166 results about "Cerebral activity" patented technology

The invention discloses an all-directional monitoring and emergency treatment system for patients in the emergency department, which belongs to the technical field of diagnosis and monitoring of emergency surgery and comprises a data input and acquisition module, a data processing module, a data processing module, a data processing module, a data processing module and a data processing module, wherein the data input and acquisition module is used for acquiring emergency treatment features including external stimulation inducements and ages of a current patient before emergency treatment; the monitoring and evaluation module is used for acquiring an epilepsy risk evaluation value, a comprehensive evaluation value and a feedback adjustment value, and collecting all-dimensional monitoring characteristics of a current patient, including amygdalia activeness, a brain activity reference level, abnormal discharge intensity, heart rate, respiratory rate, systolic pressure value and diastolic pressure value, and the monitoring and evaluation module is used for acquiring and outputting the epilepsy risk evaluation value, the comprehensive evaluation value and the feedback adjustment value; the early warning module is responsible for carrying out early warning and inputting an adjusted first-aid strategy to the first-aid execution module when the epilepsy risk assessment value, the comprehensive assessment value and the feedback adjustment value are in a continuous monitoring rising trend on the visualization module, and comprehensive and real-time monitoring and accurate assessment of the illness state of a patient and timely adjustment of first-aid measures are achieved.

The invention discloses a DIKWP-driven individualized brain-map interactive feedback system, which is used for neural rehabilitation and brain-computer interface training. The system obtains brain activity data of a patient through electroencephalogram acquisition equipment, constructs a personal brain-semantic map in combination with cognitive evaluation, and establishes a mapping relation between semantic units and brain region responses. In the training process, the DIKWP semantic analysis module performs multi-layer analysis on indexes such as reaction time, accuracy and intention achievement, generates multi-mode instant feedback such as visual sense, auditory sense or tactile sense, and performs directional reinforcement on a weak semantic domain. The system has a dynamic target optimization capability, and the difficulty can be automatically adjusted according to training performance; when attention distraction, emotion abnormity or semantic deviation is detected, a safety intervention mechanism is automatically triggered, and training effectiveness and safety are guaranteed. According to the method, closed-loop individualized rehabilitation interaction is realized, the adaptation degree and efficiency of brain-computer interface training are remarkably improved, and the method is suitable for various rehabilitation scenes such as languages, movement and cognition and has a good industrial application prospect.

Disclosed are devices, systems and methods for acquiring, analyzing, and utilizing neurofeedback to promote brain coherence. Neurofeedback is a form of biofeedback that allows an individual to regulate his / her brain activity by providing a visual metaphor of brain function, thereby making it accessible for manipulation. In some embodiments of the present technology, a system includes a brain signal detection device wearable by a subject and a computer device including a display and a brain-computer interface (BCI) configured to monitor brain signals and display visual, auditory, and / or tactile stimuli to the subject according to a neurofeedback threshold-based protocol to deliver brain signal coherence between the left and right hemispheres of a subject's brain.

Provided herein are systems, kits, and methods for monitoring brain activity. In some implementations, a system includes a plurality of wearable sensors having a housing with an extended, rounded shape are removably attached to the scalp of a patient and monitor electroencephalogram (EEG) signals. Approaches for instructing a user to position and active that wearable sensors are disclosed. Approaches for facilitating collection, synchronization, and processing of EEG signals are disclosed. Approaches for handing off control of the wearable sensors between portable computing devices are disclosed.

A wearable electroencephalography device includes an ear cuff around an auricle of an ear comprising a curved structure conforming to contours of the ear, an electroencephalography electrode positioned on the ear cuff and contacting skin for detecting brain activity signals, a wireless controller integrated with the ear cuff and processing the brain activity signals from the electroencephalography electrode, and a wireless communication module transmitting the processed brain activity signals to an external device. The wearable electroencephalography device includes a mobile application receiving the transmitted brain activity signals from the wireless communication module, analyzing the brain activity signals using machine learning algorithms to determine emotional states of a user, and providing real-time feedback regarding the emotional states through a user interface. The mobile application comprises a self-report interface allowing the user to input subjective state data and a live-feed interface displaying real-time subjective state data derived from the brain activity signals.

