71 results about "Electroencephalography" patented technology

The present application pertains to the technical field of neural stimulation. Disclosed are a temporal interference-based closed-loop multimodal neural stimulation system and method. The system comprises a temporal interference stimulation system, an electroencephalography-functional near-infrared spectroscopy sampling system, and an upper-level control system. The temporal interference stimulation system utilizes a beat-frequency electric field generated by two sets of electrodes to precisely stimulate a specified brain region. The electroencephalography-functional near-infrared spectroscopy sampling system is a bimodal collector coupling electroencephalography and functional near-infrared spectroscopy, including two parts: signal extraction and correlation analysis, and analyzes stimulation effects and adjusts stimulation schemes by integrating unified brain signal data that combines the temporal precision of EEG and the spatial precision of fNIRS. The upper-level control system includes bimodal fusion model computation, graph convolutional neural network prediction, and stimulation scheme formulation. The present application addresses the problems that traditional stimulation methods lack a closed-loop regulation system, have no means for calibration and optimization, and require a long adaptation period between the stimulation scheme and the user, thus being disadvantageous for applications.

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.

The invention relates to the field of biomedicine, and particularly discloses an epileptic focus artificial intelligence positioning system and method based on magnetic resonance and electroencephalography, and the system comprises the following contents: a data collection module is used for synchronously collecting T1 weighted magnetic resonance images and electroencephalogram signals of scalp; the data preprocessing module is used for performing brain tissue segmentation on the magnetic resonance image, obtaining structural features of each brain region, constructing vectors including whole brain structural features, and obtaining magnetic resonance structural feature vectors; artifacts of the electroencephalogram signals are removed, electroencephalogram features of all brain areas are extracted through time-frequency analysis, a matrix containing whole electroencephalogram physiological features is constructed, and the electrophysiological features are obtained; the cross-modal confidence coefficient dynamic evaluation module is used for establishing a bidirectional constraint rule to perform confidence coefficient calibration on the magnetic resonance structure feature vector and the electrophysiological feature; a positioning model construction and training module; a positioning result output module; according to the technical scheme, noise can be reduced during magnetic resonance and electroencephalogram fusion, and the epileptic focus positioning precision is high.

The invention discloses a three-dimensional convolution method for decoding an imaginary language electroencephalogram topographic map. The method solves the problem that for a multi-rhythm electroencephalogram topographic map, an existing convolutional network method is insufficient in modeling capability, so that the decoding precision of the multi-rhythm electroencephalogram topographic map in an imaginary language is remarkably reduced. In the aspect of rectangular BEAM reconstruction, a frequency band specificity self-adaptive variation Kriging interpolation model is built, an optimal variation function is automatically selected according to spatial variation characteristics of electroencephalogram power of each frequency band, a high-resolution BEAM graph sequence under multiple frequency bands and multiple time steps is built, and spatial continuity and CNN structure adaptability are considered. A double-branch three-dimensional convolutional neural network is designed, high-dimensional feature extraction is performed on space-time and space-frequency band tensors, a multi-scale structure and dynamic expression ability of neural activity are mined, discrimination of two feature vector branches is dynamically weighted, and the recognition ability of a classifier to a language imagination electroencephalogram mode is improved.

An electroencephalography dry electrode (100) and an electroencephalography measurement apparatus having same. The electroencephalography dry electrode (100) comprises a substrate and plating layers, wherein the substrate comprises a base (10) and a plurality of column members (20), an end portion of one end of each of the plurality of column members (20) is fixed to the base (10), at least the surfaces of the column members (20) are provided with the plating layers, and the plating layers on the surfaces of the column members (20) are electrically connected to the base (10).

The invention belongs to the technical field of brain function monitoring, and discloses a three-in-one non-invasive brain function monitoring system and method, a medium, equipment and application, and the monitoring method comprises the following steps: monitoring brain oxygen saturation of a critical patient; carrying out transcranial Doppler ultrasonic measurement on intracranial blood vessel blood flow velocity and ultrasonic measurement on the inner diameter of the optic nerve sheath; monitoring a quantitative electroencephalogram of the patient; various brain function monitoring series indexes and continuous changes of the indexes along with disease evolution are obtained, the pathogenic mechanism and pathogenesis of the indexes are found in time, and primary diseases and related complications of the primary diseases are treated. According to the invention, the brain oxygen saturation can avoid too low brain oxygen or low brain oxygen for a long time and too high brain oxygen due to oxygen utilization disorder; cerebral blood flow can be monitored through a transcranial Doppler (TCD) technology to find an optimal craniocerebral perfusion pressure (CPP), and a cerebral blood flow self-adjusting function is enabled to be in an optimal state; the electroencephalogram can avoid early warning of over-sedation or abnormal discharge and the like. Through organic combination of the three, noninvasive, real-time and dynamic monitoring of critical patients is realized.

