19 results about "Scalp electroencephalogram" patented technology

The invention discloses an epilepsy region positioning method and system based on brain power supply imaging and a dynamic brain network, and the method comprises the steps: obtaining T1 weighted magnetic resonance imaging data of a user, and constructing an individual three-dimensional head model through a boundary element method; acquiring scalp electroencephalogram data of a user, and preprocessing the scalp electroencephalogram data; based on an individual three-dimensional head model, performing inverse problem solving on the preprocessed scalp electroencephalogram data by using a standardized low-resolution brain power source imaging algorithm to obtain source current density signals of 68 brain regions; decomposing into six frequency bands, calculating the power spectrum density of each brain region and carrying out normalization processing, and screening effective frequency bands; based on the source current density signals of the 68 brain regions of the effective frequency band, information flow directions and intensities of different brain regions are calculated by adopting a directional transfer function method, a directional transfer function matrix of the effective frequency band is formed, and a directed brain network is constructed; and calculating a graph theory index and / or an epilepsy index of each brain region, carrying out maximum value normalization analysis, and determining an epilepsy region positioning result.

The invention discloses a brain-computer interface signal enhancement and evaluation method fusing physical information. The method comprises the following steps: firstly, based on an individual structure magnetic resonance image, constructing a personalized six-layer brain tissue anatomical model comprising a cortex, a white matter, cerebrospinal fluid, a dura mater, a skull and a scalp, and endowing differentiated conductivity parameters; secondly, neuroelectrophysiology priori knowledge such as neurodynamics and white matter anisotropic conduction is converted into a representation rule which can be learned by a neural network; then, the rules are systematically embedded into a physical information neural network in a differentiable physical constraint form, including a cortical neurodynamic equation residual error, a volume conduction equation residual error of each layer and an interlayer interface continuity constraint; and finally, training a network by using the scalp electroencephalogram signals, and jointly outputting high-fidelity intracranial electroencephalogram signals and multi-modal neurophysiological information by minimizing a loss function containing reconstruction and physical constraints. According to the method, the fidelity, the physical rationality and the clinical interpretability of signal enhancement are effectively improved.

A method of predicting a seizure occurrence or a surgical outcome includes monitoring a patient using an electroencephalography (EEG) system, including recording EEG data that indicates a seizure, and extracting a peri-ictal segment of the EEG data that includes a pre-ictal period immediately preceding the seizure, or a post-ictal period immediately following the seizure. The method also includes processing the peri-ictal segment, including generating an electrode-wise power spectral density (PSD) feature across a frequency band defined by at least one of a delta frequency range, a theta frequency range, an alpha frequency range, a beta frequency range, and a gamma frequency range. The method also includes inputting the PSD feature into a model, wherein the model predicts a seizure occurrence or a surgical outcome of the patient.

The invention discloses a signal fusion processing method for a motor imagery training brain-computer interface, and particularly relates to the technical field of neural signal processing. Multi-channel scalp electroencephalogram data of a user are collected in real time, a time-varying space covariance matrix sequence is constructed through Riemannian geometric space mapping analysis, and a phase amplitude coupling index sequence between different brain rhythms is analyzed and calculated through cross-band coupling dynamics; performing feature hierarchy joint coding on the spatial covariance matrix sequence and the phase amplitude coupling index sequence to generate a real-time neural representation tensor; calculating the distribution divergence of the real-time neural representation tensor relative to the static reference feature manifold in the initial training stage so as to quantitatively represent the mismatch degree; and finally, dynamically adjusting a weighting coefficient and a fusion structure of the multi-modal signal fusion device according to the characterization mismatching degree, and outputting a motion control instruction matched with the current brain state of the user in real time. According to the method, the motion intention decoding precision and stability in the motion imagination training process are improved.