A method for selecting an AI solution for an automated robotic process including receiving at least one functional media including information indicative of brain activity by a human engaged in a task of interest, analyzing the functional media, identifying an activity level in at least one brain region, identifying a brain region parameter and an activity parameter; identifying an action parameter based in part on the brain region parameter or the activity parameter; and selecting a component of the AI solution in part on the brain region parameter, the activity parameter, or the action parameter.

An apparatus and a method for controlling a pharmaceutical mixer of a medication for Attention Deficit Hyperactivity Disorder (ADHD) based on an assessment of a mental health state of an adolescent. The method comprises: collecting physical information of a user; verifying survey questions regarding mental health depending on the physical information and real-time brain activity data; forming additional survey questions after having verified the answers and calculating a prediction rate of an appearance of symptoms of a mental illness by using AI models; dynamically selecting the most suitable AI model for mental health assessment; verifying a set of survey questions including a plurality of sub-questions depending on the calculated prediction rate; and; outputting result data by adjusting the prediction rate; and transmitting a control signal to the pharmaceutical mixer for the ADHD medicine based on the assessment of the mental health state.

The invention provides a head-mounted diagnosis and treatment device, a brain-computer interface system and a brain activity monitoring intervention method. The head-mounted diagnosis and treatment device comprises a head-mounted structure, at least one active electrode, at least one pair of stimulation electrodes and a control module, the head-mounted structure comprises a head beam supporting part and a frontal lobe wearing part, the two ends of the frontal lobe wearing part are connected with the two opposite sides of the head beam supporting part respectively, and the active electrode is arranged on the inner side of the frontal lobe wearing part. The active electrode is arranged on the head beam supporting part and used for collecting electroencephalogram signals of a frontal lobe area contact part, the stimulation electrode is arranged on the inner side of the head beam supporting part and used for forming electrical stimulation on the frontal lobe area contact part, and the control module is electrically connected with the active electrode and the stimulation electrode and used for determining the brain function state according to the electroencephalogram signals collected by the active electrode and controlling the stimulation electrode according to the determined brain function state. Thus, electrical stimulation therapy is formed. According to the head-mounted diagnosis and treatment device, the brain-computer interface system and the brain activity monitoring intervention method, diagnosis and treatment can be integrated, and the head-mounted diagnosis and treatment device is easy to wear and portable.

Methods, systems, and devices are described for reconstructing spatially resolved electrical activity in the brain. In some example embodiments, EEG and MRI data are used to estimate volumetric distribution of electrostatic potential inside the MRI domain throughout the entire brain. Spatially and temporally varying field estimates can be generated using a brain wave model which is based on weakly evanescent transverse cortical wave propagation and constrained using the tissue properties gained from the MRI data. The disclosed techniques enable brain activity imaging with high spatial and temporal resolution, thereby providing a tool for assessing functional brain states and monitoring changes in those states in relation to various normal and pathological conditions.

Brain activity can be monitored to facilitate a vision test in a virtual reality (VR) environment using an electronic device that includes a head-mounted display (HMD) and a plurality of electrodes. The electronic device can execute a user application configured to enable a virtual vision test, and generates a VR user interface corresponding to a three-dimensional (3D) virtual environment. The electronic device renders, on the HMD, a user interface including a first visual stimulus corresponding to the virtual vision test. While displaying the visual stimulus, in real time, the electronic device collects a plurality of electrical signals by the plurality of electrodes that contact a head of a user, and determines information of at least one of a second visual stimulus following the first visual stimulus and a user response to the first visual stimulus based on the plurality of electrical signals.

A device and system for real-time visualization of the electrophysiologic activity of a brain, particularly at the cortical surface. The neural device can acquire, process, and display high-spatiotemporal-resolution electrophysiologic data in real-time across entire electrode arrays spanning many thousands of electrodes over identified anatomic regions. The system is compatible with thin-film cortical surface electrodes that record from neural tissues without damaging those tissues. The system can be used to guide diagnostic and therapeutic actions with high precision, and also provides the basis for a brain-computer interface.

In general, the subject matter described in this disclosure can be embodied in methods, systems, and program products for identifying a value of a heart rate variability metric that indicates a variation in a heart waveform of a patient; identifying a value of a brain activity metric that indicates a type of electrical activity represented by a brain waveform of the patient; providing values for a collection of metrics to a computational model, the values for the collection of metrics including the value for the heart rate variability metric and the value for the brain activity metric; and receiving, from the computational model as a result of having provided the values for the collection of metrics to the computational model, an indication of mental state of the patient.

