56 results about "Visual evoked potentials" patented technology

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 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 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 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 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 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 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.

The present invention discloses a high-frequency visual evoked potential brain-computer interface encoding and decoding method, device and robotic arm control system. The high-frequency visual evoked potential brain-computer interface encoding method comprises: displaying a plurality of instruction blocks distributed in rows and columns on a visual stimulation interface, each instruction block being jointly encoded by two frequency-phase combination pairs, and the row and column positions of each instruction block corresponding to the two frequency-phase combination pairs of the instruction block; wherein the frequency value in the frequency-phase combination pair is greater than or equal to 30Hz. When a subject gazes at an instruction block, the brain induces visual evoked potentials corresponding to the rows and columns of the instruction block, respectively, so that the row number and column number of the instruction block can be decoded instead of decoding the visual evoked potential itself to be identified, thereby solving the problem of high training cost caused by low signal-to-noise ratio of high-frequency signals in the prior art, thereby consuming lower training costs while adopting high-frequency visual stimulation frequency, achieving the effect of improving the subject's eye comfort.

The present invention relates to the field of brain-computer interface technology, and discloses a high-frequency cold-start visual evoked potential brain-computer interface system, device, equipment and medium. The system includes an instruction sequence generation module, a local dynamic flashing module, a user display interface and a decoding module, wherein the instruction sequence generation module determines the integer instruction sequence length that makes the flashing frequency within a first preset range according to the instruction target number and the device refresh rate; and determines the target instruction sequence according to the integer instruction sequence length; the local dynamic flashing module sorts the target instruction sequence by local dynamic flashing icons and determines the local dynamic flashing icon display sequence; the user display interface displays the local dynamic flashing icon display sequence to the target user; the decoding module receives the target EEG signal generated by the target user after watching the local dynamic flashing icon display sequence; the decoding module decodes the target EEG signal to obtain the target intention of the target user, thereby improving the practicality of the system.

The present invention discloses an EEG signal recognition method based on PLSR and extended FBCCA. The method comprises the following steps: regressing a sinusoidal reference template signal onto a multi-channel sampled EEG signal to obtain an EEG estimation signal; performing correlation analysis on the decomposed sub-band components of the multi-channel sampled EEG signal and the estimation signal to obtain a set of extended correlation coefficients of the sub-band components; and obtaining a set of extended correlation coefficients of the EEG signal for different targets from a set of extended correlation coefficients of different sub-band components of the EEG signal for the estimation signal. The fundamental frequency of the reference template signal corresponding to the maximum value is the target frequency of the EEG signal to be identified. The accuracy and applicability of steady-state visual evoked potential signal recognition are improved by extracting the spatial distribution relationship in the EEG signal. At the same time, the influence of multiple groups of typical variables is taken into account by expanding the main characteristic components, thereby improving the accuracy of EEG signal classification and recognition in the steady-state visual evoked potential paradigm.

The invention discloses a target identification method based on a steady-state visual evoked potential brain-computer interface and a related device, and relates to the technical field of brain-computer interfaces, and the method comprises the steps: designing a trained identification model which comprises a segment coding module, a plurality of feature extraction modules and a classification module which are connected in sequence, the feature extraction module comprises a first normalization layer, a self-adaptive frequency spectrum module, an enhanced time delay neural network module, an inverse Fourier transform layer, a second normalization layer, a splicing layer, an interactive convolution module and a first addition layer, the trained recognition model is used for determining the recognition frequency corresponding to the electroencephalogram signals, the recognition frequency serves as the target frequency, and the recognition frequency is used as the target frequency. According to the method and the device, the three core application requirements of high-precision identification, cross-subject strong generalization ability, light weight and low delay of the SSVEP-BCI system under an ultra-short time window can be met at the same time.

The invention discloses a discriminative domain adaptation-based cross-subject SSVEP (Steady-State Visual Evoked Potential) signal decoding method, which is characterized in that an end-to-end double-branch shared weight neural network is constructed, joint training of source domain data and target domain data is realized, and cross-subject migration can be completed only by using a small amount of label-free target subject data. The model adopts a multi-loss joint optimization strategy including cross entropy loss, comparison pairing loss, minimum category confusion loss and maximum mean value difference loss, collaborative enhancement of intra-class compactness, suppression of prediction confusion and fine alignment of inter-domain feature distribution. According to the method, manual feature extraction is not needed, the network can directly learn the discriminative spatial-temporal features from the original SSVEP signals, and the target domain classification precision and generalization ability are remarkably improved while the stimulation frequency related structure is kept through a discriminative domain adaptation mechanism.

