811 results about "Signal classification" patented technology

The invention discloses a motor imagery electroencephalogram signal classification method based on multi-scale feature extraction and adaptive feature fusion, which is called EEG-MAFENet for short, and belongs to the field of brain-computer interfaces. The EEG-MAFENet is provided to promote the development of a motor imagery brain-computer interface and solve the problem of low classification accuracy in the prior art. Firstly, multi-scale time domain information and channel space information of electroencephalogram signals are extracted through a multi-scale feature extraction (MFE) module, and a primary spatial-temporal feature sequence is obtained. Next, the most valuable global and local feature information is adaptively fused through an adaptive feature fusion (AFF) module to obtain an advanced fusion time feature vector; and finally, completing a classification task by a classifier module formed based on a full connection layer. Experiments prove that the classification method provided by the invention can more effectively improve the feature extraction and classification performance of the motor imagery electroencephalogram signals.

The invention provides an RSVP type unbalanced electroencephalogram signal classification method based on a self-adaptive channel mixed attention mechanism and decoupling learning, and aims to improve the feature extraction and classification performance of RSVP electroencephalogram signals. Firstly, collected RSVP electroencephalogram signals are preprocessed, including common average reference, band-pass filtering and standardization processing, and a training set and a test set are divided; then, multi-scale time and space features are extracted through a parallel convolution layer and a space convolution layer, and the features of different levels are fused through an interpolation method; the training process is decoupled into representation learning and classifier learning, the representation learning uses a triple loss function based on difficult sample sampling, and the classifier learning uses the optimized Focal Loss to perform feature learning and classifier optimization. A self-adaptive channel mixed attention mechanism is introduced, and the flexibility and robustness of the model are enhanced through dynamic weighting and multi-level feature fusion. According to the method, the problem of class imbalance in the RSVP task is solved.

The invention relates to the technical field of sound wave measurement, in particular to an ultrasonic image diagnosis system and method.According to the ultrasonic image diagnosis system and method, matching between reflection characteristics and tissue characteristics is made to have the self-adaptive learning ability through a deep neural network, the targeted recognition effect is improved in the aspect of signal classification, and based on the judgment result of the reflection characteristics, the diagnosis accuracy is improved. When boundary partitioning is carried out on a tissue area, the edge structure is judged by using the combination of three parameters of gray range, gradient direction and texture continuity, contour fuzziness caused by single index judgment is avoided, and the boundary partitioning accuracy is improved by extracting a gray distribution center, an amplitude change track and an edge area continuous change sequence. And the region positioning result is input into a support vector machine, accurate recognition of lesion properties is realized according to boundary classification comparison of morphological structure quantitative features and historical benign and malignant feature data, secondary verification of the structural form is performed after the image is formed, and the diagnosis integrity and the judgment confidence are effectively improved.

The invention provides an EEG (electroencephalogram) classification method based on multi-domain feature fusion. A multi-domain feature extraction network is constructed, the multi-domain feature extraction network mainly comprises a frequency domain feature extraction module and a space-time feature extraction module which are deployed in parallel, a feature fusion module and a classifier module, multiple view features such as a time domain, a frequency domain and a space domain can be separated, and electroencephalogram signal classification is achieved. According to the method, an efficient solution is provided for solving the problem of insufficient multi-domain feature utilization of the electroencephalogram signals, the cross-scene classification precision can be remarkably improved while the model efficiency is kept, and a technical foundation is laid for personalized deployment of brain-computer interfaces.

The invention relates to the technical field of bidirectional PCS converters, and discloses a VSG control method and device for a DSP numerical control high-frequency bidirectional PCS converter, and the method comprises the steps: carrying out the multi-signal classification of an output voltage, an output current, a DC bus voltage and a PWM modulation waveform of the PCS converter through a DSP chip, and obtaining a frequency spectrum feature vector; calculating a VSG parameter group based on the spectrum feature vector, inputting the VSG parameter group into a depth deterministic strategy gradient network for parallel training, and generating a VSG real-time control strategy; applying phase-locked loop compensation to the VSG real-time control strategy to obtain a PWM modulation signal after synchronous compensation; and carrying out real-time fault early warning on the PWM modulation signal after synchronous compensation, and carrying out seamless switching on a grid-connected and off-grid mode by adopting voltage feed-forward decoupling control to obtain a smooth transition control sequence. According to the method, the control period and the calculation load of the PCS converter under the high-frequency switching condition are reduced, and the control precision under the complex working condition is ensured.

