153 results about "EEG feature" patented technology

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.

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 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 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 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 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 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 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.

The invention provides a brain wave detection data processing method and system based on AI, and relates to the technical field of artificial intelligence, and the method comprises the steps: firstly, obtaining an original electroencephalogram signal set composed of a plurality of segments of electroencephalogram signal sequences which are continuously collected through multiple channels and have time stamps, and then carrying out the quality optimization processing of the original electroencephalogram signal set; the method comprises the following steps: acquiring an effective electroencephalogram signal set according with a detection standard, then performing feature extraction processing on the effective electroencephalogram signal set to obtain an electroencephalogram feature combination reflecting a neural activity mode, and then calling a pre-trained electroencephalogram analysis model to perform mode recognition processing on the electroencephalogram feature combination to obtain a neural activity model. An electroencephalogram detection result containing the abnormal activity time period identifier and the corresponding brain region positioning information is generated, finally, an electroencephalogram processing instruction containing space-time information is generated based on the electroencephalogram detection result and sent to the target device to trigger response operation, and the accuracy and efficiency of electroencephalogram detection are improved.

The invention relates to the field of electroencephalogram signal processing and neural engineering, and discloses a multi-mode electroencephalogram feature fusion decoding method. The method comprises the following steps: carrying out noise reduction, calibration and standardization processing on an original electroencephalogram signal to generate a standard signal; extracting mu / beta rhythm time-frequency features and Hjorth time-domain features of the signals, and generating a fusion feature vector through PCA dimension reduction fusion; performing pre-classification and weight optimization by using an SVM classifier, performing space and time sequence feature extraction and classification through a CNN-LSTM network, and outputting a classification probability; model parameters are updated according to the probability and verified, and finally real-time control signals suitable for various communication interfaces are generated. According to the method, the accuracy and robustness of electroencephalogram signal decoding are improved, and efficient and self-adaptive motor imagery brain-computer interface control is realized.

The invention discloses an electroencephalogram signal state evaluation method. The method comprises the following steps: acquiring a to-be-evaluated electroencephalogram signal and a reference electroencephalogram signal; determining a plurality of to-be-evaluated electroencephalogram features and a plurality of reference electroencephalogram features; determining a difference degree between the to-be-evaluated electroencephalogram feature and the corresponding reference electroencephalogram feature to obtain a plurality of difference degrees, and determining a comprehensive difference degree according to the plurality of difference degrees; judging whether the comprehensive difference degree is within a preset normal state range or not; if yes, determining that the to-be-evaluated person is in a normal state; if not, determining that the to-be-evaluated person is in an abnormal state; the types of the electroencephalogram characteristics to be evaluated and the reference electroencephalogram characteristics are theta wave energy characteristics, alpha wave energy characteristics, alpha wave frequency band energy characteristics, aperiodic offset characteristics and aperiodic telescopic transformation characteristics, and the abnormal state comprises fatigue and / or hypoxia. The method can quickly and accurately judge whether the person to be evaluated has an abnormal state caused by fatigue or oxygen deficit. The invention further discloses a state evaluation device, electronic equipment and a computer readable storage medium.

The application provides a kind of active and passive intention recognition method and system based on electroencephalogram signal: the method comprises: obtaining the electroencephalogram signal of test personnel;Extract the electroencephalogram feature of multiple frequency bands based on electroencephalogram signal;And based on the electroencephalogram signal power spectrum density of preset frequency band in the electroencephalogram feature of multiple frequency bands, obtain active feature and passive feature;Multiple frequency bands of electroencephalogram feature, active feature and passive feature are input into pre-trained active and passive intention recognition model, and the active and passive intention categories of test personnel are obtained by identification;Active and passive intention recognition model is trained based on the electroencephalogram signal obtained from active intention experiment and passive intention experiment.The application solves the problem that the processing of electroencephalogram signal in the prior art does not consider the influence of active and passive intention, resulting in poor accuracy of control instruction output by brain-computer interface.

The embodiment of the invention discloses a brain-controlled automobile driving intention determination method and device, equipment and a medium. The method comprises the following steps: acquiring a first electroencephalogram signal in a brain-controlled automobile in a vehicle driving process of a driver; preprocessing the first electroencephalogram signal based on a preset signal processing mode, and determining a second electroencephalogram signal corresponding to the driver; feature extraction is conducted on the second electroencephalogram signals based on a preset feature extraction mode, and electroencephalogram features corresponding to the driver are determined; and performing driving intention classification based on the electroencephalogram features and a target classification model, and determining the driving intention of the driver. Through the technical scheme of the embodiment of the invention, the vehicle driving intention of the driver can be accurately and conveniently determined, and the driving intention determination efficiency and accuracy are improved.

