Product
Patsnap Eureka
For R&D, Patsnap Eureka makes reading and utilizing patents & technical documents easy.
Patsnap Eureka AIR
Designed for self-driven R&D workflows. Generate viable solutions, solve complex R&D challenges, empower your innovation with AI.
Patsnap Eureka Materials
Designed for material experts only. Revolutionize your material R&D, from search, analyze, to developing new materials.
Feature
TechResearch
Generate reliable direction feasibility study reports for your R&D in just a few steps.
TechSeek
Discover and master advanced knowledge NOW. Basics, ideas, possibilities, all at once.
TechMind
As an expert in R&D Theories, TechMind can generates customized viable solutions instantly.
TechRisk
Analyze your overall solution with one click, know your potential R&D risks in advance.
TechMonitor
Get weekly tech updates, stay abreast of the latest tech innovations and key insights.
Patents
Literature
Hiro is an intelligent assistant for R&D personnel, combined with Patent DNA, to facilitate innovative research.
30 results about "Eeg classification" patented technology
Efficacy Topic
Property
Owner
Technical Advancement
Application Domain
Technology Topic
Technology Field Word
Patent Country/Region
Patent Type
Patent Status
Application Year
Inventor
Emotion recognition system based on depth learning and brain-computer interface as well as application
ActiveCN111616721AImplement feedbackAccurate acquisitionSensorsPsychotechnic devicesPortable EEGAcquisition apparatus
The invention discloses an emotion recognition system based on depth learning and a brain-computer interface as well as application. The emotion recognition system comprises portable EEG acquisition equipment, an emotion EEG classification module and an emotion classification display module, wherein the portable EEG acquisition equipment acquires emotion EEG signals from the brain of a subject; the emotion EEG classification module analyzes the emotion EEG signals, establishes a relation model between the EEG signals and emotions, and performs emotion classification on the input emotion EEG signals through the relation model; and the emotion classification module displays recognized emotion types and prompts the emotion state of the subject. The emotion recognition system based on depth learning and the brain-computer interface as well as application can realize accurate acquisition, effective identification and accurate classification of the emotion EEG signals, directly promote the emotion state and realize an emotion state monitoring function. The corresponding relation between the EEG signals and the emotion types is directly established, and feedback of emotions is realized.
Owner:TIANJIN UNIV +1
Electroencephalogram classification detection device based on lacuna characteristics
InactiveCN103190904AMeet the requirements for online classificationFast trainingDiagnostic recording/measuringSensorsFeature extractionAudio power amplifier
An electroencephalogram (EEG) classification detection device based on lacuna characteristics belongs to the technical field of electroencephalogram automatic detection. The EEG classification detection device comprises a multi-way EEG amplifier, a data collection card and a computer which are sequentially connected through a circuit. A signal preprocessing module, a signal segmentation module, a lacuna characteristic extraction module, a Bayes linear discriminant analysis classification module and a threshold judgment module are built in the computer. The multi-way EEG amplifier first amplifies EEG signals, then the data collection card collects the EEG signals and transmits the signals to the computer, finally the modules in the computer are utilized to conduct preprocessing and segmentation on the EEG signals and calculate the lacuna characteristics of the signals, a Bayes linear discriminant analysis classification device is utilized to classify the EEG lacuna characteristics, and the threshold judgment module is used for marking the classification and obtaining a result. The EEG classification detection device has the advantages of being simple in characteristic operation, high in practice and classification speed, high in classification accuracy and capable of achieving good classification detection effect.
Owner:SHANDONG UNIV
Motion imagination EEG classification processing method based on sparse representation classification algorithm
InactiveCN106446762AReduce computing pressureSimplify complexityCharacter and pattern recognitionBandpass filteringAlgorithm
The invention belongs to the field of EEG signal identification and artificial intelligence, and particularly relates to a motion imagination EEG classification processing method based on a sparse representation classification algorithm. The invention includes that a subject wears a scalp electrode cap with wireless or wired transmission on head in an environment with less external interference and imagines the movements based on the prompting, the EEG cap detects the EEG signals of the subject, and then the EEG signals are subjected to preliminary bandpass filtering, and the filtered EEG signals are imported to a host computer to be stored; and the host computer software organizes and marks the imported original EEG signals and makes a sample set and a test set for classification. According to the invention, only a large dictionary matrix is calculated and constructed once, the subsequent processing is calculated by means of sparse representation coefficient matrix, so that the data size of multi-channel motion imagination EEG information is greatly reduced, the data calculation pressure is reduced, and the operation speed is improved.
