CLASSIFICATION PROCESSING OF AN ELECTROPHYSIOLOGICAL SIGNAL BASED ON SPATIAL LOCATIONS OF CHANNELS OF THE SIGNAL

FIELD OF THE TECHNOLOGY

This application relates to the field of computer technologies, including a method and an apparatus for classification processing of an electrophysiological signal, a computer device, and a storage medium.

BACKGROUND OF THE DISCLOSURE

With development of computer technologies, deep learning technology has shown obvious advantages in the fields of computer vision, speech recognition and natural language processing. Therefore, deep learning technology has been gradually introduced into the classification task of electrophysiological signals by researchers.

In related technologies, the electrophysiological signals are input into a model for classification, and the model outputs classification results of the electrophysiological signals based on time domain and frequency domain features of the electrophysiological signals. However, when processing the electrophysiological signals, the model only considers the time domain and frequency domain features of the electrophysiological signals, and the undiversified feature dimensions easily lead to inaccurate classification results of the electrophysiological signals.

SUMMARY

In an embodiment, a method for classification processing of an electrophysiological signal includes acquiring an electrophysiological signal collected by an acquisition device, and acquiring a channel association feature corresponding to the acquisition device. The channel association feature indicates spatial locations of multiple acquisition channels of the acquisition device, each of the multiple acquisition channels collecting the electrophysiological signal at a respective spatial location. The method further includes extracting a time feature corresponding to the electrophysiological signal, and generating an embedded feature based on the channel association feature and the time feature, and extracting a spatial feature corresponding to the embedded feature, and obtaining a classification result corresponding to the electrophysiological signal based on the spatial feature.

In an embodiment, a method for classification processing of an electrophysiological signal includes acquiring a training electrophysiological signal collected by an acquisition device and a training label corresponding to the training electrophysiological signal, and inputting the training electrophysiological signal into an initial electrophysiological signal classification model corresponding to the acquisition device. The initial electrophysiological signal classification model includes a channel association feature corresponding to the acquisition device, the channel association feature indicating spatial locations of multiple acquisition channels of the acquisition device. The method further includes extracting a time feature corresponding to the training electrophysiological signal through the initial electrophysiological signal classification model, and generating an embedded feature based on the channel association feature and the time feature. The method further includes extracting a spatial feature corresponding to the embedded feature through the initial electrophysiological signal classification model, and obtaining a predicted label corresponding to the training electrophysiological signal based on the spatial feature. The method further includes adjusting a model parameter of the initial electrophysiological signal classification model based on a difference between the training label and the predicted label, until a convergence condition is met, to obtain a trained electrophysiological signal classification model.

In an embodiment, an apparatus for classification processing of an electrophysiological signal includes processing circuitry configured to acquire an electrophysiological signal collected by an acquisition device, and acquire a channel association feature corresponding to the acquisition device. The channel association feature indicates spatial locations of multiple acquisition channels of the acquisition device, each of the multiple acquisition channels collecting the electrophysiological signal at a respective spatial location. The processing circuitry is further configured to extract a time feature corresponding to the electrophysiological signal, and generate an embedded feature based on the channel association feature and the time feature, and extract a spatial feature corresponding to the embedded feature, and obtain a classification result corresponding to the electrophysiological signal based on the spatial feature.

Details of one or more embodiments of this disclosure are provided in the accompanying drawings and descriptions below. Other features, objectives, and advantages of this disclosure become obvious with reference to the specification, the accompanying drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

To describe the technical solutions in the embodiments of this disclosure more clearly, the following briefly introduces the accompanying drawings describing the embodiments. The accompanying drawings in the following description show merely some embodiments of this disclosure, and a person of ordinary skill in the art may still derive other drawings from these accompanying drawings.

FIG. 1 is a diagram of an application environment of a method for classification processing of an electrophysiological signal according to an embodiment.

FIG. 2 is a schematic flowchart of a method for classification processing of an electrophysiological signal according to an embodiment.

FIG. 3A is a schematic flowchart of generating a target channel association feature according to an embodiment.

FIG. 3B is a schematic structural diagram of a target channel region according to an embodiment.

FIG. 4 is a schematic flowchart of generating a target channel association feature according to another embodiment.

FIG. 5 is a schematic flowchart of generating a target channel association feature according to another embodiment.

FIG. 6 is a schematic flowchart of a method for classification processing of an electrophysiological signal according to another embodiment.

FIG. 7A is a schematic structural diagram of an electroencephalogram (EEG) signal according to an embodiment.

FIG. 7B is a schematic structural diagram of an EEG signal classification model according to an embodiment.

FIG. 8 is a structural block diagram of an apparatus for classification processing of an electrophysiological signal according to an embodiment.

FIG. 9 is a structural block diagram of an apparatus for classification processing of an electrophysiological signal according to another embodiment.

FIG. 10 is a structural block diagram of an apparatus for classification processing of an electrophysiological signal according to another embodiment.

FIG. 11 is a diagram of an internal structure of a computer device according to an embodiment.

FIG. 12 is a diagram of an internal structure of a computer device according to an embodiment.

DESCRIPTION OF EMBODIMENTS

To make objectives, technical solutions, and advantages of this disclosure clearer and more understandable, this disclosure is further described in detail below with reference to the accompanying drawings and the embodiments. It is to be understood that the specific embodiments described herein are only used for explaining this disclosure, and are not used for limiting this disclosure.

AI technology is a comprehensive subject, relating to a wide range of fields, and involving both hardware and software techniques. Basic AI technologies generally include technologies such as a sensor, a dedicated AI chip, cloud computing, distributed storage, a big data processing technology, an operating/interaction system, and electromechanical integration. AI software technologies mainly include fields such as a computer vision technology, a speech processing technology, a natural language processing technology, machine learning/deep learning, and the like.

Machine learning (ML) is a multi-field interdiscipline and relates to a plurality of disciplines such as the probability theory, statistics, the approximation theory, convex analysis, and the algorithm complexity theory. ML specializes in studying how a computer simulates or implements a human learning behavior to acquire new knowledge or skills, and reorganize an existing knowledge structure, so as to keep improving its performance. The ML is the core of the AI, is a basic way to make the computer intelligent, and is applied to various fields of AI. The ML and DL generally comprise technologies such as an artificial neural network, a belief network, reinforcement learning, transfer learning, inductive learning, and learning from demonstrations.

The solutions provided in the embodiments of this disclosure involve technologies such as ML and big data processing of AI, and are specifically described by using the following embodiments.

The method for classification processing of an electrophysiological signal provided in this disclosure is applicable to the application environment shown in FIG. 1. A terminal 102 communicates with a server 104 through a network. The terminal 102 may be, but is not limited to, a personal computer, a notebook computer, a smartphone, a tablet computer, an in-vehicle terminal, a portable wearable device, and an electrophysiological signal acquisition device. The server 104 may be implemented by an independent server, a server cluster that includes a plurality of servers, or a cloud server.

Both the terminal 102 and the server 104 may be used independently to perform the method for classification processing of an electrophysiological signal provided in the embodiments of this disclosure.

For example, the terminal acquires a to-be-classified electrophysiological signal collected by a target acquisition device, and acquires a target channel association feature corresponding to the target acquisition device, where the target channel association feature is generated based on spatial locations of multiple acquisition channels on the target acquisition device. The terminal can extract a target time feature corresponding to the to-be-classified electrophysiological signal, embed the target channel association feature into the target time feature to obtain a target embedded feature, extract a target spatial feature corresponding to the target embedded feature, and obtain a classification result corresponding to the to-be-classified electrophysiological signal based on the target spatial feature. The terminal can display the classification result.

The server acquires a training electrophysiological signal collected by a target acquisition device and a training label corresponding to the training electrophysiological signal. The server inputs the training electrophysiological signal into an initial electrophysiological signal classification model corresponding to the target acquisition device, where the initial electrophysiological signal classification model includes a target channel association feature corresponding to the target acquisition device, and the target channel association feature is generated based on spatial locations of multiple acquisition channels on the target acquisition device. The server can extract a target time feature corresponding to the training electrophysiological signal through the initial electrophysiological signal classification model, embed the target channel association feature into the target time feature to obtain a target embedded feature, extract a target spatial feature corresponding to the target embedded feature through the initial electrophysiological signal classification model, obtain a predicted label corresponding to the training electrophysiological signal based on the target spatial feature, and adjust a model parameter of the initial electrophysiological signal classification model based on a difference between the training label and the predicted label, until a convergence condition is met, to obtain a target electrophysiological signal classification model. The server can also send the target electrophysiological signal classification model to the terminal for use.

Both the terminal 102 and the server 104 may be alternatively used in cooperation to perform the method for classification processing of an electrophysiological signal provided in the embodiments of this disclosure.

For example, the server can acquire, from the terminal, a to-be-classified electrophysiological signal collected by a target acquisition device. The server classifies the to-be-classified electrophysiological signal to obtain a classification result corresponding to the to-be-classified electrophysiological signal. Further, the server may send the classification result to the terminal and the classification result is displayed on the terminal.

The server acquires, from the terminal, a training electrophysiological signal collected by the target acquisition device and a training label corresponding to the training electrophysiological signal, and trains an initial electrophysiological signal classification model based on the training electrophysiological signal and the corresponding training label to obtain a target electrophysiological signal classification model.

In an embodiment, as shown in FIG. 2, a method for classification processing of an electrophysiological signal is provided, which is described by using an example that the method is applied to a computer device shown in FIG. 1. The computer device may be the terminal 102 or the server 104 in FIG. 1. Referring to FIG. 2, the method for classification processing of an electrophysiological signal includes the following steps:

Step S202. Acquire a to-be-classified electrophysiological signal collected by a target acquisition device. For example, an electrophysiological signal collected by an acquisition device is acquired.

The acquisition device refers to a device for collecting electrophysiological signals. The target acquisition device refers to an acquisition device corresponding to the to-be-classified electrophysiological signal. The electrophysiological signal refers to a physiological signal presented by a current or voltage, for reflecting electrophysiological activities of nerve cells. The electrophysiological signal may be an electroencephalogram signal, an electromyography signal, an electrocardiogram signal, or the like.