The application discloses a scalp EEG signal-based brain internal nerve activity tracing method and system, and the method comprises the following steps: a virtual brain model is built by using a neurocluster model with biological interpretation, a whole brain map and brain physiological connection, and the number of the neurocluster model is recorded; brain internal nerve activity generated by the virtual brain model is converted into scalp EEG signals according to an EEG measurement model; a brain internal nerve activity tracing neural network is constructed; internal brain activity data with spatial characteristics and time characteristics are generated by using the virtual brain model, and corresponding EEG signals are obtained through the EEG measurement model; the obtained internal brain activity data and the corresponding EEG signals are used to construct a data set for training the brain internal nerve activity tracing neural network. The brain internal nerve activity tracing neural network is constructed by cascading a spatial module and a time module, so that the stability and accuracy of brain internal nerve activity tracing are improved.

The application discloses a Parkinson's disease freezing gait early prediction method based on electroencephalogram high-order phase characteristics. The method comprises the following steps: acquiring scalp electroencephalogram signals of multiple target brain regions of a user and performing pretreatment; applying a surface Laplace space filtering algorithm to the pretreated signals to suppress volume conduction effect; extracting multi-dimensional neuroelectrophysiological characteristics from the filtered signals in real time, wherein the characteristics at least include phase locking values of target brain region networks in frequency bands and phase-amplitude coupling strength indexes in target brain regions; inputting the multi-dimensional characteristics into a pre-trained machine learning prediction model to output a probability quantitative value of physical freezing gait of the user in future seconds. By introducing surface Laplace filtering and high-order cross-frequency coupling characteristics, the application effectively improves signal space resolution and specificity of prediction characteristics, realizes low-delay and high-accuracy early prediction, and can be used for wearable electroencephalogram devices.

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.

The present application belongs to the technical field of brain-computer interface, and relates to electroencephalogram signal processing technology, and discloses a self-adaptive scalp electroencephalogram motor imagery signal recognition method and a related device; wherein the self-adaptive scalp electroencephalogram motor imagery signal recognition method comprises the following steps: obtaining a to-be-recognized scalp electroencephalogram motor imagery signal of a target subject; based on the to-be-recognized scalp electroencephalogram motor imagery signal, using a trained self-adaptive classifier recognition model to perform signal recognition, and obtaining a scalp electroencephalogram motor imagery signal recognition result. The technical scheme of the present application can better solve the individual difference problem in EEG signal processing through the self-adaptive classifier recognition model, and can more accurately recognize motor imagery signals of different individuals.

The application aims to provide a scalp EEG-based epilepsy lesion positioning system, which is based on Granger causality theory, establishes brain function networks of patients in interictal period and ictal period respectively, comprehensively uses effective information in different periods, analyzes the connectivity difference between the initial stage of seizure and the interictal period from the network level, and analyzes the connectivity difference between the lesion area and the non-lesion area from the node level; adopts a difference quantification method to quantify the difference size of the causal flow of each lead between the interictal period and the initial stage of seizure, and sequentially identifies the seizure main frequency band, the seizure side and the seizure lead based on the difference size, so as to realize the lesion positioning and side identification of epilepsy patients.

The invention discloses a scalp electroencephalogram sensing electrode design method based on three-dimensional gradient synergy, which comprises the following steps of: 1, determining the number of structural layers of an electrode, namely at least three layers; 2, determining the component, structure and potential three-dimensional gradient requirements of each layer of material; and step 3, preparing each layer of material of the electrode and meeting the three-dimensional gradient requirements of components, structures and potentials. The method has the advantages that the gradient design of an electrode interface in three dimensions of composition, structure and potential is fully utilized, the traditional mutability high-resistance interface design is changed, the transfer and conversion resistance of ionic charges and electronic charges is greatly reduced, the charge conversion efficiency is improved, and high-fidelity sensing of electroencephalogram signals is realized; the method has the advantages of being of great significance to preparation of high-performance electroencephalogram sensing electrodes and development of high-performance brain-computer interface systems.

