151 results about "Brain state" patented technology

A computer system or method may be provided for modulating content based on a person's brainwave data, including modifying presentation of digital content at at least one computing device. The content may also be modulated based on a set of rules maintained by or accessible to the computer system. The content may also be modulated based on user input, including through receipt of a presentation control command that may be processed by the computer system of the present invention to modify presentation of content. Content may also be shared with associated brain state information.

An electrode with three-dimensional capabilities for detection and control of brain state changes of a subject. The electrode includes a disk portion having an upper surface and a lower surface, and a shaft portion secured to and extending perpendicularly outwardly from the lower surface of the disk portion; the shaft portion having an outer surface. The disk portion and shaft portion may include one or more recording or stimulating contact surfaces structured to operatively interact with the brain of a subject. Insulating material isolates each of the recording or stimulating contact surfaces from each other. At least one conductor operatively and separately connect each of the recording or stimulating contact surfaces in communication with external apparatus. The disk portion and shaft portion are structured relative to each other to operatively provide support and anchoring for each other while providing three-dimensional capabilities for detection and control of brain state changes of a subject. Modified embodiments include insertible/retractable electrode wires, both contained in channels and sheathed in axially displaceable cannulae; activating mechanisms for inserting/retracting the electrode wires and/or cannulae; and multiple shaft portions.

A system of accessing the brain of a subject to record and/or stimulate brain stares thereof. The system includes control means, first accessing means having a plurality of contact surfaces configured to operatively connect the control means to the brain of the subject to provide three-dimensional stimulation thereof; second accessing means having a plurality of contact surfaces to operatively connect the control means to the brain of the subject to provide access thereto for the purposes of obtaining three-dimensional recordings of the brain electrical activity of the subject; and a waveform bank containing a plurality of stimulating waveforms. The control means is configured to operative apply a first at least one of the stimulating waveforms of the waveform bank to the brain of the subject through the first accessing means and to assess acceptability within defined limits, as determined by preset criteria, of the subject's response to the application of the at first least one of the stimulating waveforms.

Systems and methods involve automatic activation, de-activation or modification of therapies or other medical processes based on brain state. A medical system includes a sensor system having one or more sensors configured to sense signals related to the brain state of the patient. A brain state analyzer detects various brain states, including sleep stage and/or brain seizures. A controller uses the brain state detection information to control a medical system configured to perform at least one respiratory or cardiac process. Methods involve sensing signals related to brain state and determining the brain state of a patient based on the sensed signals. At least one respiratory or cardiac medical process is controlled based on the patient's brain state.

A computer system for guiding one or more users through a brain state guidance exercise or routine, such as a meditation exercise, is provided. The computer system includes at least one computing device which may be a smart phone. A computer program which may be a mobile application runs one or more brain state guidance routines that guide at least one user through at least one brain state guidance exercise. The computing device is connected to at least one bio-signal sensor that provides biofeedback information to the computing device, and where the computer program when executed further measures performance of the at least one user relative to one or more brain state guidance related objectives by analyzing the biofeedback information based on stability of state of mind for the user. The computer program may recognize, score and reward states of meditation.

Systems and methods for developing a brain state analysis system using subject EEG data are provided. The analysis system distinguishes clinical from subclinical electrographic seizures and optionally distinguishes among different seizure onset characteristics. An algorithm trained on only clinical electrographic seizures may predict clinical seizures more accurately with fewer perceived false positives. In addition, algorithms trained on a particular onset condition may distinguish and advise on that onset condition when used by the patient.

An intelligent brain rescue instrument for identifying, monitoring, and guiding the application of brain therapies to patients with evolving brain injuries, includes an input for acquiring a multiple number of signals each indicative of a different biochemical or biophysical parameter of a patient, a computer to continuously sample each of the acquired signals and display to a user on a monitor at least some of the parameters, the displayed parameters being selected by system software embodying expert analytical rules as the most significant parameters, or as parameters having values indicative, or predictive at any time of actual, or potential future deterioration of the brain state of the patient.

A method of comparing a patient's neurological data to data that is indicative of the patient's clinical manifestation of a seizure. In some embodiments, the method includes the steps of monitoring neurological data from a patient indicative of the patient's propensity for having a seizure; automatically recording clinical manifestation data from the patient that may be indicative of the occurrence of a clinical seizure; and analyzing the automatically recorded clinical manifestation data and the monitored neurological data to determine if one of the clinical manifestation data and the neurological data indicates the occurrence of a seizure while the other does not. Systems are described including a monitoring device having a communication assembly for receiving neurological data transmitted external to a patient from a transmitter implanted in a patient; a processor that processes the neurological data to estimate the patient's brain state; and an assembly for automatically recording clinical manifestation data in response to a brain state estimate by the processor.

A method for assessing brain state by analysing mammalian brain electroencephalogram (“EEG”) recordings using an eighth order autoregressive and fifth order moving average discrete time equation.

In some embodiments, the present invention provides an exemplary inventive system that includes: an apparatus to record: individual's brain electrical activity, a physiological parameter of the individual, and iii) an environmental parameter; a computer processor configured to perform: obtaining a recording of the electrical signal data; projecting the obtained recording of electrical signal data onto a pre-determined ordering of a denoised optimal set wavelet packet atoms to obtain a set of projections; normalizing the particular set of projections of the individual using a pre-determined set of normalization factors to form a set of normalized projections; determining a personalized mental state of the individual by assigning a brain state; determining a relationship between: the physiological parameter, the environmental parameter, and the personalized mental state; generating an output, including: a visual indication, representative of the personalized mental state, and) a feedback output configured to affect the personalized mental state of the individual.