Provided is an apparatus for generating an electroencephalograph (EEG) signal, comprising a processor; and a memory comprising one or more sequences of instructions which, when executed by the processor, causes steps to be performed comprising receiving a first EEG signal and a second EEG signal; extracting a first plurality of features from the first EEG signal and a second plurality of features from the second EEG signal based on a machine learning model; generating a first reconstruction EEG signal and a second reconstruction EEG signal by swapping a same category feature among the first plurality of features and the second plurality of features based on the machine learning model so that the first EEG signal and the second EEG signal match the second reconstruction EEG signal and the first reconstruction EEG signal, respectively.

A method and system for diagnosing an idiopathic psychosis patient and improving treatment targeting for that patient. Cognitive performance is measured on the patient. Pro- and anti-saccade signals are measured on the patient. Motor inhibition is measured on the patient. EEG signals are measured on the patient. Principal components analysis is applied to the measured signals and scales to determine the most significant features. The patient is evaluating on at least 11 dimensions of neuro-cognitive performance. A trained numerical taxonomy approach is used to classify the patient as belonging to a B-SNP psychosis Biotype and the patient's condition is categorized based on the classified Biotype. The B-SNIP psychosis Biotype is used to implement targeted treatment for an individual patient. The diagnostic algorithm is continuously re-trained using new cases and new laboratory tests to improve precision of B-SNIP psychosis Biotypes diagnosis and the accuracy of selecting treatments for individual patients.

1. The name of the design product: non-invasive brain electrical collection helmet. 2. The use of the design product: for fast wearing / removing in multi-line intensive care unit (ICU) environment, realizing stable collection of left central area (C3) of head / right central area (C4) of head / central line on top of head, midpoint area (Cz) from forehead to occipital region and related sites of forehead / parietal region. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

The present invention relates to the use of agomelatine (or its prodrugs or salts) in the treatment of major depressive disorder or bipolar disorder, including the selection of patients who would benefit most from agomelatine.

The invention discloses a portable state analysis method and system based on multi-mode electroencephalogram and electrocardio, and belongs to the field of state analysis. The portable state analysis method comprises the steps that electroencephalogram signals and electrocardio signals of a subject are synchronously collected; performing preprocessing and feature extraction on the signals to obtain electroencephalogram features and electrocardio features; according to the values of the electrocardio characteristics in different physiological states, constructing difference value characteristics and ratio characteristics representing differences between the states; the electroencephalogram features, the electrocardio features and the new construction features are fused, and multi-modal features are obtained; and finally, inputting the fusion features into a pre-trained table priori data fitting network model, and outputting a classification result or a quantitative prediction value for representing the state of the subject. According to the method, through systematic state comparison feature engineering and an efficient table data model, the accuracy, generalization ability and automation level of multi-modal physiological signal analysis are improved, and the whole scheme is realized based on portable equipment and is suitable for state evaluation of various non-laboratory scenes.

A method and device for stimulating cerebrospinal fluid flow is disclosed. The method may include acquiring an electroencephalography (EEG) signal from a human subject, predicting a target feature of the EEG based on the EEG signal, and playing an auditory stimulus to coincide with the target feature of the EEG learned by a neural network. The target feature may include a peak of a slow wave. The method may include using a recurrent neural network to predict the target feature for a human subject.

The application relates to the technical field of biology, and particularly discloses a flexible implantable electroencephalogram electrode, a smart strain material can dynamically adjust the hardness through electric signal instructions, a stress sensor is matched to monitor rigidity data in real time, the hardness is increased during implantation to avoid flexural deformation, the flexible implantable electroencephalogram electrode can accurately reach a deep target brain area, the hardness is reduced after implantation to adapt to the mechanical properties of brain tissue, inflammation and immune responses caused by long-term compression are reduced, implantation accuracy and biocompatibility can be considered; a signal processing module performs feature correlation analysis on real-time rigidity data and a multimodal original data set, so that the monitoring result not only contains physiological signal information, but also fuses the adaptation state data of the electrode and brain tissue, signal interference caused by unsuitable electrode rigidity is eliminated, and the accuracy and reliability of multi-dimensional physiological state evaluation of the target brain area are improved; in this way, multi-dimensional acquisition and fusion are realized, dynamic adaptation and accurate implantation can be realized, and data correlation and evaluation accuracy are improved.

