84 results about "Visual evoked potentials" patented technology

The invention discloses a multi-modal electroencephalogram classification method under multi-source interference based on a multi-head attention mechanism, and relates to the technical field of electroencephalogram signal processing. The method comprises the following steps: synchronously acquiring three types of electroencephalogram modal signals of motor imagery, steady-state visual evoked and event-related potentials, and synchronously acquiring multi-source interference data; performing data preprocessing on the multi-mode signal data; time domain, frequency domain and spatial domain three-dimensional features of each electroencephalogram mode are extracted, and meanwhile, physical features of interference data are quantified; the multi-domain features of the three types of electroencephalogram modes serve as parallel query vectors, the multi-source interference features serve as key value vectors, interference suppression weights are dynamically distributed through a layered attention mechanism, and anti-interference enhanced multi-mode fusion features are generated; and the multi-modal fusion features are processed through a gating circulation unit and a time domain attention module, a multi-modal classification result is output, and online learning and weight updating are carried out. According to the method, a robust decoding scheme can be provided for a brain control interaction system in a complex environment.

Disclosed in the present application is a low-training-cost electroencephalogram feature decoding method for a steady-state visual evoked potential. The method comprises: collecting original steady-state visual evoked potential signals of a subject as training data; using filter banks to preprocess original steady-state visual evoked potentials Xn, so as to obtain preprocessed steady-state visual evoked potentials Xn (m); dividing the preprocessed steady-state visual evoked potentials Xn (m) into Pn sub-periods (I); on the basis of (I), obtaining spatial filters (II) of m-th sub-bands; integrating the spatial filters (II) to obtain an integrated spatial filter (III); averaging (IV) to obtain (V); copying and connecting a plurality of (V), and performing synthesis to obtain a steady-state visual evoked potential template signal (VI) having a required length; and identifying an unknown steady-state visual evoked potential K by means of the integrated spatial filter (III) and the steady-state visual evoked potential template signal (VI).

The invention discloses a brain-controlled rehabilitation equipment-oriented control strategy generation method, device and equipment and a storage medium, and relates to the technical field of signal processing, and the method comprises the following steps: acquiring a multi-channel electroencephalogram signal, and preprocessing the multi-channel electroencephalogram signal to obtain a target electroencephalogram signal comprising a steady-state visual evoked potential signal and a motor imagery signal, the target electroencephalogram signal corresponds to a preset action category; decoding the steady-state visual evoked potential signal by adopting filter group task related component analysis to obtain a correlation score vector; decoding the motor imagery signal by adopting a Mangban dynamic routing space-time network model to obtain a classification score vector; performing posterior probability distribution conversion and weighted fusion on the correlation score vector and the classification score vector to obtain fusion probability distribution; and generating a target control strategy according to the action category corresponding to the highest probability value in the fusion probability distribution. The control strategy obtained by the invention can consider both intention recognition precision and rehabilitation nerve activation effect.

The invention relates to the technical field of ophthalmology informatization, in particular to an intelligent optic nerve function evaluation method and system based on visual evoked potential, and the method comprises the steps: obtaining an electroencephalogram signal, an eye movement track and an auditory reaction signal of a subject; obtaining visual stimulation parameters, wherein the visual stimulation parameters comprise stimulation frequency, stimulation intensity and stimulation duration; on the basis of the electroencephalogram signals, the eye movement tracks and the auditory reaction signals, signal preprocessing operation is executed, and preprocessed multi-modal physiological signals are obtained; extracting features by adopting an improved space-time convolutional neural network based on the preprocessed multi-modal physiological signals to obtain optic nerve function features; according to the optic nerve function features and the visual stimulation parameters, establishing an optic nerve conduction pathway model; evaluating the optic nerve function state based on the optic nerve conduction pathway model to obtain an optic nerve function evaluation result; generating an evaluation report containing the optic nerve function evaluation result; predicting the optic nerve function change trend, and generating a prediction report.

