17 results about "Right hemisphere" patented technology

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.

The utility model discloses a connecting piece for splicing building blocks, and relates to the technical field of splicing building blocks, the connecting piece comprises a left hemisphere and a right hemisphere which are arranged in a mirror symmetry mode, the left hemisphere and the right hemisphere are spliced to form a ball shape, first building blocks are symmetrically arranged on the two sides of the left hemisphere and the right hemisphere, and second building blocks are connected to the back faces of the two first building blocks in a clamping mode. Connecting building blocks are fixedly connected to the upper ends and the lower ends of the second building blocks and the first building blocks, conical building blocks are clamped to the back faces of the two second building blocks, connecting plates are clamped to the back faces of the conical building blocks, and wing plates are arranged at the upper ends and the lower ends of the connecting plates; according to the utility model, the left hemisphere and the right hemisphere are spliced and matched, so that a stable core framework is conveniently formed, and the overall stability is improved; through clamping and connection combination of the building blocks, the connection dimension and modeling construction are enriched; finally, the problems that a traditional connecting piece is complex in structure and inconvenient to build are solved, and the using convenience and building efficiency of the splicing building block connecting piece are improved.

The utility model relates to the technical field of sound acquisition, and discloses a sound acquisition device convenient to install, which comprises a base, the lower surface of the base is fixedly connected with a first fixing plate, the bottom of the first fixing plate is provided with a clamping mechanism, the clamping mechanism comprises a first clamping jaw, the upper surface of the first clamping jaw is fixedly connected with a second ball joint, and the upper surface of the second ball joint is fixedly connected with a third ball joint. The spherical outer wall of the second ball joint is rotationally connected with a right hemisphere pin, and the inner wall of the right hemisphere pin is rotationally connected with a first ball joint. According to the utility model, when the sound acquisition device is installed on a plane or a wall surface, the clamping mechanism is disassembled, then the installation is completed by using a bolt through an installation hole, when the sound acquisition device is installed on a cylindrical rod piece or a pipeline, the adjusting bolt is rotated, the clamping is completed through the clamping jaw, the ball pin can be disassembled, and the clamping jaws with different specifications can be replaced. The clamping mechanism is added, so that the installation of the sound acquisition device on a cylindrical rod piece or a pipeline is simplified, and the installation is simple and convenient.

The invention discloses an MRI (Magnetic Resonance Imaging) image-based Alzheimer's disease classification method, which comprises the following steps of: selecting an sMRI image of a subject diagnosed for the first time, and preprocessing to obtain an sMRI grey matter image; segmenting the sMRI grey matter image into a plurality of regional images, and encoding the regional images corresponding to the half brain through a regional feature extraction network to obtain feature vector groups of the left half brain and the right half brain; inputting the feature vector groups of the left and right half brains into Transform modules of the left and right half brains, splicing the obtained features of the left and right half brains, and inputting the spliced features of the left and right half brains into a Transform module of the whole brain to obtain whole brain features; calculating cosine loss and hybrid cross entropy loss to obtain hybrid loss, and optimizing model network parameters through the hybrid loss; and based on the optimized model network, obtaining a left and right half brain branch prediction result and a whole brain prediction result, and completing Alzheimer's disease classification. According to the method, the reliability of classification and auxiliary diagnosis is improved, and the AD classification accuracy is improved.

Present disclosure describes the method and system for detecting the brain condition state of a subject. Method comprising calibrating a power zone of the system in real-time and detecting a reflected signal for each of a plurality of transmitted input signals on scanning each of lobe locations of the subject after calibration. Thereafter, method comprising validating an array generated using the plurality of transmitted input signal and corresponding reflected signal for each of the lobe locations and generating a lobe fit value for the validated array using a curve fitting technique. Subsequently, method comprising computing logarithmic ratios corresponding to four pairs of contralateral lobe location, six pairs of ipsilateral lobe locations in left hemisphere and six pairs of ipsilateral lobe locations in right hemisphere using the lobe fit value and classifying the logarithmic ratios into one of brain condition state classes by comparing with pre-labelled logarithmic ratios stored in system.