The application discloses a stroke risk assessment system and method and a storage medium. After scalp electroencephalogram signals are picked up through an electroencephalogram cap module, the signals are transmitted to a 32-channel electroencephalogram signal amplifier module through lead wires, signal amplification, filtering and ADC conversion are completed by the 32-channel electroencephalogram signal amplifier module, and then the signals are transmitted to an upper computer data acquisition visualization and risk assessment software module through a wireless mode after digital processing and packaging. The upper computer data acquisition visualization and risk assessment software module receives data and performs real-time visual display, extracts stroke electroencephalogram features, inputs a stroke risk analysis model and finally outputs a risk grade assessment result. According to the technical scheme, the accuracy of stroke risk assessment is significantly improved by combining precise electroencephalogram signal feature extraction with a machine learning algorithm.

The application discloses a consciousness level recognition device based on electroencephalogram data and a computer readable storage medium. The device can perform the following operations: obtaining multi-lead scalp electroencephalogram data of a patient to be recognized; inputting the multi-lead scalp electroencephalogram data into a trained neural network model for consciousness level recognition operation, to output a response vector composed of response intensities of each lead, the response intensity representing the correlation degree between the electroencephalogram feature of the lead and the target consciousness level; calculating the distance between the response vector and the average response vector of the target consciousness level group; and determining the consciousness level recognition result of the patient to be recognized according to the distance. The consciousness level recognition scheme based on electroencephalogram data can improve the accuracy of distinguishing between the vegetative state and the weak consciousness state, and realize objective and accurate evaluation of the consciousness level.

The invention relates to the technical field of medical detection, and discloses an electroencephalogram signal-based neuropathic pain detection and analysis system, which comprises an electroencephalogram signal acquisition module, a signal preprocessing module, a feature extraction module, a pain detection and analysis module, a result output module and a system calibration module which are electrically connected in sequence to form a closed-loop detection and analysis system, objective detection of neuropathic pain is achieved, subjective description of a patient does not need to be depended on, misdiagnosis and missed diagnosis caused by subjective factors are effectively avoided through pure objective electroencephalogram signal analysis, and a reliable objective basis is provided for clinical diagnosis and treatment; the electroencephalogram signal acquisition precision is high, electroencephalogram signals of different scalp areas are comprehensively acquired through the 32-channel electrode array, various interference signals are effectively filtered out in combination with an improved self-adaptive filtering algorithm, the signal quality is ensured, and a reliable basis is provided for feature extraction and detection analysis.

The application discloses a kind of asynchronous brain-computer interface switch control methods based on periodic visual fixation, it is related to the field of automatic control, the method includes the following steps: performing periodic fixation visual stimulation task, generates periodically changed electroencephalogram signal;Collect scalp electroencephalogram signal, signal amplification is carried out to electroencephalogram signal, analog-digital conversion and filtering processing;Characteristic signal containing fixation visual stimulation state is extracted by decoding algorithm;Characteristic signal is converted into the numerical value of virtual force;Virtual force is applied to virtual dynamics system, drives virtual dynamics system state change and oscillation;When the state of virtual dynamics system reaches the set threshold, brain-computer interface switch output instruction is triggered, and the control of brain-computer interface switch is realized;Visual stimulation and virtual dynamics system state are displayed on display device.The application constructs a new switch trigger mode with interactive trigger mode, improves the reliability of switch control, and improves the practicability of brain-computer interface system.

The embodiment of the invention discloses a motor imagery electroencephalogram signal decoding method based on neural rhythm perception, and the method comprises the steps: carrying out the preprocessing of scalp electroencephalogram signals of multiple electrode channels of a user, obtaining the aligned electroencephalogram signal tensor, obtaining the local time domain features of each electrode channel, and fusing the local time domain features to obtain a high-dimensional time sequence feature representation; and based on an adaptive neural rhythm module, obtaining an advanced feature tensor containing rich rhythm information, thereby obtaining a predicted classification result of user imagination limb movement corresponding to the multi-electrode channel scalp electroencephalogram signal. According to the method, the dominant frequency of the signal is remodeled into the two-dimensional tensor in a self-adaptive manner, so that feature representation with higher discrimination is extracted, and the decoding accuracy is remarkably improved. The method can automatically recognize the dominant frequency, can adapt to rhythm changes of different subjects or at different moments, has higher adaptability to non-stationary EEG signals, and enhances the robustness and adaptability of the model.

