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34 results about "Scalp electroencephalogram" patented technology
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Electroencephalogram monitoring device and method with epileptic seizure predicting function
InactiveCN107616793ADiagnostic recording/measuringSensorsMonitoring systemScalp electroencephalogram
The invention discloses an electroencephalogram monitoring device with an epileptic seizure predicting function and a method. The electroencephalogram monitoring device and method can be applied to ascalp electroencephalograph machine or an embedded cortex electroencephalogram monitoring system. The method of the device comprises the following steps of 1, acquiring an electroencephalogram signalof a patient through an electroencephalogram acquisition device; 2, transmitting the electroencephalogram signal to an epileptic predicting alarming device; 3, judging the state of the patient throughan epilepticprediction algorithm module in the epileptic predicting alarming device, and once the epileptic measurement module judges that the patient is in the early stage of anepileptic seizure, sound-lightalarm can be triggered, and the patient and his / her dependents are prompted to take prevention measures. The epilepticprediction algorithm moduleuses a directed transformation function as a basis and characteristics of a connection matrix among electroencephalogram signals as parameters for predicting the epilepticseizure of the patient, the epileptic seizure can be predicted several dozens of minutes in advance, and the electroencephalogram monitoring device and method have high clinical and practical significance.
Owner:UNIV OF ELECTRONIC SCI & TECH OF CHINA
Electroencephalogram source localization method based on granger causality
ActiveCN104305993AAchieve positioningImprove instabilityDiagnostic recording/measuringSensorsDiseaseInstability
An electroencephalogram source localization method based on granger causality includes the steps: recording scalp electroencephalogram signals of a plurality of leads by an electroencephalogram acquisition device and performing elementary pretreatment; respectively analyzing the granger causality among each lead and the other leads by taking each lead as an observation lead, and performing source localization among the leads according to causality indexes; counting the number of the leads capable of becoming sources, and calculating the possibility index of taking each lead capable of becoming a source as a whole brain source area to realize whole brain source localization. The electroencephalogram source localization method solves the problems of poor electroencephalogram source localization stability and uniqueness, and the possibility of taking each lead as the whole brain source area can be obtained, so that whole brain source localization is realized. Instability and non-uniqueness of solutions of electroencephalogram inverse problems can be improved to a certain extent. The electroencephalogram source localization method can be used for determining a cranial nerve system disease focus portion, localizing a neurosurgery operation and performing electroencephalogram source localization and tracking in recognition tasks, and has an important significance in scientific research and clinical practice.
Owner:INST OF BIOMEDICAL ENG CHINESE ACAD OF MEDICAL SCI
Method for automatically detecting interictal spike waves of epilepsy
ActiveCN108577834AImprove accuracyReduce false alarm rateDiagnostic recording/measuringSensorsAlgorithmSpike wave
The invention relates to a method for automatically detecting interictal spike waves of epilepsy. The method comprises the following steps: extracting, by an MEMD (multivariate empirical mode decomposition), a component in which the spike waves present from scalp electroencephalogram of a epilepsy patient, performing the signal envelop, solving a dynamic threshold curve, and positioning a possibleposition of the spike waves. The method has the advantages that the frequency band range of electroencephalogram (EEG) can be adaptively adjusted according to the electroencephalogram, and the presence position of the interictal spike wave of the epilepsy can be accurately positioned; a novel concept is provided for reducing the burden of the clinical doctor for recognizing the spike waves, and the interictal spike waves can be more accurately automatically detected; and by virtue of comparison, the comprehensive advantage of the method in the aspect of the sensitivity and error rate is better than a method based on a signal envelop distribution model, and a better spike wave detection result can be achieved.
Owner:杭州瑞尔唯康科技有限公司
Method of early detection of epileptic seizures through scalp eeg monitoring
PendingUS20210282701A1Low costReduce riskMedical data miningMechanical/radiation/invasive therapiesPhysical medicine and rehabilitationData acquisition
A system performs concurrent detection and early detection of epileptic seizure episodes, based on scalp electroencephalogram (EEG) of a patient collected through a data acquisition device in the course of the patient's normal daily activities. An early detection model, which is trained and retrained applying machine learning techniques at predetermined intervals on the collected data, enables issuing of an early warning of an upcoming seizure episode to allow the patient to take necessary preparatory actions (e.g., seeking a safe location for the episode to happen and alerting care-givers).