The application discloses a two-stage radiomics lesion identification and positioning method and device, adopts a multilayer perceptron network to analyze multi-modal image data, detects FCD by extracting features taking the gray matter as a region of interest by using a radiomics method, and the features combine shape, first-order statistics and texture features from multi-modal and wavelet images. In addition, the application also introduces asymmetric features of left and right hemispheres, avoids potential interference in the contralateral area of the unilateral FCD patient caused by the compensatory mechanism of the left and right hemispheres of epilepsy. According to the rich high-dimensional features and asymmetric features of radiomics, the sensitive features of FCD are fully explored, the two-stage detection method is combined to identify FCD abnormalities, the extracted features are more perfect, false positive results are avoided, and the accuracy and sensitivity of detecting FCD are improved from different scales from coarse to fine granularity.

The application relates to the technical field of neural image processing, and particularly discloses a brain atlas division system based on a multi-modal multi-dimensional lateralization index similarity network. The method first performs spatial uniform random sampling on the left hemisphere cortical surface of an individual, and extracts the 5-layer neighborhood of the sampling points by using the grid topological connection relationship; then, according to the cross-hemisphere vertex correspondence, the symmetric neighborhood is positioned in the right hemisphere, and the lateralization index (LI) distribution of the cortical features is calculated; by kernel density estimation modeling and morphological counter divergence algorithm, the LI-MIND correlation matrix representing the whole brain symmetry is constructed; finally, the spectral clustering algorithm is used for feature decomposition and dimension reduction of the matrix, the optimal clustering number is determined according to the contour coefficient, and the smooth individualized brain region division atlas is generated. By introducing the topological neighborhood and the lateralization distribution characteristics, the problem that the traditional brain atlas cannot effectively capture the individual organization left-right hemisphere difference is solved, and the brain region division scheme depending on the lateralization information is provided.

The application relates to the technical field of blood flow detection, and discloses a cerebral blood flow detection method and device, which comprises the following steps: acquiring detection signals corresponding to multiple brain regions of a target head; determining a to-be-processed signal in a current sliding window in the detection signals; pre-processing the to-be-processed signal to obtain a cerebral blood flow dynamic parameter time sequence corresponding to each brain region; determining a cerebral blood flow activity index, a cerebral blood flow coordination index and a left-right hemisphere symmetry index based on the cerebral blood flow dynamic parameter time sequence; the cerebral blood flow activity index is used for representing the active degree of cerebral blood flow of the whole brain, the cerebral blood flow coordination index is used for representing the cerebral blood flow coordination between brain regions, and the left-right hemisphere symmetry index is used for representing the symmetry change of blood flow perfusion and blood flow regulation between the left and right hemispheres. Through the sliding window technology and real-time signal processing, the application can realize rapid monitoring of the dynamic change of cerebral blood flow, and can comprehensively evaluate the functional state of the brain region and the mutual relationship from multiple dimensions.

This invention discloses a real-time cognitive load assessment system based on multimodal psychophysiological signals, relating to the field of cognitive load assessment technology. The system includes: an EEG signal acquisition module for acquiring bilateral EEG signals of the subject within a continuous sliding time window, wherein the bilateral EEG signals include at least a first EEG signal from a first brain region and a second EEG signal from a second brain region; a feature extraction module for analyzing a situational feature sequence characterizing bilateral brain activity based on the first and second EEG signals; and a system that constructs situational and synergistic features of bilateral brain activity. The situational features quantify the growth relationship between the left and right hemispheres in the allocation of cognitive resources by analyzing the fluctuation of the ratio of instantaneous power in the bilateral frontal lobes within a specific frequency band. The synergistic features avoid the problem of incomplete representation of the load state by single-dimensional features by analyzing the synchronization degree of instantaneous phase in the bilateral frontal lobes within a specific frequency band.

