阅读说明：本技术 一种利用脑电信号重建视觉图像的方法及装置 (Method and device for reconstructing visual image by utilizing electroencephalogram signal ) 是由 张贺晔 郝菁煜 于 2020-04-03 设计创作，主要内容包括：本申请提供了一种利用脑电信号重建视觉图像的方法及装置,包括：利用人工神经网络的自学习能力,建立脑电信号的特征向量与根据脑电信号生成的图像的图像特征之间的对应关系；其中,所述特征图像特征包括像素点值,和像素点位置；获取受试者的当前脑电信号的当前特征向量；通过所述对应关系,确定与所述当前特征向量对应的当前图像特征；具体地,确定与所述当前特征向量对应的当前图像特征,包括：将所述对应关系中与所述当前特征向量相同的特征向量所对应的图像特征,确定为所述当前图像特征。克服了脑电信号中混杂的噪声和伪影的影响,成功利用其重建图像；利用了脑电信号的时空特征,将脑电信号中的有效信息提取用于图像重建。(The application provides a method and a device for reconstructing a visual image by utilizing an electroencephalogram signal, comprising the following steps: establishing a corresponding relation between the feature vector of the electroencephalogram signal and the image feature of the image generated according to the electroencephalogram signal by utilizing the self-learning capability of the artificial neural network; wherein the feature image features comprise pixel point values, and pixel point positions; acquiring a current feature vector of a current electroencephalogram of a subject; determining the current image feature corresponding to the current feature vector according to the corresponding relation; specifically, determining a current image feature corresponding to the current feature vector includes: and determining the image characteristics corresponding to the characteristic vector which is the same as the current characteristic vector in the corresponding relation as the current image characteristics. The influence of noise and artifacts mixed in the electroencephalogram signals is overcome, and the electroencephalogram signals are successfully utilized to reconstruct images; the time-space characteristics of the electroencephalogram signals are utilized, and effective information in the electroencephalogram signals is extracted and used for image reconstruction.)

1. A method for reconstructing a visual image from an electroencephalogram signal, comprising:

establishing a corresponding relation between the feature vector of the electroencephalogram signal and the image feature of the image generated according to the electroencephalogram signal by utilizing the self-learning capability of the artificial neural network; wherein the feature image features comprise pixel point values, and pixel point positions;

acquiring a current feature vector of a current electroencephalogram of a subject;

determining the current image feature corresponding to the current feature vector according to the corresponding relation; specifically, determining a current image feature corresponding to the current feature vector includes: and determining the image characteristics corresponding to the characteristic vector which is the same as the current characteristic vector in the corresponding relation as the current image characteristics.

2. The method of claim 1,

the feature vector includes: time characteristic, waveform characteristic, and/or one-dimensional or more comprehensive characteristic composed of the characteristic extracted from the time characteristic and the waveform characteristic according to a set rule; wherein the content of the first and second substances,

the time characteristic comprises: unit time step duration;

and the combination of (a) and (b),

the waveform features include: the number of the collection points and the collection voltage value of each collection point in unit time step;

and/or the presence of a gas in the gas,

the corresponding relation comprises: a functional relationship; the feature vector is an input parameter of the functional relation, and the image feature is an output parameter of the functional relation;

determining a current image feature corresponding to the current feature vector, further comprising:

and when the corresponding relation comprises a functional relation, inputting the current feature vector into the functional relation, and determining the output parameter of the functional relation as the current image feature.

3. The method of claim 1, wherein the step of establishing a correspondence between the feature vector of the brain electrical signal and the image features of the image generated from the brain electrical signal comprises:

acquiring the time characteristic and the characteristic relation between the waveform characteristic and the characteristic vector by utilizing the learning characteristic of a cyclic artificial neural network;

acquiring time characteristics and waveform characteristics for establishing the corresponding relation;

and determining the time characteristic used for establishing the corresponding relation and the characteristic vector corresponding to the waveform characteristic through the characteristic relation.

4. The method of claim 1, wherein the step of establishing a correspondence between the feature vector of the brain electrical signal and the image features of the image generated from the brain electrical signal comprises:

acquiring sample data for establishing a corresponding relation between the feature vector and the image feature;

analyzing the characteristic and the rule of the characteristic vector, and determining the network structure and the network parameters of the artificial neural network according to the characteristic and the rule;

and training and testing the network structure and the network parameters by using the sample data, and determining the corresponding relation between the feature vector and the image feature.

5. The method according to claim 3, wherein the step of obtaining sample data for establishing a correspondence between the feature vector and the image feature comprises:

collecting the feature vectors and the image features of different subjects;

analyzing the feature vector, and selecting data related to the image features as the feature vector by combining with prestored expert experience information;

and taking the image characteristics and the data pairs formed by the selected characteristic vectors as sample data.

6. The method according to any one of claims 3 to 5,

training the network structure and the network parameters, including:

selecting a part of data in the sample data as a training sample, inputting the feature vector in the training sample into the network structure, and training through a loss function of the network structure, an activation function and the network parameters to obtain an actual training result;

determining whether an actual training error between the actual training result and a corresponding image feature in the training sample meets a preset training error;

determining that the training of the network structure and the network parameters is completed when the actual training error meets the preset training error;

and/or the presence of a gas in the gas,

testing the network structure and the network parameters, comprising:

selecting another part of data in the sample data as a test sample, inputting the feature vector in the test sample into the trained network structure, and testing by using the loss function, the activation function and the trained network parameters to obtain an actual test result;

determining whether an actual test error between the actual test result and a corresponding image feature in the test sample satisfies a set test error;

and when the actual test error meets the set test error, determining that the test on the network structure and the network parameters is finished.