Owner:NCEFALON CORP
4D transcranial focused ultrasonic neuroimaging method based on acoustoelectric effect
ActiveCN109645999AImprove time resolutionImprove spatial resolutionUltrasonic/sonic/infrasonic diagnosticsDiagnostic recording/measuringPhase correlationSonification
The invention discloses a 4D transcranial focused ultrasonic neuroimaging method based on an acoustoelectric effect. The method includes the steps that a focused ultrasonic signal generator and an energy converter are adopted to achieve generation and emission of focused ultrasonic signals, and focused ultrasonic characteristics under different parameters are determined; an electroneurographic signal collecting device composed of an electroencephalogram collecting system provided with an electroencephalogram electrode, an electroencephalogram amplifier and an electroencephalogram filter and anelectroneurographic-signal measuring system based on ultrasonic modulation is installed and connected; ultrasonic waves are spread through the cranium and focused on a certain position, and based onthe acoustoelectric effect principle, scalp electroencephalogram signals after ultrasonic modulation, namely, acoustoelectric signals are collected; through the amplitude values, the frequency and thephase position relevance of the acoustoelectric signals and activation source signals, the electroencephalogram signals are subjected to spatial encoding and demodulation, 4D neuroimaging of high space-time resolution is achieved, and multiple requirements in practical application are met.
Owner:TIANJIN UNIV
Time-varying brain network reconstruction method facing dynamic video target detection
ActiveCN112641450ACharacter and pattern recognitionDiagnostic recording/measuringPattern recognitionNeural information processing
The invention belongs to the technical field of electroencephalogram recognition, and particularly relates to a time-varying brain network reconstruction method facing dynamic video target detection. The method comprises the following steps that a scalp electroencephalogram signal of a testee under an acquired dynamic video is mapped to a cortex space, and a cortex signal with high temporal-spatial resolution is reconstructed; and an interested brain area is selected from the cortex space to serve as a network analysis node, and a cortex time-varying brain network connection graph reflecting the cognitive processing process is obtained by analyzing the neural information processing process from target search to target discovery according to cortex signal. According to the method, a neural processing mechanism of the process from search to discovery of a dynamic visual target is expanded, scalp electroencephalogram with the high temporal-spatial resolution is selected to be mapped into cortex electroencephalogram with the high temporal-spatial resolution, a finer brain activity diagram can be obtained, the brain neural information processing process is further explored through a time-varying network analysis method, the application of a brain-computer interface in an actual environment is facilitated, and the application prospect is good.
Owner:PLA STRATEGIC SUPPORT FORCE INFORMATION ENG UNIV PLA SSF IEU
Self-adaptive electroencephalogram filtering method
InactiveCN106963373AReduce distractionsSuitable for unshielded application scenariosDiagnostic recording/measuringSensorsSignal-to-noise ratio (imaging)Blind signal separation
Provided is a self-adaptive electroencephalogram filtering method. The device comprises a signal acquisition unit, an analog signal processing unit and a self-adaptive filtering unit, wherein the signal acquisition unit is used for acquiring user scalp electroencephalogram signals, spatial electromagnetic signals and electrode motion signals and producing electroencephalogram voltage signals U1, spatial electromagnetic voltage signals U2 and electrode motion digit signals D3. The analog signal processing unit is used for further amplifying the acquired voltage signals U1 and the voltage signals U2 to millivolt level, conducting filtering and AD conversion processing on the U1 and U2, respectively generating the digit signals D1 and D2 and sending the digit signals and the D3 to the self-adaptive filtering unit. The self-adaptive filtering unit utilizes the spatial electromagnetic signals D2 and the electrode motion signals D3 obtained through processing of the analog signal processing unit and adopts a common mode reference method and a blind signal separation algorithm to conduct filtering processing on the electroencephalogram signals D1. The interference of electromagnetic radiation and motion artifacts in the electroencephalogram signal acquisition process can be effectively relieved, and the signal-to-noise ratio of the electroencephalogram signals is improved.
