7 results about "Lead field" patented technology

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.

1. The name of the design product: lead disc (macro view). 2. The use of the design product: lead storage for fireworks or firecrackers. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

1. The name of the design product: lead disc (wreath). 2. The use of the design product: lead storage for fireworks or firecrackers. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

1. The name of the design product: lead disc (group flower). 2. The use of the design product: lead storage for fireworks or firecrackers. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

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.