The invention discloses a radio interference identification method based on electromagnetic spectrum monitoring, and the method comprises the following steps: S1, collecting spectrum data in an electromagnetic environment through broadband spectrum monitoring equipment, and carrying out the digital sampling processing; s2, adopting a diffusion probability model to automatically remove environmental noise and non-interference signals; s3, automatically generating an expansion data set of edge and small sample interference signals by adopting an attention-enhanced data synthesis expansion technology; s4, extracting multi-dimensional time-frequency domain dynamic correlation characteristics of the spectrum interference signal based on a Transform structure; s5, adopting an attention enhancement self-supervision algorithm to generate a self-supervision soft label of an unknown interference type; s6, dynamic back diffusion iteration is carried out through the diffusion probability model, and an interference classification result is obtained; and S7, constructing a closed-loop dynamic feedback mechanism by using an interference classification result. According to the invention, accurate identification of radio interference signals is realized, and the accuracy, dynamic adaptability and automation degree of interference signal classification are improved.

The invention relates to a parallel processing synchronous optimization method and system for high-throughput gene sequencing. The method comprises the following steps: performing channel distribution and signal acquisition initialization on a plurality of DNA samples to obtain a signal acquisition parameter matrix; performing feature extraction and clustering analysis on the fluorescence signals collected by each channel to obtain a DNA base feature sequence and a signal classification probability matrix; dynamically adjusting short-term parameters of a sequencing period through a convex optimization algorithm, and optimizing long-term sequencing performance through an integer programming algorithm to obtain a sequencing parameter optimization set and a sequencing period distribution strategy; and inputting the sequencing parameter optimization set and the sequencing period allocation strategy into a double-layer nested control model, and carrying out joint solution through an upper-layer resource allocation network and a lower-layer parameter optimization network to obtain a global optimization control parameter set. According to the method and the device, the sequencing accuracy is improved, and the utilization rate of computing resources is improved.

The invention provides a motor imagery classification neural network method based on multi-scale spatial-temporal feature fusion. According to the method, the EEG signal decoding precision in a motor imagery task is effectively improved by designing a multi-scale spatial-temporal feature fusion network. The method comprises the following steps: firstly, processing an original EEG signal by adopting standardized preprocessing; secondly, extracting features of the signals under different time scales through a multi-scale time sequence attention module, and enhancing a long-term time sequence dependency relationship in combination with a self-attention mechanism; thirdly, a self-adaptive channel weighting module is introduced, the weight of each electrode signal is dynamically adjusted, and representation of spatial features is optimized; and finally, fusing the spatio-temporal characteristics through a Transform module, capturing complex interaction of the spatio-temporal dependency relationship, and finally outputting a decoding result through a classifier. The method can effectively overcome the defects of an existing method in the aspects of spatial-temporal feature fusion and dependent modeling, improves the accuracy of EEG signal classification and the system robustness, and has a wide application prospect.

The invention relates to the technical field of intelligent diagnosis of power equipment, and provides a multi-mode fusion extra-high voltage converter transformer fault diagnosis method and system, and the method comprises the steps: collecting a multi-mode signal from a sensor of an extra-high voltage converter transformer, and classifying the signal into a plurality of modes; performing intra-modal feature reinforcement learning on the multi-modal data by adopting a SimCLR framework to obtain a discriminative representation feature vector zm of each modal; inputting the zm into a Transform branch encoder of a corresponding mode, and obtaining context feature vector enhancement representation hm of each mode; mapping the hm of different modalities to the same dimension in a unified manner, and performing weighted attention fusion to obtain feature vectors zf of all modalities after fusion; and inputting a multi-layer perceptron classifier (MLP) to obtain a fault category prediction result of the extra-high voltage converter transformer. According to the method, the multi-mode signals are processed and fused in parallel, and the fault recognition capability of the model on the extra-high voltage converter transformer in the complex operation state is improved.

A method for the classification and detection of faults in a microgrid having a distance relay, includes measuring the first plurality of voltage signals and the first plurality of current signals of the microgrid, calculating a plurality of fault-loop impedance signals, comparing the plurality of fault-loop impedance signals and a plurality of reference impedance values, inputting the plurality of difference values to a convolutional neural network (CNN)-gated recurrent unit (GRU) hybrid model, generating a reference tripping signal, defining a deep reinforcement learning (DRL) agent for a deep reinforcement learning (DRL) model. The method further includes processing a second plurality of voltage signals and current signals of microgrid and classifying the one or more fault signals into one or more fault types.