Owner:NEURACLE TECH CHANGZHOU CO LTD
Motor imagery electroencephalogram feature enhancement method and system
InactiveCN111110230AImprove recognition rateComprehensiveDiagnostic recording/measuringSensorsVisual cortexElectroencephalogram feature
The invention relates to a motor imagery electroencephalogram feature enhancement method and system. The enhancement method comprises the following steps: acquiring a multi-mode neural feedback training motor imagery electroencephalogram feature enhancement paradigm; based on the multi-mode neural feedback training motor imagery electroencephalogram feature enhancement normal form, acquiring multi-channel electroencephalogram signals of a pair of a parietal region motor cortex and an occipital region visual cortex; preprocessing the electroencephalogram signals, and determining the preprocessed electroencephalogram signals; conducting dynamic and coupled multi-level feature extraction on the preprocessed electroencephalogram signals, and determining dynamic features and coupling features of the preprocessed electroencephalogram signals; and according to the dynamic features and the coupling features, displaying feature values of the motor imagery electroencephalogram signals before enhancement and feature values of the motor imagery electroencephalogram signals after enhancement. By adopting the enhancement method and system provided by the invention, the recognition rate of effective scalp electroencephalogram signals can be improved.
Owner:YANSHAN UNIV
Scalp electroencephalogram (EEG) retrospective epileptic seizure point detection method and system
InactiveCN105249962AQuick fixImprove convenienceDiagnostic recording/measuringSensorsEeg dataEeg signal analysis
The invention belongs to the technical field of scalp electroencephalogram (EEG), and presents a scalp EEG retrospective epileptic seizure point detection method and system. The method is as follows: retrospective analysis is carried out on EEGs with various artifact EEGs being removed through a non-linear dynamics sample entropy threshold value detection method to determine the epileptic seizure point. The scalp EEG retrospective epileptic seizure point detection system includes an EEG receiving module, an epileptic seizure point determining module, and an information output module, wherein the EEG receiving module is used for receiving original EEG collected clinically; the epileptic seizure point determining module is used for analyzing and determining the retrospective epileptic seizure point through the EEG received by the EEG receiving module; the information output module is used for outputting the retrospective epileptic seizure point determined by the epileptic seizure point determining module. According to the scalp EEG retrospective epileptic seizure point detection method and system, the EEG data can be demixed in 10s, so that the epileptic seizure point can be quickly determined and the effect is obvious.
Owner:BEIJING UNION UNIVERSITY
Detecting method for multi-modal brain switch based on SSVEP and P300
ActiveCN104090653AImprove performanceImprove false positivesInput/output for user-computer interactionGraph readingAudio power amplifierDisplay device
The invention discloses a detecting method for a multi-modal brain switch based on SSVEP and P300. The method comprises the following steps: generating a scalp electroencephalogram signal by a user according to a working interface command in a display device; collecting the scalp electroencephalogram signal by an electrode cap, and transferring the signal by an I / O interface module of a computer to a signal processing module in the computer after the signal is converted by a digital-to-analogue conversion module and is amplified by a signal amplifier; copying the scalp electroencephalogram signal into two copies, and respectively performing P300 electric potential detection and SSVEP detection; classifying the control state and the idle state by combining the respective detection output of the P300 electric potential and the SSVEP, and determining goals. For the detecting method, the problem that pages with complex contents cannot be browsed for a browser based on the non-mouse control can be solved, the control speed is greatly improved, and the precision is greatly improved.
Owner:华南脑控(广东)智能科技有限公司
Electroencephalography tracing and linear correlation based positioning method for cerebral deep magnetic stimulation target
ActiveCN109568795AMagnetic stimulation is accurate and effectiveEasy to adjustMagnetotherapy using coils/electromagnetsDiagnostic recording/measuringPower flowLinear correlation
The invention relates to an electroencephalography tracing and linear correlation based positioning method for a cerebral deep magnetic stimulation target. A novel way of think and a novel method forpositioning the cerebral deep magnetic stimulation target are provided from the angle of electroencephalography tracing and linear correlation. The method comprises the steps of acquiring current intensity and three-dimensional coordinates of each dipole of cerebral deep tissue through electroencephalography tracing, calculating correlation between each scalp electroencephalogram and dipoles of the cerebral deep tissue, and selecting a scalp electrode with highest correlation as a position of the cerebral deep magnetic stimulation target. The target positioning method disclosed by the invention can enable cerebral deep magnetic stimulation to be more accurate and effective, is beneficial to magnetic stimulation intervention and regulation of the cerebral deep tissue and has an important significance in brain science researches.