Owner:HARBIN ENG UNIV
EMD and gaussian kernel function SVM-based EEG emotion classification method
InactiveCN107273841ASolving linear inseparability problemsCharacter and pattern recognitionSignal classificationDecomposition
The invention discloses an EMD and gaussian kernel function SVM-based EEG emotion classification method. Aiming at the problem that the accuracy of EEG signal classification is not high, an Empirical Mode Decomposition (EMD) technology and SVMs are combined, first EMD is performed on an EEG signal to obtain a plurality of modal components, each modal component contains effective information of different frequencies, then frequency energy is used as a quantitative criterion of each modal component, i.e., each EEG signal can obtain different characteristic values, and the characteristic values are used as characteristic values of an EEG sequence to perform a next step of sample value classification. Experiments show that the EMD and gaussian kernel function SVM-based EEG classification method can improve the accuracy of EEG signal classification.
Owner:BEIJING UNIV OF TECH
Prior knowledge-based multi-modal ethnic identity sense quantitative measurement method
InactiveCN108256579AAccurate classificationImprove objectivityCharacter and pattern recognitionDiagnostic recording/measuringFeature extractionSurvey result
The invention relates to a prior knowledge-based multi-modal ethnic identity sense quantitative measurement method and belongs to the intelligent pattern recognition and ethnology research crossing filed. According to the measurement method, ethnic minority emotion pictures are adopted as basic materials; ethnic minority identity sense survey questionnaire questions are converted into the induction materials of electrophysiological signals; after the EEG information of subjects is acquired, the EEG information implying identity senses is classified through a fuzzy neural network learning-basedEEG emotion feature extraction and classification algorithm; questionnaire survey results and EEG classification results are fused, so that ethnic identity sense quantitative measurement is realized;and the objective feeling of the subjects for the ethnic identity sense and national identity sense can be automatically analyzed. With the method of the invention adopted, the subjective influence of questionnaire surveys can be decreased, the objectivity of identity sense classification can be enhanced, and ethnic identity sense quantitative classification is more accurate and more efficient.
Owner:MINZU UNIVERSITY OF CHINA
EEG classification method based on Fisher discrimination sparse extreme learning machine
InactiveCN108122004AImprove accuracyDiscriminatingCharacter and pattern recognitionLearning machineHuman–machine interface
The invention proposes an EEG classification method based on a Fisher discrimination sparse extreme learning machine. The method comprises the steps: firstly training a structural dictionary through aFisher criterion; secondly obtaining a more discriminative sparse coefficient according to the dictionary, and obtaining a more effective feature signal; finally carrying out the classification of anew feature signal through an extreme learning machine algorithm, thereby improving the accuracy of multi-motion imaginary task classification. The method is good in application prospect in the fieldof brain-machine interfaces.
Owner:HANGZHOU DIANZI UNIV
Brain function network feature extraction method based on dynamic directional transfer function
ActiveCN111528836AImprove adaptabilityImprove dynamic characteristicsDiagnostic recording/measuringSensorsNetwork connectionMachine learning
The invention discloses a brain function network feature extraction method based on a dynamic directional transfer function. The method mainly comprises the steps of firstly, performing preprocessingsuch as common average reference and lead optimization on an original motor imagery electroencephalogram signal; secondly, calculating a network connection edge of the preprocessed electroencephalogram signal by adopting a proposed DDTF algorithm, and respectively constructing brain function networks of different frequency bands; calculating network characteristic parameter outflow information andinformation flow gain according to the brain function network, and fusing two characteristic parameters in series to serve as characteristic vectors to be sent into a support vector machine for characteristic evaluation; and finally, determining an optimal parameter and an optimal frequency band according to a recognition rate closed loop to obtain a final classification result. The method is used for constructing the motor imagery brain function network, the network parameters obtained through calculation are used for MI-EEG feature extraction, the method not only can accurately describe change characteristics of MI-EEG in a frequency domain, but also accurately reflect a dynamic evolution process of BFN, and improvement of the MI-EEG classification accuracy is greatly facilitated.