The invention discloses a calculation method for integrating task induction and intrinsic spontaneous brain function activity. The calculation method comprises the following steps: calculating a brain activation mode when an individual executes a corresponding cognitive task based on task state functional magnetic resonance imaging data and a general linear model; identifying individual large-scale nerve avalanche with spatial continuity based on resting state functional magnetic resonance imaging data; the method comprises the following steps: performing principal component analysis on resting state functional magnetic resonance data of an individual to construct a low-dimensional state space; a task-induced brain activation mode and intrinsic spontaneous nerve avalanche are projected to an individual low-dimensional state space; calculating the Euclidean distance between the task-induced brain activity and the intrinsic spontaneous nerve avalanche in the low-dimensional state space; and detecting the prediction effect of the geometric distance on the performance of the tested task through the regression model. The method is verified on a real data set, and experimental results show that the method not only can integrate two basic brain function activities, but also can significantly predict individual cognitive performance differences.

An integrated EEG-fNIRS neurofeedback system for interacting with participant's brain activity includes EEG electrodes, fNIRS detectors, at least one information receiver, a computation module, and a report generator. The EEG electrodes collect EEG signals. The fNIRS detectors collect fNIRS signals. The at least one information receiver receives and processes the collected EEG and fNIRS signals. The computation module executes an EEG and fNIRS signal processing pipeline with the EEG electrodes, the fNIRS detectors, and the information receiver. The computation module is further configured to: calculate score information based on received EEG and fNIRS signals; select a minimum score from the calculated score information; and discard an alternative score that is not selected as the minimum score, so as to enable the computation module to choose a single representative score for the shared target objective from both EEG and fNIRS signals. The report generator provides a report of the selection.

The brain control interface system comprises: a brain control interface configured to detect brain signals indicative of brain activity of a user in an environment, an input configured to obtain data indicative of a current light scene of one or more lighting devices in the environment, a memory configured to store processing methods associated with different light scenes, one or more processor configured to: select, from the processing methods stored in the memory, a processing method in accordance with the current light scene, apply the selected processing method to obtain and / or process the brain signals, derive a control command and / or a mental state of the user from the brain signals, and control the controllable device based on the derived control command and / or the derived mental state.

Embodiments described herein relate to systems, devices, and methods for use in the implementation of a brain-computer interface that integrates real-time eye-movement tracking with brain activity tracking to present and update a user interface that is strategically designed for high speed and accuracy of human—machine interaction. Embodiments described herein also relate to the implementation of a hardware agnostic brain-computer interface with specific user interface adaptations to enable high-speed, intuitive, and accurate user manipulation of applications and / or machines.

Systems and methods for predictive glucose in accordance with embodiments of the invention are illustrated. One embodiment includes glucose management device, including a brain signal recorder, and a controller, including a processor, and a memory, the memory containing a glucose monitoring application configured to direct the processor to record a brain activity signal of a user's brain using the brain signal recorder, and decode the brain activity signal to predict future glucose levels of the patient.

A computer-implemented method for decoding speech, language and related semantic neural activity includes: collecting neural signals from an array of electrodes implanted in or on a brain; extracting features from the neural signals to detect distributed signatures of linguistic encoding using non-contiguous coverage of the electrode array; and decoding linguistic units, including phonemes and semantic embeddings from the extracted features. The decoding can utilize a custom neural language model for a limited or impaired brain adapted from a generalized neural language model trained on other human brains with intact speech, linguistic and cognitive regions.

A diagnostic system and method for ADHD integrating neurodynamic enhancement and domain adaptation are proposed. This belongs to the interdisciplinary field of medical image processing and artificial intelligence. It solves the technical problems of low accuracy, poor generalization ability, and poor robustness and interpretability in existing ADHD diagnostic methods. The system includes a data input and preprocessing module, a neuromass-chaotic perturbation module, a domain adaptation module with mutual information minimization, and a multimodal graph convolutional network module. The neuromass-chaotic perturbation module captures the nonlinear dynamic characteristics of brain activity, the domain adaptation module effectively eliminates domain differences in multi-site data, graph structure constraint loss ensures the consistency of sample association, and the dynamic characteristics can correspond to the pathological mechanisms of brain functional abnormalities, providing interpretable evidence for clinical diagnosis.