Owner:INST OF BIOMEDICAL ENG CHINESE ACAD OF MEDICAL SCI
Scalp electroencephalogram collection site sorting method and system
InactiveCN108852348AImprove classification accuracyImprove performanceDiagnostic recording/measuringSensorsTime domainElectroencephalogram feature
The invention discloses a scalp electroencephalogram collection site sorting method and system. The collection site sorting method includes the steps of reading of data; preprocessing of the data, wherein original electroencephalogram signals are subjected to artifact removal and noise suppression processing, and processed electroencephalogram signals of a three-dimensional matrix are output; extraction of features, wherein the time domain features and the frequency domain features are extracted, and feature numbers, test frequency numbers and channel numbers constitute electroencephalogram feature data of the three-dimensional matrix; signature of the features, wherein the electroencephalogram feature data is subjected to feature pooling, feature clustering and data reorganization to obtain electroencephalogram signature data, recorded by the test frequency numbers and the channel numbers, of a two-dimensional matrix; correlation sorting, wherein correlations between the electroencephalogram signature data and electroencephalogram signal categories are calculated and sorted, and the channel numbers of one-dimensional vectors are output; output of results. By means of the method, arandom and divergent calculation process cannot be caused, the performance of an EEG-BCI system is improved, the data scale is reduced, the universality is high, and convenience is provided for incorporating other methods into the system.
Owner:ACADEMY OF MILITARY MEDICAL SCI
Epileptic seizure prediction method through electroencephalogram signal on the basis of multi-scale convolution and self-attention network
ActiveCN113907706AIncrease widthImprove adaptabilityDiagnostic recording/measuringSensorsSignal onTerm memory
The invention discloses an epileptic seizure prediction method through an electroencephalogram signal on the basis of multi-scale convolution and a self-attention network. The epileptic seizure prediction method comprises the following steps that a convolutional neural network is combined with a long-short term memory network, an original electroencephalogram signal segment is taken as a network input, the idea of Inception is referred, a multi-scale convolution core is used to code an electroencephalogram signal sequence, a convolution operation is combined with pooling to complete down-sampling so as to keep features while dimensionality reduction is realized, lSTM is used for extracting the time sequence characteristics of the electroencephalogram signals, but the LSTM can only learn the front-to-back information of the electroencephalogram signals and but can not learn the back-to-front information, the bidirectional long-short-term memory network is combined with an attention mechanism to carry out modeling on the time characteristics of the electroencephalogram signal fragment, so that the influence of a complicated pretreatment process and manual intervention of scalp electroencephalogram signals is reduced, and better prediction performance is obtained. The epileptic seizure prediction method has certain generalization performance and can provide a certain basis for early warning of epileptic seizure.
Owner:BEIJING UNIV OF TECH
Scalp electroencephalogram electrode rapid positioning method and positioning model
PendingCN111991081AThe process is fast, accurate and reliableFast, accurate and reliable positioningDiagnostic markersComputer-aided planning/modellingElectroencephalogram electrodeRadiology
The invention discloses a scalp electroencephalogram electrode rapid positioning method and a positioning model. The scalp electroencephalogram electrode rapid positioning method is characterized by comprising the following steps: acquiring craniofacial model parameters of a patient through skull CT or magnetic resonance MRI thin-layer scanning; preparing a craniofacial model by adopting 3D printing according to the craniofacial model parameters, and determining the positions of electrodes in the craniofacial model to form a craniofacial positioning model; and fitting the craniofacial positioning model to the scalp surface of the patient, and enabling the positions of the electrodes determined in the craniofacial positioning model to be in one-to-one correspondence to the mounting positions of the electrodes, thus completing the rapid positioning of scalp electroencephalogram electrodes. With the adoption of the scalp electroencephalogram electrode rapid positioning method and the positioning model, the rapid and accurate positioning is realized, meanwhile, the same positioning is repeated during the secondary review, and thus the comparison effect is greatly improved.
