Abstract: 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.

Abstract: A method and a system for simulating magnetic resonance echo-planar imaging artifacts. Firstly, for K-space artifacts, K-space data are restored through normal magnetic resonance images, and the K-space data are modified pertinently, and then images with artifacts are reconstructed; for susceptibility artifacts, a susceptibility model is constructed through normal magnetic resonance images, and the magnetic field distribution is reconstructed, and then the images with distortion artifacts are reconstructed. According to the present disclosure, a large number of artifact data sets with different artifact types and artifact degrees can be quickly created through a small number of normal images, thus laying a foundation for the research of identifying artifacts, eliminating or weakening artifacts.

Abstract: A system for precisely locating abnormal areas of brain fiber bundles. The system extracts fiber connections of the whole brain from diffusion magnetic resonance data, and fiber bundle pathways extracts through self-defined fiber bundle pathways or based on brain fiber bundle templates. A selected fiber bundle pathway is projected on a fiber connection result of the whole brain and finely segmented. The imaging indexes such as fractional anisotropy, mean diffusivity, intra-neurite volume fraction and orientation dispersion index are calculated from diffusion magnetic resonance data, so as to obtain the imaging index of each node of each fiber bundle pathway. These imaging indexes are configured to classify the disease group and the healthy group by a machine learning method, and which nodes on which fiber bundle pathways have abnormal changes with different diseases can be precisely located.

Abstract: A system for predicting disease with graph convolutional neural network based on multimodal magnetic resonance imaging, which extracts the radiomics information of multiple brain regions across modals as the features of nodes from multimodal magnetic resonance data, and extracts the connectomics information between brain regions to form an adjacency matrix. T1-weighted structural images extract cortical information through cortical reconstruction, and resting-state magnetic resonance data are used to calculate amplitude of low frequency fluctuations, regional homogeneity and functional connectivity. Through multimodal data preprocessing, image index extraction and structured data integration, multimodal unstructured magnetic resonance image data are integrated into unified graph-structured data, and the disease is predicted by a graph convolutional neural network method.

Abstract: An information recommendation method, an apparatus, a device, and a medium based on embedding table compression are provided. The method includes: determining, based on a preset compression ratio, to-be-compressed features and non-compressed features in a to-be-compressed embedding table of a recommendation model, generating a similarity index matrix based on a similarity between the to-be-compressed features and the uncompressed features; generating an index dictionary based on the similarity index matrix; substituting a first feature mapping dictionary based on the index dictionary to generate a second feature mapping dictionary, wherein the first feature mapping dictionary is generated based on a data set; and acquiring to-be-recommended data, replacing features in the to-be-recommended data according to the second feature mapping dictionary, inputting replaced features into the recommendation model, and outputting a prediction result.

Abstract: 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.

Abstract: A system for precisely locating abnormal areas of brain fiber bundles. The system extracts fiber connections of the whole brain from diffusion magnetic resonance data, and fiber bundle pathways extracts through self-defined fiber bundle pathways or based on brain fiber bundle templates. A selected fiber bundle pathway is projected on a fiber connection result of the whole brain and finely segmented. The imaging indexes such as fractional anisotropy, mean diffusivity, intra-neurite volume fraction and orientation dispersion index are calculated from diffusion magnetic resonance data, so as to obtain the imaging index of each node of each fiber bundle pathway. These imaging indexes are configured to classify the disease group and the healthy group by a machine learning method, and which nodes on which fiber bundle pathways have abnormal changes with different diseases can be precisely located.

Abstract: A system for predicting disease with graph convolutional neural network based on multimodal magnetic resonance imaging, which extracts the radiomics information of multiple brain regions across modals as the features of nodes from multimodal magnetic resonance data, and extracts the connectomics information between brain regions to form an adjacency matrix. T1-weighted structural images extract cortical information through cortical reconstruction, and resting-state magnetic resonance data are used to calculate amplitude of low frequency fluctuations, regional homogeneity and functional connectivity. Through multimodal data preprocessing, image index extraction and structured data integration, multimodal unstructured magnetic resonance image data are integrated into unified graph-structured data, and the disease is predicted by a graph convolutional neural network method.