The invention discloses an electroencephalogram helmet and a dry electrode tip assembly thereof, the electroencephalogram helmet comprises a shell and a dry electrode tip, and the shell is provided with an avoiding hole. And the dry electrode tip passes through the avoiding hole and is rotatably connected with the shell.

The invention discloses a multi-mode signal processing method and system based on functional ultrasound and electroencephalogram, relates to the technical field of signal processing, and solves the technical problems that ultrasonic signals and electroencephalogram signals cannot be monitored synchronously and single-mode information of an existing brain function imaging technology is incomplete. According to the technical scheme, the method is characterized in that synchronous processing and conjoint analysis are carried out on collected ultrasonic signals and electroencephalogram signals, signal alignment is achieved through a unified reference, hemodynamic characteristics and nerve electrical activity characteristics are extracted, a coupling model is constructed, brain function task decoding and brain region activation evaluation are achieved, and the brain function task is analyzed. The integrity and accuracy of brain function evaluation are improved, and the stability of cerebral blood flow imaging is improved.

1. The name of the design product: double-channel electroencephalogram head ring. 2. The use of the design product: used for collecting the double-channel electroencephalogram signals (such as the brain electrical characteristics related to concentration and relaxation) of the wearer, as well as physiological parameters such as blood oxygen saturation and heart rate, to provide data reference for daily health monitoring, concentration training, sleep quality tracking and other scenarios. 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 discloses a front-end probe for an electroencephalogram electrode and the electroencephalogram electrode and belongs to the technical field of electroencephalogram. The front-end probe comprises a barrier layer, a liquid absorption layer, a conductive layer and an interface layer; the barrier layer is formed with a containing cavity, the liquid absorption layer, the conductive layer and the interface layer are arranged in the containing cavity, the liquid absorption layer, the conductive layer and the interface layer are sequentially arranged along the direction from the outer wall to the inner wall of the barrier layer, the liquid absorption layer covers at least part of the conductive layer, the conductive layer covers at least part of the interface layer, and the interface layer is used for connecting an elastic supporting rod of the electroencephalogram electrode; along a first direction, the barrier layer has opposite top and bottom parts, the top part is provided with a through hole used for penetrating the elastic supporting rod, the bottom part is a closed structure, the side part of the barrier layer is provided with an opening, the opening is opposite to at least part of the position of the liquid absorption layer, and the side part of the barrier layer is a part between the top part and the bottom part of the barrier layer.

The invention discloses a teenager depression detection system based on a multi-task electroencephalogram biological signal, and the system comprises an electroencephalogram data collection module which guides a user to carry out an experiment according to a designed experiment normal form of an eye-opening resting task, an eye-closing resting task, an attention task and a non-attention task, and reads the electroencephalogram data of the user; the data preprocessing module is used for preprocessing the electroencephalogram data; the feature extraction module is used for performing feature extraction on the preprocessed electroencephalogram data by using the electroencephalogram biological signal learning module and the improved Transform model to obtain features related to the four tasks; the feature fusion module is used for fusing features related to the four tasks and constructing feature representation capable of comprehensively reflecting the depression state; and the depression classification module is used for classifying the features by using a classifier to obtain a depression detection result. According to the invention, efficient detection of juvenile depression is realized, and objective and accurate basis is provided for clinical diagnosis and intervention.

The invention discloses a self-adhesion conductive hydrogel physiotherapy electrode slice and a preparation method thereof, and belongs to the field of flexible electronics and rehabilitation medicine. The invention provides a self-adhesion conductive hydrogel physiotherapy electrode patch. The self-adhesion conductive hydrogel physiotherapy electrode patch is prepared from polyvinylamine, an auxiliary agent and a cross-linking agent. The polyvinylamine hydrogel prepared by the method disclosed by the invention can simultaneously realize excellent biocompatibility, ionic conductivity, tensile strength, toughness and adhesion, can replace a commercial electrode plate, and is used for detection in the aspects of myoelectricity, electroencephalogram and the like.