Specifically, the computer device may acquire the to-be-classified electrophysiological signal collected by the target acquisition device locally, or from another terminal or a server. The to-be-classified electrophysiological signal may be an electrophysiological signal collected in real time. The target acquisition device can collect electrophysiological signals in real time, and the computer device can classify a latest electrophysiological signal in real time to obtain a corresponding classification result. Certainly, the to-be-classified electrophysiological signal may alternatively be an electrophysiological signal collected historically. The electrophysiological signal collected by the target acquisition device in real time can be stored in a database of the terminal or server. The computer device can obtain the electrophysiological signal collected historically from the database, and classify the electrophysiological signal to obtain a corresponding classification result.

Step S204. Acquire a target channel association feature corresponding to the target acquisition device, the target channel association feature being generated based on spatial locations of multiple acquisition channels on the target acquisition device. For example, a channel association feature corresponding to the acquisition device is acquired. The channel association feature indicates spatial locations of multiple acquisition channels of the acquisition device, each of the multiple acquisition channels collecting the electrophysiological signal at a respective spatial location.

The acquisition device includes multiple electrodes, and different electrodes are used for collecting electrophysiological signals at different locations. One electrode corresponds to one acquisition channel. Therefore, the acquisition device includes multiple acquisition channels, and different acquisition channels are used for collecting electrophysiological signals at different locations. Neurons that are close in space are more likely to be physically connected, while neurons that are far away from each other are less likely to be physically connected. Physically connected neurons can be considered as closely linked neurons, and electrophysiological signals generated by closely linked neurons are also closely linked, interrelated and interacting. Therefore, a channel association feature can be generated based on the spatial locations of the multiple acquisition channels on the acquisition device, so that during classification of the electrophysiological signals, data processing and information fusion can be performed on the electrophysiological signals by using the channel association feature, thereby obtaining a more accurate classification result. The channel association feature is feature information for describing associations among acquisition channels. The associations among the acquisition channels may be determined according to the spatial locations of the acquisition channels. Further, locations of acquisition channels are different on different acquisition devices. Therefore, a channel association feature corresponding to each acquisition device needs to be established, and the target channel association feature refers to the channel association feature corresponding to the target acquisition device.

Specifically, the computer device may generate the channel association feature corresponding to the acquisition device based on the spatial locations of the acquisition channels on the acquisition channels. The computer device may pre-generate channel association features corresponding to the acquisition devices respectively. Therefore, after acquiring the to-be-classified electrophysiological signal collected by the target acquisition device, the computer device may directly acquire the pre-generated target channel association feature corresponding to the target acquisition device. Alternatively, the computer device can also generate the target channel association feature corresponding to the target acquisition device in real time. Therefore, after acquiring the to-be-classified electrophysiological signal collected by the target acquisition device, the computer device can acquire spatial locations of multiple acquisition channels on the target acquisition device and generate the target channel association feature in real time based on the spatial locations of the multiple acquisition channels on the target acquisition device.

In an embodiment, the computer device can determine a plurality of spatially close acquisition channels based on spatial location distances between the acquisition channels, associate the spatially close acquisition channels, and generate a channel association feature based on the associated acquisition channels. Two acquisition channels being spatially close specifically means that a spatial distance between the two acquisition channels is less than a distance threshold.

In an embodiment, in order to improve accuracy of the channel association feature, the computer device can map the spatial locations of the acquisition channels to the same plane to obtain plane locations of the acquisition channels; determine a plurality of closely related acquisition channels based on plane location distances between the acquisition channels, associate the closely related acquisition channels, and generate a channel association feature based on the associated acquisition channels. The closely related acquisition channels may be determined based on a region shape feature of a channel region formed by the acquisition channels.

Step S206. Extract a target time feature corresponding to the to-be-classified electrophysiological signal, and embed the target channel association feature into the target time feature to obtain a target embedded feature. For example, a time feature corresponding to the electrophysiological signal is extracted, and an embedded feature is generated based on the channel association feature and the time feature.

Specifically, the computer device may perform time feature extraction on the to-be-classified electrophysiological signal to obtain a target time feature corresponding to the to-be-classified electrophysiological signal, and then embed the target channel association feature into the target time feature to obtain a target embedded feature.

In an embodiment, the computer device may perform multiple time feature extractions on the to-be-classified electrophysiological signal to obtain multiple intermediate time features, and the target time feature is formed by the intermediate time features. During embedding of the target channel association feature into the target time feature, the target channel association feature can be separately embedded into each intermediate time feature to obtain an initial embedded feature corresponding to each intermediate time feature, and the target embedded feature is formed by the initial embedded features.

In an embodiment, features may be represented by a matrix. When the target channel association feature is embedded into the target time feature to obtain the target embedded feature, specifically, multiplication processing may be performed on two matrices to obtain a target embedding matrix that represents the target embedded feature.

Step S208. Extract a target spatial feature corresponding to the target embedded feature, and obtain a classification result corresponding to the to-be-classified electrophysiological signal based on the target spatial feature. For example, a spatial feature corresponding to the embedded feature is extracted, and a classification result corresponding to the electrophysiological signal is obtained based on the spatial feature.

Specifically, after obtaining the target embedded feature, the computer device may further perform spatial feature extraction on the to-be-classified electrophysiological signal to obtain a target spatial feature corresponding to the to-be-classified electrophysiological signal, and then obtain a final classification result based on the target spatial feature.

In an embodiment, the computer device can perform multiple spatial feature extractions on the target embedded feature to obtain multiple intermediate spatial features, and the target spatial feature is formed by the intermediate spatial features.

In an embodiment, the computer device can perform classification processing on the to-be-classified electrophysiological signal by means of a machine learning model to obtain a classification result corresponding to the to-be-classified electrophysiological signal. The computer device can integrate the channel association feature into a processing layer of the electrophysiological signal classification model in advance, so as to integrate the channel association feature into the electrophysiological signal classification model. In this case, after acquiring the to-be-classified electrophysiological signal collected by the target acquisition device, the computer device can acquire a target electrophysiological signal classification model corresponding to the target acquisition device, input the to-be-classified electrophysiological signal into the target electrophysiological signal classification model, and perform data processing on the to-be-classified electrophysiological signal through the target electrophysiological signal classification model to obtain a corresponding classification result. The target electrophysiological signal classification model is integrated into the target channel association feature corresponding to the target acquisition device.

In an embodiment, the obtaining a classification result corresponding to the to-be-classified electrophysiological signal based on the target spatial feature includes: performing nonlinear processing on the target spatial feature to obtain a target fitting feature; and performing classification processing on the target fitting feature to obtain the classification result.

Specifically, in the process of obtaining the classification result based on the target spatial feature, the computer device can perform nonlinear processing on the target spatial feature to obtain a target fitting feature, and then perform classification processing on the target fitting feature to obtain the classification result. The nonlinear processing can remove some redundant data in the target spatial feature, introduce a nonlinear factor, and enhance expression capability of a model. The classification processing is used for determining a final classification result.

In an embodiment, the computer device can perform classification processing on the to-be-classified electrophysiological signal by means of a machine learning model. In this case, the computer device may specifically perform the nonlinear processing through an activation function in a nonlinear layer of the target electrophysiological signal classification model, and perform classification processing through a fully-connected layer in the target electrophysiological signal classification model to output the classification result.

In the method for classification processing of an electrophysiological signal, a to-be-classified electrophysiological signal collected by a target acquisition device is acquired; a target channel association feature corresponding to the target acquisition device is acquired, the target channel association feature being generated based on spatial locations of multiple acquisition channels on the target acquisition device; a target time feature corresponding to the to-be-classified electrophysiological signal is extracted, and the target channel association feature is embedded into the target time feature to obtain a target embedded feature; and a target spatial feature corresponding to the target embedded feature is extracted, and a classification result corresponding to the to-be-classified electrophysiological signal is obtained based on the target spatial feature. In this way, the target channel association feature is generated based on the spatial locations of the multiple acquisition channels on the target acquisition device, which can represent a topology feature of the electrophysiological signal. During processing of the electrophysiological signal, time domain and frequency domain features as well as the topology feature of the electrophysiological signal is considered comprehensively, and finally an accurate classification result can be obtained, thereby improving classification accuracy of the electrophysiological signal.

In an embodiment, the acquiring a target channel association feature corresponding to the target acquisition device includes:

acquiring a target electrophysiological signal classification model corresponding to the target acquisition device, the target electrophysiological signal classification model being integrated with the target channel association feature corresponding to the target acquisition device, and being used for extracting the target time feature corresponding to the to-be-classified electrophysiological signal, embedding the target channel association feature into the target time feature to obtain the target embedded feature, extracting the target spatial feature corresponding to the target embedded feature, and obtaining the classification result corresponding to the to-be-classified electrophysiological signal based on the target spatial feature.

The electrophysiological signal classification model is a machine learning model for classifying the electrophysiological signal. There may be multiple electrophysiological signal classification models. Different acquisition devices correspond to different electrophysiological signal classification models, and one acquisition device may correspond to at least one electrophysiological signal classification model. The target electrophysiological signal classification model refers to a target electrophysiological signal classification model that corresponds to the target acquisition device and has been trained.

Specifically, the computer device can integrate the channel association feature into a machine learning model, and perform classification processing on the to-be-classified electrophysiological signal by means of the machine learning model. Therefore, after acquiring the to-be-classified electrophysiological signal collected by the target acquisition device, the computer device can directly acquire the target electrophysiological signal classification model corresponding to the target acquisition device, input the to-be-classified electrophysiological signal into the target electrophysiological signal classification model, extract the target time feature corresponding to the to-be-classified electrophysiological signal through the target electrophysiological signal classification model, embed the target channel association feature into the target time feature to obtain the target embedded feature, extract the target spatial feature corresponding to the target embedded feature through the target electrophysiological signal classification model, and obtain the classification result corresponding to the to-be-classified electrophysiological signal based on the target spatial feature. Finally, the target electrophysiological signal classification model outputs the classification result.