The embodiment of the invention discloses a control method based on an electroencephalogram signal, a medium, equipment and a product. The method comprises the steps of obtaining the electroencephalogram signal; the electroencephalogram signal comprises a steady-state visual evoked potential signal and a motor imagery signal; identifying the steady-state visual evoked potential signal to obtain a to-be-controlled object and an operation intention for the to-be-controlled object; and performing motor imagery classification on the motor imagery signal to obtain a target imagery mode; the operation intention corresponds to a preset imagination mode; if the preset imagination mode corresponding to the operation intention is consistent with the target imagination mode, generating a control instruction based on the operation intention; and controlling the to-be-controlled object to execute the control instruction. According to the embodiment of the invention, the control efficiency can be improved on the premise that the driving safety is guaranteed, the efficient human-vehicle interaction of'wanted control 'is realized, the control experience of a user is improved, and hidden potential safety hazards are avoided.

The invention relates to the technical field of electroencephalogram control, and discloses an intelligent nursing bed self-adaptive control system and method based on multi-mode electroencephalogram intention recognition, and the system comprises a signal separation module, a state judgment module, an intention feature analysis module, a lateral feature extraction module, a hierarchical intention decision module and a control instruction generation module. Acquiring an occipital area steady-state visual evoked potential signal and a motion-related cortex potential signal based on the original electroencephalogram signal of the patient; analyzing the phase locking stability of the occipital area steady state visual evoked potential signal to judge an attention effective state; analyzing the motion-related cortex potential signal to obtain an energy change sequence; analyzing a contralateral dominating mode of the patient; performing hierarchical intention decision on the patient to obtain a preliminary motion intention of the patient; generating a final control instruction of the target equipment in combination with the physiological feedback signal of the patient; according to the invention, the efficiency of adaptive control of the intelligent nursing bed based on multi-mode electroencephalogram intention recognition can be improved.

This invention discloses a method for decoding EEG features for steady-state visual evoked potentials, including: the subject's fixation frequency being f n The original steady-state visual evoked potentials collected during visual stimulation are X. n Using N fb A filter bank for X n Preprocessing is performed to obtain N fb After preprocessing, the steady-state visual evoked potential pairs are filtered to obtain useful signal components and noise signal components, and then a spatial filter is obtained to obtain the steady-state visual evoked potential template signal. This is used to acquire the unknown steady-state visual evoked potential K and obtain N. fb K is a preprocessed unknown steady-state visual evoked potential. (m) By improving and K (m) The signal-to-noise ratio, calculate N fb The filtered sum K (m) The correlation coefficient of N fb The weighted summation yields ρ n ; We obtained K and steady-state visual evoked potentials [X1,…X i ,…,X n ,...,X Nf The correlation coefficient between ], ρ i If the maximum value is reached, then the visual stimulus frequency of K is f. i The technical solution in this embodiment achieves the effect of improving the accuracy of recognizing unknown visual stimuli using a small amount of training data.

The application provides an image-based ophthalmic robot control method, system, device and medium, wherein the method comprises: acquiring an intraoperative three-dimensional OCT image sequence and a visual evoked potential signal, segmenting an optic nerve region to generate an optic nerve spatial model; performing dynamic registration on the optic nerve spatial model and the visual evoked potential signal according to an eye movement compensation algorithm to generate a fusion data body; calculating a visual function damage risk coefficient and a postoperative visual acuity recovery prediction value through a pre-trained neural network prediction model based on the fusion data body and real-time operation parameters of a robot end instrument; and dynamically generating a robot operation correction control instruction according to the damage risk coefficient and the postoperative visual acuity recovery prediction value. The method can improve the individualization accuracy of a surgical plan and the postoperative visual function protection effect.

The invention discloses a learning system and method based on steady-state visual evoked potential and transcranial magnetic regulation. The learning system comprises a display module which comprises a display interface and a flicker grid, the display interface is used for displaying a learned question, the flicker grid comprises a plurality of option boxes, each option box corresponds to an answer option, and the plurality of option boxes have different flicker frequencies; the electroencephalogram acquisition module is used for acquiring real-time electroencephalogram signals of a user and sending the real-time electroencephalogram signals to the data processing module; the data processing module is connected with the display module and the electroencephalogram acquisition module, and is used for analyzing the electroencephalogram signals acquired by the electroencephalogram acquisition module, obtaining SSVEP characteristic frequency, comparing the SSVEP characteristic frequency with flicker frequency of an option box, selecting answer options watched by a user, and displaying the options on the display module; a control instruction is output to the magnetic stimulation module according to the answering condition of the user; and the magnetic stimulation module is used for applying magnetic stimulation to the user.