51 results about "Magneto encephalography" patented technology

The invention discloses a multi-modal feature combined depression auxiliary diagnosis system. The system comprises a sampling unit which is used for constructing a multi-modal depression data set by acquiring a depression screening scale, an electroencephalogram, a magnetoencephalogram and functional magnetic resonance imaging based on acquisition equipment; the feature extraction unit is used for extracting multi-modal brain features based on the depression data set, and the multi-modal brain features comprise power spectral density obtained by electroencephalogram signals, event-related potential, micro-state, prefrontal lobe gamma frequency band power spectral density obtained by magnetoencephalogram and event-related magnetic field; gray matter volume and resting state functional connection density are obtained through functional magnetic resonance imaging; a data preprocessing unit; the diagnosis model unit is used for constructing a multi-modal depression diagnosis model and training the model on the basis of the multi-modal brain features in combination with a fusion strategy; and an analysis and prediction unit. The extracted features are comprehensive and reasonable, the defect of each mode is overcome by the feature fusion method, and the fused features are advanced.

A juvenile depression cognitive impairment subtype classification and prognosis prediction method relates to the technical field of medical treatment, and mainly comprises the following steps: performing clinical evaluation and therapeutic response evaluation on a subject, performing MRI and magnetoencephalogram data acquisition, constructing a whole brain MSN of the subject, identifying MSN abnormal characteristics, obtaining functional connection change of a frequency band when magnetoencephalogram is abnormal, and determining the cognitive impairment subtype classification and prognosis prediction of the cognitive impairment subtype of the subject. A subtype classification model is established by fusing the MSN and cognitive function evaluation data, and a prognosis prediction model is established by analyzing MSN abnormal features, functional connection changes of frequency bands during abnormality, multi-dimensional treatment reactions and high-risk behaviors. According to the method, different levels of fusion measurement are carried out on the juvenile depression with cognitive function impairment brain mechanism through multi-modal brain images, a subtype classification model with diagnosis and treatment values is established, and a prognosis prediction model with clinical transformation potential is constructed; therefore, a theoretical basis and a technical means are provided for individualized precise diagnosis and treatment of the cognitive impairment of the juvenile depression.

The invention discloses a multi-modal deep learning traceability method and system fusing magnetoencephalogram and electroencephalography, and relates to the technical field of artificial intelligence and neuroimage.Real magnetoencephalogram signals and electroencephalography signals are preprocessed and then input into a traceability model, the probability of occurrence of a source in a corresponding area is predicted, and the traceability of the source in the corresponding area is obtained by combining an imported source partition distance matrix. A final traceability result is obtained; the training process of the traceability model is as follows: constructing a generative adversarial network, and generating a multi-modal neural electrophysiological data set; inputting the multi-modal neural electrophysiological data set into a residual network of a double-branch structure, and performing stage hierarchical extraction and decoupling on magnetoencephalogram signals and electroencephalogram signals respectively; extracting features in different stages by using a multi-scale convolution module, fusing the extracted features, inputting the fused features into a classifier, defining a loss function, and updating trainable parameters of the traceability model; the traceability method improves the accuracy and generalization ability of traceability positioning.

The invention provides an automatic joint registration method for a brain MRI image and an MEG sensor. The automatic joint registration method comprises the following steps: acquiring three-dimensional face point cloud data; calculating the curvature of each point; performing comprehensive calculation based on the distance and the curvature to obtain a curvature weighted distance based on curvature weighting; sequentially selecting points from the three-dimensional face point cloud data based on the curvature weighted distance to form a sampling point set; extracting feature points based on the sampling point set; and performing registration of the three coordinate systems based on the feature points. According to the configuration, the curvature is introduced when the sampling point set is selected, so that the face salient region can be better focused, the selected sampling points are more representative, and the subsequent registration effect is improved. According to the method, automatic registration of the magnetoencephalogram equipment coordinate system, the magnetoencephalogram head coordinate system and the magnetic resonance imaging coordinate system can be achieved, and the magnetoencephalogram technology can better play the effect in diagnosis and treatment of diseases such as epilepsy.