Owner:BEIJING UNIV OF TECH
Symbol transfer entropy and brain network feature calculation method based on time-frequency energy
ActiveCN112932505AReflect the differenceComply with nonlinear dynamic characteristicsDiagnostic recording/measuringSensorsBiologyContinuous wavelet
The invention discloses a symbol transfer entropy and brain network feature calculation method based on time-frequency energy, which comprises the following steps: firstly, preprocessing collected motor imagery electroencephalogram signals (MI-EEG) based on common average reference; then, continuous wavelet transform is carried out on each lead MI-EEG, a time-frequency-energy matrix of each lead MI-EEG is obtained, time-energy sequences corresponding to each frequency in a frequency band closely related to motor imagery are spliced in sequence, and a one-dimensional time-frequency energy sequence of the lead is obtained; further, symbol transfer entropy between any two lead time-frequency energy sequences is calculated, a brain connectivity matrix is constructed, and matrix elements are optimized by using a Pearson feature selection algorithm; and finally, calculating the degree and the middle centrality of the brain function network, and forming a feature vector for MI-EEG classification. The result shows that the frequency domain feature and the nonlinear feature of the MI-EEG can be effectively extracted, and compared with a traditional feature extraction method based on the brain function network, the method has obvious advantages.
Owner:BEIJING UNIV OF TECH
Machine learning model robustness evaluation method based on noise data
ActiveCN110717602ARobustRich cognitionKernel methodsSustainable transportationOriginal dataData acquisition
The invention provides a machine learning model robustness evaluation method based on noise data. The method comprises the steps of original data set processing, noise data acquisition, model training, model prediction, accuracy reduction ratio calculation and model robustness evaluation. The original data set processing comprises the steps of collecting an original data set with a correct percentage label, and dividing an original training set and an original test set by adopting 10 times of 10-fold cross validation. The noise data acquisition comprises the following steps: on the basis of anoriginal training set, extracting t'= | D | * alpha data by adopting a stratified sampling method, and replacing a label of the data with an error label, and alpha is a noise data rate. Model training comprises the step of respectively inputting an original training set and a training set mixed with noise data to respectively construct an original model and a new model based on a common classification algorithm. Model prediction includes performing accuracy evaluation on an original model and a new model based on an original test set. Accuracy decline ratio calculation includes calculating arate of decline in accuracy of the new model relative to the original model. Model robustness evaluation comprises the steps of comparing the size of the rate of accuracy reduction in the transverse direction and the longitudinal direction, measuring the robustness of the model, and achieving the standard of judging the robustness of the model.
Owner:NANJING UNIV
Video awakening degree classification method and device and computer device
InactiveCN109190658AImprove classification accuracyCharacter and pattern recognitionEeg dataClassification methods
The invention discloses a video awakening degree classification method and device and a computer device, wherein the classification method comprises the following steps: step 1, collecting an EEG signal when a tester watches a video, and obtaining EEG data; step 2: standardizing the obtained EEG data according to columns; step 3, carrying out feature selection on the standardized EEG data to obtain EEG classification features; step 4, classifying the video according to the obtained EEG classification features. The embodiment of the invention classifies the video according to the EEG classification features by standardizing the collected data, and then extracts the EEG classification features from the EEG signals by using the SBMLR algorithm for the standardized processed data, thereby improving the accuracy of the awakening degree classification of the video.
Owner:CHINA ACADEMY OF ELECTRONICS & INFORMATION TECH OF CETC
EEG classification method adaptive to different sampling frequencies
PendingCN109009097ASimple and Effective CompletionImprove universalityDiagnostic recording/measuringSensorsSignal classificationSample length
The invention relates to an EEG (electroencephalogram) classification method adaptive to different sampling frequencies, and relates to a signal classification method. The EEG classification method adaptive to different sampling frequencies includes the steps: 1) construction of a CNN-E classification model based on a convolutional neural network; and 2) training and testing method for sample dataof different lengths. The model can be used to learn and classify EEG signals at different sampling frequencies, and can be adaptive to signals of different lengths. The model and a traditional classification method based on feature extraction can analyze the possible problems in the classification of EEG signals at different sampling frequencies. The network model CNN-E can be adaptive to various lengths of data by means of autonomous learning of the sample data, and by means of a simple and effective completion method. The experimental results show that the network model CNN-E can achieve well classification effect and preferable universality for classification of EEG signals at the same sampling frequency, classification of EEG signals at different sampling frequencies, and classification of EEG signals with different sample lengths.
