274 results about "EEG feature" patented technology

The invention relates to the technical field of flight training evaluation, and discloses an electroencephalogram feature and physiological index fused flight training evaluation system. The system comprises a physiological signal acquisition module which synchronously captures multichannel electroencephalogram original signals and body surface physiological index data, and the body surface physiological index data comprises an electrocardiograph R-R interval sequence, respiratory wave frequency amplitude and galvanic skin response amplitude; the multi-modal fusion module is used for analyzing an electrocardiograph R-R interval sequence to generate a heart rate variability feature vector and establishing dynamic association mapping of an electroencephalogram entropy value and a physiological feature vector; the cognitive state modeling module is used for generating a cognitive load index according to the dynamic association mapping and constructing a cognitive stability quantization matrix; the self-adaptive feedback module is used for receiving related data and dynamically adjusting simulated flight scene parameters; and the evaluation output module is used for integrating the data to generate a comprehensive training evaluation report containing a neurophysiological coordination degree score and an operation accuracy rating. According to the system, comprehensive evaluation and dynamic training adjustment of the cognitive state of the pilot are realized.

The invention discloses a system and method for classifying and recognizing epilepsy in electroencephalogram signals based on a time sequence. The system comprises a cross-domain mixed self-supervised learning module, a multi-scale electroencephalogram feature learning module, an epileptic seizure guided self-attention learning module and a classifier. The cross-domain hybrid self-supervised learning module comprises a time domain context prediction task unit, a channel information reconstruction task unit and a frequency spectrum masking identification task unit; the cross-domain hybrid self-supervised learning module reconstructs an input electroencephalogram signal according to a time domain, a space domain and a frequency domain to obtain an electroencephalogram representation model; the time domain context prediction task unit is used for capturing dynamic change characteristics before and after epileptic seizure by learning a time domain evolution rule of electroencephalogram signals; the channel information reconstruction task unit is used for reconstructing missing electroencephalogram signal channel data and enhancing spatial information representation of an electroencephalogram representation model; the frequency spectrum masking identification task unit trains the electroencephalogram characterization model to characterize epilepsy nerve oscillation through a random masking part electroencephalogram signal frequency component in a frequency domain; the multi-scale electroencephalogram feature learning module captures local and global level epilepsy features of an electroencephalogram representation model through different resolutions to obtain a first epilepsy electroencephalogram feature model; the epileptic seizure guided self-attention learning module optimizes the first epileptic electroencephalogram feature model by fusing time sequence and spatial features to obtain a second epileptic electroencephalogram feature model; and the classifier classifies and outputs the electroencephalogram features of the second epileptic electroencephalogram feature model based on the long short-term memory network to determine whether the epileptic seizure occurs. The invention provides an efficient, accurate and robust solution for automatic detection and clinical auxiliary diagnosis of the epileptic seizure.

The invention relates to the technical field of electroencephalogram signal processing, in particular to an ontology brain wave audio auditory perception synchronous feedback method, device and system and electronic equipment. The method comprises the following steps: receiving electroencephalogram signals of a collected user on line from electroencephalogram collection equipment through an upper computer, obtaining electroencephalogram signals of a specified frequency band from the electroencephalogram signals, and extracting corresponding electroencephalogram characteristics; according to the electroencephalogram features or preset rhythm parameters, the electroencephalogram signals of the specified frequency band are segmented into a plurality of electroencephalogram segments, a plurality of audio expressions corresponding to the electroencephalogram segments are generated, and feature parameters of the audio expressions are determined according to the electroencephalogram features of the corresponding electroencephalogram segments; generating brain wave audio representation data according to the audio representation corresponding to the electroencephalogram signals of the one or more designated frequency bands, and obtaining brain wave audio according to the brain wave audio representation data. Therefore, the physiological suitability of nerve regulation and control and the artistic expressivity of audio generation are met at the same time, and organic unification of nerve regulation and control and audio generation is achieved.