The invention discloses an auditory space attention decoding method based on a low-density electroencephalogram cross-hemisphere attention mechanism, electronic equipment and a medium, and belongs to the field of brain-computer interface and neural signal processing. The method comprises the following steps: preprocessing low-density electroencephalogram signals, and respectively mapping the low-density electroencephalogram signals into three-dimensional tensors with a time dimension and a two-dimensional space dimension according to a left hemisphere and a right hemisphere; a space-channel attention module is utilized to extract key spatio-temporal characteristics of each hemisphere; dynamic interaction between left and right hemisphere features is modeled through a cross-hemisphere attention module, and information fusion is realized; and finally, classifying the fused features, and outputting auditory attention directions. According to the method, an electroencephalogram space-time structure is reserved through three-dimensional tensor mapping, feature discrimination is enhanced in combination with a double attention mechanism, high-precision decoding can still be achieved under the low-density condition that only 4-8 channels are used, meanwhile, the method has the advantages of being low in calculation complexity and high in physiological interpretability, and the method is suitable for wearable brain-computer interfaces and intelligent hearing-aid equipment.

The application relates to a three-dimensional brain midline detection method and device, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring a brain scan image, pre-processing the brain scan image to obtain a training set image and a to-be-detected image; training a preset three-dimensional detection neural network based on the training set image to perform a left and right hemisphere segmentation task, and obtaining a hemisphere segmentation model; initializing the three-dimensional detection neural network based on network parameters of the hemisphere segmentation model, combining the training set image, training the three-dimensional detection neural network to perform a brain midline detection task, and obtaining a brain midline detection model; and detecting the input to-be-detected image based on the brain midline detection model to obtain a brain midline detection result. According to the application, the three-dimensional model training can be simultaneously performed in combination with the brain midline detection task and the left and right hemisphere segmentation task, the brain midline is determined according to the anatomical structure of the left and right hemispheres, and the problem that the brain midline detection accuracy is reduced when the brain midline structure is blocked is solved.

The application provides a learner high-order cognitive activity state recognition method, device and system, and determines a hierarchical convolution capsule network model; the hierarchical convolution capsule network model increases the length of the convolution kernel of all convolution operation channel directions to a preset value on the basis of an original capsule network model, so that the receptive field of each convolution operation can effectively capture the correlation characteristics of the corresponding electroencephalogram channels of the left hemisphere and the right hemisphere of the brain of the learner in the high-order cognitive state and the low-order cognitive state, to extract effective information representing the high-order cognitive activity state of the learner; and the hierarchical convolution capsule network increases two convolution layers in the convolution activation module of the original capsule network model, to construct a hierarchical convolution module, to extract more abundant features of different levels of electroencephalogram data, to further effectively capture information representing the high-order cognitive activity state of the learner; and the electroencephalogram data of the learner in the learning process is input into the trained hierarchical convolution capsule network model, to perceive whether the learner is in the high-order cognitive activity state.

A computer-implemented system (CIS) is provided for processing and / or analyzing non-contrast-enhance computer tomography medical imaging input data is described. The CIS contains (i) twin U-Net architectures with equal weights, which are built on a Siamese architecture, and (ii) a Dissimilar block operably linked to the two U-Net architectures, and built on top of the Siamese-U-Net architecture to form a Dissimilar-Siamese-U-Net architecture. The computer-implemented system can be used in diagnosing acute ischemic stroke and / or thromoboembolus, by analyzing separate and independent input images of the left and right hemispheres of a brain. The diagnosis is based on a detection of the presence of a hyperdense middle cerebral artery sign.

