209 results about "Cerebral activity" patented technology

For use in transcranial photobiomodulation (PBM) or brain wave or heart rate variability (HRV) biofeedback and using event-related potential (ERP) and induced and induced EEG oscillation (ERO) measurements, devices and methods for analyzing and monitoring electroencephalogram (EEG) electrical activity evoked during, prior to, and / or after a neurocognitive test of a human user to stimulate and modulate brain activity. An electroencephalogram (EEG) and photo-biometric modulation (PBM) apparatus in the form of a wearable device with headphones is described for stimulating the brain with PBM or monitoring and evaluating electrical activity generated by the brain of a person during stimulation using biofeedback training and simultaneous occurrence of EEG, also described are methods for testing the cognitive and physiological state of a person during a cognitive test using the provided devices. The PBM light is pulsed to stimulate an increase in target brain activity by providing additional energy to the mitochondria and activating other photosensitive brain processes. PBM stimulation is combined with EEG sensors for detecting EEG responses during cognitive testing using event related potentials (ERPs) and induced and induced EEG oscillations (EROs) for evaluating cognitive status and therapeutic function outcomes of a user.

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 relates to the technical field of brain imaging, in particular to a frequency-domain near-infrared spectrum measurement module and method and frequency-domain near-infrared spectroscopy equipment, comprising a detection unit and a processing unit, the detection unit is used for directly contacting with the head of a user, emitting detection light to a detected tissue and receiving a detection signal formed by reflection of the detected tissue, and the processing unit is used for processing the detection signal; amplitude data and phase data of the detection signal are extracted, and the amplitude data and the phase data are sent to the processing unit after analog-to-digital conversion; and the processing unit is used for calculating optical parameters of the detected tissue based on the converted amplitude data and phase data, and wirelessly transmitting the optical parameters to the target main control unit. Amplitude and phase detection functions are completed by the front-end measurement module in advance, the use of a high-speed ADC and a mixer is avoided, the system complexity is reduced, signal transmission between the measurement module and the main control unit depends on wireless communication, excessive cable connection is not needed, and the brain activity monitoring system is suitable for brain activity monitoring requirements in a dynamic scene.

The present solution relates to a method of smart feedback for stimulating the morphofunctional structures of the brain. The method includes assessing the state of regions of the brain to identify regions exhibiting degenerative changes in the case of diseased subjects, or selecting regions which permit an improvement in the level of intellectual functions of the brain. A user is trained to use a brain-computer interface as an input device for performing a mental exercise in a virtual environment with the aid of feedback. The brain-computer interface additionally comprises a classifier that is trained on brain activity data pertaining to the user in order to classify mental patterns in the user's brain activity as commands for mental control. The parameters of the classifier of the brain-computer interface are adaptively tuned to alter the patterns of mental control and engage the morphofunctional centres of the brain in the control process, and the user and the classifier with the adaptively tuned parameters are adaptively trained. The classifier with the adaptively tuned parameters processes information from the signals of the brain according to the tuned parameters to involve the identified regions of the brain exhibiting degenerative changes or the selected regions of the brain for improvement of the desired intellectual functions of the brain. The result is an increase in the accuracy with which a brain-computer interface signal is classified.

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.

The present invention concerns an apparatus for treating neurological disorders comprising (i) at least one electrode implantable in the brain of a patient and (ii) a processing and stimulation device connected to the at least one electrode, wherein the processing and stimulation device comprises (a) at least one stimulation module adapted to generate a stimulation signal to be sent to the at least one electrode, the stimulation signal being characterised by a plurality of parameters, (b) at least one acquisition module of a signal characteristic of cerebral activity coming from the brain of the patient adapted to determine its power in at least one frequency band, and (c) at least one control module of at least one parameter of the stimulation signal as a function of the power of the signal characteristic of cerebral activity acquired, based on a transfer function having a saturating trend, wherein the transfer function is such as to set the at least one parameter (Va, Vd, Vf) of the stimulation signal (Vstim) differently dependent on a plurality of power ranges, by keeping the at least one parameter within a predetermined stimulation range ([Vi_HighThreshold; Vi_LowThreshold]) with i=a,d,f.