The invention discloses an electroencephalogram signal analysis method, and relates to the technical field of electroencephalogram signal processing. The method comprises the following steps: collecting and preprocessing a multi-channel electroencephalogram signal; establishing a spatial filter, and separating and storing task-related signals and task-independent signals from the signals; constructing an auxiliary classifier to generate an adversarial network, training an auxiliary classifier by using the task-independent signal to judge whether task-independent noise is mixed in an artificial signal generated by a generator, and applying punishment to the generator, so as to generate a high-quality and high-fidelity task-related artificial electroencephalogram signal; and jointly inputting the generated signal and the original signal into a CNN-LSTM-Attention hybrid model for training to obtain a final spatial filtering and intention recognition model. According to the method, the problem of model performance bottleneck caused by insufficient individual training data in a steady-state visual evoked potential brain-computer interface system is effectively solved, the accuracy and robustness of patient intention recognition are remarkably improved, and meanwhile, the calibration burden of a user is relieved.

The invention discloses a multi-modal closed-loop rehabilitation training system based on a brain-computer interface and eye movement, which comprises a stimulation unit used for providing sensory stimulation for a subject, and an acquisition unit used for acquiring an eye movement signal and an electroencephalogram signal of the subject and outputting the signals to a multi-modal information processing unit; the multi-modal information processing unit is used for identifying a brain control state and an idle state based on a preset machine learning algorithm according to the eye movement signal and the electroencephalogram signal so as to obtain a motion intention generated by the subject based on sensory stimulation, generating a control instruction based on the motion intention and sending the control instruction to the second stimulation unit; the second stimulation unit outputs mechanical stimulation to the subject based on the control instruction. According to the system, high-accuracy and low-delay brain-machine switch identification is realized based on eye movement information, and high-accuracy intention identification of different actions is realized based on visual evoked potential, so that collaborative feedback is performed on a subject based on the second stimulation unit to establish a closed-loop system, the degree of participation of the subject is improved, and neural recombination is promoted.

The invention discloses an SSVEP (Steady-State Visual Evoked Potential) recognition system and method based on linear frequency modulation visual evoked potential migration. The visual stimulation generation and presentation module generates and presents a visual stimulation signal; the electroencephalogram signal acquisition module is used for acquiring Chirp-VEP and SSVEP electroencephalogram signals; the electroencephalogram signal preprocessing module is used for preprocessing the collected electroencephalogram signals; the Chirp pulse matrix construction module is used for converting a Chirp signal to obtain a Chirp pulse matrix, the Chirp-VEP decomposition module is used for constructing a least square optimization problem to solve common model parameters of Chirp-VEP, and the common model parameters are migrated to SSVEP through the SSVEP identification module to carry out feature extraction and classification. According to the method, the system overhead is reduced, the calibration time is shortened, the training cost is minimized while the optimal performance is kept, and the practicability of the brain-computer interface based on SSVEP is improved.

The invention provides an SSVEP (Steady-State Visual Evoked Potential) signal processing and screening method and device based on a brain-computer interface. The method comprises the following steps of: importing data and setting parameters; performing data denoising, and performing time domain mean value calculation on the SSVEP signals acquired by multiple channels to remove physiological artifacts; circularly processing each data block, including a data preprocessing step and a spectrum data screening step, and performing baseline drift removal, notch filtering, band-pass filtering and fast Fourier transform on each data block; and displaying a result, and outputting corresponding information according to the screening condition. The data preprocessing is performed through time domain and frequency domain filter processing, the physiological artifacts of the SSVEP signals are simply and effectively removed, and the quality of the original SSVEP signals is improved; the frequency screening step overcomes the defect that time and labor are wasted by manual screening in the past, function parameter adjustment is carried out according to SSVEP signal characteristics, fundamental harmonic characteristics and individual differences, effective SSVEP signals are rapidly screened and reserved, and powerful support is provided for subsequent tasks such as a high-quality data set.