The invention relates to a motor imagery electroencephalogram signal classification method based on multi-scale time sequence fusion. The method comprises the following steps: S1, standardizing electroencephalogram signals before inputting a network model; s2, inputting the processed data into a multi-scale channel attention convolution module, wherein the multi-scale channel attention convolution module focuses on capturing low-level spatial-temporal characteristics with discrimination from original motor imagery electroencephalogram signals (MI-EEG); s3, segmenting the electroencephalogram signal into a plurality of local time sequence segments by sliding the window; s4, inputting the data output by the sliding window into the time fusion residual network in parallel so as to further extract advanced time features from the time sequence; and S5, fusing the high-order time sequence characteristics of all windows through a full connection layer, and outputting probability prediction of a motor imagery task through a Softmax function. The method can effectively solve the problems that convolution scale division of the motor imagery network is limited, and features of all channels and advanced time features are ignored, and the average classification accuracy of the motor imagery electroencephalogram signals is improved.

The invention relates to the technical field of mechanical arm control, in particular to a mechanical arm control method and system based on motor imagery and eye movement tracking. (2) constructing an electroencephalogram signal classification model and performing training; (3) calibrating an eye movement tracking module and a mechanical arm control module; (4) inputting data collected in real time into an electroencephalogram signal classification model and an eye movement tracking module, and generating a user intention and a to-be-grabbed target label; and (5) an instruction decision is made according to the intention of the user, the to-be-grabbed target label is input into a mechanical arm control module, a mechanical arm control instruction is generated, the mechanical arm control instruction is driven to execute the grabbing action, and the control process of the mechanical arm is achieved. According to the scheme, more efficient man-machine interaction is achieved, through combination of motor imagery electroencephalogram decoding and eye movement tracking, mechanical arm selection target grabbing operation can be conducted through brain control and eye movement without manual operation, and the real-time performance and stability of a control system are improved through optimization of the decoding process.

The invention provides a cosine convolution multichannel group decoding method and system for electroencephalogram classification, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining different types of to-be-classified motor imagery electroencephalogram signals, inputting the motor imagery electroencephalogram signals into an electroencephalogram signal classification model, and outputting the classification results of the different types of motor imagery electroencephalogram signals; wherein through the cosine convolution time-frequency feature extraction module, cosine convolution kernels with different frequencies are adopted to decompose and reconstruct different frequency components, and multi-scale time-frequency features are obtained; inputting the multi-scale time-frequency features into a multi-channel group spatial feature extraction module, and carrying out convolution on data of each channel subgroup in a channel dimension by adopting an independent separable convolution branch network to obtain time-frequency-space features of electroencephalogram signals of each channel subgroup; and splicing the time-frequency-space features of the electroencephalogram signals of each channel subgroup into a fusion feature vector, and outputting the probability of the electroencephalogram signals of the motor imagery corresponding to each category of the motor imagery through a full connection layer.

An exemplary wireless soft scalp electronic system and method are disclosed that can actuate commands for a brain-machine interface (BMI) or brain-computer-interface (BCI) by performing real-time, continuous classification, e.g., via a trained neural network, of motor imagery (MI) brain signals or of steady-state visually evoked potential (SSVEP) signals. In some embodiments, the exemplary system is configured as a low-profile, portable system that includes microneedle electrodes that can acquire EEG signals for a brain-machine interface controller. The microneedle electrodes may be configured as soft imperceptible gel-less epidermis-penetrating microneedle electrodes that can provide improved contact surface area and reduced electrode impedance density, e.g., to enhance EEG signals and the signal classification accuracy. The microneedle electrodes can be further integrated with soft electronics that can be mounted locally in proximity to the electrodes to reduce obtrusive wiring and improve signal acquisition quality.

Systems and methods for operating a quantum processor. The methods comprise: training one or more quantum neural networks using modulation class data to make decisions as to a modulation classification for a signal based on one or more feature inputs for the signal; obtaining, by the quantum processor, principle components of real and imaginary components of a signal received by a communication device; and performing first quantum neural network operations by the quantum processor using the principle components as inputs to the trained one or more quantum neural networks to generate a plurality of scores, wherein each said score represents a likelihood that the received signal was modulated using a given modulation type of a plurality of different modulation types.