An electroencephalogram coding method based on a generative adversarial technology comprises the following steps: (1) designing an electroencephalogram signal acquisition experiment normal form in combination with a visual stimulation scene requirement, and completing acquisition of a tested electroencephalogram signal by utilizing electroencephalogram signal acquisition equipment according to an experiment normal form process to obtain original electroencephalogram signal data corresponding to a visual stimulation image; (2) designing an electroencephalogram feature extraction model based on a compact space-time convolutional neural network, and extracting electroencephalogram feature data related to the visual stimulation image from the electroencephalogram signals from the original electroencephalogram signal data; (3) on the basis of the visual stimulation image and the electroencephalogram feature data, constructing an electroencephalogram mapping model based on a deep residual-cyclic regression network, and simulating representation association of the electroencephalogram feature data to the visual stimulation image under the brain vision effect; and (4) a generative adversarial mechanism is introduced into the electroencephalogram mapping model, a conditional depth generative network structure is constructed, training is performed by using the visual stimulation image and the extracted electroencephalogram feature data, an electroencephalogram coding model based on visual-electroencephalogram features is finally obtained, and the electroencephalogram coding model is used for performing electroencephalogram coding on the input visual stimulation image.

The invention belongs to the cross technical field of rehabilitation engineering and neural engineering, and relates to a multi-modal signal fused fine exercise rehabilitation evaluation, regulation and control method and system, and each evaluation comprises the following steps: collecting a pressure distribution signal, a surface electromyogram signal and an electroencephalogram signal of a hand fine exercise; performing preprocessing, feature extraction and normalization processing on the three signals to obtain a pressure feature value, a surface myoelectricity feature value and an electroencephalogram feature value; calculating a conversion coefficient of a current rehabilitation stage based on the number of days that the fine motor function of the patient is damaged, and calculating weights of a pressure characteristic value, a surface myoelectricity characteristic value and an electroencephalogram characteristic value based on the conversion coefficient; performing weighted summation on the pressure characteristic value, the surface myoelectricity characteristic value and the electroencephalogram characteristic value to obtain an evaluation score; rehabilitation training parameters are regulated based on the assessment score and the number of days of impaired patient's fine motor function. According to the method, the limitation of traditional single-mode physiological signal evaluation can be broken through, and the comprehensiveness, the accuracy and the personalized adaptation capability of rehabilitation evaluation can be improved.

The invention discloses an early judgment system for cognitive impairment of Parkinson's disease based on transcranial magnetic-electroencephalogram multi-dimensional features. The early judgment system comprises a transcranial magnetic-electroencephalogram combined acquisition module, a synchronous signal preprocessing module, a multi-dimensional feature extraction module, a feature intelligent fusion module and a storage and display module, the transcranial magnetic-electroencephalogram combined acquisition module is used for disturbing the neural activity of a patient through monopulse transcranial magnetic stimulation coding and acquiring an evoked state electroencephalogram signal; the synchronizing signal preprocessing module is used for denoising and filtering the evoked state electroencephalogram signals; a multi-dimensional feature extraction module performs feature extraction on the processed evoked state electroencephalogram signals by utilizing multi-dimensional electroencephalogram feature analysis to obtain multi-parameter electroencephalogram neural activity features; the feature intelligent fusion module is used for carrying out weight distribution and feature fusion on the multi-parameter electroencephalogram neural activity features to obtain cognitive disorder multi-modal neural indexes; and the storage and display module is used for storing the cognitive disorder multi-modal nerve indexes and visualizing the cognitive disorder multi-modal nerve indexes.

The invention belongs to the technical field of multi-mode brain-computer interface signal processing, and particularly relates to a time alignment and dynamic fusion method based on electroencephalogram and functional near-infrared signals. The method comprises the following steps: neural network setting: setting a feature decoupling module for decoupling electroencephalogram features and functional near-infrared features into shared features and specific features; setting a deformable time alignment module, and performing adaptive time alignment on the shared features and the specific features based on a deformable alignment mechanism of a learnable offset delta t; the setting diagram distillation module is used for realizing fusion output of specific features and shared features by constructing a cross-modal diagram structure between samples and introducing dynamic edge weight and dual difference measurement; neural network training: constructing an electroencephalogram and functional near-infrared signal fusion loss function, and performing neural network model training; and feature fusion: inputting to-be-fused electroencephalogram and functional near-infrared signals into the trained neural network, and outputting fusion features by the neural network.