The invention provides an image-based ophthalmology robot control method, system and device and a medium, and the method comprises the steps: obtaining an intraoperative three-dimensional OCT image sequence and a visual evoked potential signal, and segmenting an optic nerve region to generate an optic nerve space model; performing dynamic registration on the optic nerve space model and the visual evoked potential signal according to an eyeball motion compensation algorithm to generate a fusion data volume; calculating a visual function injury risk coefficient and a postoperative vision recovery predicted value through a pre-trained neural network prediction model based on the fusion data body and the real-time operation parameters of the robot end instrument; and dynamically generating a robot operation correction control instruction according to the injury risk coefficient and the postoperative vision recovery predicted value. By adopting the method, the individualized precision of an operation scheme and the postoperative visual function protection effect can be improved.

The invention discloses a space coding and decoding method for steady-state visual evoked potential, which realizes mapping and identification of a target and a space characteristic by combining a multi-frequency characteristic with a canonical correlation analysis method and utilizing a space coding characteristic of a stimulation target. By monitoring and analyzing SSVEP response distribution of surrounding targets, different central targets can be effectively distinguished, so that spatial coding is realized. In the decoding process, the target frequency corresponding to the most relevant frequency of the electroencephalogram signal is identified through the maximum correlation coefficient. Then, in combination with the frequency of the surrounding target, an inclusion index is calculated to further confirm the validity of the identification. And when the inclusion degree is greater than a set threshold value, considering that the current center target is identified as an effective center target. According to the method, the recognition precision can be improved, the electroencephalogram channel requirement is reduced, the information transmission rate and the real-time performance of the system are improved through the optimized space coding scheme, and the method is particularly suitable for low-delay and multi-target recognition brain-computer interaction tasks.

Systems and methods are disclosed for assessing visual function of a user or patient. The systems and methods may contain steps, including: extracting data from an electrode site of a sensor; obtaining an average of the data across successive video presentations; applying a fast-Fourier transform to the average; determining a signal-to-noise ratio (SNR) of results of the fast-Fourier transform at tagged frequencies; determining a steady-state visual evoked potential (SSVEP) amplitude based on the SNR of the results of the fast-Fourier transform at the tagged frequencies; and determining a ratio of lower to higher spatial frequency SSVEPs to assess the vision function of the user.

The present invention relates to the fields of brain science and computer technology, and in particular to a visual brain-computer interface signal decoding method and system thereof. The method comprises constructing a visual brain-computer filter bank, acquiring EEG data, and transforming it into multiple sub-band signals; constructing a convolutional neural network to extract the signal features corresponding to each sub-band signal, and splicing the signal features along the channel to form an aggregated feature map; constructing a temporal kernel selection network to calculate the weight values ​​of the spliced ​​feature map to obtain a weighted feature map; flattening the weighted feature map to obtain a one-dimensional feature vector; and constructing a classification module to map and classify the one-dimensional feature vector and output the stimulation frequency of the EEG data. The present invention optimizes feature extraction by selecting a temporal kernel, accurately capturing the characteristic pattern of steady-state visual evoked potential signals. By expanding the receptive field of the convolution kernel, task-related feature patterns are emphasized, significantly improving the classification performance and generalization ability of the model.

The present invention discloses a square wave attack method and device for a steady-state visual evoked potential paradigm. The method includes: obtaining the set flicker frequencies of all output categories in the system to be attacked; setting the frequency of the square wave perturbation signal for the target attack category according to the set flicker frequencies of all output categories; setting the amplitude of the square wave perturbation signal according to the EEG amplitude range of the subject; setting any one channel within a predetermined range centered on the occipital region as the attacked channel; superimposing the square wave perturbation signal with the frequency and amplitude on the original signal of the attacked channel to attack the system to be attacked, so that the output of the system to be attacked is changed to the target attack category. The present invention solves the technical problem in the prior art that the perturbation signal is complex and difficult to implement.

The present application relates to the technical field of brain-computer interface, and provides a steady-state visual evoked potential brain-computer interface instruction classification method and device, wherein the method comprises: acquiring electroencephalogram data generated by a user under a frequency-semantic joint stimulation; inputting the electroencephalogram data into an electroencephalogram decoding model to obtain an instruction classification result; a frequency perception feature extractor and a semantic decoding feature extractor respectively extract frequency features and semantic features from the electroencephalogram data in parallel; and a joint decision module obtains the instruction classification result based on the frequency features and the semantic features. The present application expands the number of encodable instructions to the product of the frequency domain dimension and the semantic dimension by designing a frequency-semantic joint stimulation; the frequency perception feature extractor and the semantic decoding feature extractor can simultaneously extract frequency features and semantic features from the electroencephalogram signal, and then the joint decision module is used for fusion decision, so that the accuracy of instruction classification and the interaction efficiency are significantly improved under the premise of ensuring high comfort.