Abstract: A method and a system for simulating magnetic resonance echo-planar imaging artifacts. Firstly, for K-space artifacts, K-space data are restored through normal magnetic resonance images, and the K-space data are modified pertinently, and then images with artifacts are reconstructed; for susceptibility artifacts, a susceptibility model is constructed through normal magnetic resonance images, and the magnetic field distribution is reconstructed, and then the images with distortion artifacts are reconstructed. According to the present disclosure, a large number of artifact data sets with different artifact types and artifact degrees can be quickly created through a small number of normal images, thus laying a foundation for the research of identifying artifacts, eliminating or weakening artifacts.

Abstract: A cognitive training material generation method, a cognitive training method, a device, and a medium are provided. The cognitive training material generation method includes: acquiring a first feature and a second feature, the first feature including a multimedia material and semantic information corresponding to the multimedia material, the second feature including a magnetic resonance representation; fitting the first feature and the second feature, obtaining a semantic map according to a fitting result and a preset brain map, and acquiring target semantic information corresponding to a target point according to the semantic map; taking the first feature as input of a deep learning model and the second feature as a constraint of the deep learning model, training the deep learning model, and determining a weight parameter of the deep learning model; generating a cognitive training material according to the target semantic information and the weight parameter of the deep learning model.

Abstract: A neurovascular coupling analytical method based on an electroencephalogram and functional near-infrared spectroscopy includes: S100: acquiring an electroencephalogram signal and a brain hemodynamic signal; S110: extracting an event-related potential signal from the electroencephalogram signal; S120: extracting a time characteristic from the event-related potential signal; S130: extracting a hemodynamic response function from the brain hemodynamic signal; S140: extracting an amplitude characteristic and time characteristics from the hemodynamic response function; and S150: analyzing influence of the time characteristic of the event-related potential signal on the amplitude characteristic and the time characteristics of the hemodynamic response function to obtain a coupling result. The time characteristic of the event-related potential signal is a delay.

Abstract: A non-contact brain blood oxygen detecting system includes a mobile terminal device. The mobile terminal device includes a control module, a transmitting module, a receiving module and a display module. The control module is connected to the transmitting module, the receiving module and the display module, respectively. The transmitting module in the mobile terminal device is configured to emit dual-wavelength near-infrared light to a detected subject. The receiving module is configured to receive a light signal after propagation fed back by the detected subject, and to perform data conversion on the received light signal to obtain a digital signal containing blood oxygen information. The control module is configured to obtain the blood oxygen information of the detected subject according to the digital signal obtained by the receiving module. The display module is configured to display the blood oxygen information obtained by the control module.

Abstract: A method for generating a transcranial magnetic stimulation (TMS) coil pose atlas based on electromagnetic simulating calculation includes: constructing coil array positions and orientations of a scalp in a standard Montreal Neurological Institute (MNI) space and matching the coil array positions and orientations of the scalp to a brain space of an individual to obtain coil array positions and orientations of the brain space of the individual; using a finite element calculation method to simulate the coil array positions of the brain space of the individual to obtain induced electric field distributions of brain tissue in different coil orientations; obtaining optimal regulation effects based on the induced electric field distributions of the brain tissue; and obtaining a coil position and orientation corresponding to each optimal regulation effect as an optimal coil pose of each divided brain area of the individual, and constructing a TMS coil pose atlas of the individual.