Owner:XIAMEN UNIV
EEG classification method based on helm combined with ptsne and lda feature fusion
ActiveCN109977810BImprove classification accuracyAdding first-order normal form constraintsCharacter and pattern recognitionBiologyMachine learning
The invention discloses a motor imagery EEG classification method based on HELM combined with PTSNE manifold and LDA feature fusion, and improves the classification accuracy. In terms of feature extraction, on the one hand, using PCA combined with LDA to extract linear features can not only eliminate noise, but also consider the label information of training data; Complex nonlinear intrinsic manifold features. In terms of feature classification, the HELM algorithm with high classification accuracy is used to classify and identify motor imagery EEG signals.
Owner:BEIJING UNIV OF TECH
Emotion recognition system and application based on deep learning and brain-computer interface
ActiveCN111616721BImplement feedbackAccurate acquisitionSensorsPsychotechnic devicesPortable EEGMedicine
An emotion recognition system and application based on deep learning and brain-computer interface, comprising a sequentially connected portable EEG acquisition device, an emotional EEG classification module and an emotion classification display module, the portable EEG acquisition device collects data from the brain of a subject Emotional EEG signal, the emotional EEG classification module analyzes the emotional EEG signal, establishes a relationship model between the emotional EEG signal and emotion, and through the relationship model, the input emotional EEG signal is analyzed. Emotional classification, the emotional classification display module displays the recognized emotional types and prompts the emotional state of the subject. The emotion recognition system and application based on deep learning and brain-computer interface of the present invention can realize accurate acquisition, effective identification and correct classification of emotional EEG signals, intuitively prompt emotional states, and realize the function of emotional state monitoring. The invention directly establishes the corresponding relationship between the EEG signal and the emotion type, and realizes the feedback to the emotion.
Owner:TIANJIN UNIV +1
Motor imagery EEG classification method based on sparse representation of space-time-frequency optimized features
ActiveCN105956624BImprove accuracyImprove robustnessCharacter and pattern recognitionSignal classificationClassification methods
The invention discloses a motor imagery EEG classification method based on the sparse representation of space-time-frequency optimized features, which mainly uses linear discriminant criteria to select the most favorable lead, time segment and frequency segment for classification, and extracts EEG features through a co-space pattern algorithm. Finally, classification is performed according to the feature sparse representation. The invention includes electroencephalogram signal preprocessing, lead selection, time-frequency block selection, feature extraction and feature classification. The results show that the method of the present invention can effectively select the most favorable lead, time segment and frequency segment for classification, and the sparse representation of the features extracted by the co-space pattern algorithm can achieve better classification effect. Compared with the existing algorithms, this method can automatically select the most beneficial space-time-frequency parameters for classification, and combine the features in the optimal time-frequency block, which is conducive to improving the accuracy of motor imagery EEG signal classification.
Owner:SOUTHEAST UNIV
Electroencephalogram classification detection device based on lacuna characteristics
InactiveCN103190904BMeet the requirements for online classificationFast trainingDiagnostic recording/measuringSensorsFeature extractionAudio power amplifier
An electroencephalogram (EEG) classification detection device based on lacuna characteristics belongs to the technical field of electroencephalogram automatic detection. The EEG classification detection device comprises a multi-way EEG amplifier, a data collection card and a computer which are sequentially connected through a circuit. A signal preprocessing module, a signal segmentation module, a lacuna characteristic extraction module, a Bayes linear discriminant analysis classification module and a threshold judgment module are built in the computer. The multi-way EEG amplifier first amplifies EEG signals, then the data collection card collects the EEG signals and transmits the signals to the computer, finally the modules in the computer are utilized to conduct preprocessing and segmentation on the EEG signals and calculate the lacuna characteristics of the signals, a Bayes linear discriminant analysis classification device is utilized to classify the EEG lacuna characteristics, and the threshold judgment module is used for marking the classification and obtaining a result. The EEG classification detection device has the advantages of being simple in characteristic operation, high in practice and classification speed, high in classification accuracy and capable of achieving good classification detection effect.
Owner:SHANDONG UNIV
EEG intent decoding method based on non-negative cp decomposition model
ActiveCN111012335BImprove classification accuracyEfficient decodingMedical data miningKernel methodsPattern recognitionDecoding methods
The present invention provides an EEG intent decoding method based on a non-negative CP decomposition model. The method extracts the time component features of the EEG of different subjects in the boundary avoidance task, optimizes the feature dimension by using 2-DPCA, and uses The support vector machine classifies the features, which can reflect the differences in the EEG of the subject in different states, and the accuracy of the EEG classification of a single subject is high; and this method uses the interaction between the various modes of the EEG, and uses the channel The components and frequency components obtain the time component characteristics of the EEG, and the characteristics of the obtained EEG time components are well separable. By optimizing its dimension, it can effectively detect the left and right hand movements in the boundary avoidance task. EEG intent to decode.