In an embodiment, the target electrophysiological signal classification model includes a temporal convolution layer, a network embedding layer, a spatial convolution layer, a nonlinear layer and a fully-connected layer. The network embedding layer is integrated with the target channel association feature. The computer device can extract the target time feature corresponding to the to-be-classified electrophysiological signal through the temporal convolution layer, embed the target channel association feature into the target time feature through the network embedding layer to obtain the target embedded feature, extract the target spatial feature corresponding to the target embedded feature through the spatial convolution layer, perform nonlinear processing on the target spatial feature through the nonlinear layer to obtain the target fitting feature, perform classification processing on the target fitting feature through the fully-connected layer to obtain the classification result, and finally output the classification result.

In an embodiment, locations of acquisition channels are usually different on different acquisition devices. Therefore, an electrophysiological signal classification model corresponding to each acquisition device needs to be established. One electrophysiological signal classification model is only integrated with the channel association feature corresponding to one acquisition device, and one electrophysiological signal classification model is exclusively used for processing electrophysiological signals collected by a corresponding acquisition device. In addition, one acquisition device may also correspond to multiple electrophysiological signal classification models, and different electrophysiological signal classification models correspond to different classification tasks. For example, in a case that an electrophysiological signal is an EEG signal, a classification task for the EEG signal includes sentiment classification, motion imagination classification, attention classification, or the like, and an EEG signal classification model specially used for sentiment classification, an EEG signal classification model specially used for motion imagination classification and an EEG signal classification model specially used for attention classification can be trained. In a case that an electrophysiological signal is an electromyography (EMG) signal, a classification task for the EMG signal includes sentiment classification, muscle state classification, or the like, and an EMG signal classification model specially used for sentiment classification of the EMG signal and an EMG signal classification model specially used for muscle state classification of the EMG signal can be trained. Therefore, the target electrophysiological signal classification model may be a target electrophysiological signal classification model that corresponds to the target acquisition device and the target classification task.

In this embodiment, the channel association feature is integrated into the target electrophysiological signal classification model, and the target channel association feature only needs to be calculated once for different electrophysiological signals collected by the same acquisition device, so that classification efficiency of the electrophysiological signal can be effectively improved. In addition, a series of data processing for the to-be-classified electrophysiological signal can be implemented through the target electrophysiological signal classification model to obtain the classification result, which can further improve the classification efficiency of the electrophysiological signal.

In an embodiment, the acquiring a target electrophysiological signal classification model corresponding to the target acquisition device includes:

determining a target classification task; acquiring multiple candidate electrophysiological signal classification models corresponding to the target classification task, the multiple candidate electrophysiological signal classification models having corresponding candidate acquisition devices; and determining the target electrophysiological signal classification model corresponding to the target acquisition device from the candidate electrophysiological signal classification models.

Specifically, one acquisition device may correspond to multiple electrophysiological signal classification models. Different electrophysiological signal classification models correspond to different classification tasks. Therefore, the computer device may first determine the target classification task from multiple candidate classification tasks, and acquire multiple candidate electrophysiological signal classification models corresponding to the target classification task. The candidate electrophysiological signal classification models correspond to different candidate acquisition devices. In order to process the to-be-classified electrophysiological signal collected by the target acquisition device, the computer device can determine the target electrophysiological signal classification model corresponding to the target acquisition device from the candidate electrophysiological signal classification models, and then input the to-be-classified electrophysiological signal into the target electrophysiological signal classification model, thereby implementing classification of the to-be-classified electrophysiological signal on the target classification task.

In an embodiment, the computer device may sequentially use each of the candidate classification tasks as a target classification task. Further, the computer device may finally obtain corresponding classification results of the to-be-classified electrophysiological signal on the classification tasks. For example, in a case that the electrophysiological signal is an EEG signal, a classification task for the electroencephalogram signal includes sentiment classification, motion imagination classification, and attention classification. The computer device may first input the to-be-classified EEG signal into an EEG signal classification model for EEG sentiment classification to obtain a sentiment classification result, then input the to-be-classified EEG signal into an EEG signal classification model for EEG motion imagination classification to obtain a motion imagination classification result, and finally input the to-be-classified EEG signal into an EEG signal classification model for EEG attention classification to obtain an attention classification result.

In an embodiment, the computer device can acquire an electrophysiological signal classification request, the electrophysiological signal classification request carrying a target task identifier and the to-be-classified electrophysiological signal collected by the target acquisition device. The electrophysiological signal classification request is used for requesting classification of the to-be-classified electrophysiological signal. The computer device can determine the target classification task according to the target task identifier, determine the target electrophysiological signal classification model corresponding to the target acquisition device from multiple candidate electrophysiological signal classification models corresponding to the target classification task, and input the to-be-classified electrophysiological signal into the target electrophysiological signal classification model, thereby obtaining a classification result corresponding to the to-be-classified electrophysiological signal. The electrophysiological signal classification request can be generated according to a trigger operation acted by a user on a related interface of the terminal. For example, the user can select the target classification task and the to-be-classified electrophysiological signal collected by the target acquisition device on the related interface, and then trigger generation of the electrophysiological signal classification request. The electrophysiological signal classification request can also be automatically generated. For example, once the target acquisition device acquires the electrophysiological signal, the terminal automatically generates the electrophysiological signal classification request. The target task identifier in the electrophysiological signal classification request can include a task identifier corresponding to any one of the candidate classification tasks, and can also include task identifiers corresponding to the candidate classification tasks respectively.

In this embodiment, one acquisition device can correspond to multiple electrophysiological signal classification models, and different electrophysiological signal classification models correspond to different classification tasks. The electrophysiological signal classification models have respective functions and focus on data processing of the corresponding classification tasks. In this way, data processing is performed on the to-be-classified electrophysiological signal through a specific electrophysiological signal classification model, which can further improve the accuracy of the classification result.

In an embodiment, as shown in FIG. 3A, the target channel association feature is generated through the following steps:

Step S302. Map spatial locations of the acquisition channels to a same plane to obtain plane locations of the acquisition channels.

Step S304. Acquire a target quantity of acquisition channels from the acquisition channels as target acquisition channels to obtain a target quantity of target acquisition channels.

Step S306. Generate a target channel region based on the plane locations of the target acquisition channels. For example, a channel region is defined based on the plane locations of two or more of the multiple acquisition channels.

Specifically, the computer device can map spatial locations of the acquisition channels to the same plane to obtain plane locations of the acquisition channels in the plane. For example, spatial coordinates of the acquisition channels on the target acquisition device are in the same spatial coordinate system, and the acquisition channels can be projected to xy plane uniformly, so as to obtain plane coordinates of the acquisition channels. Further, the computer device can acquire a target quantity of acquisition channels (e.g., two or more of the acquisition channels) from the acquisition channels as target acquisition channels to obtain a target quantity of target acquisition channels, and then generate (e.g., define) a target channel region based on the plane locations of the target acquisition channels. The target channel region is a plane region surrounded by the target acquisition channels. The computer device may specifically use each of the target acquisition channels as a region vertex to obtain multiple region vertexes, and connect the region vertexes in turn according to a clockwise direction or counterclockwise direction. The target channel region is a plane region surrounded by the region vertexes and vertex connection lines. The target quantity may be set according to actual needs. For example, in a case that the target quantity is three, the computer device may determine all acquisition channel combinations that include three acquisition channels in the acquisition channels to obtain multiple acquisition channel combinations. The acquisition channel combinations each include three target acquisition channels, and each of the acquisition channel combinations can generate one target channel region. In one acquisition channel combination, the computer device may specifically use each of the three acquisition channels as a region vertex to obtain three region vertexes, and connect the region vertexes in turn according to a clockwise direction or counterclockwise direction. The target channel region is a triangular plane region surrounded by the region vertexes and connection lines.

Step S308. Associate the target acquisition channels in a case that a region shape feature corresponding to the target channel region is a preset shape feature and there is no other acquisition channel in the target channel region.

The region shape feature is feature information for describing a region shape of the target channel region.

Specifically, in a case that the region shape feature corresponding to the target channel region is a preset shape feature and there is no other acquisition channel in the target channel region, it indicates that the acquisition channels included in the target channel region are closely linked to each other. In this case, the acquisition channels corresponding to the target channel region can be associated. Therefore, in a case that two acquisition channels are associated, it can be determined that the two acquisition channels are closely linked to each other.

In an embodiment, vertex angles corresponding to region vertexes of a region may be used for reflecting a region shape feature of the region. For example, whether a triangle is an acute triangle, a right triangle, or an obtuse triangle can be determined according to the vertex angles corresponding to the three vertexes of the triangle. Whether a quadrangle is a rectangular, a trapezoid, or a rhombus can be determined according to the vertex angles corresponding to the four vertexes of the quadrangle. Therefore, the computer device can calculate vertex angles corresponding to the region vertexes of the target channel region and obtain the region shape feature corresponding to the target channel region based on the vertex angles. Correspondingly, the preset shape feature can be represented by using a reference angle range of the vertex angles. In a case that the vertex angles corresponding to the target channel region are all within the reference angle range, it can be determined that the region shape feature corresponding to the target channel region is the preset shape feature. For example, in a case that the target channel region is a triangular region, the preset shape feature may be that the vertex angles are all within a range from 0 to 90 degrees, that is, in a case that the target channel region is an acute triangle region or a right triangle region, it is determined that the region shape feature corresponding to the target channel region is the preset shape feature.

In an embodiment, alternatively, a preset channel region having the preset shape feature can be obtained, a similarity between the target channel region and the preset channel region is calculated, and it is determined that a region shape feature corresponding to the target channel region with a similarity greater than a preset threshold is the preset shape feature.

Step S310. Generate the target channel association feature based on the associated acquisition channels.

Specifically, after determining associations between the acquisition channels, the computer device can generate the target channel association feature based on the associated acquisition channels. The computer device can generate an adjacency matrix corresponding to the target acquisition device based on the associated acquisition channels and obtain the target channel association feature based on the adjacency matrix. Matrix dimensionality of the adjacency matrix is a channel quantity of the acquisition channels on the target acquisition device, and matrix values in the adjacency matrix can reflect whether any two acquisition channels are associated. For example, in a case that the matrix value is 0, it indicates that the corresponding acquisition channels are not associated; in a case that the matrix value is 1, it indicates that the corresponding acquisition channels are associated.

