Representative methods, apparatus and systems are disclosed for providing concurrent electrical stimulation and electrical recording in a human or non-human subject, such as for neuromodulation, with the apparatus coupleable to an electrode array. A representative apparatus is typically an integrated circuit including: stimulation circuits, recording circuits, and blocking circuits responsive to control signals to block the stimulation voltage or current on an electrode from a corresponding recording circuit, while other recording circuits may simultaneously record electrical signals from other electrodes and generate recorded data. A representative stimulation circuit may include current sources; a first multiplexer for current source selection; a second multiplexer for electrode selection; a switchable voltage offset circuit; a switchable grounding circuit; and a stimulation controller providing control signals to provide the electrical stimulation, such as biphasic or monophasic stimulation, and bipolar or unipolar stimulation. Off-chip communication, control, along with power and voltage level control, are also provided.

A method and computer programs for identifying time-frequency features of physiological events are disclosed. A computer system comprises filtering a set of physiological signals within each one of a plurality of time-frequency windows, obtaining a filtered set for each time-frequency window; calculating, for each time-frequency window, a given feature for the filtered set, each one of the signals of the filtered set having a given feature value, providing for each time-frequency window a set of feature values; and calculating, for each time-frequency window a first quantifier defined as a function of said set of features values and/or a second quantifier defined as a function of an empirical distribution of said set of feature values. The first quantifier can be compared with a first threshold and the second quantifier can be compared with a second threshold. The computing system can further select the time-frequency windows satisfying the first threshold and/or the second threshold.

The method of identifying a potentially surgically operable target zone in an epileptic patient's brain includes: providing a computerized platform modelling various zones of a primate brain and connectivity between said zones; providing a model of an epileptogenic zone and a model of the propagation of an epileptic discharge from an epileptic zone to a propagation zone; obtaining a patient's personalized computerized platform; deriving the potential target zones based on modularity analysis; evaluating the target zones' effectiveness by simulating epileptic seizures propagation in the personalized patient's computerized platform; evaluating the target zones' safety by simulating spatiotemporal brain activation patterns in a defined state condition and comparing the simulated spatiotemporal brain activation patterns obtained before removal of the target zone with the spatiotemporal brain activation patterns obtained after removal of the target zone; identifying the target zones which satisfy both effectiveness and safety evaluation criteria as potentially surgically operable target zones.

Systems, methods and programs for processing EEG data for display and/or automatically detecting a seizure in a patient based on one or more spectrograms created from the EEG data. EEG data from a patient may be paired into channels based on electrode locations. Spectrograms are generated from EEG data from channels, respectively. The spectrograms of different channels are grouped and a median power spectrogram (MPS) is calculated for the group. The MPS may be used to automatically determine whether the patient had a seizure by applying a machined learned model (ML) model. The ML model is trained and tested using historical EEG data from a plurality of patients. The MPS or a relationship between a plurality of MPS of different groups may be displayed on a bedside monitor in real-time for viewing by a bedside clinician.

Systems, apparatus, and methods for treating medication refractory epilepsy are disclosed. In one embodiment, a method of treating epilepsy is disclosed comprising detecting, using a first electrode array coupled to a first endovascular carrier, an electrophysiological signal of a subject. The method further comprises analyzing the electrophysiological signal using a neuromodulation unit electrically coupled to the first electrode array and stimulating an intracorporeal target of the subject using a second electrode array coupled to a second endovascular carrier implanted within a part of a bodily vessel superior to a base of the skull of the subject.

The present invention relates to an AI (Artificial Intelligence) based method for providing brain information comprising: a step 1 in which a brain signal obtaining portion of a brain signal measuring portion irradiates near-infrared ray to user's brain and detects light penetrating a cerebral cortex of the brain; a step 2 in which a brain signal processing portion of the brain signal measuring portion determines a level of oxygenation of hemoglobin in blood flow of the user's brain on the basis of the detected light; a step 3 in which a brain signal analyzing portion of the brain signal measuring portion extracts at least one brain activation area from a plurality of activated areas of the user's brain on the basis of the determined level of oxygenation of hemoglobin; and a step 4 in which a diagnosing portion determines a state of the user's brain on the basis of the at least one brain activation area extracted successively for a predetermined period of time, wherein a signal collecting operation of the brain signal obtaining portion in the step 1 is performed in a state that the user is moving.

System and method for processing, non-concurrently collected, electroencephalogram (EEG) data and resting state functional magnetic resonance imaging (rsfMRI) data, non-invasively, to create a patient-specific three-dimensional (3D) mapping of the patient's functional brain network. The mapping can be used to more precisely identify candidates of resective neurosurgery and to help create a targeted surgical plan for those patients. The methodology automatically maps the patient's unique brain network using non-concurrent EEG and resting state functional MRI (rsfMRI). Generally, the current invention merges EEG data and rsfMRI data to map the patient's epilepsy/seizure network. Correlated sections of the brain and inversely correlated sections of the brain are identified to determine which sections of the brain work in conjunction with each other and which sections work oppositely to each other during epileptic episodes.

A method for reducing epileptic seizures in a subject includes the steps of providing a vibration motor coupled to a controller configured to control vibratory motion of the vibration motor, positioning at least one vibration motor on a limb of the subject, and generating a vibratory stimulation signal configured to stimulate proprioceptive nerves in the limb to trigger kinesthetic cues that stimulate nerves in cerebellar and pontine areas of the brain to suppress seizure activity.

Methods, devices, and systems induce neuromodulation by focusing a source of stimulation through a skull/brain interface in the form of an aperture formed in the skull, a naturally occurring fenestration in the skull, or a transcranial channel. Methods, devices, and systems identify where to locate skull/brain interfaces, accessories that can be used with the interfaces, and features for controlling stimulation delivered through the interfaces. Multiple indications for the skull/brain interfaces include diagnosis and treatment of neurological disorders and conditions such as epilepsy, movement disorders, depression, Alzheimer's disease, autism, coma, and pain.

A wireless deep brain stimulation method and device.