A system can include a processing circuit. The processing circuit can receive an electroencephalogram signal from at least one electrode to sense brain activity of a neonatal patient. The processing circuit can execute a non-linear energy operator (NLEO) using the electroencephalogram signal to generate an NLEO signal. The processing circuit can generate at least one trajectory of a metric from the NLEO signal. The processing circuit can detect that the electroencephalogram signal indicates a seizure responsive to the trajectory of the metric matching a trajectory pattern.

The invention relates to a method for determining an individualized nerve regulation target spot, and the method comprises the steps: obtaining nerve image data, which are repeatedly measured for multiple times, of a subject and corresponding clinical psychological assessment data in a preset time period; calculating a brain activity index of each voxel in a preset brain region; establishing a plurality of candidate mathematical models to describe the relationship between the brain activity indexes and the clinical scores; selecting an optimal model through statistical test of fitting residual errors; generating a parameter weight map representing'symptom-activity 'association strength based on the optimal model; and finally, determining an individualized nerve regulation target according to the numerical distribution of the map. The invention also relates to a corresponding device, a processor and a computer readable storage medium thereof, and solves the problem of inaccurate target positioning caused by neglecting symptom heterogeneity and individual brain function difference in the prior art through a core methodology of ''longitudinal tracking-multi-model fitting-residual optimal selection''. The curative effect of nerve regulation and control treatment is obviously improved.

Embodiments described herein relate to systems, devices, and methods for monitoring brain activity and delivering electrical brain stimulation to a patient. In some embodiments, a system can deliver responsive electrical stimulation in a closed-loop manner and can offer real-time or near real-time monitoring of induced neurophysiological effects. After detecting a targeted brain pattern (i.e., an epileptic seizure), the system may deliver high-intensity ultra-short electrical stimulation impulses non-invasively or minimal-invasively to diminish or stop the neural oscillations underlying the epileptic seizure. The stimuli are delivered in time and space, relative to the emerging seizure rhythm patterns, such that they diminish or terminate the seizure. The system may include an implantable device including one or more electrodes electrically coupled to one or more processors. The processor(s) may be operatively coupled to a memory, one or more communication modules, and may optionally be coupled to a battery and one or more additional sensor(s).

To provide methods of correlating a brain signal to intentional and unintentional changes in a brain state.SOLUTION: Disclosed herein are methods of analysis to extract and assess brain data collected from subject animals, including humans, to detect intentional and unintentional brain activity and other unexpected signals. These signals are correlated with higher cognitive brain functions or unintended, potentially adverse events, such as a stroke or seizure, and with translation of those signals into defined trigger events or tasks.SELECTED DRAWING: Figure 6

The invention relates to a device for manipulating real objects (22) and to a hybrid real / virtual system for synchronizing neurophysiological activity with the perception and use of real objects, the device comprising: - an opaque housing (2) having an opening (3) through which at least one hand can be introduced for manipulating objects inside the opaque housing, - a screen (4) having at least two states, an opaque state in which the inside of the opaque housing is invisible and a visible state in which the inside of the housing is visible, - at least one detector (10) for detecting a thinking time of a user, - a switch (9) for controlling switching of the screen to the visible state in response to an instruction from the user, and - a processing unit (8) for collecting signals from the at least one detector (10) and the switch (9).

A non-invasive vagus nerve stimulation apparatus comprising a pair of headphones, a vagus nerve stimulator; one or more cardiac and / or brain activity sensors; and a controller; wherein the pair of headphones includes a pair of spaced apart ear-covering portions and a headband which connects the spaced apart ear-covering portions; the vagus nerve stimulator is carried by one of the ear-covering portions and includes a first arm which carries a cathode and a second arm which carries an anode; the or each cardiac and / or brain activity sensor senses activity within the body of a user and signals relating to the cardiac and / or brain activity are transmitted from the or each electrical activity sensor to the controller; and wherein the controller is carried by the pair of headphones, processes the signals relating to the sensed cardiac and / or brain activity and controls the vagus nerve stimulator to electrically stimulate the vagus nerve.