In this embodiment, spatial locations of the acquisition channels are mapped to the same plane to obtain plane locations of the acquisition channels; a target quantity of acquisition channels are acquired from the acquisition channels as target acquisition channels to obtain the target quantity of target acquisition channels; a target channel region is generated based on the plane locations of the target acquisition channels; and the target acquisition channels are associated in a case that a region shape feature corresponding to the target channel region is a preset shape feature and there is no other acquisition channel in the target channel region; and the target channel association feature is generated based on the associated acquisition channels. In this way, closely linked acquisition channels can be quickly found by analyzing the plane locations of the target acquisition channels, thereby generating an accurate target channel association feature.

In an embodiment, the generating a target channel region based on the plane locations of the target acquisition channels includes:

using each of the target acquisition channels as a region vertex to obtain multiple region vertexes, and connecting the region vertexes in turn to obtain the target channel region.

Specifically, when generating the target channel region, the computer device may use each of the target acquisition channels as a region vertex to obtain multiple region vertexes, and connect the region vertexes in turn according to a clockwise direction or counterclockwise direction. A plane region surrounded by the vertex connection lines is the target channel region corresponding to the current acquisition channels. In this way, the target channel region can be generated quickly.

In an embodiment, the region shape feature includes vertex angles corresponding to the region vertexes of the target channel region, and the associating the target acquisition channels in a case that a region shape feature corresponding to the target channel region is a preset shape feature and there is no other acquisition channel in the target channel region includes:

determining that the region shape feature corresponding to the target channel region is the preset shape feature in a case that the vertex angles are all within a reference angle range; and associating the target acquisition channels based on connection relationships between the region vertexes in a case that there is no other acquisition channel in the target channel region of which the region shape feature is the preset shape feature.

Specifically, vertex angles corresponding to region vertexes of a region may be used for reflecting a region shape feature of the region. Therefore, the computer device can calculate vertex angles corresponding to the region vertexes of the target channel region, and the region shape feature corresponding to the target channel region is formed by the vertex angles corresponding to the region vertexes of the target channel region. That is, the region shape feature includes the vertex angles corresponding to the region vertexes of the target channel region. The computer device may specifically calculate the vertex angles corresponding to the region vertexes according to plane distances between the region vertexes.

Correspondingly, in a case that a shape feature of a region is a preset shape feature, vertex angles corresponding to the region have a certain characteristic. Therefore, a reference angular range corresponding to vertex angles of a channel region whose region shape feature is the preset shape feature may be preset. In a case that each vertex angle is within the reference angle range, the computer device may determine that the region shape feature corresponding to the target channel region is the preset shape feature. Further, in a case that there is no other acquisition channel in the target channel region whose region shape feature is the preset shape feature, it indicates that there is no other redundant acquisition channel in the target channel region. The target acquisition channels forming the target channel region are a combination of closely linked acquisition channels. In this case, the target acquisition channels can be associated based on the associations between the region vertexes.

In an embodiment, the computer device may set the target quantity to three because the triangle is the most stable and dense polygon. Further, in a case that there is no other node except the three vertexes in the triangle, and the triangle is an acute triangle or a right triangle, the three vertexes of the triangle may be considered as three most closely associated nodes. In a case that there is no other node except the three vertexes in the triangle, it indicates that the three vertexes are three nodes with relatively close plane distances and no other interference inside. Further, in a case that the triangle is an acute triangle or a right triangle, it indicates that the distances between the three vertexes are appropriate and are not significantly different. In a case that the triangle is an obtuse triangle, the side corresponding to the obtuse angle is obviously much longer than the other two sides, that is, two vertexes in the obtuse triangle are relatively far apart, and the connection between the two vertexes is less tight. In a case that the three vertex angles of the generated triangle are all within a range of 0 to 90 degrees, it can be determined that the triangle is an acute triangle or a right triangle rather than an obtuse triangle. Therefore, in a case that there is no other node except the three vertexes in the triangle, and the triangle is an acute triangle or a right triangle, the target acquisition channels may be associated according to side lengths of the triangle. The two target acquisition channels connected by each side are associated. In this way, three sets of associated target acquisition channels can be obtained from one triangle.

For example, assuming that there are four acquisition channels: A, B, C, and D; three acquisition channels are arbitrarily selected from the four acquisition channels as target acquisition channels, and the three target acquisition channels are used as triangular vertexes. The triangular vertexes are connected in turn to obtain a triangle. A region surrounded by the triangle is the target channel region. As shown in FIG. 3B, a total of four triangles can be established: A-B-C, A-B-D, A-C-D, and B-C-D. The triangle A-B-C is an acute triangle without D inside. Therefore, A and B can be associated, B and C can be associated, and A and C can be associated. Although the triangle A-B-D an acute triangle, C exists inside the triangle. Therefore, there is no need to associate A, B and D. The triangle A-C-D triangle is an obtuse triangle. Therefore, there is no need to associate A, C and D. The triangle B-C-D triangle is an obtuse triangle. Therefore, there is no need to associate B, C and D.

In an embodiment, in addition to a triangle, the target channel region may be a quadrangle, that is, the computer device may also set the target quantity to four. In a case that the target channel region is a quadrangular region, the target channel region can be divided into two triangles, and there are two division manners. In a case that neither of the triangles is an obtuse triangle and there is no other acquisition channel in the triangular regions, corresponding acquisition channels can be associated according to connecting edges of the two triangles.

In the foregoing embodiments, whether the region shape feature corresponding to the target channel region is a preset shape feature can be quickly determined based on the vertex angles corresponding to the region vertexes of the target channel region.

In an embodiment, as shown in FIG. 4, the generating the target channel association feature based on the associated acquisition channels includes:

Step S402. Generate an initial channel association feature based on the associated acquisition channels.

Step S404. Perform normalization processing on the initial channel association feature to obtain the target channel association feature.

The normalization processing is used for limiting the initial channel association feature within a certain interval, so that the target channel association feature can be embedded into the target time feature subsequently. The normalization processing can be performed on the initial channel association feature through a custom formula to obtain the target channel association feature.

Specifically, in the process of generating the target channel association feature based on the associated acquisition channels, the computer device may directly generate the target channel association feature based on the associated acquisition channels. However, in order to subsequently embed the target channel association feature into the target time feature without changing an assignment range of an original signal, the computer device can also first generate the initial channel association feature based on the associated acquisition channels, and obtain the target channel association feature after performing normalization processing on the initial channel association feature.

In an embodiment, the channel association feature may be represented by a matrix, matrix dimensionality of the matrix is a channel quantity of the acquisition channels on the target acquisition device. Values in rows and columns of the target channel association matrix obtained by the normalization processing are all 1. Subsequently, in a case that the target channel association feature is embedded into the target time feature, matrix multiplication can be performed. If values in the rows and columns of the target channel association matrix are all 1, the matrix multiplication does not change the assignment range of the original signal, that is, the original signal does not increase or decrease sharply, thereby reserving the assignment interval of the original signal to a certain extent.

In this embodiment, the initial channel association feature is generated based on the associated acquisition channels, and normalization processing is performed on the initial channel association feature to obtain the target channel association feature. The normalization processing can improve validity and accuracy of the target channel association feature, so that the target channel association feature is conveniently embedded into the target time feature subsequently.

In an embodiment, as shown in FIG. 5, the performing normalization processing on the initial channel association feature to obtain the target channel association feature includes:

Step S502. Generate an initial channel association matrix based on the initial channel association feature.

Specifically, the computer device can generate the initial channel association matrix based on the initial channel association feature. The initial channel association feature is expressed in a matrix form to facilitate subsequent calculation processing.

In an embodiment, the generating an initial channel association matrix based on the initial channel association feature includes any one of the following manners:

determining matrix dimensionality of the initial channel association matrix based on the channel quantity of the acquisition channels on the target acquisition device, setting matrix values corresponding to the associated acquisition channels to a first preset threshold, and setting matrix values corresponding to other acquisition channels to a second preset threshold, to obtain the initial channel association matrix; or

determining matrix dimensionality of the initial channel association matrix based on the channel quantity of the acquisition channels on the target acquisition device, determining matrix values corresponding to the associated acquisition channels based on spatial location distances between the associated acquisition channels, and setting matrix values corresponding to other acquisition channels to a second preset threshold, to obtain the initial channel association matrix.

Specifically, the initial channel association matrix may be an unweighted network, which only includes associations between the acquisition channels. Therefore, in the process of generating the initial channel association matrix, the computer device can use the channel quantity of the acquisition channels on the target acquisition device as matrix dimensionality of the initial channel association matrix, set matrix values corresponding to the associated acquisition channels to a first preset threshold, and set matrix values corresponding to other acquisition channels to a second preset threshold, to obtain the initial channel association matrix. The first preset threshold and the second preset threshold may be set as required.

For example, the acquisition channels on the target acquisition device include A, B, C and D, and the channel quantity is 4. A, B and D can form an acute triangle without C inside, and B, C and D can form an acute triangle without A inside. Therefore, A-B, A-D, B-D, B-C and C-D have associations respectively. If the first preset threshold is set to 1 and the second preset threshold is set to 0, the generated initial channel association matrix can be:

$\begin{matrix} 0 & 1 & 0 & 1 \\ 1 & 0 & 1 & 1 \\ 0 & 1 & 0 & 1 \\ 1 & 1 & 1 & 0 \end{matrix}$

Row elements of the initial channel association matrix represent A, B, C and D in turn, and column elements represent A, B, C and Din turn.

The initial channel association matrix alternatively may be a weighted network, which includes associations and spatial location distances between the acquisition channels. Therefore, in the process of generating the initial channel association matrix, the computer device can determine matrix dimensionality of the initial channel association matrix based on the channel quantity of the acquisition channels on the target acquisition device, determine matrix values corresponding to the associated acquisition channels based on spatial location distances between the associated acquisition channels, and set matrix values corresponding to other acquisition channels to the second preset threshold, to obtain the initial channel association matrix. A reciprocal of the spatial location distance may be used as a matrix value.

