239 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.

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 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 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 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 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 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 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 invention discloses a continuous dynamic emotion recognition method, system and device, a medium and a product, and relates to the field of emotion recognizing.The method comprises the steps that electroencephalogram signal data and eye movement signal data of a target subject are obtained; determining electroencephalogram features and eye movement features according to the electroencephalogram signal data and the eye movement signal data; based on a deep learning model, the electroencephalogram features and the eye movement features are fused to obtain multi-modal fusion features, and the continuous dynamic emotion category of the target subject is determined according to the multi-modal fusion features; the deep learning model comprises a cross-modal attention mechanism layer, a deep typicality correlation analysis layer, a convolutional neural network, a Transform network and a full connection layer. According to the invention, the accuracy and real-time performance of continuous dynamic emotion recognition are improved.

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 discloses an intelligent asthenopia detection method, system and device, and relates to the field of asthenopia detection.The method comprises the steps that feature extraction is conducted on electroencephalogram information and eye movement information to obtain an electroencephalogram feature set and an eye movement feature set; respectively inputting the electroencephalogram feature set and the eye movement feature set into a plurality of preset asthenopia detection models to obtain a plurality of asthenopia detection results; determining a final asthenopia detection result according to the plurality of asthenopia detection results; wherein different preset asthenopia detection models correspond to different machine learning models, and each preset asthenopia detection model is obtained by training the machine learning model according to the asthenopia detection sample set. According to the invention, real-time, efficient, objective and accurate asthenopia detection can be realized.

The invention discloses a motor imagery late fusion decoding method based on EEG-fNIRS data, and the method comprises the steps: preprocessing the EEG-fNIRS data, and obtaining EEG data and fNIRS data of a specified size; using a depth separable convolution feature extraction module based on an Inception architecture to extract EEG feature information, and using an fNIRS feature extraction module to extract fNIRS feature information; inputting the EEG feature information into a self-adaptive average pooling layer, changing the dimension of the EEG feature information to be consistent with the dimension of the fNIRS feature information, and splicing the changed EEG feature information and fNIRS feature information to obtain mixed features; and inputting the mixed features into a multi-layer perceptron for motor imagery task classification. According to the method, EEG data with high time resolution and fNIRS data with high spatial resolution can be combined, so that EEG and fNIRS feature information is fused and complemented, and the classification performance of motor imagery tasks is improved.

The embodiment of the invention belongs to the technical field of data processing, and relates to a user state detection method which comprises the following steps: acquiring respiration data, electroencephalogram data and basic sensing data of a user; performing feature extraction on the respiration data and the electroencephalogram data to obtain respiration feature data and electroencephalogram feature data; performing data fusion on the respiration feature data and the electroencephalogram feature data to obtain a joint feature vector; determining a target state detection model in each preset state detection model according to the basic sensing data; and performing state detection on the user according to the target state detection model and the joint feature vector, and determining the state of the user. According to the method and the device, the physiological data of the user is acquired from different dimensions, and the acquired data is subjected to multi-modal fusion and conjoint analysis, so that the user state detection in different scenes is realized, diversified scene requirements are met, and the accuracy of the intelligent wearable equipment in detecting the user state is 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 provides a multi-parameter fusion mood disorder assessment system based on a time sequence dynamic graph network, and the system comprises a data collection unit which is used for collecting an electroencephalogram signal of a to-be-assessed patient; the electroencephalogram coding unit is used for extracting electroencephalogram features corresponding to the electroencephalogram signals; the time sequence dynamic graph network feature extraction unit is used for constructing a time sequence dynamic graph network sequence related to various electroencephalogram parameter combinations and extracting related time sequence dynamic graph network features; the demographic information coding unit is used for coding the demographic information to obtain demographic coding features; and the hierarchical multi-level gating unit is used for predicting and obtaining a mood disorder evaluation result based on the sequential dynamic graph network characteristics corresponding to the electroencephalogram parameter combinations obtained by screening of the sequentially arranged multi-level gating layers. According to the method, the problem that in the prior art, a mood disorder evaluation system does not consider the correlation between multi-parameter combination and tasks and does not perform brain network model screening in a hierarchical gating mode, so that the evaluation accuracy is limited is solved.

The invention discloses an emotion prediction method and system based on electrocardiographic and computerized electrical coupling. The method comprises the following steps: acquiring electrocardiosignal data and electroencephalogram data of a patient to be predicted; on the basis of a feature analysis algorithm, according to the electrocardiosignal data and the electroencephalogram signal data, determining electrocardiosignal feature data and electroencephalogram feature data; based on a coupling analysis algorithm, determining coupling characteristic data according to the electrocardiosignal data and the electroencephalogram signal data; and inputting the electrocardio feature data, the electroencephalogram feature data and the coupling feature data into a trained emotion prediction model to obtain an emotion prediction result of the to-be-predicted patient. Therefore, the accuracy and the stability of emotion prediction can be improved, accurate evaluation of the emotion state of the patient is realized, and a reliable basis is provided for emotion management and related medical intervention.