The invention relates to a brain-computer interface robot control method and system based on specific electroencephalogram characteristics, and the method comprises the steps: obtaining an electroencephalogram signal of a user in real time through an electroencephalogram signal collection device; predefined interpretable physiological features are extracted from the electroencephalogram signals; the key parameters capable of explaining the physiological features are directly input into a pre-constructed real-time decoding model based on a rule engine, and a robot control instruction is generated through a preset deterministic mapping rule; and the robot is driven to execute corresponding actions according to the control instruction, so that the purpose of accurately and stably controlling the brain-controlled robot is achieved.

The invention relates to the technical field of biomedical engineering, in particular to a waking-up system based on multi-modal electroencephalogram characteristic dynamic evaluation and closed-loop regulation, which comprises a multi-modal electroencephalogram acquisition module for acquiring multi-modal original electroencephalogram signals; the multi-dimensional awakening related electroencephalogram feature fusion extraction module is used for extracting a multi-dimensional fusion feature vector; the consciousness level and waking-up response dynamic evaluation module is used for loading an off-line constructed and completed deep learning model, carrying out real-time dynamic evaluation and generating a waking-up response dynamic evaluation result; the personalized closed-loop awakening regulation and control decision module is used for generating and dynamically updating a closed-loop awakening regulation and control scheme by adopting an awakening personalized self-adaptive regulation and control algorithm; and the multi-modal awakening regulation and control execution and man-machine interaction module is used for executing multi-modal awakening nerve regulation and control output and providing a visual dynamic evaluation result, awakening regulation and control parameters and a man-machine interaction interface for medical personnel and family members of the patient. Therefore, the problems of single electroencephalogram acquisition mode, one-sided electroencephalogram feature extraction and the like in the prior art are solved.

The invention discloses a face and electroencephalogram feature fused fatigue driving detection method, which comprises the following steps of: acquiring a face video and an electroencephalogram signal in a driving process as a data set; performing feature extraction on the face video in the data set by adopting a multi-scale neural network to obtain a face feature tensor; extracting features from the electroencephalogram signals in the data set through a convolutional neural network to obtain an electroencephalogram feature tensor, taking the electroencephalogram feature tensor and a face feature tensor as input of a fusion model, performing feature fusion training, and performing fatigue detection on images or videos in the driving process by adopting the trained model. According to the method, firstly, a simulation driving platform is built to simulate real road driving behaviors, and electroencephalogram signals and facial features of a driver in the whole simulation driving process are collected; thirdly, constructing a lightweight multi-scale neural network model, and extracting fatigue features; finally, a facial feature and electroencephalogram signal fusion detection model is constructed, and the driving state is accurately recognized.

The invention provides a brain wave epilepsy detection method based on Transform. The method comprises the following steps: acquiring EDF data of a multi-channel electroencephalogram from a database, processing the EDF data, generating a CSV file, and performing data preprocessing on the generated CSV file; defining a plurality of machine learning models, and independently training the models on the EEG feature data to obtain the classification performance of the models; predicting a probability vector by using a machine learning model to construct Transform model input data; the attention of the Transform model is used for dynamic training, and a trained Transform fusion model is obtained; and outputting a prediction result, and storing the trained Transform fusion model. According to the method, more efficient model fusion is realized through dynamic fusion, and the accuracy and generalization ability of epilepsy detection are improved.

The present embodiment provides a method for studying instinctive fear based on EEG features, comprising: outputting different stimulation signals, wherein different stimulation signals correspond to different fear stimulus intensities; obtaining a subject's original EEG signal, performing noise reduction processing on the original EEG signal to obtain an EEG signal; performing time-frequency analysis on the EEG signal to obtain EEG information; extracting EEG features from the EEG information; and analyzing the EEG features to obtain results of the instinctive fear study. Also provided is a device for studying instinctive fear based on EEG features. The experimental design for studying human instinctive fear provided by the present embodiment can provide in-depth research into the mechanisms of human instinctive fear.