For example, the acquisition channels on the target acquisition device include A, B, C and D, and the channel quantity is 4. A-B, A-D, B-D, B-C and C-D have associations respectively. The spatial location distance between A and B is x1, the spatial location distance between A and D is x2, the spatial location distance between B and D is x3, the spatial location distance between B and C is x4, and the spatial location distance between C and D is x5. The reciprocal of the spatial location distance can be used as the matrix value.

In this case, the generated initial channel association matrix can be:

$\begin{matrix} 0 & \frac{1}{x 1} & 0 & \frac{1}{x 2} \\ \frac{1}{x 1} & 0 & \frac{1}{x 4} & \frac{1}{x 3} \\ 0 & \frac{1}{x 4} & 0 & \frac{1}{x 5} \\ \frac{1}{x 2} & \frac{1}{x 3} & \frac{1}{x 5} & 0 \end{matrix}$

Row elements of the initial channel association matrix represent A, B, C and D in turn, and column elements represent A, B, C and Din turn.

Step S504. Acquire a cell matrix, and fuse the cell matrix and the initial channel association matrix to obtain an intermediate channel association matrix.

Step S506. Acquire a diagonal matrix corresponding to the initial channel association matrix, and fuse the diagonal matrix and the intermediate channel association matrix to obtain a target channel association matrix.

Step S508. Obtain the target channel association feature based on the target channel association matrix.

The cell matrix refers to a square matrix in which diagonal elements are non-zero and other elements are zero. The diagonal matrix refers to a matrix in which all elements except a main diagonal are zero. The diagonal matrix corresponding to the initial channel association matrix refers to a matrix in which elements on a main diagonal are determined according to matrix values of the initial channel association matrix, while elements except the main diagonal are all zero.

Specifically, in order to subsequently embed the target channel association feature into the target time feature without losing respective electrophysiological signals of the acquisition channels, the computer device can acquire the cell matrix, fuse the cell matrix and the initial channel association matrix to obtain an intermediate channel association matrix. For example, the cell matrix is a square matrix in which elements on the diagonal are 1 and other elements are 0. The matrix dimensionality of the cell matrix is the channel quantity of the acquisition channels on the target acquisition device. The computer device can perform matrix addition processing on the cell matrix and the initial channel association matrix to obtain an intermediate channel association matrix. Because the elements on the diagonal of the initial channel association matrix are 0, if the initial channel association matrix is not fused with the cell matrix, the target embedded feature obtained by subsequent calculation does not include related information of the respective electrophysiological signals of the acquisition channels, which is not conducive to the classification of electrophysiological signals.

Further, in order to subsequently embed the target channel association feature into the target time feature without changing the assignment interval of the original signal, values in rows and columns of the intermediate channel association matrix need to be converted into 1. Therefore, the computer device can acquire the diagonal matrix corresponding to the initial channel association matrix, fuse the diagonal matrix and the intermediate channel association matrix to obtain a target channel association matrix. Finally, the computer device can obtain the target channel association feature based on the target channel association matrix. For example, the target channel association matrix is used as the target channel association feature.

In an embodiment, the normalization processing can be implemented by the following formula:

$\hat{A} = D^{- \frac{1}{2}} \tilde{A} D^{- \frac{1}{2}}, \tilde{A} = A + I, D = diag (\sum_{j}, \sum_{j}, \dots \dots, \sum_{j})$

A represents the initial channel association matrix, which may also be referred to as an initial adjacency matrix. I represents the cell matrix. Á represents the intermediate channel association matrix. D represents the diagonal matrix corresponding to the initial channel association matrix, and diag(Σ_j custom-character , Σ_j, . . . , Σ_j) represents elements on the diagonal line of the diagonal matrix. Σ_j represents a sum of all elements in the first row in Ã. Σ_j represents a sum of all elements in the second row in Ã. Σ_j represents a sum of all elements in the n^throw in Ã. n represents the channel quantity of the acquisition channels on the target acquisition device. Ā represents the target channel association matrix.

In an embodiment, the to-be-classified electrophysiological signal includes electrophysiological signals corresponding to the acquisition channels respectively, and the extracting a target time feature corresponding to the to-be-classified electrophysiological signal includes:

acquiring at least one temporal convolution kernel; separately extracting time sub-features corresponding to the electrophysiological signals based on a same temporal convolution kernel to obtain multiple time sub-features respectively corresponding to each of the at least one temporal convolution kernel; generating an intermediate time feature based on the multiple time sub-features corresponding to the same temporal convolution kernel, to obtain at least one intermediate time feature corresponding to the at least one temporal convolution kernel respectively; and obtaining the target time feature based on the at least one intermediate time feature.

The to-be-classified electrophysiological signal is a multi-channel time sequence signal, and the to-be-classified electrophysiological signal includes the electrophysiological signals corresponding to the acquisition channels respectively. The electrophysiological signal can reflect changes in voltage or current over time.

Specifically, different temporal convolution kernels are used for extracting time features with different viewing angles. For example, one temporal convolution kernel focuses on extracting a starting time feature of the signal, and one temporal convolution kernel focuses on extracting an end time feature of the signal. In the process of extracting the target time feature corresponding to the to-be-classified electrophysiological signal, the computer device can separately extract time features of the electrophysiological signals corresponding to the acquisition channels based on the temporal convolution kernel, that is, separately extract the time sub-features corresponding to the electrophysiological signals based on the same temporal convolution kernel. Because there are multiple temporal convolution kernels, multiple time sub-features corresponding to each temporal convolution kernel can finally be obtained. Further, the computer device may generate an intermediate time feature based on the multiple time sub-features corresponding to the same temporal convolution kernel. For example, the multiple time sub-features corresponding to the same temporal convolution kernel is spliced to obtain the intermediate time feature. Finally, the computer device may obtain the target time feature based on the intermediate time features. For example, the target time feature is formed by the intermediate time features.

In an embodiment, the size of the to-be-classified electrophysiological signal is T*C, where T represents a length of a single electrophysiological signal, that is, a time range corresponding to the electrophysiological signal, and C represents a channel quantity. The temporal convolution kernel is a two-dimensional convolution kernel with a size of n*1, where the value of n is adjustable. Different temporal convolution kernels may correspond to the same or different n, and matrix values of different temporal convolution kernels can be the same or different. The temporal convolution kernel only performs convolution on the to-be-classified electrophysiological signal in the time dimension, and only extracts the time feature.

In an embodiment, the target electrophysiological signal classification model includes a temporal convolution layer for extracting a time feature of the to-be-classified electrophysiological signal. The temporal convolution layer includes at least one temporal convolution kernel. After inputting the to-be-classified electrophysiological signal into the target electrophysiological signal classification model, the computer device can extract the target time feature corresponding to the to-be-classified electrophysiological signal through the temporal convolution layer. In the temporal convolution layer, time sub-features corresponding to the electrophysiological signals are extracted based on a same temporal convolution kernel to obtain multiple time sub-features corresponding to each of the temporal convolution kernels; an intermediate time feature is generated based on the multiple time sub-features corresponding to the same temporal convolution kernel, to obtain intermediate time features corresponding to the temporal convolution kernels; and the target time feature based on the intermediate time features.

In this embodiment, the temporal convolution layer includes at least one temporal convolution kernel, and different temporal convolution kernels are used for extracting time features with different viewing angles, so that the target time feature can include time features with multiple angles, thereby improving richness and accuracy of the target time feature.

In an embodiment, the target time feature includes multiple intermediate time features, and the embedding the target channel association feature into the target time feature to obtain a target embedded feature includes:

separately embedding the target channel association feature into the intermediate time features to obtain initial embedded features corresponding to the intermediate time features, and obtaining the target embedded feature based on the initial embedded features.

Specifically, in a case that the target time feature is a single time feature matrix, the computer device can directly embed the target channel association feature into the target time feature to obtain the target embedded feature. In a case that the target time feature includes multiple time feature matrices, that is, the target time feature includes multiple intermediate time features, the computer device needs to separately embed the target channel association feature into the intermediate time features to obtain initial embedded features corresponding to the intermediate time features, and then the target embedded feature is generated from the initial embedded features.

In this embodiment, in a case that the target time feature includes multiple intermediate time features, the target channel association feature needs to be separately embedded into the intermediate time features to obtain the initial embedded features corresponding to the intermediate time features, and the target embedded feature is obtained based on the initial embedded features. In this way, a more accurate target embedded feature can be obtained.

In an embodiment, the channel association feature includes association sub-features corresponding to the acquisition channels respectively, the intermediate time feature includes time sub-features corresponding to the acquisition channels respectively. The separately embedding the target channel association feature into the intermediate time features to obtain initial embedded features corresponding to the intermediate time features includes:

in a current intermediate time feature, embedding an association sub-feature corresponding to a same acquisition channel into a corresponding time sub-feature, to obtain embedded sub-features corresponding to the acquisition channels respectively; and obtaining an initial embedded feature corresponding to the current intermediate time feature based on the embedded sub-features.

Specifically, the channel association feature includes association sub-features corresponding to the acquisition channels respectively, and the intermediate time feature includes time sub-features corresponding to the acquisition channels respectively. In a case that the target channel association feature is embedded into the intermediate time feature, the association sub-feature and time sub-feature corresponding to the same acquisition channel need to be embedded. Therefore, using the current intermediate time feature as an example, the computer device can embed the association sub-feature corresponding to the same acquisition channel into the corresponding time sub-feature, to obtain embedded sub-features corresponding to the acquisition channels respectively; and then an initial embedded feature corresponding to the current intermediate time feature is generated from the embedded sub-features. Similarly, initial embedded features corresponding to the intermediate time features respectively can be finally obtained by performing similar processing on other intermediate time features.

In this embodiment, the association sub-feature and time sub-feature corresponding to the same acquisition channel are embedded to obtain a more accurate initial embedded feature.