The present invention provides a deep learning method for a magnetoencephalogram spike wave localization model. The deep learning method comprises: performing data preprocessing on multi-channel magnetoencephalogram signals; performing preliminary screening of spike waves, and performing regional multi-channel weighted fusion of the screened multi-channel magnetoencephalogram signals to obtain band data; calculating manual feature information of the band data; and, inputting the band data and the corresponding manual feature information as a whole into a neural network to train the magnetoencephalogram spike wave localization model. With such a configuration, the intrinsic correlation between multi-channel magnetoencephalogram signals and the dynamic characteristics of the spike wave signal time series are taken into account, thereby overcoming the problems existing in the prior art. In one embodiment of the present invention, the effect of the scheme is further improved by partitioning the magnetoencephalogram channels, adopting a multi-channel weighted fusion strategy in each partition, and constructing a neural network architecture combining a gated recurrent unit and an attention mechanism.

The invention discloses an MEG data bad segment detection method based on a variational auto-encoder and clustering, and belongs to the technical field of magnetoencephalogram data processing. The method comprises the following steps: collecting cranial nerve activity original data, extracting time domain features and frequency domain features, inputting the time domain features and the frequency domain features into a variational auto-encoder, and converting the time domain features and the frequency domain features into low-dimensional potential variables; taking the sum of the reconstruction loss and the KL divergence as a total loss function in the variational auto-encoder, and minimizing the total loss function through a back propagation algorithm; extracting low-dimensional potential variables in the variational auto-encoder, and performing clustering analysis on the low-dimensional potential variables by using a trained clustering algorithm to distinguish bad segments from good segments; and decoding the MEG bad segment data identified by the trained clustering algorithm back to the original feature space. According to the method, the robust performance is shown in the low-signal-to-noise-ratio, large-source and multi-source environments, and the strong robustness and the excellent imaging performance are shown.

A method for predicting a development level difference of language and communication capability normality of a healthy infant or toddler. The method comprises obtaining electroencephalogram (EEG) or magnetoencephalogram (MEG) waveform data of a healthy infant or toddler caused by external auditory stimulus; obtaining, from the EEG or MEG waveform data, quantitative data that represents measurement index data of a central nervous system caused by the external auditory stimulus; and using a data set to train a machine learning classifier, so as to obtain a prediction result. Further provided are a relevant tool and an integrated system.

The invention provides a cold start method of a brain-computer interface, which relates to the technical field of brain-computer interfaces, and comprises the following steps: collecting an initial signal of a target user by using brain-computer interface equipment; the initial signal comprises electroencephalogram data and magnetoencephalogram data; preprocessing the initial signal to obtain a preprocessed signal; performing feature extraction on the preprocessed signal to obtain a signal feature; inputting the signal features into a trained electroencephalogram activity classification model to obtain a classification result; and generating a feedback result according to the classification result, and sending the feedback result to the target user. The cold start method of the brain-computer interface effectively solves the problems that a system based on transfer learning is poor in adaptive capacity and slow in response in the cold start stage of a new user. According to the method, individuation, real-time feedback and online learning are emphasized, so that a more effective and flexible user adaptation scheme is provided, the interactive experience of the user is smoother, and the overall performance of the system and the user satisfaction degree are improved.

The invention discloses a magnetoencephalogram-based epilepsy lesion positioning method and system and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the preprocessing of a structure image of a patient, carrying out the registration with the magnetoencephalogram of the patient, carrying out the grid division of a cerebral cortex region based on the preprocessed structure image and a registration result, and obtaining a forward model of the patient; performing preprocessing and spine wave detection on the magnetoencephalogram to obtain a spine wave time point sequence; based on the forward model and the ratchet wave time point sequence, using a magnetic dipole algorithm to calculate a source coordinate and a source direction of a ratchet wave time point, and classifying the ratchet wave time point sequence according to the source coordinate and the source direction to obtain a clustering result; calculating the source coordinate and the source direction of the class center of each class in the clustering result by using a magnetic dipole algorithm; generating a clinical report of the patient; according to the positioning method, the problems of large epilepsy diagnosis difference and error proneness caused by level difference of different doctors are solved, and the epilepsy focus position can be quickly determined.

