A method for optimizing stimulation for a patient having a stimulator device such as a Deep Brain Stimulation (DBS) device is disclosed. Test stimulation is provided at initial combinations of lead positions and values of a stimulation parameter such as amplitude, neural potentials are measured for each combination, and a neural response score is determined using the measured neural potentials. A next combination of position and a value of the stimulation parameter to test is determined using the neural response scores. This process repeats iteratively until a stopping criterium is met. The neural response scores and then used determine optimal therapeutic stimulation for the patient. Neural response measurements can also be used to exclude certain positions or stimulation values during subsequent optimization testing.

A method includes obtaining a first imaging scan and a second imaging scan of a single subject brain. The first imaging scan is converted to a first dataset, and the second imaging scan is converted to a second dataset. A sequence-adaptive multimodal segmentation algorithm is applied to the first dataset and the second dataset. The sequence-adaptive multimodal segmentation algorithm performs automatic intensity-based tissue classification to generate a first labelled dataset and a second labeled dataset. The first labeled dataset and the second labeled dataset are automatically co-registered to each other to generate a transformation matrix based on the first labeled dataset and the second labeled dataset. The transformation matrix is applied to align the first dataset and the second dataset.

A processing device is configured to interface with a region of the brain of a subject that is responsible for forming concepts without sensory input. The processing device receives brain signals representative of at least one concept formed by the region of the brain without sensory input, and processes the received brain signals so as to convert the at least one concept to data that is representative of a tangible form of the at least one concept. In certain embodiments, the processing device processes data that is representative of at least one concept to be formed by the region so as to convert the data into one or more brain signals, and selectively providing the one or more brain signals to the region of the brain such that the at least one concept represented by the data is formed by the region of the brain without sensory input.

Provided herein is a computing system for optimizing a waveform, in communication with an implantable pulse generator, and including a computing device including a memory device and a processor communicatively coupled to the memory device. The processor is configured to: retrieve historical waveform data associated with a plurality of waveforms used in therapeutic sessions for a plurality of patients, the historical waveform data including a plurality of waveform parameters; analyzing the historical waveform data to determine preferred waveform parameters; determining that a patient is starting a new therapeutic session using the patient therapeutic device; displaying each of the preferred waveform parameters; prompting the user to accept or modify the displayed waveform parameters; optimizing the waveform parameters for the therapeutic session; and transmitting the optimized waveform parameters to the patient therapeutic device to start the therapeutic session.

There is provided a method of generating deep brain stimulation signals, the method comprising receiving a plurality of sensor signals from a corresponding plurality of sensors on or in a subject, and using the received sensor signals to generate a plurality of stimulation signals for application at a corresponding plurality of target sites in the brain of the subject. There is further provided a method of generating stimulation signals, the method comprising receiving a plurality of sensor signals from a corresponding plurality of sensors on or in a subject, and using the received sensor signals to generate a plurality of stimulation signals for application at a corresponding plurality of target sites on or in the subject using a model of the response of neurons in the subject to the stimulation signals that models neural tissue as a plurality of coupled populations of neurons.

A system for modeling neurological activity includes a computer having one or more processors, one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more storage devices. The program instructions are configured to receive electroencephalogram (“EEG”) data generated by an EEG device coupled to a plurality of electrodes disposed on a brain, the EEG data comprising a plurality of waveforms representative of electrical activity detected by the plurality of electrodes over a period of time; generate a graphical brain model representative of the brain; to convert the EEG data into a graphical EEG model representative of electrical activity; integrate the EEG model with the brain model, thereby enabling visualization of and interaction with the EEG model within the context of the brain model; and communicate the integrated EEG and brain model to a display.

Provided is an electrode having an internal space, wherein the internal space is formed by a film including a layer containing a conductive material (conductive layer). Also provided is a method for producing an electrode, including a step (a) of forming a film including a layer containing a polymer compound (polymer compound layer) and a layer containing a conductive material (conductive layer); and a step (b) of allowing the film to form a tubular shape in a self-organized manner, using, s a driving force, a strain gradient in the thickness direction of the film.

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

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

An intracranial apparatus that may be used for electrophysiological monitoring and stimulation of brain tissue of a patient. Embodiments comprise a stylet including a body section and a tip configured to separate brain tissue, a sheath including a body section having an outer surface defining a central opening configured to receive the stylet such that the tip of the stylet extends beyond the body section of the sheath, and a plurality of electrodes disposed at the outer surface of the sheath and configured to be connected to the brain tissue surrounding the sheath for stimulating and/or monitoring.