6 results about "Neurophysiology" patented technology

Disclosed in the present invention are a motor-related electroencephalographic signal source localization method and system based on an MNE-GAN. The method comprises the following steps: synthesizing virtual multi-channel electroencephalographic data; constructing a brain electrical source data generation network based on minimum norm estimation (MNE); constructing a brain electrical source data discriminator; preprocessing the virtual electroencephalographic data; training an MNE-GAN model on the basis of the virtual electroencephalographic data; and using the trained model to perform source localization on real electroencephalographic data. In the present invention, an MNE-based generative adversarial network (GAN) is used to provide a new brain electrical source localization method. A minimum norm constraint is added to restrict generated brain electrical source data to satisfy physical prior knowledge, thereby facilitating the research into non-invasive neurophysiological mechanisms and the improvement of the electroencephalographic decoding accuracy.

The invention discloses a neurophysiology heuristic electroencephalogram emotion recognition method based on deep learning, and the method comprises the steps: firstly carrying out the preprocessing of an EEG signal, constructing potential feature input representing an emotional state, inputting the potential feature input to an emotional response adjacent embedded module, and forming an emotional state space-time view; secondly, inputting the emotional state space-time view into a layered space-time Transform module, and outputting enhanced emotional channel representation; and then inputting the emotion channel representation into a channel independent time sequence embedding module to generate a cross-time-scale multi-view time sequence representation, and inputting the cross-time-scale multi-view time sequence representation into a hierarchical dual attention Transform module to obtain time representation. And finally, based on the time characterization, the electroencephalogram emotional state is judged, and a corresponding emotion recognition result is output. According to the method, the extracted emotion features have higher separability among different emotion categories, and the stability and accuracy of emotion recognition are improved.

The application discloses a kind of MNE-GAN-based motion-related electroencephalogram signal source positioning method and system.Method includes the following steps: virtual multi-channel electroencephalogram data synthesis;Electroencephalogram source data generation network construction based on minimum norm estimation MNE;Electroencephalogram source data discriminator construction;Virtual electroencephalogram data preprocessing;MNE-GAN model training based on virtual electroencephalogram data;The source positioning of real electroencephalogram data is carried out using the trained model.The application uses the generative adversarial network based on MNE to propose a new electroencephalogram source positioning method, by adding minimum norm constraint limit generated electroencephalogram source data meets physical prior knowledge, is conducive to non-invasive neurophysiological mechanism research and electroencephalogram decoding precision promotion.

The invention belongs to the technical field of rehabilitation training, and discloses a self-adaptive VR rehabilitation training method and system driven by a mirror neuron system. The system comprises a neurophysiology and motion behavior feature enhancement module, a VR scene adaptive control module, a neurophysiology and motion behavior coupling analysis module and a diagnosis interpretation early warning report processing module, and is used for cleaning a multi-modal time sequence data set to obtain cleaned multi-modal data; the method comprises the following steps of: obtaining neurophysiology and motion behavior characteristics after related brain region characteristics of a mirroring neuron system are enhanced, obtaining a parameter dynamic adjustment record report, obtaining a neurophysiology and motion behavior coupling map, and performing analysis based on the neurophysiology and motion behavior coupling map to obtain an interpretable diagnosis report and an early warning report. The method has the remarkable advantages of high multi-modal fusion data quality, good self-adaptive mechanism optimization effect and great transformation effect from the system to clinical use.

The invention relates to a method and device for analyzing visual and neural function changes after visual training, and relates to the technical field of visual neural function evaluation.The method comprises the steps that in the visual training process of a target object, visual brain physiological data of different training groups and trained binocular visual functions are synchronously collected through EEG-fNIRS, and the visual brain physiological data are obtained; the method comprises the following steps of: acquiring physiological data signals, performing preprocessing and feature extraction on the acquired physiological data signals to obtain corresponding signal features, performing analysis based on the extracted signal features to determine a first analysis result and a second analysis result, and finally analyzing dynamic interaction of electroencephalogram and brain oxygen by utilizing EEG-fNIRS in combination with the two analysis results and binocular visual functions. According to the method, the change analysis result of the visual and neurological functions after visual training is obtained, the examination of organically combining the traditional visual function with the neurological function is realized, the improvement of the visual function is analyzed, and the influence of the visual function on the brain function can be revealed from the neurophysiology level.