The invention discloses a universal brain-computer interface decoding method, and relates to the technical field of neural network algorithms. In order to solve the technical problems that an existing brain-computer interface decoding algorithm is poor in universality, performance is reduced under a complex normal form, and light weight and high classification performance are difficult to balance, the method comprises the steps that original electroencephalogram signals are preprocessed and enhanced through TRCA (Task Related Component Analysis); and decoding is realized in combination with an enhanced deep neural network containing causal convolution, expansion convolution and a multi-layer full-connection layer. The method is verified by experiments under three normal forms of steady-state visual evoked potential SSVEP, high-frequency SSVEP and SSPVEP, not only maintains the light weight of the network to reduce the over-fitting risk, but also shows the classification performance superior to that of a traditional algorithm and an existing deep learning algorithm when the normal form difficulty is improved and the data length is increased, has strong universality and practicability, and is suitable for popularization and application. The method can be widely applied to brain-computer interface related fields such as medical rehabilitation and virtual reality.

The application provides a steady-state visual evoked potential classification method and device and electronic equipment. The method comprises the following steps: obtaining a steady-state visual evoked potential signal; based on a plurality of different frequency band bandpass filters, extracting short time domain window data corresponding to each frequency band in the plurality of different frequency bands from the steady-state visual evoked potential signal; inputting the short time domain window data corresponding to the plurality of frequency bands into a pre-trained hybrid network model to obtain a classification result of the steady-state visual evoked potential signal; wherein the hybrid network model comprises a convolutional neural network and a bidirectional gated recurrent unit, the convolutional neural network is used to obtain a fusion feature map based on the short time domain window data corresponding to the plurality of frequency bands, and compress the fusion feature map into one-dimensional data; the bidirectional gated recurrent unit is used to obtain the classification result of the steady-state visual evoked potential signal based on the one-dimensional data. The application can accurately classify the short time domain window potential signal data and improve the classification performance.

The present invention relates to a hybrid brain-computer interface method that combines visual and auditory weak stimulation. Its technical characteristics are: determining the video stimulation interface, the number of visual stimulation targets and the visual stimulation attributes, and realizing the visual weak stimulation function by means of high-frequency flickering of the peripheral visual field; realizing the auditory weak stimulation function by applying binaural frequency-divided low-sound-pressure-level auditory stimulation signals to the left and right ears; performing visual weak stimulation and auditory weak stimulation at the same time, and selectively paying attention to the auditory stimulation of the left or right ear to complete the task of the hybrid paradigm; while performing visual weak stimulation and auditory weak stimulation, extracting the steady-state visual evoked potential characteristics and auditory steady-state response characteristics of the user's electroencephalogram signal and performing identification and classification to generate coding instructions. The present invention combines the visual weak stimulation method with the auditory weak stimulation method, and uses two electroencephalogram signals for encoding at the same time, thereby improving the naturalness and friendliness of human-computer interaction, and can encode more targets using fewer frequencies.

The invention provides a steady-state visual evoked potential brain-computer interface system for snowflake point weak flicker coding, and the system comprises a stimulation presentation module which is used for presenting a stimulation interface of snowflake point weak flicker; each stimulation target comprises a corresponding frequency and initial phase combination, and the frequencies and initial phase combinations corresponding to different stimulation targets are different; the stimulation interface is used for guiding the sight line of the user to be transferred to a current stimulation target when each trial starts; the electroencephalogram collection module is used for collecting electroencephalogram signals of the occipital area of the user through a dry electrode head ring, and the dry electrode head ring comprises a plurality of movable electrodes, a reference electrode and a grounding electrode; the signal processing module is used for processing the electroencephalogram signal to identify a steady-state visual evoked potential and acquiring a frequency and phase combination of a stimulation target corresponding to the electroencephalogram signal; and the interaction control module is used for outputting a corresponding control instruction according to the frequency and phase combination of the stimulation target identified by the signal processing module to realize brain-computer interaction.