A deep learning model based on a multi-scale network and application in brain state monitoring are provided. A model establishing method comprises steps of: preprocessing and multi-scale transforming a measured multichannel signal; obtaining a multi-scale weighted recursive network and a cross-recursive rate matrix corresponding to the multi-scale weighted recursive network of the multichannel signal in all scales; extracting the network indexes of the multi-scale weighted recursive network at different scales; at each scale, retaining relatively large elements in the cross recursive rate matrix and obtaining an unweighted adjacent matrix and a multi-scale unweighted recursive network corresponding thereto; for each value of a variable in the set range, obtaining the multi-scale unweighted recursive network and the adjacent matrix corresponding to the multi-scale unweighted recursive network, extracting the network indexes of the multi-scale unweighted recursive network at different scales, calculating the integral of the network indexes when the variable is changed in the set range, and the integral as the final network index of the multi-scale unweighted recursive network under each scale; and training the deep learning model and monitoring a brain state.

A non-invasive product customization system and a method of customizing a product formulation is provided. Brain activity of a user is detected in response to an input of a product formulation into a brain of the user via a sensory nervous system of the user. A mental state of the user is detected based on the detected brain activity. The product formulation is modified based on the determined mental state of the user. The modified product formulation may be presented to the user in a manner that modulates the mental state of the user.

An apparatus for assessing the brain state of a patient comprises at least one sensor to monitor at least one physiological variable, which provides at least one output signal. In addition, the apparatus for assessing the brain state of a patient comprises a computer processor for receiving the at least one output signal, wherein the computer processor filters the at least one output signal and applies pattern recognition techniques in evaluating the brain state of the subject. The computer processor comprises a first filtering module, which removes transient events not indicative of a brain state, and a second filtering sub-module, which groups together multiple modalities on the basis of classification techniques and incorporates additional information regarding mental states.

The disclosure provides an apparatus and method for assessing brain plasticity by measuring electrical brain biomarkers, for example, with a near real-time analysis of electrical brain biomarkers, where an increase or decrease in at least one biomarker is indicative of a state of brain plasticity in response to a stimulus or treatment. Brain plasticity can be measured with or without an added stimuli, for example, to determine the best time for learning. Also provided is a method for treating a neurological disease or trauma by applying an electrical or drug stimulus to a patient, where the stimulus is increased or decreased depending on the changes of electrical brain biomarker of the patient. This treatment can occur in near real-time, so a course of treatment can be tailored immediately to a patient's needs.

The invention discloses an electroencephalogram signal analysis method and an electroencephalogram signal analysis device to identify the brain state. The method comprises the following steps of: segmenting the acquired electroencephalogram signal; analyzing the state of each segment of electroencephalogram signal and preprocessing to remove interference; and calculating an ordering entropy of each segment of electroencephalogram signal, and determining the activity state of the brain according to ordering entropy values. The invention can be used for analyzing electroencephalogram signals under the conditions of different cognitive function activities of the brain and physiological statues, and can be particularly applied to studying the changes of brain activities in sleep and the like. Starting from a phase space, ordering entropies are adopted for statistical analysis of the structural change modes of the electroencephalogram signals to disclose the uncertainty, stable degree and information content of the electroencephalogram signals.

The invention discloses a method for automatically real-time estimating anesthesia depth based on ordering entropy, at first acquiring original electrobrain signal in real time; then sectioning the electrobrain data by a movable window technology; then recognizing the brain state and preprocessing the electrobrain signals in different states by different methods; at last calculating ordering entropies of each data section and estimating the anesthesia depth based on the ordering entropy values. The ordering entropy is a novel method for evaluating dynamics system complexity, the conception is simple, the computation speed is fast, the anti-interference performance is strong and the precision is high. The present invention provides an accurate real-time method for monitoring the influence of the anaesthetic dose to the electrobrain activity and provides objective evidences for the anesthesia physician to take appropriate measure.

The invention discloses an analysis method and application of EEG signals based on a complex network. The analysis method of the EEG signals based on the complex network comprises the following steps: constructing multi-scale level limited penetrable visibility graph complex networks; calculating characteristic indexes of each multi-scale level limited penetrable visibility graph complex network; combining a support vector machine to classify the EEG signals, namely using a leave-one-out cross-validation and support vector machine classifier to classify all two-dimensional index vectors, and using a ten-fold cross-validation and support vector machine classifier to classify all the two-dimensional index vectors. According to the invention, multi-scale ideas and level limited penetrable visibility graph theories are combined to construct an EEG multi-scale level limited penetrable visibility graph complex network so as to extract complex network indexes, and the support vector machine classifier in machine learning is combined to realize high-accuracy classification for different EEG signals. The analysis method and application of the EEG signals based on the complex network can be applied to smart head-mounted wearable equipment, and sleep EEG signals are measured through analyzing the smart wearable equipment to monitor the brain state of a user, furthermore, necessary early warning can be provided.