In an embodiment, the extracting a target spatial feature corresponding to the target embedded feature includes:

acquiring at least one spatial convolution kernel; separately performing spatial feature extraction on the target embedded feature based on the at least one spatial convolution kernel to obtain at least one intermediate spatial feature corresponding to the at least one spatial convolution kernel respectively; and obtaining the target spatial feature based on the at least one intermediate spatial feature.

Specifically, different spatial convolution kernels are used for extracting spatial features with different viewing angles. In the process of extracting the target spatial feature corresponding to the target embedded feature, the computer device can perform spatial feature extraction on the target embedded feature based on the spatial convolution kernel to obtain an intermediate spatial feature. Because there are multiple spatial convolution kernels, respective intermediate spatial features corresponding to the spatial convolution kernels can be obtained finally. Further, the computer device can obtain the target spatial feature based on the intermediate spatial features. For example, the target spatial feature is formed by the intermediate spatial features.

In an embodiment, the size of the to-be-classified electrophysiological signal is T*C, where T represents the length of a single electrophysiological signal, that is, a time range corresponding to the electrophysiological signal, and C represents a channel quantity. The spatial convolution kernel is a two-dimensional convolution kernel with a size of 1*C. Different spatial convolution kernels have different matrix values. The spatial convolution kernel only performs convolution on the target embedded feature in a channel dimension, fuses embedded features corresponding to different acquisition channels, and only extracts a spatial feature.

In an embodiment, the target electrophysiological signal classification model includes a spatial convolution layer for extracting the spatial feature of the to-be-classified electrophysiological signal, and the spatial convolution layer includes at least one spatial convolution kernel. The computer device can extract the target spatial feature corresponding to the target embedded feature through the target electrophysiological signal classification model. In the spatial convolution layer, spatial feature extraction is performed on the target embedded feature separately based on the at least one spatial convolution kernel to obtain at least one intermediate spatial features corresponding to the at least one spatial convolution kernel respectively; and the target spatial feature is obtained based on the at least one intermediate spatial feature.

In this embodiment, the spatial convolution layer includes at least one spatial convolution kernel, and different spatial convolution kernels are used for extracting spatial features with different viewing angles, so that the target spatial feature may include spatial features with multiple angles, thereby improving richness and accuracy of the target spatial feature.

In an embodiment, as shown in FIG. 6, a method for classification processing of an electrophysiological signal is provided, which is described by using an example that the method is applied to a computer device shown in FIG. 1. The computer device may be the terminal 102 or the server 104 in FIG. 1. Referring to FIG. 6, the method for classification processing of an electrophysiological signal includes the following steps:

Step S602. Acquire a training electrophysiological signal collected by a target acquisition device and a training label corresponding to the training electrophysiological signal.

The training electrophysiological signal refers to an electrophysiological signal used for model training. The training label refers to a correct classification result corresponding to the training electrophysiological signal.

Specifically, the computer device can acquire the training electrophysiological signal collected by the target acquisition device and the training label corresponding to the training electrophysiological signal, and perform supervised training on an initial electrophysiological signal classification model corresponding to the target acquisition device based on the training electrophysiological signal and the corresponding training label to obtain a target electrophysiological signal classification model corresponding to the target acquisition device. For example, the computer device can train an EEG signal classification model based on an EEG signal with a known classification result, train an EMG signal with a known classification result, and train an ECG signal with a known classification result.

Step S604. Input the training electrophysiological signal into an initial electrophysiological signal classification model corresponding to the target acquisition device, the initial electrophysiological signal classification model including a target channel association feature corresponding to the target acquisition device, the target channel association feature being generated based on spatial locations of multiple acquisition channels on the target acquisition device. For example, the training electrophysiological signal is input into an initial electrophysiological signal classification model corresponding to the acquisition device. The initial electrophysiological signal classification model includes a channel association feature corresponding to the acquisition device, and the channel association feature indicates spatial locations of multiple acquisition channels of the acquisition device.

The initial electrophysiological signal classification model refers to an electrophysiological signal classification model to be trained.

Specifically, the computer device can generate a target channel association feature corresponding to the target acquisition device based on the spatial locations of the multiple acquisition channels on the target acquisition device, and integrate the target channel association feature into the initial electrophysiological signal classification model corresponding to the target acquisition device. Further, the computer device can acquire the training electrophysiological signal collected by the target acquisition device and the training label corresponding to the training electrophysiological signal, and perform supervised training on the initial electrophysiological signal classification model based on the training electrophysiological signal and the corresponding training label to obtain the target electrophysiological signal classification model.

For the specific process of generating the target channel association feature, reference may be made to the content of the foregoing related embodiments, and details are not described herein.

Step S606. Extract a target time feature corresponding to the training electrophysiological signal through the initial electrophysiological signal classification model, and embed the target channel association feature into the target time feature to obtain a target embedded feature. For example, a time feature corresponding to the training electrophysiological signal is extracted through the initial electrophysiological signal classification model, and an embedded feature is generated based on the channel association feature and the time feature.

Step S608. Extract a target spatial feature corresponding to the target embedded feature through the initial electrophysiological signal classification model, and obtain a predicted label corresponding to the training electrophysiological signal based on the target spatial feature. For example, a spatial feature corresponding to the embedded feature is extracted through the initial electrophysiological signal classification model, and a predicted label corresponding to the training electrophysiological signal based on the spatial feature is obtained.

The predicted label refers to a predicted classification result outputted by the initial electrophysiological signal classification model.

Specifically, the computer device can extract the target time feature corresponding to the training electrophysiological signal through the initial electrophysiological signal classification model, embed the target channel association feature into the target time feature to obtain the target embedded feature, extract the target spatial feature corresponding to the target embedded feature, and obtain the predicted label corresponding to the training electrophysiological signal based on the target spatial feature.

For the specific process of performing data processing on the inputted data by the model, reference may be made to the content of the foregoing related embodiments, and details are not described herein.

In an embodiment, the initial electrophysiological signal classification model is a lightweight convolutional neural network. A lightweight network is more suitable for a training set with a small quantity of samples, and can effectively suppress over-fitting. A training sample of the initial electrophysiological signal classification model (that is, a training electrophysiological signal X∈R^N*T*C) is used as an input of the network, where N represents a sample quantity, T represents a single-channel length of a sample, that is, a time range corresponding to a single-channel electrophysiological signal, and C represents a channel quantity of the sample. The initial electrophysiological signal classification model includes a temporal convolution layer, a network embedding layer, a spatial convolution layer, a nonlinear layer and a fully-connected layer. First, the training sample is transformed, to expand an original three-dimensional sample to be four-dimensional, where a dimension with a size of 1 is added to the second dimension. That is, the size of the original sample is N*T*C, and the size of the sample after transformation is N*1*T*C. The transformed sample is inputted to the temporal convolution layer, and a time feature corresponding to the electrophysiological signal of each acquisition channel is extracted through the temporal convolution layer to obtain a time feature map (that is, the target time feature) X^t∈R^N*D*T*C. D represents a feature map depth, that is, a feature map quantity, and one temporal convolution kernel corresponds to one feature map. The temporal convolution layer only performs convolution in the time dimension and only extracts a time feature. Then, a time feature map is multiplied by a normalized network adjacency matrix (that is, the target channel association feature) through the network embedding layer, and network embedding is performed to obtain a feature map after the network embedding X^g∈R^N*D*T*C. Then, the feature map after the network embedding is inputted to the spatial convolution layer for spatial fusion, and nonlinear fitting capability of the network is enhanced through the nonlinear layer. Finally, one fully-connected layer is used for performing feature classification to obtain the predicted label. The spatial convolution layer only performs convolution in the channel dimension.

Step S610. Adjust a model parameter of the initial electrophysiological signal classification model based on a difference between the training label and the predicted label, until a convergence condition is met, to obtain a target electrophysiological signal classification model.

The target electrophysiological signal classification model refers to a trained electrophysiological signal classification model.

Specifically, the computer device inputs the training electrophysiological signal into the initial electrophysiological signal classification model corresponding to the target acquisition device to obtain the predicted label corresponding to the training electrophysiological signal, then calculates a training loss value based on the difference between the training label and the predicted label, performs back propagation updating based on the training loss value, and adjusts the model parameter of the initial electrophysiological signal classification model, until the convergence condition is met, to obtain the target electrophysiological signal classification model. The convergence condition may be at least one of the following: the training loss value is less than a loss value threshold, the number of model iterations reaches an iteration threshold, or the like.

In an embodiment, a cross-entropy loss function can be selected as a loss function of the model. Model parameter updating is performed by using a classical gradient back propagation algorithm, and an Adam optimizer with a default parameter setting can be selected for parameter optimization.

In an embodiment, one acquisition device may correspond to multiple electrophysiological signal classification models. Different electrophysiological signal classification models correspond to different classification tasks. Therefore, the training label may be a training label corresponding to the training electrophysiological signal under a target classification task. In this case, the computer device obtain the target electrophysiological signal classification model based on the training electrophysiological signal and the corresponding training label. The target electrophysiological signal classification model is designed to output the classification result corresponding to the to-be-classified electrophysiological signal under the target classification task.

In the foregoing method and apparatus for classification processing of an electrophysiological signal, the computer device, and the storage medium, a training electrophysiological signal collected by a target acquisition device and a training label corresponding to the training electrophysiological signal are acquired; the training electrophysiological signal is inputted into an initial electrophysiological signal classification model corresponding to the target acquisition device, the initial electrophysiological signal classification model including a target channel association feature corresponding to the target acquisition device, the target channel association feature being generated based on spatial locations of multiple acquisition channels on the target acquisition device; a target time feature corresponding to the training electrophysiological signal is generated through the initial electrophysiological signal classification model; the target channel association feature is embedded into the target time feature to obtain a target embedded feature; a target spatial feature corresponding to the target embedded feature is extracted through the initial electrophysiological signal classification model; a predicted label corresponding to the training electrophysiological signal is obtained based on the target spatial feature; and a model parameter of the initial electrophysiological signal classification model is adjusted based on a difference between the training label and the predicted label, until a convergence condition is met, to obtain a target electrophysiological signal classification model. In this way, the target channel association feature is generated based on the spatial locations of multiple acquisition channels on the target acquisition device, which can represent a topology feature of the electrophysiological signal. During processing of the electrophysiological signal, time domain and frequency domain features as well as the topology feature of the electrophysiological signal is considered comprehensively, and finally an accurate classification result can be obtained, thereby improving classification accuracy of the electrophysiological signal. In addition, the target channel association feature is integrated into the model, and the target channel association feature only needs to be calculated once for different electrophysiological signals collected by the same acquisition device, thereby effectively improving classification efficiency of the electrophysiological signal.