The invention provides an emotion prediction method and device based on electroencephalogram signals. The method comprises the steps that the electroencephalogram signals of a target user are acquired; performing time-frequency analysis on the electroencephalogram signal to obtain a target electroencephalogram characteristic signal, and extracting electroencephalogram characteristics of different frequency bands from the target electroencephalogram characteristic signal to obtain multi-dimensional electroencephalogram characteristics; calibrating the resting emotional state baseline according to the multi-dimensional electroencephalogram features to obtain a calibrated emotional state baseline, and extracting features corresponding to the calibrated emotional state baseline to obtain calibrated electroencephalogram features; inputting the calibrated electroencephalogram features into an emotion prediction model, performing emotion prediction by using the emotion prediction model, and outputting to obtain an emotion prediction result; and executing real-time emotion monitoring and feedback based on the emotion prediction result. According to the method and the device, the problem of low emotion recognition precision caused by neglecting data individual differences and non-stationary features in an emotion recognition process in the prior art is solved.

The invention discloses a motor imagery electroencephalogram signal decoding method and system based on a Sinc filter, and belongs to the field of brain-computer interfaces, and the method comprises the following steps: carrying out convolution calculation on a motor imagery electroencephalogram signal and the Sinc filter to obtain a frequency characteristic signal of the motor imagery electroencephalogram signal; performing channel connection on the characteristic signals of the motor imagery electroencephalogram signals of different frequency bands to generate high-dimensional electroencephalogram characteristic signals; obtaining a parallel multi-branch space-time convolutional network, performing time domain feature extraction, spatial feature extraction and average pooling dimension reduction processing on the high-dimensional electroencephalogram feature signal to obtain a channel weight, and performing weighted calculation on the target feature map based on the channel weight to obtain a space-time feature map; and obtaining class labels of the motor imagery electroencephalogram signals after mapping processing and activation processing, and obtaining a decoding result. According to the method, the Sinc filter and the space-time convolutional neural network are combined, and the accuracy and robustness of motor imagery electroencephalogram signal classification are improved.

The invention discloses a motor imagery early fusion decoding method based on EEG-fNIRS. The method comprises the steps that EEG and fNIRS signals in a motor imagery task are acquired and preprocessed; eEG and fNIRS feature extraction and alignment modules are used for extracting and aligning EEG feature information and fNIRS feature information respectively; performing deep fusion on the EEG and fNIRS time dimension alignment features by using a bidirectional cross attention module to obtain EEG-fNIRS early fusion features; the EEG-fNIRS early fusion features are input into a Transform encoder, different time step information is fused in a self-adaptive mode through an attention weighted pooling module, and EEG-fNIRS fusion features are obtained; and inputting the EEG-fNIRS fusion feature into a multi-layer perceptron to output a motor imagery task category. According to the method, space-time coupling characteristics of EEG and fNIRS signals can be fully utilized, deep fusion of cross-modal characteristics is realized, and the decoding performance of a motor imagery task is remarkably improved.

The invention relates to the technical field of brain-computer interfaces, in particular to an emotion perception interaction method and system for a brain-computer interface earphone, and the method comprises the following steps: obtaining an electroencephalogram signal and a voice signal of a user; preprocessing the electroencephalogram signal and the voice signal; electroencephalogram features are extracted from the preprocessed electroencephalogram signals, and voice features are extracted from the preprocessed voice signals; performing multi-modal feature fusion based on the electroencephalogram features and the voice features to obtain fusion features; identifying an emotional state of the user based on the fused feature; adjusting earphone output parameters according to the emotional state; obtaining user feedback; and updating the model based on user feedback; the deep fusion of the electroencephalogram and the voice signal is realized through the innovative DAMCLN algorithm, and the accuracy of emotion recognition is remarkably improved. Test results show that the emotion recognition accuracy of the method reaches 92.5% and is far higher than that of the prior art; intelligent parameter adjustment based on the emotional state of the user is realized, and the score of the user reaches 8.7.

The invention discloses an electrocardio sleep staging method based on electroencephalogram and electrocardio coupling learning, which is used for improving a UNet basic feature extraction structure so that the UNet basic feature extraction structure can extract multi-level and multi-scale richer information. Through a pooling layer, a convolution layer, a normalization layer and an activation layer, fusion processing and scale normalization are respectively carried out on basic electrocardio and electroencephalogram features, and a mean square error similarity function is adopted to supervise coupling learning, so that features learned by electrocardio are similar to electroencephalogram as much as possible, and more effective features used for electrocardio sleep staging are extracted. And the accuracy and efficiency of (electrocardio) sleep staging are effectively improved.