Owner:YANSHAN UNIV
A kind of electroencephalogram signal classification method, device, equipment and medium
ActiveCN109766845BImprove accuracyCharacter and pattern recognitionVisual evoked potentialsSignal classification
The present application provides a method for classifying EEG signals, the method comprising: obtaining an EEG signal sequence to be identified; inputting the EEG signal sequence to be identified into an EEG signal classification model to obtain the EEG signal sequence to be identified EEG classification information, the EEG classification information includes motor imagery EEG signals and steady-state visual evoked potential EEG signals.
Owner:XUANWU HOSPITAL OF CAPITAL UNIV OF MEDICAL SCI +1
Voting strategy classification method of motor imagery EEG signal based on extremely fast learning machine
ActiveCN103413050BReduce randomnessImprove classification accuracySpecial data processing applicationsLearning machineFeature vector
The invention belongs to the field of mode recognition and a brain-machine interface and discloses a motor imagery electroencephalogram voting strategy sorting method based on extreme learning machines. The motor imagery electroencephalogram voting strategy sorting method comprises the following steps: dividing an original motor imagery electroencephalogram into S sections of sub-signals; carrying out dimensionality reduction on each section of sub-signal by a principal component analysis method; carrying out secondary dimensionality reduction on a feature vector subjected to the dimensionality reduction by a linear discrimination analysis method; carrying out the same processing on the S sections of sub-signals to finally obtain S (K-1)-dimensional feature vectors, and combining the S (K-1)-dimensional feature vectors to finally obtain an S*(K-1)-dimensional feature; and transmitting the S*(K-1)-dimensional feature into a plurality of ELM (Extreme Learning Machine) sorting devices so as to obtain a final sorting result by utilizing a voting sorting strategy. The invention provides a voting sorting strategy based on the ELMs; compared with a traditional multi-time ELM average accuracy scheme, the method provided by the invention has the advantages that the sorting accuracy is improved under the condition of not influencing the training sorting low consumption.
Owner:BEIJING UNIV OF TECH
Multi-sensory-mode BCI-VR control method and system and VR equipment
PendingCN113552941AHigh precisionImprove user experienceInput/output for user-computer interactionCharacter and pattern recognitionPhysical medicine and rehabilitationFeature extraction
The invention discloses a multi-sensory-mode BCI-VR control method and system and VR equipment, and the method comprises the steps: obtaining an EEG signal which is triggered by a VR scene and comprises a plurality of sensory signals corresponding to different sensory modes; performing feature extraction and classification recognition processing on the sensory signals to obtain EEG classification information corresponding to the sensory signals; and combining the EEG classification information to obtain an identification result of the EEG signal. According to the method, the traditional VR equipment is combined with the multi-sensory-mode BCI, and the SSVEP stimulation is fused to simulate the real environment, so that the adaptability and fatigue feeling of direct repeated stimulation on the brain of the user caused by the fact that the SSVEP single-visual-mode BCI depends on target object flicker stimulation are relieved, the visual fatigue is relieved, and the use experience of the user is improved.
Owner:上海厉鲨科技有限公司
Mass face library retrieval method based on man-machine collaboration
PendingCN112949369AQuick searchExact searchDigital data information retrievalCharacter and pattern recognitionCognitive capabilityImage retrieval
The invention discloses a massive face image library retrieval method based on man-machine collaboration. The low-level features extracted by computer vision are different from the high-level semantic features captured by human vision, so that the computer vision retrieval result cannot reach the expectation of people. Face images are small in difference in low-level contour features, and similar faces are difficult to distinguish only depending on computer vision. The method comprises the following steps: 1, establishing an image library; 2, training an EEG classification model by using an EEG signal generated by watching the portrait by a person; and 3, performing online iteration by using an EEG classification model, and retrieving a target image required by the testee from the image data. The face image retrieval method is different from a traditional face retrieval method, fast, accurate and robust massive face image retrieval is achieved by combining the powerful cognitive ability of the human brain with the fast computing ability and the massive storage ability of a computer, and the face image retrieval method has remarkable application value.