7. The method of claim 6,

training the network structure and the network parameters, further comprising:

when the actual training error does not meet the set training error, updating the network parameters through an error loss function of the network structure;

activating a function and the updated network parameters to retrain through the loss function of the network structure until the retrained actual training error meets the set training error;

and/or the presence of a gas in the gas,

testing the network structure and the network parameters, further comprising:

and when the actual test error does not meet the set test error, retraining the network structure and the network parameters until the retrained actual test error meets the set test error.

8. An apparatus for reconstructing a visual image from an electroencephalogram signal, comprising:

the establishing module is used for establishing a corresponding relation between the feature vector of the electroencephalogram signal and the image feature of the image generated according to the electroencephalogram signal by utilizing the self-learning capability of the artificial neural network; wherein the feature image features comprise pixel point values, and pixel point positions;

the acquisition module is used for acquiring the current feature vector of the current electroencephalogram signal of the subject;

the determining module is used for determining the current image characteristics corresponding to the current characteristic vectors through the corresponding relations; specifically, determining a current image feature corresponding to the current feature vector includes: and determining the image characteristics corresponding to the characteristic vector which is the same as the current characteristic vector in the corresponding relation as the current image characteristics.

9. An apparatus comprising a processor, a memory, and a computer program stored on the memory and capable of running on the processor, the computer program when executed by the processor implementing the method of any one of claims 1 to 7.

10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 7.

Technical Field

The application relates to the field of medical detection, in particular to a method and a device for reconstructing a visual image by utilizing an electroencephalogram signal.

Background

The scientific community has been trying to break through the limits of understanding and detection techniques for brain working mechanisms. For example, brain-machine interface research for machine-driven direct control of disabled persons is a relatively successful area of research that can have a direct impact on the life of the user. While research in cognitive neuroscience has been attempting to determine which parts of the human visual cortex and brain are responsible for the visual cognitive processes, it has not been determined, but it has been determined that brain activity records contain information about the class of visual objects. This consideration asks the question whether such brain's activity patterns can be identified in order to extract useful information about the observed scene content. This information is then used in conjunction with a conditional generative model to reconstruct a realistic-minded image.

In fact, the human visual cortex accounts for around 30% of the total cortical area, making it much larger than the other sensory cortex, which means that the presentation of visual information in the brain is apparently the most complex of all sensory processes. In 2012, "national library of science bio-book, b.n. pasley et al, describe a method of reconstructing (part of) a speech stimulus based on human auditory cortex data, which is obtained from an array of cortical surface electrodes. Compared with the acquisition of electroencephalogram signals, the method is less influenced by noise, and the needed generation model is simpler. However, such practice for visual signal extraction is prohibitive because it requires a craniotomy procedure.

The process of reconstructing human vision differs from its perception in that it requires an understanding of whether and how brain signals recorded by existing devices record visual content. There are some studies that attempt to solve this problem, for example, by identifying the visible categories through different visual stimuli. In 2015, b.kaneshiro et al proposed training a classifier in the american public library of science-ONE to identify target classes from a topographic map generated from brain-electrical signals. However, the linear classifier adopted cannot well reflect the space-time dynamic characteristics of the electroencephalogram signal, so the obtained accuracy is low (more than 12 classes and 29%). Similar work is carried out in the journal of neuroscience methods of A.X.Stewart et al 2014, but the original electroencephalogram data is firstly processed through independent component analysis and then input into a support vector machine classifier, and the classifier only has the task of distinguishing two categories. While these works are certainly interesting, they still have some limitations (relatively simple classification models, fewer classes of objects) that do not allow the spatio-temporal dynamics of brain electrical signals to be studied at a deeper level.

Disclosure of Invention

In view of the above, the present application is directed to a method and apparatus for reconstructing a visual image from brain electrical signals that overcomes or at least partially solves the problems, comprising:

a method for reconstructing a visual image using brain electrical signals, comprising:

establishing a corresponding relation between the feature vector of the electroencephalogram signal and the image feature of the image generated according to the electroencephalogram signal by utilizing the self-learning capability of the artificial neural network; wherein the feature image features comprise pixel point values, and pixel point positions;

acquiring a current feature vector of a current electroencephalogram of a subject;

determining the current image feature corresponding to the current feature vector according to the corresponding relation; in particular, determining a current image feature corresponding to the current feature vector comprises: and determining the image feature corresponding to the feature vector which is the same as the current feature vector in the corresponding relation as the current image feature.

Further, the air conditioner is provided with a fan,

the feature vector includes: time characteristic, waveform characteristic, and/or one-dimensional or more comprehensive characteristic composed of the time characteristic and the characteristic extracted from the waveform characteristic according to a set rule; wherein the content of the first and second substances,

the time characteristic comprises: unit time step duration;

and the combination of (a) and (b),

the waveform features include: the number of the acquisition points and the acquisition voltage value of each acquisition point in unit time step;

and/or the presence of a gas in the gas,

the corresponding relation comprises: a functional relationship; the feature vector is an input parameter of the functional relation, and the image feature is an output parameter of the functional relation;

determining a current image feature corresponding to the current feature vector, further comprising:

and when the corresponding relation comprises a functional relation, inputting the current feature vector into the functional relation, and determining the output parameter of the functional relation as the current image feature.

Further, the step of establishing a correspondence between the feature vector of the electroencephalogram signal and the image feature of the image generated from the electroencephalogram signal includes:

acquiring the time characteristic and the characteristic relation between the waveform characteristic and the characteristic vector by utilizing the learning characteristic of a cyclic artificial neural network;

acquiring time characteristics and waveform characteristics for establishing the corresponding relation;

and determining the time characteristic used for establishing the corresponding relation and the characteristic vector corresponding to the waveform characteristic through the characteristic relation.