This invention relates to the field of brain-computer interface (BCI) data processing technology, and discloses a BCI data processing method that integrates steady-state motor visual evoked potentials and motor imagery. The method includes: generating a bimodal task configuration table; generating a bimodal synchronized stimulation sequence; acquiring and labeling multi-lead EEG signals; extracting visual response features and motor imagery response features; performing motor intention recognition; and generating rehabilitation feedback information. Compared to the single visual evoked paradigm in existing technologies, especially under conditions of significant differences in subjects' motor imagery abilities and low EEG noise ratios, this invention addresses the technical problem of failing to achieve stable motor intention recognition. By synchronously binding motor visual stimulation with motor imagery tasks and jointly extracting visual frequency response and motor imagery desynchronization features, stable recognition of motor intention is achieved, improving the recognition accuracy of rehabilitation BCI training.

The application discloses an eye irradiation device, which comprises a visual stimulation module, a signal acquisition module, a control module and a light source module; the visual stimulation module is used for providing visual stimulation to a first user to induce a visual evoked potential signal; the signal acquisition module is used for acquiring the visual evoked potential signal of the first user; the control module is connected with the signal acquisition module and the visual stimulation module, and is used for determining an irradiation opportunity and generating an irradiation control signal according to the visual evoked potential signal of the first user; and the light source module is connected with the control module, and is used for emitting irradiation light to the eyes of the first user at the irradiation opportunity according to the irradiation control signal.

This invention discloses a brain-controlled cotton candy machine control method based on attention level and SSVEP (Steady State Visual Evoked Potential), belonging to the field of brain-computer interface and food processing equipment integration technology. This invention employs a NeuroSci wireless EEG acquisition system, using SSVEP steady-state visual evoked potentials to achieve flavor selection (original, strawberry, pineapple), and utilizes frontal electrodes to collect EEG signals to calculate attention level. An STM32F407ZGT6 is used as the main controller to establish a precise mapping relationship between attention level grading and the cotton candy machine's motor speed. This is combined with servo motors for automatic quantitative feeding, infrared monitoring for candy card reversal and obstacle clearance, and touch display, voice commands, and multimodal audio-visual feedback. A state machine logic is used to complete the entire process of automatic control, from initialization and preheating to interaction, production, and completion. This invention achieves closed-loop control of "EEG acquisition—signal analysis—device execution," solving the problems of cumbersome operation, poor interactivity, and limited functionality in traditional cotton candy machines. It features intelligent operation, high stability, and high safety, and can be used in parent-child interaction, brain science popularization, and attention training scenarios, possessing high application and promotion value.

A neurofeedback system includes an EEG apparatus, a presentation apparatus and a controller. The controller is configured to: (i) cause the presentation apparatus to display an overlaid image to the user that comprises a first image flickering at a first frequency and a second image flickering at a second frequency different than the first frequency, the first image being an affective distractor stimulus image and the second image being a task-relevant stimulus image, (ii) receive from the EEG apparatus a number of first steady-state visual evoked potential (SSVEP) signals generated in response the first image of the overlaid image and a number of second SSVEP signals generated in response the second image of the overlaid image, and (iii) calculate feedback indicative of how much attention of the was user allocated to the task-relevant stimulus image versus how much attention of the user was allocated to the affective distractor stimulus image.

The application discloses a brain-computer interface system and method, which comprises a visual stimulation module, a collection module and an analysis module. The visual stimulation module provides a plurality of visual stimulation targets, each of which comprises a background gray adjustable display area and a visual stimulation area formed in the display area. The visual stimulation area does not completely fill the display area, and the display area and the visual stimulation area jointly constitute a visual stimulation code. The collection module is used for collecting brain electrical signals generated by a user to the visual stimulation code. The analysis module extracts features in the brain electrical signals and identifies a visual stimulation target currently gazed by the user. The application adopts a contrast coding method and can be used on a common refresh rate display to realize a high-frequency multi-target steady-state visual evoked potential brain-computer interface system.