The invention relates to a magnetoencephalography registration method, a magnetoencephalography registration device, magnetoencephalography registration equipment, a storage medium and a program product. Generating a helmet face model including the first facial feature of the testee and the first marker feature corresponding to the marker arranged on the brain magnetic helmet; according to the brain nuclear magnetic data of the testee, generating a scalp face model including a second facial feature of the testee; according to the design parameters of the brain magnetic helmet, acquiring a helmet device model comprising second marker features corresponding to the plurality of markers; registering the helmet face model with the scalp face model according to the first facial feature and the second facial feature to obtain a registered helmet face model including the registered first marker feature; and according to the registered first marker feature and the second marker feature, registering the helmet device model with the registered helmet face model to obtain a registered helmet device model.

Aspects provide systems and methods for utilizing quantum computing systems for processing of data generated by quantum sensor. For example, magnetic field data may be captured from a brain using a quantum sensor array. This magnetic field data may then be processed using a quantum computing apparatus including a plurality of qubits in order to generate a model of the brain.

Aspects provide systems and methods for utilizing quantum computing systems for processing of data generated by quantum sensor. For example, magnetic field data may be captured from a brain using a quantum sensor array (132). This magnetic field data may then be processed using a quantum computing apparatus (110) including a plurality of qubits (114) in order to generate a model of the brain.

The application discloses a multi-modal deep learning source tracing method and system combining magnetoencephalogram and electroencephalogram, relates to the field of artificial intelligence and neuroimaging technology, and inputs real magnetoencephalogram signals and electroencephalogram signals into a source tracing model after preprocessing, predicts the probability of the occurrence of corresponding regional sources, combines an imported source partition distance matrix, and obtains a final source tracing result; the training process of the source tracing model is as follows: a generative adversarial network is constructed to generate a multi-modal neuroelectrophysiological data set; the multi-modal neuroelectrophysiological data set is input into a double-branch structure residual network to perform stage hierarchical extraction and decoupling on the magnetoencephalogram signals and the electroencephalogram signals respectively; a multi-scale convolution module is used to extract features at different stages, the extracted features are input into a classifier after being fused, and a loss function is defined to update trainable parameters of the source tracing model; and the source tracing method improves the accuracy and generalization ability of source positioning.

The invention relates to a method for inferring epileptogenicity of a brain region not observed to be recovered or not observed to be not recovered in a seizure activity of a brain of an epilepsy patient, comprising the steps of: providing a computerized model modeling individual regions of a primate brain and connectivity between said regions; providing said computerized model with a model capable of reproducing the dynamics of a seizure in a primate brain; providing structural data of a brain of an epilepsy patient and using said structural data to individualize the computerized model in order to obtain a virtual epilepsy patient (VEP) brain model; translating a state space representation of the virtual epilepsy patient (VEP) brain model into a probabilistic programming language (PPL) using probabilistic state transitions in order to obtain a probabilistic virtual epilepsy patient brain model (BVEP); and acquiring electroencephalogram or magnetoencephalogram data of the brain of the patient and fitting the probabilistic virtual epilepsy patient brain model against said data in order to infer the epileptogenicity of said brain region not observed.