Owner:ANHUI PROVINCIAL HOSPITAL
Method for identification of pathological brain activity from scalp electroencephalogram
ActiveCN111093471AMedical automated diagnosisDiagnostic recording/measuringCerebral activityStatistical analysis
The present invention relates to a computer-implemented method for detecting pathological brain activity patterns from a scalp electroencephaiographic signal, the method comprising the steps of obtaining (A) an electroencephaiographic signal as a function of multiple channels and time; identifying (C), for each channel, the zero- crossings of the electroencephaiographic signal over a fixed threshold; generating a zero-crossing representation of at least a segment of the obtained electroencephaiographic signal with the identified zero-crossings; obtaining (D) a reference family of real functions of time and channels from a zero-crossing statistical analysis of zero-crossing representation of pre-recorded electroencephaiographic signals; calculating (E) a matching score by comparing said zero-crossing representation of a segment of the electroencephaiographic signal with at least one reference function from the reference family of functions; and computing the matching score as a functionof time by sliding the at least one reference function from the reference family of functions over the electroencephaiographic signal.
Owner:波尓瑟兰尼提公司 +5
Electrode placement position determining device and method for scalp electroencephalogram
ActiveCN105708452ADetermine convenient, fast and accurateEasy to operateDiagnostic recording/measuringSensorsElectrode placementMeasurement device
The invention relates to a measurement device for determining an electrode placement position and used for scalp electroencephalogram. The measurement device comprises four main hollow tapes, four side-edge hollow tapes, a top button and eight sliding buttons, wherein the initial ends of the four main hollow tapes are fixed to one point by the top button; the main hollow tapes and the side-edge hollow tapes are provided with hollow tracks extending along the extending direction of the tapes and the hollow tracks are used for allowing the sliding buttons to slide; each side-edge hollow tape is connected with the hollow tracks of the two adjacent main hollow tapes respectively through the two sliding buttons arranged in the hollow track of the side-edge hollow tape, so that each sliding button slides in the hollow tracks of one side-edge hollow tape and the corresponding main hollow tape simultaneously, and scales are marked on the hollow tapes. In use, the measurement device covers the surface of the brain of a patient; the electrode placement position can be conveniently, rapidly and accurately positioned, the operation is simple, an operation process or a measurement result can be easily accepted by a doctor, and finally, an accurate electroencephalogram result is conveniently obtained.
Owner:孟艳林 +1
Depression and bipolar disorder brain network analysis method based on dual-channel phase synchronization feature fusion
The invention discloses a depression and bipolar disorder brain network analysis method based on dual-channel phase synchronization feature fusion. The method comprises the following steps: constructing phase synchronization matrixes by using resting scalp electroencephalogram signals and respectively adopting a phase delay index, a weighted phase delay index and a phase lock value synchronization index, and fusing the phase synchronization matrixes, so that brain lesion areas of patients with depression and bipolar disorder can be effectively distinguished. Compared with the prior art, the method provided by the invention has the following advantages that more effective information can be obtained by fusing the three synchronous features to detect a weak interaction relationship among the signals, and whether the electrode signals of the brain region are in a synchronous state or not can be found favorably, and thus the lesion brain region can be identified effectively. Experiments show that compared with a healthy control group, the method has the advantage that the difference between the frontal lobe and the parietal lobe of a patient with depression and bipolar disorder is relatively large.
Owner:BEIJING UNIV OF TECH
Dynamic brain function network generation method, system and equipment
ActiveCN113274037AHigh precisionSolving the Problem of Analysis BiasCharacter and pattern recognitionDiagnostic recording/measuringNetwork generationResting state fMRI
The invention discloses a dynamic brain function network generation method, system and equipment, wherein the method comprises the steps of: obtaining the scalp electroencephalogram data of a tested person, and carrying out artifact removal preprocessing, so as to obtain a high-quality scalp electroencephalogram signal; based on the high-quality scalp electroencephalogram signal, obtaining a micro-state time sequence of the tested person, and for a specified micro-state, determining a closed-loop period of the specified micro-state in the micro-state time sequence; determining a resting state function network of the tested person in the closed-loop period based on the time sequence signal in the closed-loop period; and based on the plurality of resting state function networks, determining a dynamic resting state function network changing along with the micro-state time sequence in the specified micro-state period, and carrying out network topology characteristic analysis. According to the technical scheme provided by the invention, a more accurate dynamic brain function network can be generated.