The application provides a deep learning-based electroencephalography signal artifact separation method and system. The method comprises: acquiring an original multi-channel electroencephalography signal; performing feature extraction on the original multi-channel electroencephalography signal through an encoder to obtain encoded features; inputting the encoded features into a backbone model, the backbone model being based on a cross transformer, and performing joint modeling on the dependence relationship of the encoded features in the time dimension and the channel dimension through an attention mechanism; generating synthetic artifact channel features based on the features output by the backbone model; and reconstructing signals through a decoder based on the features output by the backbone model and the synthetic artifact channel features, and synchronously outputting an electroencephalography net signal after removing artifacts and a corresponding artifact estimation signal. The method provided by the application can automatically complete artifact separation from the original electroencephalography signal without human participation in the identification and screening process of independent components.

The invention discloses a device and a method for evaluating the stability of a right leg driving loop for electroencephalogram acquisition, and belongs to the technical field of bio-electricity signal acquisition. The device comprises a signal triggering device, an injection signal generating device, a double-path acquisition device, a data processing unit and a configurable human body impedance simulation network. The method comprises the following steps: setting test parameters through an upper computer, and controlling an injection signal generator to inject a sweep frequency sinusoidal signal to a to-be-tested right leg driving loop; the double-path acquisition device synchronously acquires an injection point signal and a loop response signal; and the data processing unit performs Fourier transform on the signal, calculates open-loop amplitude-frequency and phase-frequency characteristics of a loop, and automatically calculates a phase margin and a gain margin to quantitatively evaluate the stability. According to the method, the problems of low precision, poor authenticity, poor repeatability and the like of the existing simulation and general instrument test method are solved, an electroencephalogram acquisition scene can be truly restored, and high-precision and automatic loop stability test is realized.

This invention discloses a dance rehabilitation training system integrating EEG and EEG sensors. The system includes an EEG headband and EEG sensors, each with built-in EEG and EEG acquisition modules. The dance rehabilitation training process includes: device wearing and initialization; personalized physiological baseline acquisition; simultaneous acquisition and preprocessing of dual-modal signals; multimodal feature fusion and emotion recognition, obtaining emotional state through dual-model fusion and smoothing; personalized safety threshold decision-making and intervention level determination; multi-channel dance intervention execution; emergency stress handling mechanism; closed-loop iteration and real-time control; and finally, generation of an evaluation report. This invention solves the problems of static intervention parameters, low feedback accuracy, and easy triggering of stress responses in traditional dance training by using dual-modal signal fusion, personalized safety threshold modeling, and closed-loop real-time control, achieving real-time, precise, and safe personalized rehabilitation intervention.

1. The name of the design product: digital electroencephalograph (NL-4208). 2. The use of the design product: as a device for electrotherapy rehabilitation, for measuring brain electrical signals, and assisting in the diagnosis of brain diseases. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

This invention relates to a real-time assessment and prediction method for mental fatigue based on multimodal signals and deep learning, addressing the problem that existing mental fatigue monitoring technologies rely heavily on static feature analysis of single physiological signals. First, it integrates dynamic brain function network features from electroencephalography (EEG) with time-frequency domain features of EEG to achieve feature fusion driven by multi-domain EEG features. Further, it automatically associates PPG signals with fatigue stages based on weighted K-means clustering, using the center value of heart rate to calibrate the fatigue level, eliminating the need for subjective labeling and achieving objective recalibration of mental fatigue levels. Finally, it uses a temporal deep convolutional network model to achieve real-time assessment and prediction of mental fatigue levels. This invention can accurately assess and predict mental fatigue levels in real time, and issue warning signals to operators with high levels of mental fatigue, providing technical support for real-time monitoring and warning of mental fatigue, and further reducing the rate of operational errors caused by mental fatigue.

Systems and methods for electroencephalography headband apparatuses including a strap member, an amplifier device, and electrode assemblies each including a capsule receiving structure having a detent feature configured to resist rotation of a gel capsule positioned there within, a stabilization structure having a central attachment section and a plurality of leg members positioned radially outward from a central axis of the electrode assembly, and a gel delivery structure configured to be positioned at least partially within the capsule receiving structure and to rotatably attach to the stabilization structure such that the gel delivery structure may be able to rotate independently of the stabilization structure. The gel delivery structure may include a plurality of teeth members configured to engage with and lacerate a lower surface of the gel capsule and a plurality of gel delivery channels configured to deliver gel from the gel capsule to patient scalp.