The present invention relates to the field of image processing technology, and in particular to a method for image diagnosis and positioning of a supplementary motor area based on a resting state, comprising: acquiring a user's brain magnetic resonance image based on an MRI scanner, creating a database, and storing the acquired user's brain magnetic resonance image; acquiring the user's brain magnetic resonance image, and removing high-frequency physiological noise and low-frequency drift in the user's brain magnetic resonance image; acquiring the supplementary motor area, and performing low-frequency pulsation amplitude analysis, local consistency analysis, and functional connection abnormality analysis; the present invention utilizes brain maps for preliminary positioning and functional connection mutual verification, can more accurately determine the position of the supplementary motor area, reduce positioning errors, and evaluate the state of the supplementary motor area from two aspects of local brain activity and functional connection abnormality analysis, and based on this method, reduces the interference of subjective factors, realizes the quantification of diagnostic indicators, and makes the results more objective and reliable.

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 invention discloses a cognitive activity prediction method integrating network topology and physical constraints. The method comprises the following steps: (1) constructing a brain network adjacency matrix based on resting state functional magnetic resonance imaging data and a functional connection method; (2) according to a brain template, calculating a physical distance between brain regions to obtain a spatial constraint matrix; (3) considering the functional hierarchy difference between brain intervals to construct functional asymmetric path connection; (4) calculating a shortest path between brain intervals based on the functional asymmetry path connection matrix and considering physical space constraints so as to predict cognitive brain activities; and (5) executing the model through a leave-one-out method to obtain the predicted brain activity of each brain region, and finally calculating the similarity between the predicted brain activity and the real brain activity. The method not only considers the topological attribute of the brain network, but also combines the spatial geometry and functional hierarchy characteristics of the brain. The method is verified on a real data set, and experimental results show that the accuracy of cognitive activity prediction is remarkably improved.

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.

[Problem] The purpose of the present invention is to provide a brain activity measuring device that is capable of measuring brain activity over a wide region. [Solution] The brain activity measuring device comprises: a brain diagnosis probe; a detection signal reception unit for receiving a detection signal from the brain diagnosis probe; and a signal analysis unit for analyzing the detection signal received by the detection signal reception unit and obtaining measurement information for measuring brain activity. The brain diagnosis probe has multiple sheet-shaped probe units, and each of the multiple sheet-shaped probe units is placed inside the skull via a hole prepared in the skull of the living organism in question.

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.

Blood glucose states based at least on sensed brain activity data. Blood glucose states may be predicted states using prediction models, and may be real-time or future glucose states. One or more wearable sensors, optionally adapted for use in either single channel or dual channel sensing, can record brain activity signals and communicate brain activity signal data to a remote device.

To provide a brain activity measuring device and a brain activity measuring system free from tightening on to the temporal region by a frame, which hardly gives uncomfortable feeling when mounted.SOLUTION: An elastic cushion body 13 that is self-supported from a connector 5 of a frame body 2 and is erected inwardly is disposed inside the frame body 2 mounted on the head of a headset, and an electrode 15A to abut against the head through a connection sheet 16 is disposed on an apex 13c of the cushion body 13. The tip of a swing part 16B is fixed to a second part 2B of the frame body 2 when a part sticking out sideways from the cushion body 13 of the connection sheet 16 is represented as the swing part 16B. The swing part 16B is installed between the end of the cushion body 13 and the inside of the second part 2B of the frame body 2, and the electrode 15B is disposed in the swing part 16B.SELECTED DRAWING: Figure 1

The invention discloses a group brain regulation and control method and device based on multi-modal sensory stimulation. The method comprises the following steps: acquiring intercerebral synchronization baseline levels of a plurality of users; implementing in-phase auditory, tactile, or visual stimuli at a specific frequency to the user to manipulate synchronized neural activity at a specific brain region of the user; obtaining an inter-brain synchronization result of the user again; and according to the inter-brain synchronization result of the user before and after the implementation of the multi-modal sensory stimulation, a visual report reflecting the inter-brain synchronization improvement condition before and after the group brain regulation is provided for the user. According to the method, the synchronism of brain activities is manipulated by implementing in-phase sensory stimulation with specific frequency to the user, the problems of high cost, complex operation, low user acceptability, limited popularization and application and the like of the existing group brain regulation and control technology based on electrical stimulation are solved, and a practical means is provided for promoting interpersonal interaction.

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.