The invention relates to the technical field of medical instruments, and discloses a transcranial magnetic stimulation therapy-based stimulation coil positioning system and method.The system comprises a biological signal sensing module and a positioning decision module, the biological signal sensing module acquires multi-modal physiological data including cortical electric signals, hemodynamic data and magnetoencephalogram data in real time, and the positioning decision module determines whether the cortical electric signals, the hemodynamic data and the magnetoencephalogram data are in real time; and a target spot positioning instruction is generated through dynamic noise suppression and feature fusion of the modeling processing layer. The modeling processing layer comprises a signal preprocessing module and a collaborative optimization module, the signal preprocessing module carries out space-time segmentation, anomaly filtering and standardized registration on original data, and the collaborative optimization module realizes frequency domain denoising, anatomical correlation modeling and multi-dimensional fusion of multi-modal data through a biological noise filtering layer, a target point correction layer and a positioning execution layer. The method comprises the steps of data acquisition, noise suppression, feature fusion and positioning instruction generation. Through multi-modal data fusion and dynamic modeling, the positioning accuracy and clinical adaptability are improved, and the method is suitable for nerve regulation personalized treatment.

The present invention belongs to the field of biological signal processing technology. The present invention discloses a method and device for generating magnetoencephalography (MEG) evoked signals. The method comprises: determining the alignment cost of all signals in the MEG signal based on the minimum cost path between any two trial signals in the MEG signal, wherein the minimum cost path is used to represent the time-minimized alignment cost between the corresponding two trial signals; determining the temporal similarity weight coefficient of the signal for each trial based on the alignment cost; and generating the MEG evoked signal based on the signal for each trial and the corresponding temporal similarity weight coefficient. The method provided by the present invention effectively solves problems in the prior art, such as the inability to effectively extract signals of interest from MEG signals and the inability to effectively reduce the impact of signal jitter.

The present disclosure relates to a camera for a magnetoencephalogram and a magnetoencephalogram, and belongs to the field of medical devices. The camera for the magnetoencephalogram includes: a camera assembly; a camera fixing assembly, which is configured to provide an installation space for the camera assembly to fix the camera assembly; a moving assembly, which is movably connected to the camera fixing assembly and is configured to drive the camera fixing assembly to move; and a slide rail assembly, which is movably connected to the moving assembly and is configured to provide a moving track for the moving assembly. The camera can be used in magnetoencephalogram detection, which can solve the current technical problem of the lack of cameras suitable for magnetoencephalograms, realize the multi-directional movement of the camera, and fully meet the complex and diverse monitoring requirements in magnetoencephalogram detection.

This invention provides a deep learning model training method for classifying temporal lobe epilepsy lateralization, belonging to the fields of brain science signal processing, medical artificial intelligence, and deep learning technology. The method includes preprocessing and source reconstruction of magnetoencephalography (MEG) signals; obtaining a brain region time-series matrix by selecting brain regions of interest; constructing a multi-band adjacency matrix; constructing a node feature matrix based on the adjacency matrix; and defining and training a deep learning model based on the node feature matrix. The deep learning model is used to classify temporal lobe epilepsy lateralization. Based on MEG brain network characteristics, this invention automatically identifies whether a subject belongs to the healthy group, the left TLE group, or the right TLE group, thereby providing an objective basis for the clinical judgment of temporal lobe epilepsy lateralization.

The present disclosure provides an AI-based clinical decision support system comprising an input module configured to receive clinical information comprising brain scan data, an analysis module configured to parse the clinical information using statistical measures from functional connectivity analysis with counterfactual explanations to identify brain connectivity patterns associated with brain disorders, and an output module configured to present a recommended diagnosis and explanation comprising attribution information identifying connectivity features contributing to the diagnosis. The brain scan data comprises functional magnetic resonance imaging, electroencephalography, and magnetoencephalography data. The statistical measures comprise functional connectivity analysis and graph theory metrics including degree metrics, betweenness centrality measures, and clustering coefficients. The analysis module comprises a functional connectivity engine configured to process brain scan data and generate connectivity features, a feature bank configured to store connectivity features, and modeling backbones configured to analyze connectivity features using machine learning techniques.