The invention discloses a device for awareness level evaluation and a storage medium. The apparatus comprises: a processor; the device realizes the following operations: collecting clinical information of a person to be assessed and a task state electroencephalogram signal under a target stimulation normal form; extracting frequency domain characteristics of a specific frequency band and spatial-temporal characteristics of a target event related potential based on the task state electroencephalogram signal, and combining the frequency domain characteristics and the spatial-temporal characteristics into a corresponding electroencephalogram topographic map; inputting the corresponding electroencephalogram topographic map into a multi-modal large language model, and performing image feature extraction by using an image encoder to obtain electroencephalogram features; inputting clinical information into the multi-modal large language model, and performing text feature extraction by using a text encoder to obtain text features; and performing cross-modal attention calculation fusion on the electroencephalogram features and the text features by using a cross-modal fusion module to realize consciousness evaluation so as to output a consciousness evaluation result. By means of the scheme, the consciousness level of the patient can be automatically and accurately evaluated.

The invention relates to the field of education, and discloses a teaching window control method which comprises the following steps: synchronously acquiring eye movement tracking data, electroencephalogram signals and teaching semantic flow intensity in real time through a multi-modal sensor to form a multi-modal perception data input source; inputting the multi-modal perception data into a quantum annealing optimization module, constructing a Hamiltonian model based on semantic coupling strength and physical display constraints, and generating a window layout candidate parameter set in parallel; and performing symplectic geometry manifold correction on the candidate parameter set, calculating a physical adaptability boundary of the compensation display device through a differential geometry contact coefficient, and screening out a feasible solution space meeting curvature constraint. According to the method, the eye movement track and the electroencephalogram characteristics are fused through the multi-mode sensing unit, real-time layout optimization driven by the cognitive state is achieved, the limitation of separation of physiological signals and interface design is broken through, and window arrangement is made to accurately accord with the instantaneous cognitive load level of a learner.

The invention relates to the technical field of image processing, and particularly discloses a high-quality video reconstruction method based on electroencephalogram signals, which comprises the following steps: preprocessing electroencephalogram data, down-sampling video frames, and segmenting the electroencephalogram data by using a sliding window technology; selecting a channel with high correlation with video reconstruction; electroencephalogram time features are extracted; electroencephalogram space-time features are extracted; electroencephalogram time-frequency features are extracted; the electroencephalogram time-space-frequency features are fused; extracting the category and semantic information of the electroencephalogram; adopting a comparative learning method to align electroencephalogram-text-picture features; generating a reference video frame by using a stable diffusion model; and generating a high-quality video by using the video diffusion model. According to the method, the information of electroencephalogram features is further deeply extracted, the semantic content of the video is remarkably enhanced, meanwhile, the introduced video diffusion model gradually constructs a clear and natural picture through a step-by-step denoising process, the consistency between video frames is remarkably improved, and therefore the high-quality video rich in details is generated.

The invention provides an expression recognition method and device based on multi-modal data, edge computing equipment and a medium, and the method comprises the steps: obtaining a to-be-recognized image and an electroencephalogram signal of a testee, the to-be-recognized image comprising a human face image of the testee, the electroencephalogram signals are acquired by electroencephalogram signal acquisition equipment when the testee is in different emotional states; preprocessing the to-be-recognized image to obtain a facial feature map; preprocessing the electroencephalogram signal to obtain an electroencephalogram feature map; performing data fusion on the facial feature map and the electroencephalogram feature map to obtain multi-modal fusion data; and performing expression recognition based on the facial feature map, the electroencephalogram feature map and the multi-modal fusion data. The method provided by the invention is helpful for improving the accuracy of expression recognition, capturing subtle expression differences, enhancing the reliability and stability of expression recognition, and better adapting to differences of different individuals.