The invention relates to an electroencephalogram traceability model training method and device, equipment and a medium. The method comprises the following steps: constructing a standardized graph structure data set containing multiple individual electroencephalogram signals, a structure connection group and a source activity true value, and training a graph neural network by adopting a meta-learning framework to extract a common rule of a cross-individual brain connection group and a traceability mapping relationship, so as to form a pre-training model with strong generalization ability; for a new individual, only key parameters associated with connection group features in the model are adjusted through a parameter efficient fine tuning technology, and rapid migration of pre-training meta-knowledge to individual specific connection is realized; finally, while individualized traceability precision is kept, computing resources and data volume required for model adaptation are greatly reduced, the problem of traceability deviation caused by individual brain structure difference in a traditional method is effectively solved, and feasibility and efficiency of an electroencephalogram traceability technology in clinical practice are remarkably improved.

Techniques for guiding treatment of a test subject having a neurological disorder are presented. The techniques may include: producing an outlier rate data set for a test subject from a scalp electroencephalography (EEG) data set obtained from the test subject over a selected time; using the outlier rate data set to predict whether a given neurological medication (NM) will be effective in treating the test subject if the given NM is administered to the test subject to produce an NM efficacy prediction; and providing the NM efficacy prediction. Additional methods as well as related systems and computer readable media are also presented.

1. The name of the design product: EEG acquisition impedance detection graphical user interface of electronic equipment. 2. The use of the design product: for displaying interface content. 3. The design points of the design product: in the graphical user interface. 4. The picture or photo that best shows the design points: front view. 5. The electronic equipment is a conventional design, and other views are omitted. 6. The use of the graphical user interface: for impedance detection graphical user interface in EEG acquisition. The human-computer interaction mode: human-computer interaction can be realized by mouse dragging, sliding, placing and clicking. The front view is the main interface of the EEG acquisition impedance detection operation interface, the interface change state diagram 1 is the electrode configuration file import interface displayed after clicking the Select option in EEG_V1.0-25fd0 in the front view state, the interface change state diagram 2 is the electrode configuration file selection interface displayed after clicking the Import option in the pop-up window in the interface change state diagram 1 state, the interface change state diagram 3 is the interface displayed after clicking a specific electrode configuration file in the interface change state diagram 2 state, the interface change state diagram 4 is the electrode configuration file import success interface displayed after clicking the select folder option or double-clicking the selected file in the interface change state diagram 3 state, the interface change state diagram 5 is the interface displayed after checking the square option box right of EEG_32_V1.0 in the pop-up window in the interface change state diagram 4 state, and the interface change state diagram 6 is the impedance preview interface displayed after clicking the preview icon right of EEG_32_V1.0 in the interface change state diagram 5 state.

This invention relates to the field of perioperative neurological monitoring technology, and particularly to a postoperative cognitive quantitative assessment system and method that integrates electroencephalography (EEG) and near-infrared spectroscopy. The system includes: a baseline anchoring module, which extracts preoperative scalp EEG and near-infrared spectral signal features, concatenates them into a first vector, and calculates the covariance to generate a preoperative baseline manifold; a matrix construction module, which generates a cross-modal manifold matrix based on real-time bimodal signals within a time window; a feature extraction module, which calculates the geodesic distance between the cross-modal manifold matrix and the preoperative baseline manifold, projects it onto the tangent space using a logarithmic mapping to generate a tangent matrix, and combines the geodesic distance to generate a topological feature vector; a quantitative assessment module, which inputs the vector into a softmax function layer to output a probability distribution and calculates a cognitive assessment index; and a closed-loop intervention module, which outputs an intervention command when the index meets preset conditions and provides a timestamp to reset the time window. This system achieves closed-loop manifold-based assessment and dynamic monitoring of postoperative cognitive status.

A three-dimensional convolution method for imagined language electroencephalography (EEG) topography decoding. It solves the problem of the deficiency of the existing convolutional neural network method in modeling ability for multi-rhythm EEG topography, which leads to a significant decline in decoding accuracy in imagined language. In the aspect of rectangular BEAM reconstruction, a frequency-specific adaptive variogram kriging interpolation model is built, which automatically selects the optimal variogram function according to the spatial variation characteristics of the EEG power of each frequency band, and constructs a high-resolution BEAM map sequence under multi-frequency and multi-time steps, taking into account the spatial continuity and the adaptability of the CNN structure. A double-branch three-dimensional convolutional neural network is designed to extract high-dimensional features from spatial-time and spatial-frequency tensors, respectively, to explore the multi-scale structure and dynamic expression ability of neural activity. The discriminative ability of the two feature vector branches is dynamically weighted to improve the recognition ability of the classifier for the EEG pattern of language imagination.