnostic system is configured to direct an acoustic stimulation generator to an acoustic stimulation generator to apply acoustic stimulation having a plurality of stimulus frequencies to a recipient of a cochlear implant during an insertion procedure in which an electrode lead communicatively coupled to the cochlear implant is inserted into a cochlea of the recipient, direct the cochlear implant to use an electrode disposed on the electrode lead to record a plurality of evoked response signals during the insertion procedure corresponding to the plurality of stimulus frequencies and representative of evoked responses that occur within the recipient in response to the acoustic stimulation applied to the recipient, and determine, based on one or more evoked response signals included in the plurality of evoked response signals, an insertion state of the electrode lead within the cochlea of the recipient.

Subject measurement systems can generate indications of neurological state, disease, dysfunction, or injury using a machine learning model. The machine learning model can include at least one encoding model and a sequential model pre-trained to perform language processing tasks. The machine learning model can further include a classifier configured to output classifications. The at least one encoding model, sequential model, and at least one decoding model can be jointly trained to predict timeseries output, thereby adapting the pre-trained sequential model for use with neurologically relevant input domains, such as medical images, EEG data, evoked response data, speech data, or the like. The at least one encoding model, sequential model, and classifier can be jointly trained to output indications of neurological state, disease, dysfunction, or injury. A subject measurement system can then generate such indications using patient data and the at least one encoding model, sequential model, and classifier.

A neuromodulation device comprises stimulus electrodes and sense electrodes. A stimulus source provides stimuli to evoke a neural response on a neural pathway. A signal sensed at the sense electrodes includes an evoked neural response. A control unit controls the stimulus source to provide the neural stimulus according to a stimulus intensity parameter; measures an intensity of the evoked response; and completes a feedback loop using the measured intensity and controller parameters to control the stimulus intensity parameter to maintain the measured intensity at a target value. Optimal values of the controller parameters are determined from a representative value of a characteristic of the feedback loop, the representative value having been derived from data of previously programmed patients, and / or the processor is configured to determine optimal values of the one or more controller parameters from a predetermined value of an amplification parameter of the feedback loop.

Systems and methods for closed-loop control of electrostimulation based on signal features derived from evoked neural activities are disclosed. A system comprises an electrostimulator to deliver a stimulation therapy, a sensing circuit to sense an evoked response induced by the stimulation therapy, and a controller circuit to identify, from a plurality of candidate signal features, a target signal feature that satisfies a performance criterion for distinguishing a first stimulation effect according to a first stimulation setting from a second stimulation effect according to a different second stimulation setting. The target signal feature can be identified based on evaluations of a discrimination metric for each of the candidate signal features. The controller circuit can determine a value of the target signal feature from the sensed evoked response, and control the electrostimulator to adjust the stimulation therapy based on the value of the target signal feature.