Owner:SUZHOU INST OF BIOMEDICAL ENG & TECH CHINESE ACADEMY OF SCI +1
Methods and Apparatus for Detection and Imaging of Epileptogenicity from Scalp High-Frequency Oscillations
PendingUS20210106247A1Facilitate clinical managementEasy to manageElectroencephalographySensorsPattern recognitionScalp electroencephalogram
A system and method to facilitate the analysis of long-term scalp EEG recordings and the investigation of underlying epileptogenicity. Pathological high frequency oscillations are identified and extracted from the EEG recordings and electrophysiological source imaging is used to reconstruct cortical activity, which can be used as an aid in surgical resection for the treatment of epilepsy.
Owner:CARNEGIE MELLON UNIV
Microstate analysis method based EEG real-time detection and analysis platform
PendingCN111643075ARealize integrated operationReal-time monitoring statusDiagnostic recording/measuringSensorsScalp electroencephalogramUSB
The invention relates to a microstate analysis method based EEG real-time detection and analysis platform. The platform includes an EEG electrode array (1) used for collecting scalp electroencephalogram, a slave computer (2) used for scalp electroencephalogram acquisition, preprocessing and microstate analysis and a host computer (3) for displaying analyzed results; the slave computer (2) includesan ADC module (4), a DSP operation chip (5) and a USB communication module (6); the EEG electrode array (1) and the ADC module (4) collect scalp electroencephalogram signals; an EEG preprocessing module (7) in the DSP operation chip (5) preprocesses an original EEG signal, so that a clean EEG signal (10) for analysis can be obtained; and the preprocessed EEG signal is input to a microstate analysis module (11) to carry out further analysis and processing so as to obtain the analyzed results and specific characteristic parameters.
Owner:TIANJIN UNIV
Nerve feedback training instrument used for brain memory function improvement on basis of electroencephalogram
InactiveCN102319067BEnhance memoryDiagnostic recording/measuringSensorsMedicineScalp electroencephalogram
The invention relates to a nerve feedback training instrument used for the brain memory function improvement on the basis of electroencephalogram. The scalp electroencephalogram collected in the brain activity process can be used for carrying out quantitative detection on the real-time state of the memory, the electroencephalogram rhythm waves presenting the memory level are shown to users to guide the users to consciously regulate the electroencephalogram rhythm waves, and the goal of improving the memory level is reached. The instrument is characterized in that firstly, an electroencephalogram collection module is used for collecting the electroencephalogram of the users under the classical memory task, and the electroencephalogram rhythm waves presenting the memory level are extracted; and then, the real-time memory state of the brain is described through an electroencephalogram analysis module and is fed back and output to the users in a striking and attractive mode. The users can directionally regulate the electroencephalogram rhythm waves according to the real-time feedback, and the goal of improving the memory is reached. The memory electroencephalogram of the users is used as the feedback signals of the system in a nerve feedback system for the first time, and the invention provides a new idea for the application direction of the nerve feedback system.
Owner:BEIJING NORMAL UNIVERSITY
Electric model for conducting cortex electroencephalogram into scalp electroencephalogram
Owner:JIANGSU UNIV OF SCI & TECH
Method for identification of pathological brain activity from scalp electroencephalogram
ActiveUS20210093247A1Medical automated diagnosisDiagnostic recording/measuringPattern recognitionMedicine
A computer-implemented method for detecting pathological brain activity patterns from a scalp electroencephalographic signal, the method including the steps of obtaining (A) an electroencephalographic signal as a function of multiple channels and time; identifying (C), for each channel, the zero-crossings of the electroencephalographic signal over a fixed threshold; generating a zero-crossing representation of at least a segment of the obtained electroencephalographic signal with the identified zero-crossings; obtaining (D) a reference family of real functions of time and channels from a zero-crossing statistical analysis of zero-crossing representation of pre-recorded electroencephalographic signals; calculating (E) a matching score by comparing the zero-crossing representation of a segment of the electroencephalographic signal with at least one reference function from the reference family of functions; and computing the matching score as a function of time by sliding the at least one reference function from the reference family of functions over the electroencephalographic signal.