Further, the step of establishing a correspondence between the feature vector of the electroencephalogram signal and the image feature of the image generated from the electroencephalogram signal includes:

obtaining sample data for establishing a correspondence between the feature vectors and the image features;

analyzing the characteristic and the rule of the characteristic vector, and determining the network structure and the network parameters of the artificial neural network according to the characteristic and the rule;

and training and testing the network structure and the network parameters by using the sample data, and determining the corresponding relation between the feature vector and the image feature.

Further, the step of obtaining sample data for establishing a correspondence between the feature vector and the image feature includes:

collecting the feature vectors and the image features of different subjects;

analyzing the feature vector, and selecting data related to the image features as the feature vector by combining with prestored expert experience information;

and taking the image features and the selected data pairs formed by the feature vectors as sample data.

Further, the air conditioner is provided with a fan,

training the network structure and the network parameters, including:

selecting a part of data in the sample data as a training sample, inputting the feature vector in the training sample into the network structure, and activating a function and the network parameters to train through a loss function of the network structure to obtain an actual training result;

determining whether an actual training error between the actual training result and a corresponding image feature in the training sample meets a preset training error;

determining that the training of the network structure and the network parameters is completed when the actual training error meets the preset training error;

and/or the presence of a gas in the gas,

testing the network structure and the network parameters, comprising:

selecting another part of data in the sample data as a test sample, inputting the feature vector in the test sample into the trained network structure, and testing by using the loss function, the activation function and the trained network parameters to obtain an actual test result;

determining whether an actual test error between the actual test result and a corresponding image feature in the test sample satisfies a set test error;

and when the actual test error meets the set test error, determining that the test on the network structure and the network parameters is finished.

Further, the air conditioner is provided with a fan,

training the network structure and the network parameters, further comprising:

when the actual training error does not meet the set training error, updating the network parameters through an error loss function of the network structure;

activating a function and the updated network parameters to retrain through the loss function of the network structure until the retrained actual training error meets the set training error;

and/or the presence of a gas in the gas,

testing the network structure and the network parameters, further comprising:

and when the actual test error does not meet the set test error, retraining the network structure and the network parameters until the retrained actual test error meets the set test error.

An apparatus for reconstructing a visual image using brain electrical signals, comprising:

the establishing module is used for establishing a corresponding relation between the feature vector of the electroencephalogram signal and the image feature of the image generated according to the electroencephalogram signal by utilizing the self-learning capability of the artificial neural network; wherein the characteristic image features comprise pixel point values and pixel point positions;

the acquisition module is used for acquiring the current feature vector of the current electroencephalogram signal of the subject;

the determining module is used for determining the current image characteristic corresponding to the current characteristic vector according to the corresponding relation; specifically, determining a current image feature corresponding to the current feature vector includes: and determining the image characteristics corresponding to the characteristic vector which is the same as the current characteristic vector in the corresponding relation as the current image characteristics.

An apparatus comprising a processor, a memory and a computer program stored on the memory and being executable on the processor, the computer program, when executed by the processor, implementing the steps of the method for reconstructing a visual image from brain electrical signals as described above.

A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method for reconstructing a visual image from brain electrical signals as set forth above.

The application has the following advantages:

in the embodiment of the application, the corresponding relation between the feature vector of the electroencephalogram signal and the image feature of the image generated according to the electroencephalogram signal is established by utilizing the self-learning capability of the artificial neural network; wherein the characteristic image features comprise pixel point values and pixel point positions; obtaining a current feature vector of a current electroencephalogram signal of a subject; determining the current image feature corresponding to the current feature vector according to the corresponding relation; specifically, determining a current image feature corresponding to the current feature vector includes: and determining the image characteristics corresponding to the characteristic vector which is the same as the current characteristic vector in the corresponding relation as the current image characteristics. The influence of noise and artifacts mixed in the electroencephalogram signals is overcome, and the electroencephalogram signals are successfully utilized to reconstruct images; the time-space characteristics of the electroencephalogram signals are utilized, and effective information in the electroencephalogram signals is extracted and used for image reconstruction, and is not just simple classification.

Drawings

In order to more clearly illustrate the technical solutions of the present application, the drawings needed to be used in the description of the present application will be briefly introduced below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive labor.

FIG. 1 is a flowchart illustrating steps of a method for reconstructing a visual image from brain electrical signals according to an embodiment of the present application;

FIG. 2 is a schematic flow chart illustrating a method for reconstructing a visual image using electroencephalogram signals according to an embodiment of the present application;

FIG. 3 is a structural diagram of a recurrent artificial neural network of a method for reconstructing a visual image using electroencephalogram signals according to an embodiment of the present application;

fig. 4 is a schematic structural diagram of a feature vector extraction framework of an electroencephalogram signal of a method for reconstructing a visual image by using an electroencephalogram signal according to an embodiment of the present application;

FIG. 5-a is a schematic diagram of a method for reconstructing a visual image using electroencephalogram signals according to one embodiment of the present application, the method using feature vectors of electroencephalogram signals to reconstruct an image;

5-b is a schematic diagram of the initial score and classification result of a reconstructed image of a method for reconstructing a visual image using electroencephalogram signals according to an embodiment of the present application;

FIG. 6 is a block diagram illustrating an apparatus for reconstructing a visual image using electroencephalogram signals according to an embodiment of the present application;

fig. 7 is a schematic structural diagram of a computer device according to an embodiment of the present invention.