The invention discloses a cardiac flow experience closed-loop regulation and control method and system based on sensory movement synchronization. The method comprises the following steps: acquiring initial electroencephalogram data of a user, extracting frequency domain periodic and non-periodic electroencephalogram characteristics related to cardiac flow experience to obtain an initial electroencephalogram characteristic value, randomly presenting rhythmic sensory stimulation at a preset rhythm, and executing corresponding behavior response by the user; calculating a sensory motion synchronization index; according to the sensory motion synchronism index and a preset interval, the sensory motion synchronism index is stabilized in the preset interval by adjusting the rhythm; extracting frequency domain periodic and non-periodic electroencephalogram features again, and calculating the variable quantity of the relative initial value; judging whether the sensory stimulation presentation rhythm is adjusted or not in combination with the variable quantity and a preset standard; and if the rhythm needs to be adjusted, repeatedly executing the previous steps until the variable quantity of each electroencephalogram characteristic value is adjusted to a preset standard. The system can adaptively adjust the interaction content, optimizes the heart stream experience according to the electroencephalogram characteristics of the user, and improves the immersion and pleasure of the user.

The invention relates to the technical field of brain-computer interfaces, and discloses a brain-computer interaction normal form, an implementation method, a brain-computer interaction method and a brain-computer interaction system. The brain-computer interaction normal form presents the brain electrical stimulation interface to a user stimulation attention eye, and presents the environment video to a user video attention eye at the same time; and performing far-near switching between the brain electrical stimulation interface and the environment video based on the active and passive attention selection mechanism of the user. The brain-computer interaction method has an observation mode and a control mode, and switching between the observation mode and the control mode is carried out based on eye actions of a user. In the observation mode, the first-person view angle video and the third-person view angle video are synchronously tiled and presented to the main canvas from left to right; in the control mode, electroencephalogram characteristics of the user are obtained in real time, and when it is judged that the electroencephalogram characteristics are control-state electroencephalogram characteristics, the corresponding electroencephalogram stimulation types are determined, and control instructions corresponding to the electroencephalogram stimulation types are output. According to the method, the misoperation of the user can be limited to the maximum extent, and high-performance immersive brain-computer interaction in a complex scene is realized.

The invention discloses an electroencephalogram voice annotation calibration and scheduling system and method for individual differences. The system comprises an electroencephalogram signal collecting and preprocessing module, a voice instruction receiving and recognizing module, a rapid individual calibration module, a dynamic task scheduling module, an electroencephalogram feature online decoding and annotation module and an annotation storage and feedback module. Through cooperative work of the two core modules, namely the rapid individual calibration module and the dynamic task scheduling module, the problems that decoding performance is reduced during cross-subject and cross-task switching caused by individual differences, and multi-task labeling efficiency is low due to task scheduling strategy stiffness are solved accurately. Data persistent storage, model online updating and user interaction feedback are completed through storage, optimization and feedback links, periodic retraining is carried out by utilizing daily successful labeling data through a model continuous learning link, and finally, a zero-threshold individual rapid adaptation and high-efficiency multi-task collaborative labeling target is achieved.

The invention discloses an emotion recognition method, device and equipment based on electroencephalogram signals and a medium, and relates to the technical field of electroencephalogram signal emotion recognition. The method comprises the steps that resting-state electroencephalogram signals and task-state electroencephalogram signals are preprocessed, and the preprocessed resting-state electroencephalogram signals and the preprocessed task-state electroencephalogram signals are obtained; performing feature extraction on the pre-processed resting-state electroencephalogram signals and the pre-processed task-state electroencephalogram signals to obtain resting-state electroencephalogram features and task-state electroencephalogram features; calculating a task state-resting state differential feature and / or a state transition differential feature between a resting state task and a task state task according to the resting state electroencephalogram feature and the task state electroencephalogram feature; and inputting the task state-resting state difference feature and / or the state transition difference feature after the generalized difference processing into a trained emotion recognition model to obtain an emotion recognition prediction result of the target user, thereby improving the emotion recognition precision.

The invention discloses an obsessive-compulsive disorder diagnosis system based on brain multi-modal indexes, and belongs to the field of mental diseases. The problem of lack of a cross-modal feature fusion mechanism is solved. The system comprises an electroencephalogram signal acquisition unit used for acquiring an EEG signal of a testee under a preset stimulation normal form and executing preprocessing operation; the brain imaging data acquisition unit is used for synchronously acquiring brain structure imaging data and brain function imaging data of the testee; the multi-modal data fusion unit is used for extracting frequency band power spectrum density characteristics and event-related potential amplitude or incubation period characteristics from the EEG signals; performing standardization processing on the EEG features, the sMRI structural features and the fMRI functional features; integrating modal features by adopting a weighted average fusion algorithm; screening fused feature subsets through a recursive feature elimination method; and the diagnosis model unit is used for inputting the fusion feature vector into a trained SVM classification model and outputting an obsessive-compulsive disorder diagnosis result. Used in the medical field.