The invention discloses a motor imagery electroencephalogram signal classification method and device, a terminal and a storage medium, and relates to the field of biomedical signal processing.The method comprises the steps that electroencephalogram signals based on user motor imagery are obtained and preprocessed, and electroencephalogram features are determined; performing multi-scale spatio-temporal feature extraction and space and channel decoupling reconstruction on the electroencephalogram features, and determining target spatio-temporal enhancement features; and classifying the target space-time enhancement features through a classification output layer, and determining a classification result. Due to the fact that space and channel decoupling reconstruction is carried out on the features, redundant correlation of the cross-electrode electroencephalogram signals is systematically eliminated, and the problems that in the prior art, space and channel information are jointly processed, information redundancy is caused, and calculation burden is increased can be effectively solved.

This application relates to the field of data processing technology, and in particular to a data fusion method and system based on neural pathways. The method includes the following steps: First, acquiring electroencephalogram (EEG) data and eye-tracking (EMT) data, aligning them using timestamps, and preprocessing them separately; then, extracting EEG features based on the preprocessed EEG data; extracting EMT features based on the preprocessed EMT data; next, fusion of features based on the EEG and EMT features by downsampling the data; finally, evaluating the synergy of the fused features. This application constructs a complete evaluation system by building a framework from a stimulus-visual paradigm to a signal acquisition platform and analysis methods related to neural structures. The time delay between the most relevant points in the time domain of eye-tracking and EEG features is used as an indicator of brain-eye synergy. The difference between these indicators has interpretability based on physiological structures, constituting a novel evaluation indicator for brain-eye synergy when assessing dynamic visual acuity.

This invention discloses a multi-task behavioral-EEG synchronous monitoring method for schizophrenia screening, relating to the field of schizophrenia screening. The method includes: designing a multi-task temporal loading paradigm based on clinical screening needs; simultaneously collecting EEG signals and behavioral data of subjects during the execution of the multi-task temporal loading paradigm, and preprocessing the collected raw data; extracting multidimensional features from the preprocessed data, wherein the multidimensional features include EEG feature groups, behavioral feature groups, and time-varying feature groups; constructing a multimodal fusion discriminant model, inputting the multidimensional features into the multimodal fusion discriminant model, and outputting a baseline risk probability and a schizophrenia risk index. The advantages of this invention are: achieving efficient, accurate, and objective screening of the core cognitive dimensions of schizophrenia, effectively improving the sensitivity and specificity of screening.

This invention relates to the field of music signal recognition technology, and more particularly to a method for recognizing the degree of music enjoyment based on music-EEG feature fusion. The method includes: extracting the Mel-frequency cepstral coefficients of the music signal; preprocessing and performing Fast Fourier Transform on the Mel-frequency cepstral coefficients to obtain the spectral feature values ​​of the music signal; and sequentially filtering, reducing dimensionality, and decorrelating the spectral feature values ​​of the music signal; extracting the Mel-frequency cepstral coefficients of the EEG signal; optimizing and updating the order of the Mel-frequency cepstral coefficients of the EEG signal so that the comprehensive error value between the EEG feature reconstruction signal and the original EEG signal meets a preset condition; aligning the music feature coefficients and the EEG feature reconstruction signal in the feature dimension and time axis using the FastDTW algorithm; and recognizing the degree of music enjoyment of the subject based on the alignment result. This invention effectively improves the robustness and accuracy of music recognition.

The invention discloses a portable state analysis method and system based on multi-mode electroencephalogram and electrocardio, and belongs to the field of state analysis. The portable state analysis method comprises the steps that electroencephalogram signals and electrocardio signals of a subject are synchronously collected; performing preprocessing and feature extraction on the signals to obtain electroencephalogram features and electrocardio features; according to the values of the electrocardio characteristics in different physiological states, constructing difference value characteristics and ratio characteristics representing differences between the states; the electroencephalogram features, the electrocardio features and the new construction features are fused, and multi-modal features are obtained; and finally, inputting the fusion features into a pre-trained table priori data fitting network model, and outputting a classification result or a quantitative prediction value for representing the state of the subject. According to the method, through systematic state comparison feature engineering and an efficient table data model, the accuracy, generalization ability and automation level of multi-modal physiological signal analysis are improved, and the whole scheme is realized based on portable equipment and is suitable for state evaluation of various non-laboratory scenes.