Detailed Description

In order to make the aforementioned objects, features and advantages of the present application more comprehensible, the present application is described in further detail with reference to the accompanying drawings and the detailed description. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

Referring to fig. 2, it should be noted that, in any embodiment of the present invention, the electroencephalogram signal is recorded when a display image is observed for a subject, and the image output is obtained by a generator, which learns to associate the processed electroencephalogram signal feature vector with the image class observed when the signals are recorded. The present invention defaults to brain electrical signals that inherently encode vision-related information, whether it be low-level responses to visual stimuli or high-level cognitive processes associated with more complex brain activities (e.g., recognition and understanding).

Referring to fig. 1, a method for reconstructing a visual image using brain electrical signals according to an embodiment of the present application is shown, including:

s110, establishing a corresponding relation between a feature vector of the electroencephalogram signal and image features of an image generated according to the electroencephalogram signal by utilizing the self-learning capability of the artificial neural network; wherein the feature image features comprise pixel point values and pixel point positions;

s120, obtaining a current feature vector of a current electroencephalogram of the subject;

s130, determining a current image feature corresponding to the current feature vector according to the corresponding relation; specifically, determining a current image feature corresponding to the current feature vector includes: and determining the image characteristics corresponding to the characteristic vector which is the same as the current characteristic vector in the corresponding relation as the current image characteristics.

In the embodiment of the application, the corresponding relation between the feature vector of the electroencephalogram signal and the image feature of the image generated according to the electroencephalogram signal is established by utilizing the self-learning capability of the artificial neural network; wherein the characteristic image features comprise pixel point values and pixel point positions; obtaining a current feature vector of a current electroencephalogram signal of a subject; determining the current image feature corresponding to the current feature vector according to the corresponding relation; specifically, determining a current image feature corresponding to the current feature vector includes: and determining the image characteristics corresponding to the characteristic vector which is the same as the current characteristic vector in the corresponding relation as the current image characteristics. The influence of noise and artifacts mixed in the electroencephalogram signals is overcome, and the electroencephalogram signals are successfully utilized to reconstruct images; the time-space characteristics of the electroencephalogram signals are utilized, and effective information in the electroencephalogram signals is extracted and used for image reconstruction, and is not just simple classification.

Next, a method of reconstructing a visual image using an electroencephalogram signal in the present exemplary embodiment will be further described.

As described in the step S110, the self-learning capability of the artificial neural network is used to establish a correspondence between the feature vector of the electroencephalogram signal and the image feature of the image generated from the electroencephalogram signal; wherein the feature image features comprise pixel point values, and pixel point positions.

For example: and analyzing the display state rule of the electroencephalogram corresponding to the image characteristics by utilizing an artificial neural network algorithm, and finding out the mapping rule between the characteristic vector of the electroencephalogram of the subject and the image characteristics through the self-learning and self-adaptive characteristics of the artificial neural network.

For example: the method can utilize an artificial neural network algorithm, collect the characteristic vectors of the electroencephalograms of a large number of different subjects (including but not limited to one or more of age, weight, sex, state of illness and the like), select the characteristic vectors and the image characteristics of the electroencephalograms of a plurality of subjects as sample data, learn and train the neural network, fit the relation between the characteristic vectors and the image characteristics of the electroencephalograms by the neural network by adjusting the weight between the network structure and the network nodes, and finally enable the neural network to accurately fit the corresponding relation between the characteristic vectors and the image characteristics of the electroencephalograms of different subjects.

In one embodiment, the feature vector includes: time characteristic, waveform characteristic, and/or one-dimensional or more comprehensive characteristic composed of the characteristic extracted from the time characteristic and the waveform characteristic according to a set rule; wherein the content of the first and second substances,

the time characteristic comprises: unit time step duration;

and the combination of (a) and (b),

the waveform features include: the number of the acquisition points and the acquisition voltage value of each acquisition point in unit time step;

in an embodiment, the correspondence includes: and (4) functional relation.

Preferably, the feature vector is an input parameter of the functional relationship, and the image feature is an output parameter of the functional relationship;

determining a current image feature corresponding to the current feature vector, further comprising:

and when the corresponding relation comprises a functional relation, inputting the current feature vector into the functional relation, and determining the output parameter of the functional relation as the current image feature.

Therefore, the flexibility and convenience of determining the current image features can be improved through the corresponding relations in various forms.

In one embodiment, the step of establishing a correspondence between a feature vector of the brain electrical signal and an image feature of an image generated from the brain electrical signal includes:

acquiring the time characteristic and the characteristic relation between the waveform characteristic and the characteristic vector by utilizing the learning characteristic of a cyclic artificial neural network;

acquiring time characteristics and waveform characteristics for establishing the corresponding relation;

and determining the time characteristic used for establishing the corresponding relation and the characteristic vector corresponding to the waveform characteristic through the characteristic relation.

For example: the method can utilize a cyclic artificial neural network algorithm to learn and train the cyclic artificial neural network by collecting and collecting waveform characteristics of a large number of different subjects (including but not limited to one or more of age, sex, occupation and the like) under different time characteristics, selecting a plurality of preset time characteristics, waveform characteristics of the plurality of subjects and feature vectors of electroencephalograms as sample data, and finally enabling the cyclic artificial neural network to accurately fit different time characteristics and corresponding relations between the waveform characteristics of the different subjects and the feature vectors of the electroencephalograms by adjusting a network structure and weights among network nodes.

Referring to FIG. 3, as an example, an RNN-based encoder is used to analyze a brain electrical signal over time and learn to encode it into a feature vector of the brain electrical signal, which contains visually relevant portions of the brain electrical signal and information about categories.