The present invention relates to the technical fields of human-computer interaction and brain informatics, and in particular to a closed-loop neurofeedback method and device based on haptic memory electroencephalogram signal conditioning. The method comprises: collecting electroencephalogram signals of a subject, and performing real-time online data preprocessing; designing a haptic memory stimulation task, activating the haptic memory of the subject, and recording a corresponding electroencephalogram signal response; performing feature extraction and analysis on a corresponding electroencephalogram signal to obtain an electroencephalogram feature related to haptic memory; and on the basis of a haptic memory electroencephalogram feature signal, designing a closed-loop neurofeedback system, the closed-loop neurofeedback system monitoring the electroencephalogram signals of the subject in real time, performing real-time stimulation on the basis of a preset haptic stimulation task, and recording a corresponding electroencephalogram response. In the technical solution of the present invention, the haptic memory electroencephalogram feature signal is combined with the closed-loop neurofeedback system, thus providing new ideas and methods for the diagnosis and treatment of haptic memory-related diseases.

The invention relates to a brain fatigue real-time evaluation and prediction method based on a multi-modal signal and deep learning, and solves the problem that the existing brain fatigue monitoring technology mostly depends on static feature analysis of a single physiological signal. Firstly, EEG dynamic brain function network features and EEG time-frequency domain features are fused, and feature fusion driven by time-frequency-space multi-domain EEG features is achieved; furthermore, on the basis of weighted K-means clustering, PPG signals are automatically associated with a fatigue stage, the fatigue level is calibrated with the heart rate center value, subjective labeling is not needed, and objective re-calibration of the brain fatigue degree is achieved; and finally, realizing real-time evaluation and prediction of the brain fatigue degree based on the time sequence deep convolutional network model. The brain fatigue degree can be evaluated and predicted in real time with high precision, an early warning signal is sent to an operator with high brain fatigue degree, technical support is provided for brain fatigue real-time monitoring and warning, and the rate of misoperation accidents caused by brain fatigue is further reduced.

The invention discloses an EEG motion image classification modeling system based on self-supervised contrast learning and a modeling method thereof, and the system comprises an obtaining module which is used for obtaining EEG data; the domain self-adaptive alignment module is used for aligning EEG data of the source domain and the target domain; the data enhancement module is used for performing enhancement operation on the unlabeled source domain EEG data; the mask modeling module is used for performing mask of partial time points on the enhanced EEG data and extracting EEG feature representation through an encoder; the multi-view space-time attention module is used for processing EEG features and generating EEG feature vectors; the comparative learning module is used for realizing reconstruction of an original sequence through a reconstruction decoder and carrying out comparative learning and self-supervised pre-training; and the classification module optimizes model parameters through classification loss and applies the model parameters to target domain data to realize cross-domain classification. According to the method, the individual variability of the EEG signals can be effectively processed, and the accuracy of motion image classification and the cross-domain adaptive capacity of the model are improved.

The invention relates to a cognitive state classification method based on EEG-fNIRS spatio-temporal fusion features, which comprises the following steps of: setting an experiment according to a mental calculation experiment normal form, and synchronously acquiring EEG data and fNIRS data of a tested mental calculation task; preprocessing the two collected data to obtain electroencephalogram signal data and hemoglobin concentration change data; eEG signal data and hemoglobin concentration change data are taken, data enhancement is carried out through a time window, then the data are sent to a space-time fusion network, EEG features, oxyhemoglobin HBO features and deoxyhemoglobin HBR features are obtained, fusion features are obtained through fusion, and an EEG classification result, an fNIRS classification result and a fusion feature classification result are obtained. According to the method, the characteristics of the EEG signal and the fNIRS signal are effectively combined, the overfitting problem in modal deep learning is relieved, and cognitive state classification can be accurately carried out.

