17 results about "Lead field" patented technology

The invention discloses a nerve regulation and control intervention system based on time domain interference electrical stimulation, and the system comprises a magnetic resonance image registration module which receives and registers an imported magnetic resonance image, and obtains a to-be-processed MRI image; the head model tissue segmentation module is used for performing head model tissue segmentation on the to-be-processed MRI image; the stimulation simulation module is used for performing stimulation simulation based on the tissue segmentation structure to obtain a front lead field matrix; the intervention coordinate selection module is used for determining a target brain region corresponding to a target spot brain region coordinate position needing stimulation intervention and putting the target brain region into a running sequence; the electrode position arrangement optimization module is used for verifying, optimizing and finely adjusting the electric field intensity of the target brain region under different electrode arrangement schemes to obtain an electrode configuration parameter result and displaying the electrode configuration parameter result; the parameter importing module is used for receiving the selected electrode configuration parameters and imported preset electrical stimulation parameters; and the electrical stimulation module is used for performing electrical stimulation on the target brain region of the target object. According to the application, the accuracy and the effectiveness of nerve regulation and control treatment can be realized.

The invention relates to a method for determining parameters of a double-target time interference stimulating electrode and a stimulating method. The method comprises the following steps of: inputting a lead field matrix, coordinates of two target spots and an electrode frequency; generating a random solution and multiplying the random solution with the lead field matrix to obtain a corresponding high-frequency electric field; inputting a Grossman formula, and obtaining two groups of envelope electric fields with frequencies of # imgabs0 # and delta fHz; performing Hilbert transform and space vector summation on the two groups of envelope electric fields to form a total matrix; constructing a target point matrix; solving the ratio of the average electric field intensity of the target spot matrix to the average electric field intensity of the total matrix, and if the ratio is less than b, removing a corresponding random solution; if the ratio is larger than b, the corresponding random solution is reserved, and a new random solution is generated; and iteration is carried out to a suspension condition, and if suspension is carried out, the reserved random solution is output. Through the arrangement, an electrode feasible solution for simultaneously stimulating double target spots through time interference stimulation can be effectively found.

This application discloses a method and system for forward and reverse calculation of brain electric fields based on custom channels. The method comprises: displaying a three-dimensional head model of a subject to be stimulated; responding to an electrode channel editing instruction for the displayed three-dimensional head model, determining a list of electrode channel coordinates currently enabled on the three-dimensional head model; converting each electrode channel coordinate in the electrode channel coordinate list into magnetic resonance imaging coordinates using a preset affine transformation matrix; if a preset known lead field matrix contains electrode channel coordinates in the electrode channel coordinate list that have not been subjected to finite element calculation, recalculating the lead field matrix based on the magnetic resonance imaging coordinates to obtain a target lead field matrix; and responding to an inverse positioning and navigation instruction, using the target lead field matrix as a calculation parameter for the MOVEA algorithm to generate and output electrode positions and current parameters for the target brain region. Using embodiments of the present application, it is possible to precisely adapt to individual brain structural differences while effectively avoiding stimulation risk areas.

This application discloses a method and related equipment for cortical signal reconstruction based on a physically constrained diffusion model. The method includes: acquiring non-invasive EEG signals from the head of a target subject; extracting conditional feature vectors from the non-invasive EEG signals; inputting the conditional feature vectors into a trained diffusion model for denoising to obtain transient latent variables; obtaining transient ECoG estimates based on the transient latent variables and a trained physical-sensory variational autoencoder; calculating a theoretical signal based on the transient ECoG estimates and a preset lead field matrix; updating the transient latent variables based on the difference between the theoretical signal and the non-invasive EEG signal until a target latent variable is obtained; the preset lead field matrix represents the transmission relationship from the cortical source space to the scalp sensor space; and obtaining the ECoG reconstructed signal based on the target latent variable and the trained physical-sensory variational autoencoder. The embodiments of this application can accurately predict ECoG using EEG. This application can be widely applied in the field of electroencephalography (EEG) technology.

The invention discloses a sparse electroencephalogram signal source positioning method based on functional magnetic resonance guidance, and relates to the crossing field of electroencephalogram signal processing and neuroimaging technologies. The method mainly comprises three parts of fMRI-guided sparse source space construction, subject specificity forward model construction and multi-target regularization inverse problem solving. The method comprises the following steps: firstly, screening high-activation voxels through BOLD signal intensity of fMRI, and constructing a sparse source space through connected component analysis and representative point selection; then, a subject specific boundary element (BEM) head model is constructed based on the structural MRI, and electrode registration and lead field matrix calculation are completed; and then a multi-target regularization model fusing data fidelity, space compactness and intensity consistency is constructed, optimal estimation of source current intensity is obtained through analysis and solution, and finally a positioning result of brain power activation is output. According to the method, the advantages of high time resolution of the EEG and high spatial resolution of the fMRI are fully played, the limitations of inverse problem morbidity, low spatial resolution and insufficient multi-mode fusion in traditional brain power supply positioning are effectively solved, and brain power supply positioning with high precision, noise resistance and high interpretability is realized; and a new scheme is provided for cognitive neuroscience research, nervous system disease diagnosis and brain-computer interface development.