This disclosure further provides an application scenario, and the above method for classification processing of an electrophysiological signal is applied to the application scenario. Specifically, the application of the method for classification processing of an electrophysiological signal in the application scenario is as follows:

The method for classification processing of an electrophysiological signal provided in this disclosure is applicable to an EEG signal classification task. An EEG signal is an electrophysiological signal obtained by amplifying and recording a scalp electrical signal by an electronic instrument (that is, the acquisition device), and is a multi-channel time sequence. Referring to FIG. 7A, the acquisition device includes multiple electrodes, one electrode corresponds to one acquisition channel, and a complete EEG signal includes EEG signals corresponding to multiple acquisition channels. 702 in FIG. 7A may represent one electrode.

1. Establish a Brain Network, and Fuse the Brain Network into an Initial EEG Classification Model

The EEG signal is a multi-channel time sequence obtained by recording discharges of brain neurons from the scalp by a precise electronic instrument. Each channel corresponds to a location on the scalp of human brain. The channels have certain associations in three-dimensional space, and the association is helpful for the fusion of channel information. Therefore, EEG signal classification can be implemented based on a brain network. The brain network is a network used for describing functional connections of the brain, which includes nodes and connection edges. The node represents a component of the brain, that is, one acquisition channel or electrode, and the connection edge represents a connection relationship between components, that is, an association between acquisition channels or electrodes. The brain network can be represented by a channel association feature.

1-1. Establish an Initial Brain Network

A spatial brain network refers to a brain network established based on spatial location information of the electrodes, and is used for measuring a possible physical connection between different electrodes. Apparently, neurons that are close in space are more likely to be physically connected, while neurons that are far away from each other are less likely to be physically connected. The initial brain network is established as follows:

(1) Map three-dimensional coordinates of electrodes to a two-dimensional system to obtain two-dimensional coordinates corresponding to the electrodes.

(2) Considering each electrode as a vertex of a triangle, obtain side lengths of the triangle by calculating a distance between every two vertexes.

(3) When and only when the following conditions are met, determine that a triangles and sides of the triangles exist: {circle around (1)} the triangle does not include any nodes except three vertexes; {circle around (2)} the triangle should be an acute triangle or a right triangle (that is, the triangle cannot be an obtuse triangle).

(4) Count all existing triangles, and construct a spatial brain network by using sides of each triangle as connection edges of the network, to obtain an initial adjacency matrix corresponding to the initial brain network. The dimensionality of the initial adjacency matrix is a node quantity (that is, the electrode quantity or channel quantity), and a numerical value represents a connection edge value between every two nodes. In a case that the brain network is an unweighted network, values in the initial adjacency matrix only include 0 or 1. In a case that the brain network is a weighted network, data in the initial adjacency matrix includes 0 and a reciprocal of a spatial distance between two electrodes corresponding to each connection edge.

1-2. Perform Normalization Processing on the Initial Brain Network to Obtain a Target Brain Network

First, the initial adjacency matrix A and the cell matrix I are summed based on the formula Ã=A+I to obtain an intermediate adjacency matrix Ã. This is because the original multi-channel signal needs to be multiplied with the normalized adjacency matrix during subsequent network embedding. If the cell matrix is not added, each channel signal after the calculation does not include information of the channel, which is not conducive to the subsequent analysis.

Then, normalization processing is performed on the initial brain network through the formula

$\hat{A} = D^{- \frac{1}{2}} \tilde{A} D^{- \frac{1}{2}}$

to obtain a target brain network (that is, the target channel association feature). The normalization processing can transform the initial adjacency matrix into a matrix of rows and columns in which values are 1, so that an assignment interval of the original signal is not altered during subsequent brain network fusion. The target brain network is a structural brain network used for describing physical connections or locational relationships between neurons or regions of the brain.

1-3. Fuse the Target Brain Network into the Initial EEG Classification Model to Serve as a Component of the Initial EEG Classification Model

The target brain network is constructed based on the spatial location relationship of the acquisition channels of the EEG signal, and the target brain network is integrated into a model structure of a deep learning model to fully utilize spatial information between the acquisition channels and integrate highly connected acquisition channels, thereby improving the performance of the EEG signal classification model.

Further, if the target brain network is integrated into the initial EEG classification model, one initial EEG classification model corresponds to one acquisition device. In this way, for different samples corresponding to acquisition channels at the same location, the brain network only needs to be calculated once, which is more efficient and suitable for a brain-computer interface system that needs real-time control.

2. Perform Supervised Training on the Initial EEG Signal Classification Model to Obtain a Target EEG Signal Classification Model

Supervised training is performed on the initial EEG signal classification model based on the training EEG signal and the corresponding training label to obtain the target EEG signal classification model.

3. Perform EEG Signal Classification Through the Target EEG Signal Classification Model

Referring to FIG. 7B, the target electrophysiological signal classification model includes a temporal convolution layer, a network embedding layer, a spatial convolution layer, a nonlinear layer, and a fully-connected layer that are sequentially connected. The network embedding layer is integrated with the target brain network (that is, the target channel association feature). After a to-be-classified EEG signal is inputted into the target EEG signal classification model, a target time feature corresponding to the to-be-classified EEG signal can be extracted through the temporal convolution layer. The target time feature is embedded into the target brain network through the network embedding layer to obtain a target embedded feature. A target spatial feature corresponding to the target embedded feature is extracted through the spatial convolution layer. Nonlinear processing is performed on the target spatial feature the through the nonlinear layer to obtain a target fitting feature, and classification processing is performed on the target fitting feature through the fully-connected layer to obtain a classification result corresponding to the to-be-classified EEG signal.

In this embodiment, end-to-end decoding of the EEG signal can be implemented without artificial feature extraction, making the model more universal. In addition, the spatial brain network is integrated into the construction of the deep learning model, and topological connections between electrodes are introduced while the original signal feature is preserved, so that multi-channel signals are better fused, thereby obtaining a more accurate EEG classification result. Moreover, the network is built based on the spatial locations of the electrodes, and signal samples with the same electrode location have the same spatial brain network, which achieves high computation efficiency and is suitable for the brain-machine interface system that needs real-time control. For example, for a user with hand disability, the computer device can acquire an EEG signal of the user in real time. Motion imagination classification is performed on the EEG signal through the target EEG signal classification model. In a case that the classification result shows that the user is currently imagining the left hand, the computer device can drive a left mechanical arm to move.

It should be understood that in addition to the application to the EEG classification task, the method for classification processing of an electrophysiological signal in this disclosure can also be applied to other electrophysiological signal classification tasks, for example, an ECG classification task, an EMG signal classification task, or the like. For example, in a case that a user is exercising, muscle state classification can be performed on an EMG signal of the user through the target EMG signal classification model. In a case that a muscle state is muscle fatigue, prompt information is generated to prompt the user to rest in time.

It is to be understood that, steps in flowcharts of FIG. 2 to FIG. 6 are displayed in sequence based on indication of arrows, but the steps are not necessarily performed in sequence based on a sequence indicated by the arrows. Unless otherwise explicitly specified in this specification, execution of the steps is not strictly limited, and the steps may be performed in other sequences. In addition, at least some steps in FIG. 2 to FIG. 6 may include a plurality of steps or a plurality of stages, and these steps or stages are not necessarily performed at a same time instant, but may be performed at different time instants. The steps or stages are not necessarily performed in sequence, but may be performed by turn or alternately with other steps or at least part of steps or stages in other steps.

In an embodiment, as shown in FIG. 8, an apparatus for classification processing of an electrophysiological signal is provided. The apparatus may include software modules or hardware modules, or include a combination of the software and hardware modules to become a part of a computer device. The apparatus specifically includes: a signal acquisition module 802, a channel association feature acquisition module 804, a signal processing module 806, and a signal classification module 808.

The signal acquisition module 802 is configured to acquire a to-be-classified electrophysiological signal collected by a target acquisition device.

The channel association feature acquisition module 804 is configured to acquire a target channel association feature corresponding to the target acquisition device, where the target channel association feature is generated based on spatial locations of multiple acquisition channels on the target acquisition device.

The signal processing module 806 is configured to extract a target time feature corresponding to the to-be-classified electrophysiological signal through a target electrophysiological signal classification model, and embed the target channel association feature into the target time feature to obtain a target embedded feature.

The signal classification module 808 is configured to extract a target spatial feature corresponding to the target embedded feature through the target electrophysiological signal classification model, and obtain a classification result corresponding to the to-be-classified electrophysiological signal based on the target spatial feature.

In an embodiment, the channel association feature acquisition module is further configured to acquire the target electrophysiological signal classification model corresponding to the target acquisition device, where the target electrophysiological signal classification model is integrated with the target channel association feature corresponding to the target acquisition device, and is used for extracting the target time feature corresponding to the to-be-classified electrophysiological signal, embedding the target channel association feature into the target time feature to obtain the target embedded feature, extracting the target spatial feature corresponding to the target embedded feature, and obtaining the classification result corresponding to the to-be-classified electrophysiological signal based on the target spatial feature.

In an embodiment, the channel association feature acquisition module is further configured to determine a target classification task; acquire multiple candidate electrophysiological signal classification models corresponding to the target classification task, the multiple candidate electrophysiological signal classification models having corresponding candidate acquisition devices; and determine the target electrophysiological signal classification model corresponding to the target acquisition device from the candidate electrophysiological signal classification models.