The application provides a specific neurofeedback training system for improving anxiety based on multi-modal fusion, which forms specific electroencephalogram signals through the projection strategy of magnetoencephalogram signals to electroencephalogram, and is used for the regulation and improvement of anxiety emotion. The application belongs to the technical field of medical treatment, and comprises an electroencephalogram acquisition module, a real-time processing module and a visual feedback module which are connected with each other; a complete treatment closed loop from signal acquisition, signal processing, signal feedback and signal acquisition is realized. The application decodes and analyzes electroencephalogram signals in real time, extracts features, and uses a specific mapping model of multi-modal feature fusion to establish a specific electroencephalogram mapping signal reflecting the real-time activity of core brain regions related to emotion (such as amygdala), which has stronger spatial accuracy and symptom specificity; the specific mapping signal is applied to neurofeedback treatment, which can assist in formulating individualized and multi-course neuroregulation training, improving the anxiety emotion of users, and providing a non-invasive and convenient neuroregulation platform for users.

A machine learning model based on neural data (e.g., electroencephalogram (EEG) data, magnetoencephalogram (MEG), and / or magnetic resonance imaging (MRI) data) may be used to predict the effectiveness of future diagnoses and / or interventions (e.g., early interventions) of autistic (autistic) spectrum disorders (ASD) in an individual child. The training of the model may be based on experimental data obtained in a child who previously accepts an intervention.

A system for classifying working memory task magnetoencephalography based on machine learning, including: the magnetoencephalography data acquisition module configured to acquire magnetoencephalography data of a subject in different working memory task states; the magnetoencephalography data preprocessing module configured to control the quality of magnetoencephalography data in different working memory tasks and separate noises and artifacts; the magnetoencephalography source reconstruction module configured for sensor signal analysis and source reconstruction analysis for the data processed by the magnetoencephalography data preprocessing module; and the machine learning classification module is configured to classify the working memory tasks to which the subjects belong by taking power time series as features. The present disclosure integrates the complete analysis pipeline from preprocessing to source reconstruction of the working memory magnetoencephalography data, classifies the working memory task magnetoencephalography data, and is of great significance to the study of working memory decoding and brain memory related mechanisms.

The utility model relates to a brain magnetic helmet structure and a brain magnetic mapping instrument. The utility model is suitable for the technical field of medical instruments. According to the technical scheme, the brain magnetic helmet structure comprises an equipment main body lower half part, and a lower helmet matched with the rear half part of the head of the human body is arranged at the intersection of the upper surface and the front end face of the equipment main body lower half part; an upper helmet matched with the front half part of the human head is arranged at the intersection of the lower surface and the front end face of the equipment main body upper half part; the sliding rail assembly is arranged between the equipment main body lower half part and the equipment main body upper half part, and the equipment main body upper half part can be installed on the equipment main body lower half part in a front-back moving mode relative to the equipment main body lower half part through the sliding rail assembly; the front end positioning mechanism can position the upper half part of the equipment main body at a first position where the equipment main body moves back and forth, and when the upper half part of the equipment main body is positioned at the first position, the upper helmet corresponds to the lower helmet in vertical position; and the rear end positioning mechanism can position the upper half part of the equipment main body at a second position moving back and forth.

The application provides a kind of child magnetoencephalogram personalized stimulation paradigm optimization method based on reinforcement learning, comprising: step S1, the multi-channel magnetoencephalogram signal of the child to be measured is continuously collected by using optically pumped magnetometer array, and behavior data and physiological data are synchronously collected;Step S2, the magnetoencephalogram signal feature, behavior feature and physiological feature are extracted, and real-time state feature vector is fused and constructed;Step S3, the real-time state feature vector is input into the reinforcement learning model to obtain the prediction Q value of stimulation adjustment action, and the parameters of magnetoencephalogram stimulation paradigm of the child to be measured are dynamically adjusted based on the prediction Q value;Step S4, based on the magnetoencephalogram stimulation paradigm after parameter adjustment, the new multi-channel magnetoencephalogram signal, behavior data and physiological data of the child to be measured are collected, and the comprehensive reward value is calculated to update the reinforcement learning model.The beneficial effect is that the application can intelligently and dynamically adjust the magnetoencephalogram stimulation paradigm, and improve the multi-channel magnetoencephalogram signal quality of the child to be measured.