Because the existing method tries to directly process a multi-channel time brain electrical sequence, the method only connects the time sequence in series into a single feature vector and ignores local time dynamics. To account for temporal dependencies, long-short term memory recurrent neural networks are employed.

The EEG signature encoder of this example is shown in FIG. 3 as being comprised of a standard Long Short-Term Memory artificial neural network (LSTM) layer and a non-linear layer. At each time step, the input s (·, t), i.e. the set of values from all channels at time t (128 channels of extracted electroencephalogram signal), is fed into the LSTM layer; when all time steps have been processed, the final output state of the LSTM layer enters the fully connected layer with the ReLU nonlinearity. The output of the result is the characteristic vector of the brain electrical signal, and it should be noted that the result is ideally a compact representation of the visual class, namely discriminative brain activity information. The encoder and classifier are trained end-to-end by appending a classification layer and performing gradient descent optimization (supervised by the image class displayed when the input signal is recorded).

In an embodiment, a specific process of "establishing a correspondence between a feature vector of the electroencephalogram signal and an image feature of an image generated from the electroencephalogram signal" in step S110 may be further described with reference to the following description.

The following steps are described: acquiring sample data for establishing a corresponding relation between the feature vector and the image feature;

in a further embodiment, a specific process of acquiring sample data for establishing a corresponding relationship between the feature vector and the image feature may be further described in conjunction with the following description.

The following steps are described: collecting the feature vectors and the image features of different subjects;

for example: data collection: collecting feature vectors and corresponding image features of subjects with different health conditions; collecting the feature vectors of the subjects of different ages and the corresponding image features; and collecting the feature vectors of the subjects with different sexes and the corresponding image features.

Therefore, the operation data are collected through multiple ways, the quantity of the operation data is increased, the learning capacity of the artificial neural network is improved, and the accuracy and the reliability of the determined corresponding relation are improved.

The following steps are described: analyzing the feature vector, and selecting data related to the image features as the feature vector by combining with prestored expert experience information (for example, selecting the feature vector influencing the image features as an input parameter, and using a specified parameter as an output parameter);

for example: the feature vector in the relevant data of the diagnosed subject is used as an input parameter, and the image feature in the relevant data is used as an output parameter.

The following steps are described: and taking the image features and the data pairs formed by the selected feature vectors as sample data.

For example: and using part of the obtained input and output parameter pairs as training sample data and using part of the obtained input and output parameter pairs as test sample data.

Therefore, the collected characteristic vectors are analyzed and processed to obtain sample data, the operation process is simple, and the reliability of the operation result is high.

The following steps are described: analyzing the characteristic and the rule of the characteristic vector, and determining the network structure and the network parameters of the artificial neural network according to the characteristic and the rule;

for example: analyzing the feature vector of the electroencephalogram signal and the image features of the image generated according to the electroencephalogram signal can preliminarily determine the basic structure of the network, the number of input and output nodes of the network, the number of hidden layers of the network, the number of hidden nodes, the initial weight of the network and the like.

The network structure is a Generative countermeasure network (GAN);

and/or the presence of a gas in the gas,

the network parameters comprise: at least one of the number of convolution layers, the size of convolution kernel, the number of convolution kernels, the number of normalization layers, the number of pooling layers, the number of cavity convolution residual blocks, the number of full-link layers, the initial weight, and the offset value.

Referring to FIGS. 2-4, as an example, the method disclosed herein uses a conditional GAA network of generators in the N framework generates images. In the original formula, the generative model G (z | y) will be randomly input from P_z(z) noise distribution and condition y are mapped to target data distribution P_data(x) In that respect Under given conditions, the discriminant model D (x | y) predicts the probability that a data point belongs to the target distribution. The generator and the arbiter are trained simultaneously so that the arbiter tries to maximize the probability (from P) of assigning the correct label to the "true" data_data(x) And "false" data (from P)_G(z | y)), and the generator attempts to maximize the probability that the discriminator erroneously produced a "true" sample. In other words, these two models play the following very small game defined by the value function V (D, G):

in practice, from a training point of view, this means that a correct sample S is given_c＝(x_c，y_c) Including true data and correct conditions, and a false sample S consisting of false data and arbitrary conditions_w＝(x_w，y_w) The negative log-likelihood discriminator loss is calculated as follows:

the loss of the generator is determined by the same false samples S_wThe method comprises the following steps:

in the present invention, the condition vector associated with each image is the feature vector of the average brain electrical signal over all images of each class and all objects (calculated by the network described in the previous section).

In the generator, the condition y is appended to the random noise vector z, and a series of transposed convolutions amplifies the concatenated input to the output color image. The discriminator takes as input an image of the same size (either the actual image or the generated image). After several convolution layers of reduced feature map size, the condition y associated with the input image is spatially copied from the second convolution layer to the last convolution layer and appended to the feature map set, on the basis of which the final probability estimation is performed.

Meanwhile, in practical applications, the present invention modifies the previously given discriminator loss function: instead of training the discriminator with true images using the correct conditions and with false images using arbitrary conditions, an error sample consisting of true images and error conditions is provided, and feature vector vectors of representative brain electrical signals of different classes are randomly selected, which forces the discriminator to learn how to distinguish whether the true images have the correct conditions without any explicit information. Thus, the correct sample S is given_c＝(x_c,y_c) And an erroneous sample S_w1＝(x_c,y_w) And S_w2＝(x_w,y_w) The discriminator loss becomes:

optionally, a specific process of training the network structure and the network parameters in the step "training and testing the network structure and the network parameters and determining the correspondence between the feature vectors and the image features" may be further described in conjunction with the following description.