The invention provides a method and device for controlling an intelligent sound production system to relieve carsickness based on electroencephalogram signals. The method comprises the following steps that S1, electroencephalogram signal data of passengers in the carsickness process are collected; step S2, preprocessing the electroencephalogram signals, and extracting electroencephalogram characteristics including frequency bands of # imgabs0 # waves, # imgabs1 # waves, # imgabs2 # waves and # imgabs3 # waves; s3, calculating a brain wave comprehensive variation index CVEI as an objective evaluation index of the carsickness grade based on the extracted electroencephalogram characteristics; s4, performing carsickness grade classification according to the brain wave comprehensive variation index CVEI of the passenger by using a carsickness grade identification model, wherein the carsickness grade identification model is constructed by fusing a convolutional neural network and an attention mechanism; and step S5, according to the carsickness grade classification result of the passenger, playing the audio which corresponds to the carsickness grade and is used for relieving carsickness in the audio sample library. The garment can effectively relieve carsickness of passengers.

The invention provides a user intention classification method based on a multi-task learning time-frequency double-branch network, and the method comprises the steps: data preprocessing: employing LaBrM to extract motor imagery and N-back task features from an original EEG signal, and generating a final EEG feature embedding vector; based on an adaptive spectrum feature fusion attention module and a multi-scale expansion factor convolution time feature extraction module, features of EEG feature embedding vectors are extracted respectively; fusing the extracted time features and spectrum features; and classifying the fused features through a multi-task classifier. According to the method, the distribution characteristics of the electroencephalogram signals in the time domain and the frequency domain are fully utilized, and accurate recognition of intention information in multiple cognitive tasks of the user is achieved.

The invention requests to protect a motor imagery electroencephalogram signal enhancement method based on graph attention. The method comprises the following steps: firstly, carrying out multi-stage preprocessing on an original electroencephalogram signal, including band-pass filtering and baseline drifting, and removing artifacts in combination with independent component analysis, then, extracting time information of each channel by utilizing a one-dimensional convolutional network so as to capture time sequence characteristics in a motor imagery process, on the basis, constructing a graph attention network, taking the electroencephalogram signal channels as nodes, and taking the electroencephalogram signal channels as the nodes; the method comprises the following steps of: dynamically modeling and optimizing the internal relation between channels, strengthening motor imagery related channels such as C3, C4 and Cz by combining priori knowledge, further introducing a time attention network to identify and enhance key time slices rich in discrimination information, and finally, forming a high-dimensional feature matrix by fusing space-enhanced and time-enhanced electroencephalogram features, so as to realize the recognition and enhancement of the motor imagery related channels. And inputting into a classifier for pattern recognition.

The invention discloses a sleep spindle wave intelligent identification method, system and device, a medium and a product, and relates to the field of electroencephalogram characteristic wave identification, and the method comprises the following steps: obtaining a sleep electroencephalogram signal time sequence; performing band-pass filtering processing on the sleep electroencephalogram signal time sequence to obtain a filtered electroencephalogram signal time sequence; respectively segmenting the sleep electroencephalogram signal time sequence and the filtering electroencephalogram signal time sequence into overlapped window signals with different lengths to obtain a time slice data set; according to the time slice data set, a spindle wave recognition model is adopted, and whether spindle waves exist in the sleep electroencephalogram signal time sequence or not and the starting time and the duration time of the spindle waves are determined; wherein the spindle wave recognition model is constructed based on a convolutional neural network, and is obtained by adopting a training sample set for training in advance. The spindle waves with different durations and amplitudes can be detected, and the recognition accuracy of the sleep spindle waves is improved.

The invention discloses an electroencephalogram feature recognition method for motion intention prediction in a lower limb alternate stepping process. The electroencephalogram feature recognition method comprises the following steps: acquiring an electroencephalogram signal of a subject before the subject executes an action under the requirement of a lower limb alternate stepping motion execution normal form; preprocessing the electroencephalogram signal to obtain an initial signal, performing label labeling on the initial signal according to an action type, and forming a data set by a label and the initial signal; constructing a neural network model, wherein the neural network model comprises a multi-scale grouping space-time convolution module, a self-attention mechanism module for updating external memory based on Top-k gating and a grouping causal time convolution module; and training the neural network model in a supervised manner by using the data set to obtain a prediction model for predicting the action type. According to the method provided by the invention, the movement intention of the patient can be recognized by the movement brain-computer interface before the lower limb alternate movement, the control of the rehabilitation robot on the specific lower limb movement by the brain intention is realized, and the method has an important value on the clinical application of a brain-controlled rehabilitation robot system.