The invention discloses a method for sharing the contribution degree of an ERP component in consciousness state evaluation and a medium, and the method comprises the steps: introducing preset disturbance in a time window corresponding to a target ERP component for to-be-analyzed multi-lead scalp electroencephalogram data, so as to obtain shielded multi-lead scalp electroencephalogram data; inputting the to-be-analyzed multi-lead scalp electroencephalogram data and the shielded multi-lead scalp electroencephalogram data into the trained neural network model, and performing consciousness state evaluation operation to output a first prediction result and a second prediction result which are composed of prediction probabilities of leads; calculating an absolute difference value between the first prediction result and the second prediction result as a prediction difference; and determining the contribution degree of the target ERP component in consciousness state evaluation based on the prediction difference. By means of the scheme, quantitative analysis of the contribution degree of each ERP component in consciousness state evaluation can be achieved, and the accuracy of consciousness state evaluation is improved.

The application discloses a method and a medium for sharing contribution of ERP components in consciousness state evaluation. The method comprises the following steps: introducing a preset disturbance in a time window corresponding to a target ERP component to obtain multi-lead scalp electroencephalogram data after shielding, for multi-lead scalp electroencephalogram data to be analyzed; inputting the multi-lead scalp electroencephalogram data to be analyzed and the multi-lead scalp electroencephalogram data after shielding into a trained neural network model respectively, performing a consciousness state evaluation operation, and outputting a first prediction result and a second prediction result composed of prediction probabilities of each lead; calculating an absolute difference value between the first prediction result and the second prediction result as a prediction difference; and determining the contribution of the target ERP component in the consciousness state evaluation based on the prediction difference. By using the scheme of the application, quantitative analysis of the contribution of each ERP component in the consciousness state evaluation can be realized, and the accuracy of the consciousness state evaluation is improved.

The application discloses a brain-computer interface signal enhancement and evaluation method fusing physical information. First, based on individual structural magnetic resonance images, a personalized six-layer brain tissue dissection model containing the cortex, white matter, cerebrospinal fluid, dura mater, skull and scalp is constructed, and differentiated conductivity parameters are given. Second, neurophysiological prior knowledge such as neural dynamics and white matter anisotropic conduction is converted into a representation rule that can be learned by a neural network. Then, these rules are embedded in the physical information neural network in the form of differentiable physical constraints, including the residual error of the cortical neural dynamics equation, the residual error of the volume conduction equation of each layer, and the continuity constraint of the interlayer interface. Finally, the scalp electroencephalogram signal is used to train the network, and by minimizing the loss function containing the reconstruction and physical constraints, a high-fidelity intracranial electroencephalogram signal and multi-modal neurophysiological information are jointly output. The application effectively improves the fidelity, physical reasonableness and clinical interpretability of signal enhancement.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle control method and system based on EEG and fNIRS multi-modal feature fusion, and the method comprises the steps: synchronously obtaining scalp electroencephalogram and near-infrared blood oxygen signals, extracting the electroencephalogram frequency band power spectrum density to construct a fast variable feature matrix, and obtaining a fast variable feature matrix; extracting oxyhemoglobin concentration variation time sequence characteristics to construct a slow variable characteristic matrix, and compensating a slow variable timestamp by using a phase-space reconstruction algorithm to realize alignment; taking the aligned slow variable as a query matrix, taking the fast variable as a key matrix and a value matrix, and obtaining a fusion feature matrix through attention mechanism fusion; inputting the discrete instruction and the continuous variable into a double-branch network to decode the discrete instruction and the continuous variable respectively, and splicing to generate an intention decoding instruction; and finally, calculating a cognitive load index by integrating the concentration variable quantity, dynamically allocating weights of an intention decoding instruction and a bottom-layer autonomous safety control law, and generating a final instruction to be issued and executed. According to the invention, the problems of low control dimension and easy out-of-control air crash are solved.