The invention discloses an EIT reconstruction method and system based on cross-domain learning and physical guidance. The method comprises the following steps: setting conditions; collecting voltage measurement data, and constructing a two-dimensional voltage data matrix; defining source domain data and target domain data; performing feature coding on the data of the two domains, and introducing physical bias based on a lead field similarity matrix in the coding process; and inputting the coded data into the same decoder to perform conductivity image reconstruction, and performing cross-domain consistency learning in combination with a loss function. The system comprises a condition definition module, a data preprocessing module and a model training module. By using the method and the device, the problems of reconstruction resolution reduction and structure distortion caused by insufficient measurement information can be effectively relieved, so that the image reconstruction quality under a low-electrode condition is remarkably improved. The method can be widely applied to the field of electrical impedance images.

The invention relates to a multi-channel transcranial magnetic stimulation coil array control method, which comprises the following steps: solving a lead field matrix of each coil according to brain structure data of a subject and adopted multi-channel transcranial magnetic stimulation coil array arrangement; calculating the distribution of an induced electric field generated by each coil, and establishing an optimization function based on Elasticic-net regression by taking minimization of a difference value between the distribution of the induced electric field and the distribution of a target electric field as a target; respectively solving the optimization functions under different regularization intensities to obtain a plurality of alternative current time derivative vectors; and selecting the alternative current time derivative vector which enables the coil energy consumption to be lowest and the number of activated coils to be small, and controlling the current passing through each coil in the coil array according to a selection result. The method can accurately control the coil current to generate the required induction electric field, is widely applicable to a multichannel transcranial magnetic stimulation coil array, and has the advantages of high calculation speed, high flexibility and high usability.

Methods and systems for estimating spatial locations of brain activity sources from EEG signals with fMRI-comparable resolution are disclosed. The method processes EEG signals with structural MRI data by computing a lead field matrix based on electrode coordinates and anatomical information. Brain activity sources are estimated using this matrix and a predefined source estimation algorithm. Energy levels for each source are calculated over a predefined time window and convolved with a hemodynamic response function. The resulting data undergoes spatial transformation into subject-specific space before mapping to standard brain template space. A deep neural network model comprising convolutional core and temporal combination components processes this mapped data to generate estimated BOLD signals. Application of a gray matter voxel mask during training and inference enhances spatial accuracy. The system implements this processing pipeline with at least one processor and memory configured to execute these operations.

The invention discloses a structural source interference-oriented spatial filtering brain magnetic signal denoising method and system, and belongs to the technical field of brain magnetic signal processing. According to the method, suppression of non-task related sources is realized through a two-stage subspace projection mechanism from a space domain to a time domain. The method comprises the following steps: firstly, acquiring an original multi-channel brain magnetic signal, limiting a lead field matrix source space based on space prior, and only reserving an ROI (Region of Interest) internal source point; secondly, through spatial domain filtering, the ROI source point lead field is expanded into a signal subspace, an original signal is projected to the subspace, and preliminary separation of a target signal and non-target brain region background interference is achieved; and finally, applying time domain subspace projection to the interference component, extracting a time domain interference subspace, and constructing a projection operator to eliminate residual interference. According to the method, the source reconstruction precision and the analysis reliability of downstream cognitive / clinical indexes are remarkably improved, and an efficient and accurate solution is provided for OPM-MEG task state signal processing.

The invention relates to an effect evaluation method for time interference stimulation. The method comprises the following steps: inputting a lead field matrix, a feasible solution matrix, a target point coordinate, a preset stimulation range and nuclear magnetic image data with electric field value distribution; calculating an envelope space electric field distribution matrix; calculating a brain total electric field; calculating a total prime number based on the envelope space electric field distribution matrix; calculating a total electric field in the limited area and a total electric field in the non-limited area based on the envelope space electric field distribution matrix, the nuclear magnetic image data with the electric field value distribution, the target point coordinates and a preset stimulation range; based on the envelope space electric field distribution matrix, the target point coordinates and a preset stimulation range, calculating the number of voxels in the limited area and the number of voxels in the non-limited area; and calculating the average electric field of the brain, the average electric field in the limited area and the average electric field in the non-limited area. Through the setting, the quality of a plurality of feasible solutions in time interference stimulation can be better evaluated and measured.