Selecting a part of data in the sample data as a training sample, inputting the feature vector in the training sample into the network structure, and training by a loss function of the network structure, an activation function and the network parameters to obtain an actual training result;

specifically, a loss function is minimized through a gradient descent algorithm, network parameters are updated, a current neural network model is trained, and an actual training result is obtained;

determining whether an actual training error between the actual training result and a corresponding image feature in the training sample meets a preset training error; determining that the training of the network structure and the network parameters is completed when the actual training error meets the preset training error;

specifically, when the actual training error satisfies the preset training error, and the currently trained model converges, it is determined that the training of the network structure and the network parameters is completed.

More optionally, training the network structure and the network parameters further includes:

when the actual training error does not meet the set training error, updating the network parameters through an error loss function of the network structure; retraining the activation function and the updated network parameters through the loss function of the network structure until the retrained actual training error meets the set training error;

for example: and if the test error meets the requirement, finishing the network training test.

Therefore, the reliability of the network structure and the network parameters is further verified by using the test sample for testing the network structure and the network parameters obtained by training.

Optionally, a specific process of testing the network structure and the network parameters in the step of training and testing the network structure and the network parameters using the sample data and determining the correspondence between the feature vectors and the image features may be further described in conjunction with the following description.

Selecting another part of data in the sample data as a test sample, inputting the feature vector in the test sample into the trained network structure, and testing by using the loss function, the activation function and the trained network parameters to obtain an actual test result; determining whether an actual test error between the actual test result and a corresponding image feature in the test sample satisfies a set test error; and when the actual test error meets the set test error, determining that the test on the network structure and the network parameters is finished.

As described in step S120 above, obtaining a current feature vector of the current electroencephalogram of the subject;

as described in step S130 above, the current image feature corresponding to the current feature vector is determined according to the corresponding relationship.

For example: and identifying the feature vector of the electroencephalogram signal of the subject in real time.

Therefore, the current image characteristics of the electroencephalogram signals are effectively identified according to the current characteristic vectors based on the corresponding relation, so that accurate judgment basis is provided for diagnosis of testers, and the judgment result is high in accuracy.

In an alternative example, the determining the current image feature corresponding to the feature vector in step S130 may include: and determining the image characteristics corresponding to the characteristic vector which is the same as the current characteristic vector in the corresponding relation as the current image characteristics.

In an optional example, the determining, in step S130, a current image feature corresponding to the feature vector may further include: when the corresponding relation can comprise a functional relation, inputting the current feature vector into the functional relation, and determining the output parameter of the functional relation as the current image feature.

Therefore, the current image features are determined according to the current feature vector based on the corresponding relation or the functional relation, the determination mode is simple and convenient, and the reliability of the determination result is high.

For example, the artificial neural network model obtained by training is used to detect the image features of each sample in the test set.

In an alternative embodiment, the method may further include: and verifying whether the current image characteristic is consistent with the actual image characteristic.

Optionally, when a verification result that the current image feature does not conform to the actual image feature is received and/or it is determined that there is no feature vector in the correspondence that is the same as the current feature vector, at least one maintenance operation of updating, correcting, and relearning the correspondence may be performed.

For example: the device can not acquire the actual image characteristics, and the device can only acquire the actual image characteristics by feedback operation of a tester, namely, if the device intelligently judges the image characteristics, the tester feeds back that the image characteristics do not accord with the actual state through operation, and the device can only acquire the image characteristics.

And verifying whether the current image characteristic is consistent with the actual image characteristic (for example, displaying the actual image characteristic through an AR display module to verify whether the determined current image characteristic is consistent with the actual image characteristic).

And when the current image features do not accord with the actual image features and/or the corresponding relation does not have the feature vector which is the same as the current feature vector, performing at least one maintenance operation of updating, correcting and relearning on the corresponding relation.

For example: the current image feature can be determined according to the maintained corresponding relation and the current feature vector. For example: and determining the image characteristics corresponding to the characteristic vector which is the same as the current characteristic vector in the maintained corresponding relation as the current image characteristics.

Therefore, the corresponding relation between the determined feature vector and the image feature is maintained, and the accuracy and the reliability of the determination of the image feature are favorably improved.

Referring to FIGS. 4, 5-a and 5-b, in one specific implementation, all code was completed using Python on a Linux (Ubuntu16.04) system, and the method of the present invention was trained and tested on a NVIDIA Tesla P4024 GB GPU graphics card. Using a deep learning pytorech framework, an Adam optimizer was utilized.

The generator takes a cascade vector of 100-dimensional random noise and a feature vector of 128-dimensional electroencephalogram signals as input. Such input then goes through 5 transposed convolutional layers: the first layer upsamples the vector 4 times spatially, while each of the other layers doubles in size at each step, so the output image size is 64 x 64. The number of feature maps starts at 512 for the first layer and is halved for each layer before outputting the last layer of the 3-channel (color) image.

The discriminator consists of four convolutional layers and two fully connected layers. A 64 x 64 image is taken as input and the feature map is similarly reduced in size by half in each convolution step. After the final convolutional layer, where the feature map size is 4 × 4 (spatially appending the condition vectors), two fully connected layers reduce the number of features to 1024 and 1, the latter being sigmoid probability estimates for the input image/condition pairs. The number of feature maps in the convolutional layers starts from 64 on the first layer, doubling each layer before the layer is fully connected. Both the generator and the evaluator include batch normalization modules and ReLU nonlinearities.

The effectiveness of the present invention was verified by experiments on 12000 128-channel brain signal maps generated from 2000 images observed in 6 subjects, respectively.