In order to solve the problem of inaccurate classification caused by fixed time-frequency resolution and lack of physical rationality in sample enhancement in a traditional classification method, the invention relates to a partial discharge signal classification method based on a physical constraint generative adversarial network, and the method comprises the following steps: extracting a partial discharge signal and a historical partial discharge signal, and carrying out the double-window time-frequency coding of the partial discharge signal and the historical partial discharge signal; correspondingly obtaining a fused time-frequency graph and a historical fused time-frequency graph; determining a corresponding discharge type label based on the historical partial discharge signal, establishing a physical constraint generative adversarial network, and training the physical constraint generative adversarial network in the historical fusion time-frequency graph based on the discharge type label; and inputting the fusion time-frequency graph into the trained physical constraint generative adversarial network to obtain a fusion feature graph corresponding to the fusion time-frequency graph, performing time sequence modeling on the fusion feature graph, mapping the fusion feature graph into a time sequence feature vector, and performing classification on the corresponding partial discharge signals based on the time sequence feature vector.

The invention discloses an electric vehicle charging load prediction method and system, and the method comprises the steps: collecting and obtaining the historical charging load time series data of an electric vehicle, and employing an ICEEMDAN model to carry out the decomposition of the charging load time series data, and obtaining a plurality of components; performing sample entropy calculation on the components, and performing signal classification by adopting K-medoids clustering according to a sample entropy result to obtain a high-frequency component F1, an intermediate-frequency component F2 and a low-frequency component F3; carrying out secondary decomposition on the high-frequency component F1 by adopting a characteristic mode decomposition (FMD) model to obtain a plurality of components; the MTS-Mixers model, the Crossform model and the DeepESN model are optimized by using an improved artemisinin AO optimization algorithm, and a high-frequency component F1, an intermediate-frequency component F2 and a low-frequency component F3 are predicted; the prediction results of the AO-MTSMixers model, the AO-Crossform model and the AO-DeepESN model are reconstructed, and the prediction result of the charging load is obtained; compared with a traditional method, the charging load prediction accuracy can be improved, and powerful support is provided for charging use and management of the electric vehicle.

The invention discloses a power distribution network fault knowledge graph construction method and system based on multi-modal data, equipment and a medium, and belongs to the technical field of intelligent power grids, and the method comprises the steps: carrying out the change matching of spatial position signals and meteorological parameters based on regional monitoring space-time records, meteorological environment parameters and equipment operation state data, dividing time periods, and obtaining a power distribution network fault knowledge graph; and analyzing according to spatial position association, and screening data overlapping to obtain a multi-modal data association distribution table. According to the invention, through matching and screening of space-time records, meteorological parameters and equipment state data, multi-modal data integration and abnormal feature extraction are realized, and in combination with cross analysis of signal fluctuation and meteorological conditions, abnormal signal classification and path association are optimized, and a fault propagation rule is accurately analyzed. The overall optimization significantly improves the efficiency of abnormal recognition, fault location and propagation analysis of the power distribution network, enhances the operation reliability and fault processing capability, reduces data redundancy and errors, and improves the safety and stability of the power grid.

The invention discloses a beam forming anti-interference method and device in a low-earth-orbit satellite communication system, and the method comprises the steps: building a low-earth-orbit satellite communication system model based on a planar array antenna, and receiving a signal through a receiving end of the low-earth-orbit satellite communication system; wherein the signals received by the receiving end comprise user signals transmitted by a plurality of ground nodes and interference signals generated by an external interference source; estimating the arrival angles of the user signals and the interference signals by using a multiple signal classification algorithm; by converting a maximized SINR problem into a power minimization problem under an arrival angle gain constraint, inter-user interference and external interference are suppressed so as to solve and obtain a beam forming matrix. According to the invention, the multi-node communication requirement of the satellite communication system can be met, the anti-interference of the satellite receiving end is realized, and the capacity of the satellite communication system is improved.

