In one example, the disclosure describes a method comprising receiving, by processing circuitry, information indicative of one or more evoked compound action potential (ECAP) signals. The one or more ECAP signals are sensed by at least one electrode carried by a medical lead. The processing circuitry determining that at least one characteristic value of the one or more ECAP signals is outside of an expected range. Responsive to determining that the at least one characteristic value of the one or more ECAP signals is outside of the expected range, the processing circuitry performs a lead integrity test for the medical lead.

A device for recording evoked neural responses, comprising one or more stimulus electrodes and one or more sense electrodes. The device has a stimulus source for providing a stimulus to be delivered from the stimulus electrodes to a neural pathway in order to give rise to an evoked action potential on the neural pathway. The device has measurement circuitry for recording a neural compound action potential signal sensed at the sense electrodes. Crosstalk cancellation circuitry is configured to produce a stimulus crosstalk cancellation signal, and is configured to inject the stimulus crosstalk cancellation signal into the measurement circuitry. The stimulus crosstalk cancellation signal is configured to cancel a stimulus crosstalk voltage arising upon the one or more sense electrodes as a result of delivery of the stimulus.

A method for context-aware self-calibration includes measuring for a plurality of time segments, at least one feature of at least one biosignal or each of at least one channel. Each biosignal is created in response to a user imagining an intended direction for each time segment. An object is moved along an actual decoded direction determined by an output of a decoder configured to correlate for each time segment the at least one feature to the intended direction. The decoder self-calibrates to minimize for each time segment, an error between the actual decoded direction, and the intended direction inferred subsequent to the respective time segment.

An automated EP analysis apparatus for monitoring, detecting and identifying changes (adverse or recovering) to a physiological system generating the analyzed EPs, wherein the apparatus is adapted to characterize and classify EPs and create alerts of changes (adverse or recovering) to the physiological systems generating the EPs if the acquired EP waveforms change significantly in latency, amplitude or morphology.

An automated EP analysis apparatus for monitoring, detecting and identifying changes (adverse or recovering) to a physiological system generating the analyzed EPs, wherein the apparatus is adapted to characterize and classify EPs and create alerts of changes (adverse or recovering) to the physiological systems generating the EPs if the acquired EP waveforms change significantly in latency, amplitude or morphology.

Described is an apparatus for locally monitoring nerve activity that may be incorporated into a nerve ablation catheter. Such a catheter is equipped with magnetic sensing for both identifying nerves and assessing the success of the ablation. The catheter is also equipped with an ablation instrument for both stimulating and destroying nerve tissue.

A brain machine interface system for use with an electroencephalogram to identify a behavioral intent of a person is disclosed. The system includes an electroencephalogram configured to sense electromagnetic signals generated by a brain of a person. The electromagnetic signals include a time component and a frequency component. A monitor monitors a response of the person to a stimulus and a characteristic of the stimulus. A synchronization module synchronizes the sensed electromagnetic signals with the response and the characteristic to determine a set of electromagnetic signals corresponding to the monitored response and the characteristic. A processor processes the set of electromagnetic signals and extracts feature vectors. The feature vectors define a class of behavioral intent. The processor determines the behavioral intent of the person based on the feature vectors. A brain machine interface and a method for identifying a behavioral intent of a person is also disclosed.

A brain machine interface system for use with an electroencephalogram to identify a behavioral intent of a person is disclosed. The system includes an electroencephalogram configured to sense electromagnetic signals generated by a brain of a person. The electromagnetic signals include a time component and a frequency component. A monitor monitors a response of the person to a stimulus and a characteristic of the stimulus. A synchronization module synchronizes the sensed electromagnetic signals with the response and the characteristic to determine a set of electromagnetic signals corresponding to the monitored response and the characteristic. A processor processes the set of electromagnetic signals and extracts feature vectors. The feature vectors define a class of behavioral intent. The processor determines the behavioral intent of the person based on the feature vectors. A brain machine interface and a method for identifying a behavioral intent of a person is also disclosed.

A method for functional analysis of neurophysiological data by decomposing neurophysiological data and EEG signal to form a plurality of signal features. The signal features may then optionally be analyzed to determined one or more patterns.

A device for examining a pathological interaction between different brain areas, including a stimulation unit, which administers identical stimuli to a patient in a sequential manner, wherein the stimuli stimulate neurons of the patient in the brain areas to be examined, a measuring unit for recording measurement signals that represent a neural activity of the stimulated neurons, and a control and analysis unit for controlling the stimulation unit and for analyzing the measurement signals. The control and analysis unit transforms the measurement signals into the complex plane, examines the distribution of the phases of stimuli of the measurement signals absorbed by the measuring unit in response to the stimuli delivered to the patient, and determines the probability, with which the phase distribution differs from a uniform distribution, in order to ascertain whether a pathological interaction between the brain areas exists.