The invention of a device and system for reduction of tinnitus defects and hearing disorders in the ear and to correct the defects along with real-time feedback with the help of brain output signal recording technology to determine the amount of corrections is related to a device and system to control and reduce hyperacusis, tinnitus and hearing loss and a flexible wearable device that can be placed on a patient's head and also is a portable wearable device that can be connected to a mobile phone and a pair of true wireless earbuds wirelessly. This invention is a system of applying a magnetic field to the auditory cortex of the human brain to reduce hearing defects. This hat is able to simultaneously treat the patient with rTMS and monitor brain activity using fNIRS technology so that it provides accuracy in magnetic stimulation and monitoring of brain reactions.

The invention discloses an ADHD diagnosis system and method fusing neurodynamic enhancement and domain adaptation. The method belongs to the technical field of medical image processing and artificial intelligence crossing. The technical problems that an existing ADHD diagnosis method is low in recognition accuracy, poor in generalization ability and poor in robustness and interpretability are solved. The system comprises a data input and preprocessing module, a neural quality-chaotic disturbance module, a mutual information minimization domain adaptation module and a multi-modal graph convolutional network module. Nonlinear dynamic characteristics of brain activity are captured through the nerve quality-chaos disturbance module, domain differences of multi-site data are effectively eliminated through the domain adaptation module, sample correlation consistency is guaranteed through graph structure constraint loss, the dynamic characteristics can correspond to pathological mechanisms of brain dysfunction, and an interpretable basis is provided for clinical diagnosis.

The invention discloses a task-driven fNIRS decoding neural feedback training method and a task-driven fNIRS decoding neural feedback training system, which are characterized in that an individualized decoder is constructed by utilizing task state fNIRS data in a pretest stage, and a preselected feature set with the strongest discriminating power is screened out, so that a stable brain activity mode highly related to a specific cognitive function is targeted to neural feedback training; therefore, a foundation is laid for realizing accurate nerve regulation and control; in a real-time training stage, the system continuously decodes the brain activity into a positive state instead of a negative state as a feedback index, so that a subject can induce a target neural mode through self-regulation without being exposed to external unfavorable stimulation completely, and the target neural mode can be used as a target neural mode. The training pertinence and nerve specificity of advanced cognitive functions such as interference control are enhanced, a closed-loop training mode based on data driving and an individualized model can be applied to treatment of psychological and mental disorders, and discomfort and risks possibly brought by a traditional exposure therapy are effectively avoided.

The present application relates to the technical field of EEG signal monitoring and processing, and in particular to a method for decoding motor imagery based on EEG and its source estimation, the method comprising: acquiring EEG data of a target user; determining source estimation data of the whole brain based on the EEG data of the target user; determining source estimation data of different brain regions based on the source estimation data of the whole brain and the acquired brain region segmentation map, wherein the source estimation data of different brain regions include source estimation data of part of the whole brain regions or source estimation data of all the whole brain regions; combining the EEG data and the source estimation data of the different brain regions to determine the brain activity of the target user, and determining the motor imagery classification of the target user based on the brain activity of the target user. The collected EEG data of the user can be converted into activation data of the brain source space, and the EEG data and the source space data can be combined to comprehensively perform motor imagery decoding to improve the accuracy of motor imagery decoding.

1. The name of the design product: the cursor of the wireless electroencephalogram control graphical user interface for electronic equipment. 2. The use of the design product: an electronic equipment. 3. The design points of the design product: the graphical user interface. 4. The picture or photo that best shows the design points: design 1 front view. 5. Design 1 is designated as the basic design. 6. The use of the graphical user interface: for receiving and displaying control instruction signals from the brain-computer interface software analyzing the brain activity of the patient. 7. The human-computer interaction mode of the graphical user interface: interacting by clicking or dragging the cursor to control the graphical user interface displayed on the screen panel by analyzing the electroencephalogram signals of the patient, or interacting by operating the components of the electronic equipment, specifically, by controlling the mouse hover (design 1), the left mouse click (design 2), the left mouse double-click (design 3), the right mouse click (design 4), and the mouse drag (design 5) in the graphical user interface to realize human-computer interaction. 8. The change state description of the graphical user interface: designs 1-4 belong to dynamic graphical user interfaces, and their appearances automatically change from the front view to change state figure 1 and change state figure 2 in turn; design 5 belongs to a graphical user interface, and its appearance changes from the front view to interface change state figure 1 and interface change state figure 2 in turn through two drags. 9. Other circumstances that need to be explained: the colors of designs 2 and 3 are claimed. 10. The part covered by the blue translucent block in the view does not constitute part of the appearance design claimed.