Owner:BIOSERENITY +5
Device and method for determining electrode placement position for scalp electroencephalography
ActiveCN105708452BDetermine convenient, fast and accurateEasy to operateDiagnostic recording/measuringSensorsElectrode placementScalp electroencephalogram
The invention relates to a measurement device for determining an electrode placement position and used for scalp electroencephalogram. The measurement device comprises four main hollow tapes, four side-edge hollow tapes, a top button and eight sliding buttons, wherein the initial ends of the four main hollow tapes are fixed to one point by the top button; the main hollow tapes and the side-edge hollow tapes are provided with hollow tracks extending along the extending direction of the tapes and the hollow tracks are used for allowing the sliding buttons to slide; each side-edge hollow tape is connected with the hollow tracks of the two adjacent main hollow tapes respectively through the two sliding buttons arranged in the hollow track of the side-edge hollow tape, so that each sliding button slides in the hollow tracks of one side-edge hollow tape and the corresponding main hollow tape simultaneously, and scales are marked on the hollow tapes. In use, the measurement device covers the surface of the brain of a patient; the electrode placement position can be conveniently, rapidly and accurately positioned, the operation is simple, an operation process or a measurement result can be easily accepted by a doctor, and finally, an accurate electroencephalogram result is conveniently obtained.
Owner:孟艳林 +1
Self-adhesion fixed scalp electroencephalogram electrode and electroencephalogram signal acquisition method
InactiveCN112043264ARelieve pressureImprove accuracyDiagnostic recording/measuringSensorsHead scalpAnatomy
The invention discloses a self-adhesion fixed scalp electroencephalogram electrode and an electroencephalogram signal acquisition method. The scalp electroencephalogram electrode comprises a comb tooth electrode, and an adhesion medium capable of adhering the comb tooth electrode to the scalp is arranged on the surface, facing the scalp direction, of the comb tooth electrode. The electroencephalogram signal acquisition method comprises the following steps of S1, selecting the comb tooth electrode as the scalp electroencephalogram electrode; S2, adhering the adhesion medium to the collection surface; S3, before the coating medium is subjected to film formation, pushing aside the hair at the position of a to-be-detected brain area, placing a signal acquisition electrode in the to-be-detectedbrain area, slightly pressing the signal acquisition electrode to make full contact with the scalp, and enabling the signal acquisition electrode to be fixed to the scalp under the action of the adhesion medium; S4, starting to collect electroencephalogram signals and recording the electroencephalogram signals; and S5, after the acquisition work is finished, wetting and wiping with alcohol to enable the signal acquisition electrode to fall off from the to-be-detected brain area, and wiping the acquisition surface of the signal acquisition electrode with alcohol to completely remove the adhesion medium on the acquisition surface.
Owner:江苏集萃脑机融合智能技术研究所有限公司
Emotion perception method and system for natural human-computer interaction
PendingCN113128353AReduce complexityImprove efficiencyInput/output for user-computer interactionCharacter and pattern recognitionEmotion perceptionElectroencephalogram feature
The invention discloses an emotion perception method for natural human-computer interaction. The emotion perception method comprises the following steps: S1, collecting scalp electroencephalogram signals in positive, mild and negative emotional states; s2, preprocessing the data acquired in the step S1; s3, constructing a training sample and a test sample; s4, training a deep convolutional neural network; and S5, testing the classification effect by the test set. The invention further discloses an emotion perception system for natural human-computer interaction. According to the electroencephalogram emotion recognition method, the high-dimensional spatial-temporal feature information in the electroencephalogram is learned through the spatial-temporal deep convolutional neural network, so that the efficiency and accuracy of an emotion recognition task are improved. According to the method, the problems of lost part of useful electroencephalogram feature information and feature extraction usefulness verification generated by inputting the convolutional neural network after feature extraction are solved, the complexity of emotion recognition is reduced, and the efficiency and accuracy of emotion recognition are improved.