Owner:HANGZHOU DIANZI UNIV
SSVEP EEG classification method based on convolutional neural model augmented with EMD data
ActiveCN109299751BReduce fatigueImprove usabilityCharacter and pattern recognitionNeural architecturesEeg dataData transformation
The present invention relates to the algorithm of brain-computer interface, artificial intelligence and pattern recognition, more specifically, relates to the SSVEP EEG classification method based on the convolutional neural model enhanced by EMD data, which can be applied to the fields of medical equipment, human-computer interaction, robot control, etc. . This method first preprocesses the original EEG data. After preprocessing, the empirical mode decomposition method is used to decompose the original EEG data, and a large number of artificial EEG data that conform to the time-frequency domain characteristics of the original EEG signal are generated by mixing. The original EEG data is merged and used for parameter training of the neural network, so as to achieve the effect of effectively training network parameters with a small amount of EEG data. Finally, the complex Morlet wavelet transform is used to generate the EEG tensor, and the original time domain data is converted into a tensor dictionary integrating time domain, frequency domain and spatial information as the input of the neural network, and the convolutional neural network model is used to enhance the data. EEG training set for classification.
Owner:NANKAI UNIV
An EEG Signal Classification Method Based on Improved Deep Residual Group Convolutional Network
ActiveCN113065526BReflect EEG characteristicsImprove accuracyCharacter and pattern recognitionNeural architecturesSignal classificationTest set
The invention belongs to the field of pattern recognition and EEG signal processing, and relates to an EEG signal classification method based on an improved ResNeXt network; it includes four parts: EEG signal acquisition, preprocessing, feature extraction, and training ResNeXt classification network; training ResNeXt classification network is Refers to: dividing the training set and test set; building an improved ResNeXt EEG signal classification network; training the improved ResNeXt EEG signal classification network; building an improved ResNeXt EEG signal classification network refers to: improving on the basis of ResNeXt, and convolving the groups The middle layer of the convolutional layer of each block module increases the direct connection operation, speeds up the speed of model convergence, reduces the test error of the model, and improves the generalization ability; the invention speeds up the convergence speed of the classification model, compared with the convolutional neural network brain The electrical classification model, the improved ResNeXt classification model is easier to optimize, effectively improves the gradient explosion problem in the deep training model, and can greatly deepen the number of layers of the network while avoiding the degradation of the classification model.
Owner:JILIN UNIV
EEG classification model generation method, device and electronic equipment
Owner:SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
EEG classification transfer learning method and system based on Euclidean alignment and Procuses analysis
ActiveCN111832427AInput/output for user-computer interactionCharacter and pattern recognitionPattern recognitionCentering matrix
The invention discloses an EEG classification transfer learning method and system based on Euclidean alignment and Procuses analysis, and belongs to the field of brain-computer interfaces based on motor imagery. The method comprises the following steps: performing covariance alignment on previous user and new user feature matrixes; performing mean value alignment on the feature matrixes of the previous users and the new users; calculating the category center of the previous user according to the label of the previous user and the feature matrix of the previous user after the mean value is aligned, and calculating the category center of the new user according to a pseudo label of the new user and the feature matrix of the new user after the mean value is aligned; constructing a previous user and new user category center matrix, and calculating a rotation matrix for aligning the category center of the new user with the category center of the corresponding previous user through an orthogonal Principal analysis method; and multiplying the feature matrix and the rotation matrix of the new user after mean value alignment to obtain finally aligned new user data.
Owner:HUAZHONG UNIV OF SCI & TECH
A Classification Method of Elm Motor Imagery EEG Based on AR Coefficient Space
InactiveCN104951797BImprove classification accuracyImprove stabilityCharacter and pattern recognitionMedicineAlgorithm
The present invention relates to an ELM motor imagery EEG classification method based on the AR coefficient space. First, the motor imagery EEG signal of a single channel is used as a random signal, and the p-order AR model is used for fitting, expressed as: ; and then the Burg algorithm is used Solve the undetermined coefficients a1, a2, ..., ap of the AR model; then combine the p-order AR coefficients of m channel motor imagery EEG signals into an AR coefficient vector, and embed it into the original optimization problem of ELM network parameter training Among them, the optimal external weight β is solved, and the ELM classification algorithm based on the AR coefficient space is constructed. The method of the invention improves the classification accuracy and classification speed of motor imagery EEG signals.