The utility model provides a photostimulation assembly for treating brain function related diseases, which adopts a light transmission piece capable of circumferentially emitting near-infrared light, the light transmission piece is spirally arranged in an accommodating cavity of a bearing piece, and a light uniformizing layer is arranged on one side of a light transmission part of the bearing piece, so that the near-infrared light can be transmitted to the light transmission piece, and the light transmission piece can transmit near-infrared light to the bearing piece. The near-infrared light can be uniformly irradiated to the head of a patient, light spots with a certain area can be emitted to a head treatment area at a short distance, meanwhile, relatively uniform distribution of light power is guaranteed, and the treatment effect on patients with brain function related diseases in some special scenes is greatly improved. Meanwhile, the photostimulation assembly is not interfered by a high-intensity magnetic field in a treatment area of the head of the patient, and can be combined with magnetoencephalography, functional magnetic resonance imaging and other technologies, so that the change condition of the brain function state of the patient can be monitored in time while the brain function related diseases are treated by adopting photostimulation; the method can be used for evaluating the treatment effect of photostimulation, guiding the photostimulation treatment scheme and the like.

The invention provides a children magnetoencephalogram personalized stimulation normal form optimization method based on reinforcement learning, and the method comprises the steps: S1, continuously collecting multi-channel brain magnetic signals of a to-be-detected child through an optical pump magnetometer array, and synchronously collecting behavior data and physiological data; s2, brain magnetic signal features, behavior features and physiological features are extracted and fused to construct a real-time state feature vector; s3, inputting the real-time state feature vector into a reinforcement learning model to obtain a predicted Q value of a stimulation adjustment action, and dynamically adjusting parameters of a magnetoencephalogram stimulation normal form of the to-be-tested child based on the predicted Q value; and S4, acquiring new multichannel brain magnetic signals, behavior data and physiological data of the to-be-tested child based on the parameter-adjusted brain magnetic map stimulation normal form, and calculating to obtain a comprehensive reward value to update the reinforcement learning model. The method has the advantages that the magnetoencephalogram stimulation normal form can be intelligently and dynamically adjusted, and the multi-channel magnetoencephalogram signal quality of the child to be tested is improved.

The invention provides a method of determining whether a subject has a neurological dysfunction associated with a signal in a particular electroencephalogram (EEG) or magnetoencephalogram (MEG) frequency range, the method comprising: obtaining an EEG power spectrum from the subject; and obtaining a metric quantifying the magnitude of power in particular frequency range metric quantifying the power in the power spectrum in the particular frequency range, wherein the metric summarises the power in said frequency range corrected using an estimate of the power in said frequency range that is attributable to background signal that is specific to said frequency range, wherein the metric is indicative of the presence and / or severity and / or direction of a neurological dysfunction. Related methods and devices are also described.

The application discloses a magnetometer depth adjustment method of a wearable magnetoencephalograph, and comprises the following steps: obtaining two-part three-dimensional information of a subject's helmet and face based on an optical scanner, and obtaining the position and direction of the magnetometer relative to the scalp surface of an MRI image by two-step registration; calculating the minimum distance of the bottom surface of the magnetometer moving along the probe axial direction to the scalp based on the position and direction of the magnetometer and the scalp in the MRI coordinate system; and based on the distance, using a motor push rod or other control method to make the magnetometer travel a corresponding distance along the axial direction. The magnetometer depth adjustment method has the characteristics of automation, high precision and high efficiency, can automatically calculate the optimal distance of magnetometer depth adjustment, assist in helmet design, improve the magnetoencephalic source positioning precision and the helmet scalp adhesion, simplify the depth adjustment process, facilitate the use of medical staff, and further promote the medical application of magnetoencephalography.

1. Name of the product in this design: Horizontal magnetoencephalography (MEG) instrument. 2. Purpose of this design: To detect weak magnetic field signals generated by active intracranial nerves in a non-invasive manner, and to analyze the characteristics and location of active intracranial nerve sources. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.