The invention belongs to the technical field of man-machine interaction, and particularly relates to a man-machine interaction method and device based on a mixed brain-computer interface, and the method comprises the steps: collecting and preprocessing a visual evoked potential SSVEP signal and a motor imagery signal, and carrying out the classification and recognition of the SSVEP signal and the motor imagery signal, the control intention of the user is analyzed according to the classification and recognition result and converted into a keyboard and mouse operation instruction capable of being recognized by a computer, and keyboard and mouse interaction operation and electroencephalogram signal characteristics made by the user within a specified time period are recorded and stored in a database; reproducing an event information script for executing the keyboard and mouse operation instruction through event playback; by analyzing the electroencephalogram signal characteristics of the user and matching the electroencephalogram signal characteristics with the electroencephalogram characteristics at the corresponding time in the database, a script file of keyboard and mouse operation is generated in a self-adaptive mode so as to achieve intention behaviors of the user. The invention provides a man-machine interaction method capable of flexibly realizing complex operation of a keyboard and a mouse through ideas.

The invention discloses a manipulator situation awareness space target recognition pre-judgment method, system and device based on a brain-computer interface and a storage medium, and the method comprises the following steps: S1, forming a plurality of grabbing modes based on different types of grabbing electroencephalogram signals of a user; s2, on the basis of the captured image of the captured target, predicting a moving path of the captured target and confirming a capturing mode; s3, based on the predicted moving path and grabbing mode of the grabbed target, grabbing the grabbed target; compared with the prior art, the brain-computer interface normal form of the steady-state visual evoked potential is adopted, the user gazes the instructions corresponding to various different grabbing modes on the display screen, and the instructions induce the user to generate the steady-state visual evoked potential through different flicker frequencies; the electroencephalogram signals are collected through an electroencephalogram signal amplifier and an electroencephalogram interface electrode, and typicality correlation analysis processing is carried out.

The invention provides a neurosurgery ward quick response method based on a double-normal-form layered brain-computer interface system, and relates to the technical field of medical information. The system consists of a motor imagery subsystem, a steady-state visual evoked potential subsystem, a protocol management module, an event response module and a state machine management module, and is interconnected with a ward terminal. According to the method, emergency triggering is achieved through motor imagery normally-open monitoring, stable visual evoked potential high-precision interaction is switched to when the illness state is stable or medical care confirms, and the emergency state can be returned by preemptive interruption; and the state machine module is switched among three states and records operation indexes to realize log auditing and parameter setting. According to the method, quick starting, low false triggering and accurate transmission are considered, and the emergency response efficiency and the communication safety of the ward are improved.

The invention relates to the technical field of vehicles, in particular to a vehicle component control system, a vehicle and a method.The system comprises the steps that a plurality of stimulation targets are displayed in turns in a preset loop playing mode on the basis of wearable equipment, and when the duration of watching any stimulation target by a user is longer than the preset duration, electroencephalogram signals of the user are collected and sent to the wearable equipment; the processing module obtains intention recognition information of a user according to the electroencephalogram signal, and sends the intention recognition information to the wearable device for display and / or sends the intention recognition information to the control module, so that a target control component and control parameters of the target control component are determined according to the intention recognition information; and controlling the target control component according to the control parameters. Therefore, the problems that the portability and the application range are limited due to the fact that steady-state visual evoked potential stimulation is displayed on an external screen, and meanwhile the monitoring accuracy is reduced due to the fact that the user state is visually monitored through a camera and a computer and is easily interfered by factors such as illumination and shielding are solved.

Uses of ADNF polypeptides in therapy are provided. Accordingly, there is provided a method of treating a disease associated with visual evoked potential impairment and / or speech impairment that is not due to vocal disturbance and in which the subject suffers from the visual evoked potential impairment and / or speech impairment, the method comprising administering to the subject a therapeutically effective amount of an ADNF polypeptide, wherein said ADNF polypeptide has a neurotrophic / neuroprotective activity in an in vitro cortical neuron culture assay.