Owner:ANHUI UNIVERSITY
Method for removing BCG artifacts through synchronous EEG-fMRI electroencephalogram signals
ActiveCN113081001AAvoid human errorLower requirementDiagnostic recording/measuringSensorsData setNMR - Nuclear magnetic resonance
The invention discloses a method for removing BCG artifacts through synchronous EEG-fMRI electroencephalogram signals. The method comprises the following steps: collecting and preprocessing multi-channel scalp electroencephalogram signals in eye opening and closing states in a common environment and a nuclear magnetic resonance environment, then performing data segmentation on the multi-channel scalp electroencephalogram signals, and constructing an eye opening and closing state data set; respectively training an eye-opening network model and an eye-closing network model for removing BCG artifacts by utilizing the eye-opening and eye-closing state data sets; according to the model, a network architecture model BCGGAN based on a CycleGAN is adopted; and the BCGGAN comprises a CycleGAN, an auto-encoder constraint and an intermediate feature constraint. According to the method, while the BCG artifacts are removed as far as possible, electroencephalogram information can be better and effectively reserved.
Owner:HANGZHOU DIANZI UNIV
Method for positioning scalp electroencephalogram epilepsy region based on artificial intelligence
ActiveCN114677379AAvoid the effects of poor trainingImprove recognition accuracyImage analysisCharacter and pattern recognitionPattern recognitionElectro encephalogram
The invention relates to the field of artificial intelligence, in particular to a scalp electroencephalogram epilepsy region positioning method based on artificial intelligence. Comprising the steps that historical electroencephalogram waveform images are collected and compressed, and a sparse coding image of each historical waveform image is obtained; a neural network is used for training, and the LOSS function historical value of each sparse coding image is obtained; calculating a mean value of historical values of the LOSS function of the sparse coding image, and obtaining an oscillation sparse coding image; calculating the similarity between the oscillation sparse coding image and each sparse coding image, and obtaining an associated sparse coding image; and reconstructing an LOSS function of the neural network, and outputting a labeled image. According to the technical means provided by the invention, the LOSS function of the neural network is re-constructed, so that the recognition accuracy of the neural network can be effectively improved, and the influence of a relatively poor neural network training result caused by inaccurate manual annotation is effectively avoided.
Owner:恒泰利康(西安)生物技术有限公司 +1
Scalp EEG feature extraction and classification method based on end-to-end convolutional neural network
ActiveCN110263606BImprove robustnessNot prone to severe overfittingCharacter and pattern recognitionBand-pass filterClassification methods
The invention discloses a method for extracting and classifying scalp EEG features based on an end-to-end convolutional neural network. Data enhancement is performed on training data, and then the enhanced training data is used to train the convolutional neural network; the data to be detected is input into the convolutional neural network for further processing. The feature extraction and classification steps are: S1. Filter and process the original scalp EEG signal with a band-pass filter to obtain the signal x θ 、x μ and x β ;S2, pair signal x θ 、x μ and x β Perform multi-scale temporal convolution and spatial convolution to extract features; S3, perform pooling operation on the feature map output by the convolutional layer; S4, perform feature fusion after pooling, and then send it to the fully connected layer to perform abstract feature extraction on the input Integration; S5, the output of the fully connected layer is sent to the softmax layer for classification. The present invention applies a brand-new data enhancement technology in the training stage. In the test stage, the data is first passed through the filter bank, and then input into multiple convolutional neural network branches to perform multi-scale convolution operations, which reduces the over-fitting phenomenon and improves the classification. Accuracy.
Owner:周军
Brain-computer interface decoding method based on intracranial electroencephalogram and scalp electroencephalogram fusion
PendingCN113951896AAccurate and real-time feedbackImprove classification accuracyDiagnostic recording/measuringSensorsPattern recognitionHead scalp
The invention discloses a brain-computer interface decoding method based on intracranial electroencephalogram and scalp electroencephalogram fusion. The brain-computer interface decoding method comprises the steps of synchronously collecting intracranial electroencephalogram signals and scalp electroencephalogram signals, preprocessing the electroencephalogram signals, extracting electroencephalogram signal features and jointly decoding the electroencephalogram signals. The motor imagery intention of a user is decoded by fusing the characteristics of an intracranial local field potential signal LFP and a scalp electroencephalogram EEG signal, the classification accuracy is high, the robustness is good, and accurate real-time feedback can be provided in rehabilitation training based on a brain-computer interface, so that a good rehabilitation effect is achieved, and a thought and a method are provided for combined application of the intracranial electroencephalogram signals and the scalp electroencephalogram signals.
Owner:ZHEJIANG LAB +1
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