In an embodiment, as shown in FIG. 9, the apparatus further includes:

a channel association feature generation module 801, configured to map the spatial locations of the acquisition channels to a same plane to obtain plane locations of the acquisition channels; acquire a target quantity of acquisition channels from the acquisition channels as target acquisition channels to obtain a target quantity of target acquisition channels; generate a target channel region based on the plane locations of the target acquisition channels; associate the target acquisition channels in a case that a region shape feature corresponding to the target channel region is a preset shape feature and there is no other acquisition channel in the target channel region; and generate the target channel association feature based on the associated acquisition channels.

In an embodiment, the channel association feature generation module is further configured to use each of the acquisition channels as a region vertex to obtain multiple region vertexes, and connect the region vertexes in turn to obtain the target channel region.

In an embodiment, the region shape feature includes vertex angles corresponding to the region vertexes of the target channel region. The channel association feature generation module is further configured to determine that the region shape feature corresponding to the target channel region is the preset shape feature in a case that the vertex angles are all within a reference angle range; and associate the target acquisition channels in a case that there is no other acquisition channel in the target channel region whose region shape feature is the preset shape feature.

In an embodiment, the channel association feature generation module is further configured to generate an initial channel association feature based on the associated acquisition channels; and perform normalization processing on the initial channel association feature to obtain the target channel association feature.

In an embodiment, the channel association feature generation module is further configured to generate an initial channel association matrix based on the initial channel association feature; acquire a cell matrix, and fuse the cell matrix and the initial channel association matrix to obtain an intermediate channel association matrix; acquire a diagonal matrix corresponding to the initial channel association matrix, and fuse the diagonal matrix and the intermediate channel association matrix to obtain a target channel association matrix; and obtain the target channel association feature based on the target channel association matrix.

In an embodiment, the channel association feature generation module is further configured to determine matrix dimensionality of the initial channel association matrix based on a channel quantity of the acquisition channels on the target acquisition device, set matrix values corresponding to the associated acquisition channels to a first preset threshold, and set matrix values corresponding to other acquisition channels to a second preset threshold, to obtain the initial channel association matrix. The channel association feature generation module is further configured to determine matrix dimensionality of the initial channel association matrix based on a channel quantity of the acquisition channels on the target acquisition device, determine matrix values corresponding to the associated acquisition channels based on spatial location distances between the associated acquisition channels, and set matrix values corresponding to other acquisition channels to a second preset threshold to obtain the initial channel association matrix.

In an embodiment, the to-be-classified electrophysiological signal includes electrophysiological signals corresponding to the acquisition channels respectively. The signal processing module is further configured to acquire at least one temporal convolution kernel; separately extract time sub-features corresponding to electrophysiological signals based on a same temporal convolution kernel to obtain multiple time sub-features corresponding to each of the at least one temporal convolution kernel; generate an intermediate time feature based on the multiple time sub-features corresponding to the same temporal convolution kernel to obtain at least one intermediate time feature corresponding to the at least one temporal convolution kernel respectively; and obtain the target time feature based on the at least one intermediate time feature.

In an embodiment, the target time feature includes multiple intermediate time features. The signal processing module is further configured to separately embed the target channel association feature into the intermediate time features to obtain initial embedded features corresponding to the intermediate time features, and obtain the target embedded feature based on the initial embedded features.

In an embodiment, the channel association feature includes association sub-features corresponding to the acquisition channels respectively, and the intermediate time feature includes time sub-features corresponding to the acquisition channels respectively. The signal processing module is further configured to: in a current intermediate time feature, embed an association sub-feature corresponding to a same acquisition channel into a corresponding time sub-feature, to obtain embedded sub-features corresponding to the acquisition channels respectively; and obtain an initial embedded feature corresponding to the current intermediate time feature based on the embedded sub-features.

In an embodiment, the signal classification module is further configured to acquire at least one spatial convolution kernel; perform spatial feature extraction on the target embedded feature separately based on the at least one spatial convolution kernel to obtain at least one intermediate spatial feature corresponding to the at least one spatial convolution kernel respectively; and obtain the target spatial feature based on the at least one intermediate spatial feature.

In an embodiment, the signal classification module is further configured to perform nonlinear processing on the target spatial feature to obtain a target fitting feature; and perform classification processing on the target fitting feature to obtain the classification result.

In an embodiment, as shown in FIG. 10, an apparatus for classification processing of an electrophysiological signal is provided. The apparatus may include software modules or hardware modules, or include a combination of software and hardware modules to become a part of a computer device. The apparatus specifically includes: a data acquisition module 1002, a data input module 1004, a data processing module 1006, a label prediction module 1008, and a reference adjustment module 1010.

The data acquisition module 1002 is configured to acquire a training electrophysiological signal collected by a target acquisition device and a training label corresponding to the training electrophysiological signal.

The data input module 1004 is configured to input the training electrophysiological signal into an initial electrophysiological signal classification model corresponding to the target acquisition device, the initial electrophysiological signal classification model including a target channel association feature corresponding to the target acquisition device, the target channel association feature being generated based on spatial locations of multiple acquisition channels on the target acquisition device.

The data processing module 1006 is configured to extract a target time feature corresponding to the training electrophysiological signal through the initial electrophysiological signal classification model, and embed the target channel association feature into the target time feature to obtain a target embedded feature.

The label prediction module 1008 is configured to extract a target spatial feature corresponding to the target embedded feature through the initial electrophysiological signal classification model, and obtain a predicted label corresponding to the training electrophysiological signal based on the target spatial feature.

The reference adjustment module 1010 is configured to adjust a model parameter of the initial electrophysiological signal classification model based on a difference between the training label and the predicted label, until a convergence condition is met, to obtain a target electrophysiological signal classification model.

For a specific limitation on the apparatus for classification processing of an electrophysiological signal, refer to the limitation on the method for classification processing of an electrophysiological signal above. Details are not described herein again. Each module in the foregoing apparatus for classification processing of an electrophysiological signal may be implemented entirely or partially by software, hardware, and a combination thereof. The foregoing modules may be built in or independent of one or more processors of a computer device in a hardware form, or may be stored in a memory of the computer device in a software form, so that the processor invokes and performs an operation corresponding to each of the foregoing modules.

In an embodiment, a computer device is provided. The computer device may be a server, and an internal structure diagram thereof may be shown in FIG. 11. The computer device comprises a processor (including processing circuitry), a memory (including a non-transitory computer-readable storage medium), and a network interface that are connected by using a system bus. The processor of the terminal is configured to provide computing and control capabilities. The memory of the terminal comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer-readable instruction, and a database. The internal memory provides an environment for running of the operating system and the computer-readable instruction in the non-volatile storage medium. The database of the computer device is configured to store data such as candidate electrophysiological signal classification models, a target electrophysiological signal classification model, and the like. The network interface of the computer device is configured to communicate with an external terminal through a network connection. The computer-readable instruction is executed by the processor to implement the method for classification processing of an electrophysiological signal.

In an embodiment, a computer device is provided. The computer device may be a terminal, and an internal structure diagram thereof may be shown in FIG. 12. The computer device includes a processor, a memory, a communication interface, a display screen, and an input apparatus that are connected by using a system bus. The processor of the terminal is configured to provide computing and control capabilities. The memory of the terminal comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer-readable instruction. The internal memory provides an environment for running of the operating system and the computer-readable instruction in the non-volatile storage medium. The communication interface of the computer device is configured to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented by using WIFI, an operator network, near-field communication (NFC), or other technologies. The computer-readable instruction is executed by the processor to implement the method for classification processing of an electrophysiological signal. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen. The input apparatus of the computer device may be a touch layer covering the display screen, or may be a key, a trackball, or a touch pad disposed on a housing of the computer device, or may be an external keyboard, a touch pad, a mouse, or the like.

A person skilled in the art may understand that, the structures shown in FIG. 11 and FIG. 12 are only block diagrams of partial structures related to the solution of this disclosure and do not limit the computer device to which the solution of this disclosure is applied. Specifically, the computer device may include more or fewer components than those in the drawings, or some components are combined, or a different component deployment is used.

In an embodiment, a computer device is further provided, including a memory and one or more processors, the memory storing computer-readable instructions, the computer-readable instructions, when executed by the one or more processors, implementing the steps in the foregoing method embodiments.

In an embodiment, a computer-readable storage medium is provided, storing computer-readable instructions, the computer-readable instructions, when executed by one or more processors, implementing the steps in the foregoing method embodiments.

In an embodiment, a computer program product or a computer program is provided. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The one or more processors of the computer device read the computer instructions from the computer-readable storage medium, and the one or more processors execute the computer instructions, to cause the computer device to perform the steps in the method embodiments.

A person of ordinary skill in the art may understand that all or some of the procedures of the methods of the foregoing embodiments may be implemented by computer-readable instructions instructing relevant hardware. The computer-readable instructions may be stored in a non-volatile computer-readable storage medium. When the computer-readable instructions are executed, the procedures of the embodiments of the foregoing methods may be included. Any reference to a memory, a storage, a database, or another medium used in the embodiments provided in this disclosure may include at least one of a non-volatile memory and a volatile memory. The non-volatile memory may include a read-only memory (ROM), a magnetic tape, a floppy disk, a flash memory, an optical memory, and the like. The volatile memory may include a random access memory (RAM) or an external cache. For the purpose of description instead of limitation, the RAM is available in a plurality of forms, such as a static RAM (SRAM) or a dynamic RAM (DRAM).

The technical features in the foregoing embodiments may be randomly combined. For concise description, not all possible combinations of the technical features in the embodiment are described. However, provided that combinations of the technical features do not conflict with each other, the combinations of the technical features are considered as falling within the scope recorded in this specification.

The foregoing disclosure includes some exemplary embodiments of this disclosure which are not intended to limit the scope of this disclosure. Other embodiments shall also fall within the scope of this disclosure.

	Number	Date	Country
Parent	PCT/CN2022/078970	Mar 2022	US
Child	17992565		US

CLASSIFICATION PROCESSING OF AN ELECTROPHYSIOLOGICAL SIGNAL BASED ON SPATIAL LOCATIONS OF CHANNELS OF THE SIGNAL

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