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 an electroencephalogram signal classification method based on a dynamic gating hybrid expert model, which focuses on efficient detection of electroencephalogram signals and comprises the following steps: 1, acquiring electroencephalogram signal data and preprocessing the electroencephalogram signal data to generate a standardized electroencephalogram signal sample; 2, constructing a dynamic gating hybrid expert model composed of a shared feature layer, a hybrid expert module and a dynamic gating network; 3, designing a mixed loss function containing classification loss and load balancing loss; 4, training a dynamic gating hybrid expert model based on the hybrid loss function; and 5, realizing electroencephalogram signal classification by utilizing the trained dynamic gating hybrid expert model. By means of a dynamic expert selection mechanism, expert combination is optimized in real time through a gating network, and the electroencephalogram signal classification sensitivity is effectively improved; meanwhile, a hardware sensing architecture is introduced, multiplication operation is greatly reduced, energy consumption optimization and the classification accuracy of the electroencephalogram signals are remarkably improved, and the application value of the electroencephalogram signals in the medical field is enhanced.

The invention discloses an ADHD classification method based on electroencephalogram signals and a channel selection attention mechanism, and particularly relates to the technical field of electroencephalogram signal classification, and the method comprises the steps: obtaining the public electroencephalogram data of ADHD children, ADD children and HC children in task states, carrying out the preprocessing of the data, carrying out the analysis of independent components, separating data components, removing artifact components, and carrying out the classification of the ADHD children, the ADD children and the HC children; the method comprises the steps of obtaining pure electroencephalogram signals, segmenting data and dividing a data set, designing an ADHD electroencephalogram signal classification algorithm based on a multi-scale channel selective attention mechanism and a neural network, capturing features of different scales through a multi-scale CNN, focusing on correlation enhancement features among channels by using the channel selective attention mechanism, and classifying the electroencephalogram signals to obtain an ADHD electroencephalogram signal classification algorithm based on the multi-scale channel selective attention mechanism. And finally, capturing a long-distance dependency relationship through a self-attention mechanism and fusing global features.

The invention discloses a rapid identification method applied to defibrillation requirements of an AED device, and belongs to the technical field of medical equipment. According to the method, multiple links including acquisition, preprocessing, feature extraction, mode classification, real-time classification and defibrillation triggering work cooperatively, and advanced signal classification and mode recognition technologies are introduced, so that efficient and accurate electrocardiosignal analysis is realized in the AED device, the response speed and accuracy in the emergency treatment process are remarkably improved, and the emergency treatment efficiency is improved. Multi-dimensional features of electrocardiosignals are extracted and input into a pattern classification model, automatic recognition of arrhythmia is achieved, whether defibrillation operation needs to be carried out or not can be accurately judged, the possibility of misjudgment is reduced, self-optimization can be carried out according to new electrocardiosignal data through a dynamic learning and retraining mechanism, and the accuracy of defibrillation is improved. The adaptive capacity to a complex heart rhythm mode is improved, the first-aid response time is effectively shortened, the safety of a patient is guaranteed, and the first-aid efficiency and the treatment effect are improved.

The invention relates to a dynamic weighted electroencephalogram signal classification method capable of resisting individual differences. The method comprises the following steps: step 1, acquiring motor imagery electroencephalogram signals of multiple subjects by using electroencephalogram acquisition equipment; 2, preprocessing the collected electroencephalogram signals, wherein the preprocessing comprises filtering, denoising and segmenting; step 3, constructing a hybrid expert model based on CNN-Transform; step 4, training the hybrid expert model; and 5, performing classification prediction on the electroencephalogram signals of the target subject. According to the method, the advantages of CNN and Transform are combined, and a hybrid expert mechanism of dynamic routing is introduced, so that the classification performance and generalization ability of the model in a cross-subject scene are remarkably improved.

Disclosed are system and techniques for classifying events such as emergencies. A system can include a computer system to perform operations including: receiving sensor signals from a group of devices at a location, determining whether one or more of the sensor signals exceed expected threshold levels, in response to determining that the one or more of the sensor signals exceed the expected threshold levels, correlating the sensor signals, classifying the correlated sensor signals into an emergency event based on applying an artificial intelligence (AI) model to the correlated sensor signals, the AI model having been trained to classify the correlated sensor signals into a type of emergency, determine a spread of the emergency event, and determine a severity level of the emergency event, generating, based on information associated with the classified emergency event as output from the AI model, emergency response information, and returning the emergency response information.