The invention relates to the technical field of internet big data, in particular to a hemorrhagic side and non-hemorrhagic side cerebrospinal fluid volume quantification method based on deep learning, which comprises the following steps: S1, acquiring N brain CT images to be processed; s2, inputting N brain CT images into the trained end-to-end multi-task collaborative architecture network, and respectively predicting and outputting a corresponding cerebrospinal fluid segmentation result, a brain midline prediction result and a cerebral hematoma segmentation result; s3, partitioning the left hemisphere and the right hemisphere of the brain based on the brain midline prediction results of all the brain CT images; s4, the number of hematoma pixel points of the left hemisphere and the right hemisphere of the brain is calculated based on the partitioning result of the left hemisphere and the right hemisphere of the brain and in combination with the hematoma segmentation results of all the brain CT images, and the hemorrhagic side and the non-hemorrhagic side of the brain are determined; and S5, based on the cerebrospinal fluid segmentation results of all the brain CT images, respectively calculating the hemispherical cerebrospinal fluid volumes of the hemorrhagic side and the non-hemorrhagic side of the brain and the volume difference. According to the method, the accuracy and objectivity of cerebrospinal fluid volume quantification can be improved.

The invention discloses a depression recognition system based on electroencephalogram signals, and the system comprises a data collection module which is used for collecting multi-channel electroencephalogram signals of a target object in a target state; the data processing module is used for preprocessing the multi-channel electroencephalogram signal to obtain an electroencephalogram signal to be recognized; the intelligent recognition module is used for performing depression recognition according to the to-be-recognized electroencephalogram signal based on a pre-trained target recognition model to obtain a target recognition result; wherein the target recognition model comprises a double-branch spatiotemporal feature extraction unit, a spatiotemporal feature fusion unit, a cross attention unit and a classification unit, the double-branch spatiotemporal feature extraction unit is used for performing spatiotemporal feature extraction on the left and right hemisphere electroencephalogram signals, and the spatiotemporal feature fusion unit is used for performing global fusion on multi-period features of the left and right hemispheres; and the cross attention unit is used for determining a dependency relationship between the global fusion features of the left and right hemispheres based on a multi-head attention mechanism. According to the scheme, the accuracy and reliability of depression identification can be improved.

The invention relates to the technical field of blood flow detection, and discloses a cerebral blood flow detection method and equipment, and the method comprises the steps: obtaining detection signals corresponding to a plurality of brain regions of a target head; determining a to-be-processed signal in the current sliding window in the detection signal; preprocessing the to-be-processed signal to obtain a cerebral blood flow dynamic parameter time sequence corresponding to each brain region; based on the cerebral blood flow dynamic parameter time sequence, determining a cerebral blood flow activity index, a cerebral blood flow coordination index and a left and right hemisphere symmetry index; the cerebral blood flow activity index is used for representing the cerebral blood flow activity degree of the whole brain, the cerebral blood flow coordination index is used for representing the cerebral blood flow collaboration between brain areas, and the left and right hemisphere symmetry index is used for representing the symmetric change of blood flow perfusion and blood flow regulation of the left and right hemispheres. Through a sliding window technology and real-time signal processing, the dynamic change of cerebral blood flow can be rapidly monitored, and the functional state and the mutual relation of the brain region can be comprehensively evaluated from multiple dimensions.

The invention relates to an image visual enhancement processing method, in particular to a visual enhancement processing method based on an artery spin marking perfusion image, and solves the problems that perfusion signals are lost or distorted and a post-processing method is insufficient to effectively correct bias field effects and motion artifacts in an image in the existing artery spin marking. According to the method, the original brain perfusion image is processed, the overall quality and the signal-to-noise ratio of the image are improved, and meanwhile, the signal intensity and consistency are ensured; by utilizing anatomical information of a standard brain template, the accuracy of removing non-brain tissues is improved, and meanwhile, interference caused by motion artifacts is effectively reduced; the contrast ratio of the image is improved through the mirror image overturning and rigid body registration technology; the perfusion difference of the left hemisphere and the right hemisphere is quantified through the ratio image, gridding processing is carried out on the ratio image, the ratio average value in each grid is calculated, then spatial distribution of a high-perfusion area and a low-perfusion area is clearly displayed through a visual colored tape mapping method, and visual enhancement processing is achieved.