The application discloses a real-time electroencephalography source imaging system, comprising: a data input module for continuously and real-timely acquiring brain source activity sampling data and lead field matrix; a sampling data generation module for constructing an electroencephalography sampling data generation model according to the brain source activity sampling data; a covariance matrix calculation module for real-timely calculating the covariance of all brain source activity sampling data; a real-time weight matrix estimation module for real-timely calculating the weight vector of a new beamformer; and a real-time imaging module for real-timely calculating and solving the amplitude of a new brain source activity source for real-time imaging. The application can reconstruct the space-time characteristics of brain source activity in real time under Gaussian and real brain noise conditions, can efficiently process electroencephalography data with an ultra-long sampling time, and the result is robust.

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A method for determining the functional topography of a peripheral nerve (10) of a user comprising the steps of prearranging an electrode (100) comprising a number n of channels ci, with i=1, 2 . . . , n, arranging the electrode (100) in such a way that each channel is in contact with the peripheral nerve (10) at a respective contact point pi, with i=1, 2 . . . , n, generating a model of a cross section S of the peripheral nerve (10) where the area A of the cross section S comprises a number m of areas aj, with j=1, 2, . . . , m, computing a lead field matrix L=[Rj,i], wherein Rj,i is a value that describes the electrostatic relationship between an area aj and a contact point pi of the cross section S, periodic acquisition, by the electrode (100), of a number n of voltage values Vki at instants tk, with k=1, 2, . . . , S, obtaining a voltage matrix V=[Vk,i], with i=1, 2, . . . , n, where Vki is the voltage value determined by the channel ci at the contact point pi at the instant tk, periodic acquisition, by at least one medical device, of a number r of values of physiological signals Pk,h of the user at instants tk, with k=1, 2, . . . , s, obtaining a matrix of the physiological signals P=[Pk,h], with h.=1, 2, . . . , r, where Pk k is the value of the h-th physiological signal determined at the instant tk, computing a discrimination matrix=D=[dh,i], D being function of the matrices V=[Vk,i] and P=[Pk,h], where dh,i is the discrimination coefficient which represents the correlation between the h-th physiological signal Pk,h and the i-th voltage value Vk,i referred to a same instant ty computing a spatial filtering matrix ΦDBF=[φh,j], φk,j being the localization index which represents the correlation between the h-th physiological signal and the area aj of said cross section S, generating a functional topography of said peripheral nerve (10), for each h-th physiological signal, wherein each area aj, is graphically identified as a function of the corresponding value φh,j associated with it by the spatial filtering matrix ΦDBF.

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The application belongs to the technical field of electroencephalogram signal processing, and proposes a personalized matrix accuracy compensation method based on standard head model EEG signal re-reference in the auditory attention decoding scene: multi-channel EEG signals in the auditory scene are collected; a lead field matrix is calculated based on the standard head model; the collected multi-channel EEG signals are preprocessed and input into a personalized matrix compensation module to obtain corrected EEG signals; the corrected EEG signals are sequentially input into subsequent modules in the neural network and an end-to-end optimization method is adopted, the auditory attention classification task loss and the personalized matrix compensation loss are jointly trained, the parameter change of the personalized matrix is constrained, and the spatial mixing error caused by the mismatch of the standard head model is compensated, to obtain the best personalized matrix parameters. The application can correct the standard head model re-reference results for subjects without individual real head models, and is suitable for various downstream electroencephalogram decoding models.

The invention discloses a brain electric field forward and reverse calculation method and system based on a user-defined channel. The method comprises the steps that a three-dimensional head model of a to-be-stimulated object is displayed; in response to an electrode channel editing instruction for the displayed three-dimensional head model, determining a coordinate list of an electrode channel currently started on the three-dimensional head model; through a preset affine transformation matrix, converting each electrode channel coordinate in the electrode channel coordinate list into a magnetic resonance brain image coordinate; under the condition that the electrode channel coordinates which are not subjected to finite element calculation in the electrode channel coordinate list exist in a preset known lead field matrix, recalculating the lead field matrix according to the magnetic resonance brain image coordinates to obtain a target lead field matrix; and in response to a reverse positioning navigation instruction, taking the target lead field matrix as a calculation parameter of an MOVEA algorithm, and generating and outputting an electrode position and a current parameter of the target brain area. By adopting the embodiment of the invention, the brain structure difference of individuals can be accurately adapted, and the stimulation risk area can be effectively avoided.