The invention relates to the technical field of seismic event monitoring, in particular to a seismic signal classification and identification method based on multi-feature fusion. According to the method, firstly, each piece of seismic data is preprocessed to obtain preprocessed seismic data, then time domain features and frequency domain features are extracted based on the preprocessed seismic data, step weights of the time domain features and step weights of the frequency domain features are set respectively, the time domain features and the frequency domain features are fused in combination with the step weights, and the time domain features and the frequency domain features are fused. The method comprises the steps of obtaining fusion features according to the real-time seismic data, inputting the fusion features into a seismic signal classification model to train the seismic signal classification model, obtaining a trained seismic signal classification model, and finally inputting the real-time seismic data into the trained seismic signal classification model to obtain the seismic type of the real-time seismic data. The weights can be allocated according to the importance of the features, so that the influence of the key features on the classification result of the seismic signal classification model is greater, and the classification accuracy is improved.

The invention belongs to the technical field of electromyographic signal classification detection, discloses an electromyographic signal classification detection method combining SNN and HDC, provides an electromyographic signal classification detection framework combining SNN and HDC, and aims to realize ultra-low power consumption operation. In the framework, the SNN executes event-driven feature extraction by using a random untrained weight, so that the calculation overhead is reduced to the greatest extent; and the HDC realizes anti-noise classification through high-dimensional representation. The integration of the two not only realizes real-time detection of energy conservation, but also is particularly suitable for being applied to resource-limited scenes such as wearable equipment and the like. The method provided by the invention realizes 95% of average accuracy (the peak accuracy is 96.44%) in three types of fatigue recognition tasks, and the training speed is 5.7 times faster than that of a one-dimensional convolutional neural network (1D-CNN) and 45 times faster than that of a five-dimensional long-short-term memory network (5D-LSTM). Even under the condition that only 20% of training data is used, the method can still keep the accuracy of 90% or above, and the high efficiency and robustness of the method in actual deployment are fully proved.

The invention discloses a bearing vibration signal processing model construction method, a model and a signal classification method, which are used for constructing time domain and frequency domain loss functions by calculating a plurality of feature vector distances so as to train an encoder. According to the method, by minimizing the difference value of different distances in the time domain and frequency domain loss functions, an encoder can be guided to extract similar feature representations in signals, so that the similar feature representations are gathered in a feature space, and dissimilar features are separated. When a trained encoder (a convolutional neural network with an attention mechanism) processes a vibration signal of an unknown fault type, some feature representations in the signal are different from those of other signals, so that the feature representations are gathered in a feature space and are far away from the feature representations of other signals, and the fault detection accuracy is improved. Therefore, the recognition of the vibration signal of the unknown fault type is effectively realized.

The invention discloses a multi-source domain adversarial electroencephalogram signal classification method and system based on multi-task learning. The method comprises the steps that electroencephalogram signal feature samples of a target domain O and a plurality of source domains S are obtained respectively; performing time scrambling on the electroencephalogram signal characteristic sample of each source domain S to form a plurality of electroencephalogram signal characteristic samples of a time sub-domain P; mixing the electroencephalogram signal feature sample of each source domain S with the electroencephalogram signal feature sample of the target domain O to form electroencephalogram signal feature samples of a plurality of mixed sub-domains Q; inputting the electroencephalogram signal feature samples of the source domain S, the time sub-domain P and the mixed sub-domain Q into a domain adversarial neural network for training to form a branch prediction result; and performing voting prediction on the branch prediction result to determine a final prediction result of the target domain O. According to the method, the learning ability of the multi-source domain adversarial model is enhanced by introducing multiple adversarial tasks irrelevant to the tasks, so that the robustness and generalization of the features are improved, and the accuracy of electroencephalogram signal classification prediction is improved.

The heart electrophysiological signal classification method comprises the steps that electrocardiosignals and magnetocardiogram signals are obtained, filtering and denoising preprocessing is conducted on original ECG signals and original MCG signals respectively, heart beats are divided through R-wave positioning, a two-dimensional spectrogram is obtained after Fourier transform, and the two-dimensional spectrogram is used for classifying the electrocardiosignals and the magnetocardiogram. After feature extraction, respectively establishing a multi-domain cardiac function signal data set; a convolutional recurrent neural network is constructed, synchronous processing of two different forms of heart electrophysiological signals is realized through a double-input model, shallow signal features extracted by the convolutional neural network are input into a time sequence feature extraction module formed by the recurrent neural network, local saliency features and time sequence features of ECG and MCG signals are fused, and a time sequence feature extraction module is constructed. And finally, ECG and MCG signal features for classification are formed. And inputting the fused features into a nonlinear classification layer, performing model training by using Softmax in combination with a cross entropy loss function, and outputting a classification result.