The performance of the method was evaluated using two angles:

(1) how feature vector coding architecture of electroencephalogram signals extracts meaningful representation from original electroencephalogram signals induced by visual stimulation

(2) Using the Initial Score (IS), the authenticity and variety of the generated images are estimated, and the quality of the generated images IS judged according to its classification accuracy on the batch of generated images under consideration.

Note that 6 subjects participated in the experiment and were given a visual image of the object while electroencephalogram data was recorded. All subjects were evaluated by a specialist to rule out health conditions or drugs that might alter normal brain activity.

Subjects were presented with 50 images from 40 different object categories, for a total of 2000 images per subject. Each image class is presented at intervals of 25 seconds (0.5 seconds per image) and then paused for 10 seconds where black images are displayed. The black image is used to "refresh" any high level class information present in the previous image. The total run time for each experiment was 1400 seconds (23 minutes 20 seconds). The experimental protocol is detailed in table 1.

Number of classes	40
		Number of images per category	50
Total number of images	2000
		Visualizing order	Sequence of
Time shown in each graph	0.5s
		Time of pause between each class	10s
Number of nodes	4
		Time of each day	350s
Total time of day	1400s

TABLE 1

An Activap5 capacitor and 128 active low impedance, low noise electrodes were used in acquiring the signal. Using 4 32 channel Brainvision6 high precision, low delay signal amplifiers (precision model: braimamp DC), a qualified technician was present during the performance of the experiment, and the use of a conductive abrasive gel ensured that the skin impedance was always kept below 10 kilo-ohms. The acquired brain electrical signals are filtered in operation (i.e., during the acquisition phase) by an integrated hardware notch filter (49-51hz) and a second order Butterworth (bandpass) filter with a frequency boundary of 14-70 hz. This frequency range contains the necessary bands (α, β, and γ) that are most significant in the visual recognition task. The sampling frequency is set to 1000 hz and the quantization resolution is set to 16 bits.

The electroencephalogram signal data set is divided into training, verifying and testing sets which respectively account for 80% (1600 pictures), 10% (200) and 10% (200). By image segmenting the samples, rather than segmenting by brain electrical signals (the number of brain electrical signals for each image is as many as the number of subjects), it can be ensured that all the brain electrical signals generated by a single image are not divided into different data sets. Training was performed using Adam gradient descent (learning rate initialized to 0.001) with 16 per batch. All layer sizes in the model (stacked LSTMs and underlying non-linear layers) are set to 128. The model and training hyper-parameters are adjusted on the validation set.

Referring to fig. 4, several configurations of feature vector extraction networks for several electroencephalograms were tested.

As shown in part a of fig. 4, the public LSTM: the feature extraction network consists of several layers of LSTM layers. In each time step t, the first layer receives the input s (·, t) (in this sense, "common" means that all EEG channels are initially fed to the same LSTM layer); if there are other LSTM layers, the output of the first layer (which may be of a different size than the original input) is provided as an input to the second layer, and so on. The output of the deepest LSTM layer of the last step is represented as an EEG feature of the entire input sequence.

As shown in section b of fig. 4, channel LSTM + common LSTM: the first encoding layer consists of a plurality of LSTMs, each LSTM being connected to only one input channel: for example, a first LSTM processes input data s (1,), a second LSTM processes s (2,), etc. Thus, the output of each "channel LSTM" is characteristic of the single channel data. The second coding layer then performs inter-channel analysis by receiving as input the concatenated output vectors of all channels LSTMs. As described above, the output of the last step, the deepest LSTM, is used as a vector of eigenvalues of the brain electrical signal.

As shown in section c of fig. 4, the common LSTM + output layer: similar to the common LSTM architecture, but with an additional output layer added after the LSTM (linear combination of inputs followed by the ReLU nonlinearity) to increase the model capacity with little computation (if compared to the two-layer common LSTM architecture). In this case, the eigenvalue vector of the brain wave is the output of the last layer.

The results of the experiment are shown in table 2. The electroencephalogram feature vector extracted by the best frame structure can reach the accuracy of more than 80% in a classification experiment, namely, the frame of the method is proved to be capable of extracting a part with class features from an original electroencephalogram signal.

TABLE 2

Referring to fig. 5-a and 5-b, examples of some of the 40 classes in the dataset are shown. It can be seen that the generator is able to capture information about classes in the electroencephalogram feature values and generate images of the corresponding classes, which confirms that the generator and discriminator can distinguish different input/output classes using conditional EEG features, and that there is a difference in the effect of the images generated in the different classes because the 40 ImageNet classes selected exhibit a high intra-class variance in the appearance and size of the subject.

Therein, the global (all classes) and the initial fraction (IS) of each class are also calculated. In the first case, 50000 image samples (1250 per class) were generated; in the second case, 50000 image samples are generated for each class and an initial score is calculated for each class. The results are shown in Table 3. It can be seen that the initial score of the reconstructed image of the present invention approximates the best result of 8.07 on CIFAR-10 (the initial score on ImageNet or a subset thereof has not been published). Although the initial score obtained is different from the score calculated by CIFAR-10, it should be noted that training ImageNet is more difficult than CIFAR-10, as detailed below:

(1) ImageNet (64X 64) has higher resolution than CIFAR-10 (32X 32);

(2) ImageNet (class 40) has more categories than CIFAR-10 (class 10);

(3) ImageNet (1200-1300 per class) has less training data in each class than CIFAR-10 (6000 per class);

(4) ImageNet has higher within-class variance than CIFAR-10

Since the initial score cannot measure the correctness of the generated image according to the correspondence with the condition vector, an evaluation is made with the aim of verifying that the image generated under a given condition (average of the values of the features of each type of brain electrical wave) is similar to the image of the correct class. To this end, class probability distributions are computed over the initial network using 50000 image samples previously generated (1250 images per class). The correct classification rate is 63%, which is much higher than 2.5% of random guesses over 40 classes, but this indicates that the generated image is sufficiently realistic to make automatic classification meaningful.

