A method and apparatus for preventing or terminating seizures, by stimulating a brain with at least two implanted electrodes, each implanted in a different one of at least two regions of the brain, with a frequency to emulate and/or disrupt neuronal synchrony. Upon detecting a potential or actual seizure occurrence, the frequency is electrically applied to the brain upon the detection to preempt or terminate the potential or actual seizure occurrence.

An active skull replacement system including an implant having an area A, an upper surface, and a bottom surface, adapted to be implanted at least in part into a skull of a subject so to substitute a portion of the skull, the bottom surface arranged to face at least in part a cranial cavity, and having a first wireless bidirectional data communication device, a device operably connected to the bottom surface of the implant, the device adapted to at least one of stimulate a physiological response and record a physiological parameter of the subject, and an external reader adapted to be placed on the scalp of the subject and including a second wireless bidirectional data communication device configured to communicate with the first wireless bidirectional data communication device of the implant to operate the device, wherein the external reader and the implant are fixed and aligned among each other through a magnetic device.

An apparatus for measuring intracranial dynamics comprises the at least one sensing device (100): an electroencephalo-graphic electrode arrangement, which senses direct-current electroencephalographic signals from the brain, an optic measurement Marrangement (120), which directs optic radiation toward the brain through the cranium, and receives the optic radiation reflected and/or scattered therefrom, and/or a capacitive sensor arrangement (130), which senses electric potential signals of the head. The apparatus additionally comprises a data processing arrangement (150), which receives electric signals from the at least one sensing device (100), and determine data on at least one of the following dynamics: glymphatic activity, water within the cranium, brain tissue movements, water and/or electrolyte movements and intracranial pressure based on said electric signals from the at least one sensing device (100). The data processing arrangement (150) then outputs at least one piece of the data on the dynamics through a user interface (152).

A neural device system that can include a neural device configured to sense data associated with the subject or receive control input, an external device communicably coupled to the neural device, a storage medium communicable coupled to the external device, and one or more communications interfaces between the neural device, the external device, and the storage medium or components thereof, wherein the one or more communications interfaces comprise an encryption protocol.

A system for performing deep brain stimulation on a brain of a patient is disclosed. The system comprises one or more recording arrays, at least one stimulation array, and a processor. The recording arrays, which may be minimally invasively inserted to a target recording site of the brain, include recording electrodes having a diameter of less than about 1 mm and having a spacing of less than about 1 mm therebetween. The stimulation array, which may be inserted to a target stimulation site within the deep brain, includes at least one stimulation electrode. The processor is configured to receive one or more recorded signals from the recording arrays at the target recording site, determine a neurological state of the brain based on the recorded signals, and deliver electrical stimulation to the target stimulation site through the stimulation array based on the neurological state in order to electrically stimulate the brain.

A systems and methods for calibrating a neural device using transfer learning techniques. The methods can include aggregating calibration data across a user population to define a global dataset, identifying similar data segments across the global dataset to define a task-independent training dataset, training a feature extraction model based on the task-independent training dataset to define a trained, task-independent feature extraction model, receiving the calibration data from a user calibrating the neural device, and calibrating a user-specific feature extraction model using the trained, task-independent feature extraction model and the calibration data.

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.

An apparatus for storing data records associated with a medical monitoring event in a data structure. An implanted device obtains data and stores the data in the data record in a first data structure that is age-based. Before an oldest data record is lost, the oldest data record may be stored in a second data structure that is priority index-based. The priority index may be determined by a severity level and may be further determined by associated factors. The implanted device may organize, off-load, report, and/or display a plurality of data records based on an associated priority index. Additionally, the implanted device may select a subset or composite of physiologic channels from the available physiologic channels based on a selection criterion.

The invention relates to a method for automatically detecting elements of interest in electrophysiological signals, and to a detector for implementing such a method. The method according to the invention comprises steps in which: electrophysiological signals are delivered; a whitened time-frequency representation of said electrophysiological signals is produced; a threshold is set; this threshold is applied to the whitened time-frequency representation; and, in the whitened time-frequency representation, local maxima that are higher than or equal to the applied threshold are detected.

An initial set of parameters for operating one or more detection tools is automatically derived and subsequently adjusted so that each detection tool is more or less sensitive to signal characteristics in a region of interest. Detection tool(s) may be applied to physiological signals sensed from a patient (such as EEG signals) and may be configured to run in an implanted medical device that is programmable with the parameters to look for rhythmic activity, spiking, and power changes in the sensed signals, etc. A detection tool may be selected and parameter values derived in a logical sequence and/or in pairs based on a graphical representation of an activity type which may be selected by a user, for example, by clicking and dragging on the graphic via a GUI. Displayed simulations allow a user to assess what will be detected with a derived parameter set and then to adjust the sensitivity of the set or start over as desired.