Abstract: A method for generating a transcranial magnetic stimulation (TMS) coil pose atlas based on electromagnetic simulating calculation includes: constructing coil array positions and orientations of a scalp in a standard Montreal Neurological Institute (MNI) space and matching the coil array positions and orientations of the scalp to a brain space of an individual to obtain coil array positions and orientations of the brain space of the individual; using a finite element calculation method to simulate the coil array positions of the brain space of the individual to obtain induced electric field distributions of brain tissue in different coil orientations; obtaining optimal regulation effects based on the induced electric field distributions of the brain tissue; and obtaining a coil position and orientation corresponding to each optimal regulation effect as an optimal coil pose of each divided brain area of the individual, and constructing a TMS coil pose atlas of the individual.

Abstract: A neurovascular coupling analytical method based on an electroencephalogram and functional near-infrared spectroscopy includes: S100: acquiring an electroencephalogram signal and a brain hemodynamic signal; S110: extracting an event-related potential signal from the electroencephalogram signal; S120: extracting a time characteristic from the event-related potential signal; S130: extracting a hemodynamic response function from the brain hemodynamic signal; S140: extracting an amplitude characteristic and time characteristics from the hemodynamic response function; and S150: analyzing influence of the time characteristic of the event-related potential signal on the amplitude characteristic and the time characteristics of the hemodynamic response function to obtain a coupling result. The time characteristic of the event-related potential signal is a delay.

Abstract: A non-contact brain blood oxygen detecting system includes a mobile terminal device. The mobile terminal device includes a control module, a transmitting module, a receiving module and a display module. The control module is connected to the transmitting module, the receiving module and the display module, respectively. The transmitting module in the mobile terminal device is configured to emit dual-wavelength near-infrared light to a detected subject. The receiving module is configured to receive a light signal after propagation fed back by the detected subject, and to perform data conversion on the received light signal to obtain a digital signal containing blood oxygen information. The control module is configured to obtain the blood oxygen information of the detected subject according to the digital signal obtained by the receiving module. The display module is configured to display the blood oxygen information obtained by the control module.

Abstract: A method and system for detecting brain activity may be disclosed, the method including performing multi-channel synchronous collections of brain electrical signals and cerebral cortex blood oxygen signals simultaneously and ensuring synchronicity of the collected signals among channels by collecting the signals at multiple locations simultaneously. A system may include a functional near-infrared light source emission module, which may employ the frequency division multiplexing technique. A multi-functional joint collection helmet may access the light source signal emitted from the emission module, then may be processed by a near-infrared detection module. Further, the near-infrared detection module may detect optical signals of the scalp, while a brain electricity detection module detects electrical signals of the scalp. Finally, a central control unit may synchronize data collected from the detected signals and may control a variety of functional modules and upload the data to a host computer.

Abstract: A method for storing data of photoelectrically synchronous brain activity recording, said method comprising: generating data when a photoelectrically synchronous brain activity detection system is operating; generating from said data a data storage file comprising a basic information data segment, a near-infrared spectrum data segment and a brain electrical activity data segment, and sequentially storing said data segments into a .neg file in binary form according to the above order. The method can store comprehensive test information, flexibly configure the near-infrared and brain electrical measurement information, and realize synchronous storage of near-infrared data and brain electrical data and maintain file version compatibility.

Abstract: A method for storing data of photoelectrically synchronous brain activity recording, said method comprising: generating data when a photoelectrically synchronous brain activity detection system is operating; generating from said data a data storage file comprising a basic information data segment, a near-infrared spectrum data segment and a brain electrical activity data segment, and sequentially storing said data segments into a .neg file in binary form according to the above order. The method can store comprehensive test information, flexibly configure the near-infrared and brain electrical measurement information, and realize synchronous storage of near-infrared data and brain electrical data and maintain file version compatibility.

Abstract: Disclosed are a method and system for brain activity detection. The method is: performing multi-channel synchronous collections of brain electrical signals and cerebral cortex blood oxygen signals simultaneously, and ensuring synchronicity of the collected signals among channels, and collecting said brain electrical signals and said cerebral cortex blood oxygen signals of all locations at the same time.