The application discloses a kind of indoor CO2 concentration and temperature interaction influence on human cognition test method, belong to indoor environment control field, mainly include: screening tester participating in experiment;Exposure environment construction: prepare environment cabin and set experimental condition, the experimental condition includes: set different CO2 concentration level, set different temperature level;Cognitive performance measurement: in exposure environment, different types of cognitive task test are carried out, and subjective response, physiological parameter and electroencephalogram are measured;Measurement data analysis: the measurement data under different types of cognitive tasks are processed, and the sensitivity of different types of cognitive tasks to different components in the environment is compared.The application supplements the existing evidence on the effects of increasing carbon dioxide concentration and temperature on humans by combining the use of psychological, physiological and neurological tests and comparing different cognitive tasks. At the same time, a deep learning cognitive comfort model is constructed based on time-domain EEG features, providing new insights into the effects of environmental factors on cognition and suggesting potential applications for optimizing cognitive comfort through environmental control.

The present application relates to a kind of automatic sleep staging method, its steps include: the EEG original data is processed to obtain EEG feature map, while the EOG original data is processed to obtain EOG feature map;EEG feature map and EOG feature map are multiplied and fused, and highlight feature map is obtained;Highlight feature map is directly added with EEG feature map and EOG feature map and fused to obtain initial fusion feature map;Initial fusion feature map is processed to obtain the most weight feature map by weight adjustment;Initial fusion feature map and the most weight feature map are multiplied and fused to obtain the final fusion feature map with weight information;According to the weight information of final fusion feature map, each sleep stage prediction probability is output.The automatic sleep staging method described in the present application is further trained and optimized, does not need too much artificial intervention, and can be simply and effectively applied in sleep staging of healthy individuals and patients with consciousness disorders.

The invention provides a building image generation method and device and electronic equipment, and the method comprises the steps: obtaining electroencephalogram data corresponding to a preset image, carrying out the feature extraction of the electroencephalogram data, and obtaining a target electroencephalogram feature; inputting the target electroencephalogram feature into an emotion recognition model, and processing the target electroencephalogram feature by the emotion recognition model to obtain a target emotion vector; determining a semantic prompt word according to the target emotion vector, inputting the semantic prompt word into an image generation model, and processing the semantic prompt word through the image generation model to obtain an initial building image; performing emotion recognition on the initial building image to obtain an image emotion vector; and determining the similarity between the image emotion vector and the target emotion vector, and taking the initial building image as a target building image in response to the fact that the similarity is greater than or equal to a preset similarity threshold. According to the method and the device, the building image starting from individual emotion is generated, and the emotion expression ability of the building image and the adaptation degree of user experience are remarkably improved.

The invention particularly relates to a neural decoding method based on multi-scale electroencephalogram signals and heterogeneous visual stimulation alignment, which comprises the following steps: firstly, performing frequency domain transformation, processing and inverse mapping on original EEG signals through an FADE module to effectively integrate frequency specific information; then, the signals are input into an ACC module, and the module achieves self-adaptive multi-channel relation modeling based on a functional relation instead of a fixed anatomical position through a dynamic clustering center and a cross attention mechanism; then, the encoded EEG features and CLIP visual features are subjected to alignment training under a joint loss function, and the loss function optimizes the semantic fidelity and discrimination of the features at the same time; finally, the EVA frame obtained through training can be used for heterogeneous visual stimulation decoding tasks such as zero-sample image retrieval, video classification and high-quality image reconstruction. According to the method, the capture capability of the EEG features on visual semantic information and the generalization of the model can be effectively improved, and the challenge of multi-scale and multi-modal neural decoding is solved to a certain extent.

The invention provides a sleep staging and sleeping posture judgment method, system and device and a storage medium. The sleep staging and sleeping posture judgment method at least comprises the following steps that A, monitoring is started; b, signal acquisition; c, signal preprocessing; d, feature extraction and preliminary judgment; and E, multi-modal information fusion decision making. The step D specifically comprises the following steps: D1, EEG feature extraction; d2, calculating the attitude of the IMU; d3, preliminarily staging the sleep; and D4, sleep posture and body movement judgment. The invention provides a sleep staging and sleep posture judgment method, system and device based on electroencephalogram and posture information fusion and a storage medium, and the core lies in that sleep posture and body movement judgment results monitored by an IMU module are used as assistance, and sleep staging results based on EEG are verified, corrected and supplemented. Finally, an anti-interference and multi-dimensional fused sleep report is output, and technical support and assistance are provided for implementation of subsequent disease diagnosis, rehabilitation training and the like.