Described herein is a system and method for controlling a computing system by an AI network based upon an electroencephalograph (EEG) signal from a user. The user's EEG signals are first detected as the user operates an existing controller, during which time the associated artificial intelligence (AI) system learns by correlating the EEG signals with the commands received from the controller. Once the AI system determines that there is sufficient correlation to predict the user's actions, it can take control of the computing system and initiate commands based upon the user's EEG signal in place of the user's actions with the controller. At this point, weights in the AI network may be locked so that further commands from the controller, or the lack thereof, do not reduce correlation with the EEG signals. In some embodiments, the AI network may initiate commands faster than the user would be able to do.

The present invention is directed to a method for determining a paroxysmal slow waves event (PSWE) so as to determine blood-brain barrier dysfunction (BBBD) or increased risk of developing a neurological disease or disorder in a subject.

Embodiments of the instant invention relate to applying motor learning to promote remyelination following demyelination in a subject having a condition or disease. In certain embodiments, applying motor learning alone or in combination with vagus nerve stimulation (VNS) induces the production of new and preserves surviving oligodendrocytes. In accordance with certain embodiments of the disclosure, motor learning, when properly timed, enhances oligodendrogenesis after injury and recruits mature oligodendrocytes to participate in remyelination through the generation of new myelin sheaths. In other aspects of the disclosure, VNS paired with motor learning enhances remyelination following demyelination.

A computing device for use in a system for mapping brain activity of a subject includes a processor. The processor is programmed to select a plurality of measurements of brain activity that is representative of at least one parameter of a brain of the subject during a resting state. Moreover, the processor is programmed to compare at least one data point from each of the measurements with a corresponding data point from a previously acquired data set from at least one other subject. The processor is also programmed to produce at least one map for each of the measurements based on the comparison of the resting state data point and the corresponding previously acquired data point. The processor may also be programmed to categorize the brain activity in a plurality of networks in the brain based on the map.

A system (1) for estimating the brain blood volume and/or brain blood flow and/or depth of anesthesia of a patient, comprises at least one excitation electrode (110E) to be placed on the head (20) of a patient (2) for applying an excitation signal, at least one sensing electrode (110S) to be placed on the head (20) of the patient (2) for sensing a measurement signal caused by the excitation signal, and a processor device (12) for processing said measurement signal (VC) sensed by the at least one sensing electrode (110S) for determining an output indicative of the brain blood volume and/or the brain blood flow. Herein, the processor device (12) is constituted to reduce noise in the measurement signal (VC) by applying a non-linear noise-reduction algorithm. In this way a system for estimating the brain blood volume and/or the brain blood flow of a patient is provided which may lead to an increased accuracy and hence more exact estimates.

A multi-site probe for interfacing with the central nervous system includes one more structures disposed perpendicularly on a backing layer, where each structure includes a braided sleeve over a flexible or rigid core, and a delivery-vehicle. The structures may include optrodes and electrodes. The location of the probe may be determined through the application of combinatorics.

Embodiments for a cerebrospinal fluid flow detector for detecting the flow of cerebrospinal fluid are disclosed. In some embodiments, the cerebrospinal fluid flow detector includes a casing with a rotatable wheel having a plurality of radially extending arms disposed therein. The rotatable wheel is in communication with a channel having a distal end in communication with an inlet port and a proximal end in communication with an outlet port such that the flow of cerebrospinal fluid through the channel causes the rotatable wheel to rotate. In some embodiments, each radially extending arm includes at least one radiopaque marker in which movement of the rotatable wheel caused by fluid flow through the channel allows an X-ray imaging apparatus to detect the difference in position of a respective radiopaque marker at multiple times caused by rotation of the rotatable wheel.

Apparatus, systems, and methods are provided for generating a quantified indicator of cognitive skills in an individual. In certain configurations, the apparatus, systems, and methods can be implemented for enhancing cognitive skills in an individual.

A method for obtaining a near-infrared spectroscopy (fNIRS) cerebral signal in a subject includes: placing a near-infrared emitter and respective proximal and distal near-infrared detectors on a skin of a head of a subject; during a baseline recording stage with the subject in resting-state, record near-infrared signals, the recorded signals including a baseline deep-signal and a baseline shallow-signal; calculate a scaling factor between amplitudes of the baseline deep-signal and the baseline shallow-signal at a given task-frequency; with the subject undergoing a cyclic cerebral stimulation at the task-frequency during a stimulation recording stage, record near-infrared signals, the recorded signals comprising a shallow-signal and a deep-signal; and applying the scaling factor to the shallow-signal, calculating the cerebral signal at the task-frequency as a difference between the deep-signal and the scaled shallow-signal, at the task-frequency.

The present disclosure relates to a method and a system for detecting a neurological disease and an eye gaze-pattern abnormality related to the neurological disease of a user. The method comprises displaying stimulus videos on a screen of an electronic device and simultaneously filming with a camera of the electronic device to generate a video of the user's face for each one of the stimulus videos, each one of the stimulus videos corresponding to a task. The method further comprises providing a machine learning model for gaze predictions, generating the gaze predictions for each video frame of the recorded video, and determining features for each task to detect the neurological disease using a pre-trained machine learning model.