Owner:FUZHOU UNIV
An EEG classification method based on entropy of dynamic functional connectivity
The invention discloses an EEG classification method based on the entropy value of dynamic functional connection. First, the obtained original EEG signals are preprocessed, and then filtered; then the phase synchronization analysis method is used to calculate the EEG signals of each frequency band at each time. Point the phase relationship between each two channels to obtain the dynamic functional connection matrix; then calculate the time domain entropy of the phase relationship value between the two channels one by one to obtain the information entropy of each edge to measure the time of each edge of the EEG functional network The complexity of the domain; the dynamic functional connection entropy of each frequency band is used as the classification feature of the EEG functional network, and the adaptive improvement classifier is trained to obtain multiple adaptive improvement classifiers and the corresponding classification accuracy; The samples are combined for classification. The problem of low classification accuracy in the existing EEG signal classification methods is solved.
Owner:北京大智商医疗器械有限公司
A multi-class EEG classification method based on dual-rule active extreme learning machine
ActiveCN109800651BRelieve pressureCutting costsCharacter and pattern recognitionLearning machineCosine similarity
The invention proposes a multi-class EEG classification method based on a dual-rule active over-limit learning machine. The method of the present invention adopts the core idea of active learning, first evaluates the uncertainty of unlabeled samples according to the extreme learning machine classifier, and secondly eliminates unlabeled samples with high similarity according to the cosine similarity rule, and obtains the most valuable small number of unlabeled samples Labeling, and then use the filtered data to train the extreme learning machine, maximize the use of the internal information of the labeled EEG signal, thereby reducing the dependence on the labeled EEG data, and obtain a higher multi-classification of motor imagery tasks accuracy. This method has broad application prospects in the field of brain-computer interface.
Owner:HANGZHOU DIANZI UNIV
A method for EEG classification based on frequency-band attention residual network
ActiveCN109846477BFlexible accessDiagnostic recording/measuringSensorsClassification methodsInformatics
An EEG classification method based on frequency-band attention residual network belongs to the field of pattern recognition and bioinformatics. First, preprocess the original EEG signal to remove noise and artifacts in the signal; then, use a fixed-length sliding window to segment the preprocessed N-lead EEG signal to obtain a total of S-segment N-lead sub-signals ; Then use the wavelet packet decomposition to decompose and reconstruct the S segment N derivation sub-signals, each sub-band signal of each data segment is decomposed into F sub-band signals; after that, the multi-conductor decomposition results of each frequency band are converted into electrode correlation matrix; and then use the electrode correlation matrix of F frequency bands as the input of the frequency band attention residual network to complete the classification of EEG signals. Compared with the existing technology, the present invention has the advantages that the frequency band attention module is used to obtain frequency band importance weights, and a personalized frequency band attention distribution is given to each sample.
Owner:BEIJING UNIV OF TECH
EEG classification method and system based on EEG classification based on Ou's alignment and PROCRUSESTES analysis
ActiveCN111832427BInput/output for user-computer interactionCharacter and pattern recognitionCentering matrixLearning methods
Owner:HUAZHONG UNIV OF SCI & TECH
A method for calculating symbol transfer entropy and brain network characteristics based on time-frequency energy
ActiveCN112932505BReflect the differenceComply with nonlinear dynamic characteristicsDiagnostic recording/measuringSensorsBiologyEeg classification
The invention discloses a method for calculating symbol transfer entropy and brain network characteristics based on time-frequency energy. First, the collected motor imagery EEG signals (MI-EEG) are preprocessed based on a common average reference; MI-EEG performs continuous wavelet transform to obtain its time-frequency-energy matrix, and splices the time-energy sequences corresponding to each frequency in the frequency band closely related to motor imagery in turn to obtain the one-dimensional time-frequency energy of the lead sequence; then, calculate the sign transfer entropy between any two lead time-frequency energy sequences, construct a brain connectivity matrix, and use the Pearson feature selection algorithm to optimize the matrix elements; finally, calculate the degree and betweenness centrality of the brain functional network , constituting the feature vector for MI‑EEG classification. The results show that the present invention can effectively extract the frequency domain features and nonlinear features of MI-EEG, and has obvious advantages compared with the traditional feature extraction method based on brain function network.
Owner:BEIJING UNIV OF TECH
Who we serve
- R&D Engineer
- R&D Manager
- IP Professional
Why Patsnap Eureka
- Industry Leading Data Capabilities
- Powerful AI technology
- Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.
© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com