An implantable array, such as an electrode array, is provided. The array is suitable for being placed in anatomic tissue of a human or animal body, and has a structure for referencing predefined distinct points of the implantable electrode array in magnetic resonance images. A structure is arranged in a predefined portion of the implantable array, where the structure has multiple patterns, each pattern having a predefined form and formed from a material having a magnetic susceptibility which is different from the magnetic susceptibility of the anatomic tissue surrounding the implantable array when placed in the human or animal body. Each pattern is in a predefined spatial relationship with one of the predefined distinct points.

Systems and methods for evaluating neurologic function of a subject are described. An odorant, auditory and/or somatosensory generator is configured to deliver a sensory stimulation to the subject, a plurality of electrodes are configured to be attached to the subject, and a handheld EEG control unit is configured to control the odorant, auditory and/or somatosensory generator, process the neural signals from the plurality of electrodes and generate an assessment of neurologic function of the subject.

One embodiment of an exemplary ambulatory seizure monitoring method calculates a phase lock value synchrony level of a neurological signal of an individual; detects an onset of a seizure event for the individual by comparing the phase lock value synchrony level with a patient threshold for the individual; and transmits a notification to a remote communication device indicating the onset of the seizure event for the individual.

A method receives continuous EEG data for a long duration of time from at least one electrode intracranially implanted in a subject. The method determines a current or predicted brain state from the EEG data using an artificial intelligence (AI) model.

The present invention discloses a brain activity analysis method and apparatus, which is based on a nonlinear waveform decomposition technology, wherein the changes of the intrinsic features in brain waves are decomposed and demodulated to extract the modulation signals of the components, including the frequency-modulation signals and the amplitude-modulation signals. The present invention further uses a feature mask to determine whether to proceed further decomposition and demodulation of the extracted modulation signals. If not, the multidimensional changes of the intrinsic features are obtained according to the feature mask. Then, quantitation and identification is performed to obtain the status of brain function. The present invention not only effectively increases the accuracy of the identification but also uses the feature mask to obviously reduce the complexity and the load of computation.

A behavioral state of a brain is classified by automatically selecting one or more sensors based on the signals received from each sensor and one or more selection criteria using one or more processors, calculating at least one measured value from the signal(s) of the selected sensor(s), classifying the behavioral state as: (a) an awake state whenever the measured value(s) for the selected sensor(s) is lower than a first threshold value, (b) a sleep state (N2) whenever the measured value(s) for the selected sensor(s) is equal to or greater than the first threshold value and the measured value(s) is not greater than a second threshold value, or (c) a slow wave sleep state (N3) whenever the measured value(s) from the selected sensor(s) is greater than the first threshold value and the measured value(s) is greater than the second threshold value, and providing a notification of the classified behavioral state.

To enable real-time seizure warnings, systems and methods of the present disclosure include a wearable sensor in communication with processors that are configured to receive from the wearable sensor data streams associated with a user that include biomarker data parameters. The processors utilize a seizure forecasting machine learning model to predict a pre-ictal period probability associated with a forecasted time segment based on values of the data streams. The processors determine a segment value for an integration window of a history pre-ictal period probabilities for the forecasted time segment and previously forecasted time segments and determine a pre-ictal period based on the segment value exceeding a pre-ictal probability threshold. The processors determine a pre-ictal risk indication include a seizure treatment administration and cause a computing device to produce the pre-ictal risk indication to indicate a predicted risk of a seizure.

A device for measuring eye movement in a human subject comprises a housing, at least one stimulator mounted to the housing, and a sensor. The at least one stimulator is configured to provide stimulus to one or both eyes of the subject. The sensor is configured to collect information related to movement of one or both eyes of the subject. The device also includes a user interface that is configured to control the at least one stimulator and display information collected by the camera.

A method of detecting a seizure includes collecting volatile organic compounds with a collector material of a collector; separating a mixture of the volatile organic compounds into its constituent chemicals with a gas chromatography column; ionizing the constituent chemicals to create ionized chemicals and detecting the ionized chemicals; and analyzing the ionized chemicals to identify seizure-indicative volatile organic compounds.

A system for calculating an indicator associated to a brain activity of a subject, the system including an acquisition module configured to acquire at least an epoch of electroencephalographic signal of a subject from a plurality of electrodes and a data processing module configured to carry out the steps of: calculating an average vector (V.sub.A) using as input of an autoencoder neural network (aNN) an electroencephalographic signals (ES) of a subject acquired from a plurality of electrodes; detecting (DET) the presence of at least a predefined pattern in the consecutive average values of the average vector (V.sub.A); and generating an indicator of brain activity (Idx) of the subject when detecting the predefined pattern.