For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.

Referring to fig. 6, there is shown an apparatus for reconstructing a visual image using an electroencephalogram signal according to an embodiment of the present application, including:

the establishing module 610 is used for establishing a corresponding relation between the feature vector of the electroencephalogram signal and the image feature of the image generated according to the electroencephalogram signal by utilizing the self-learning capability of the artificial neural network; wherein the characteristic image features comprise pixel point values and pixel point positions;

an obtaining module 620, configured to obtain a current feature vector of a current electroencephalogram of a subject;

a determining module 630, configured to determine, according to the corresponding relationship, a current image feature corresponding to the current feature vector; specifically, determining a current image feature corresponding to the current feature vector includes: and determining the image characteristics corresponding to the characteristic vector which is the same as the current characteristic vector in the corresponding relation as the current image characteristics.

In one embodiment, the feature vector includes: time characteristic, waveform characteristic, and/or one-dimensional or more comprehensive characteristic composed of the characteristic extracted from the time characteristic and the waveform characteristic according to a set rule; wherein the content of the first and second substances,

the time characteristic comprises: unit time step duration;

and the combination of (a) and (b),

the waveform features include: the number of the acquisition points and the acquisition voltage value of each acquisition point in unit time step;

and/or the presence of a gas in the gas,

the corresponding relation comprises: a functional relationship; the feature vector is an input parameter of the functional relation, and the image feature is an output parameter of the functional relation;

determining a current image feature corresponding to the current feature vector, further comprising:

and when the corresponding relation comprises a functional relation, inputting the current feature vector into the functional relation, and determining the output parameter of the functional relation as the current image feature.

In one embodiment, the establishing module 610 includes:

the characteristic relation establishing submodule is used for acquiring the time characteristic and the characteristic relation between the waveform characteristic and the characteristic vector by utilizing the learning characteristic of the cyclic artificial neural network;

a time characteristic and waveform characteristic obtaining submodule for obtaining a time characteristic and a waveform characteristic for establishing the corresponding relationship;

and the characteristic vector determining submodule is used for determining the characteristic vector corresponding to the time characteristic used for establishing the corresponding relation and the waveform characteristic through the characteristic relation.

In one embodiment, the establishing module 610 includes:

the obtaining submodule is used for obtaining sample data used for establishing a corresponding relation between the feature vector and the image feature;

the analysis submodule is used for analyzing the characteristics and the rules of the characteristic vectors and determining the network structure and the network parameters of the artificial neural network according to the characteristics and the rules;

and the training submodule is used for training and testing the network structure and the network parameters by using the sample data and determining the corresponding relation between the feature vector and the image feature.

In one embodiment, the obtaining sub-module includes:

a collection sub-module for collecting the feature vectors and the image features of different subjects;

the analysis submodule is used for analyzing the feature vector, and selecting data related to the image features as the feature vector by combining pre-stored expert experience information;

and the sample data generation submodule is used for taking the image characteristics and the data pairs formed by the selected characteristic vectors as sample data.

In one embodiment of the present invention, the substrate is,

the training submodule includes:

a training result generation submodule, configured to select a part of the sample data as a training sample, input the feature vector in the training sample to the network structure, and train through a loss function of the network structure, an activation function, and the network parameter to obtain an actual training result;

the training result error judgment submodule is used for determining whether the actual training error between the actual training result and the corresponding image feature in the training sample meets a preset training error or not;

a training completion determination submodule configured to determine that the training of the network structure and the network parameters is completed when the actual training error satisfies the preset training error;

and/or the presence of a gas in the gas,

a test sub-module for testing the network structure and the network parameters, the test sub-module comprising:

a test result generation submodule, configured to select another part of the sample data as a test sample, input the feature vector in the test sample into the trained network structure, and perform a test with the loss function, the activation function, and the trained network parameter to obtain an actual test result;

the test result error judgment submodule is used for determining whether the actual test error between the actual test result and the corresponding image feature in the test sample meets a set test error;

and the test completion judging submodule is used for determining that the test on the network structure and the network parameters is completed when the actual test error meets the set test error.

In one embodiment of the present invention, the substrate is,

the training submodule further comprises:

a network parameter updating submodule, configured to update the network parameter through an error loss function of the network structure when the actual training error does not meet the set training error;

the first retraining submodule is used for retraining the activation function and the updated network parameters through the loss function of the network structure until the actual training error after retraining meets the set training error;

and/or the presence of a gas in the gas,

the test submodule further comprises:

and the second retraining submodule is used for retraining the network structure and the network parameters when the actual test error does not meet the set test error until the retrained actual test error meets the set test error.

Referring to fig. 7, a computer device of a method for reconstructing a visual image using brain electrical signals of the present invention is shown, which may specifically include the following:

the computer device 12 described above is embodied in the form of a general purpose computing device, and the components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.

Bus 18 represents one or more of any of several types of bus 18 structures, including a memory bus 18 or memory controller, a peripheral bus 18, an accelerated graphics port, and a processor or local bus 18 using any of a variety of bus 18 architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus 18, micro-channel architecture (MAC) bus 18, enhanced ISA bus 18, audio Video Electronics Standards Association (VESA) local bus 18, and Peripheral Component Interconnect (PCI) bus 18.

Computer device 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer device 12 and includes both volatile and nonvolatile media, removable and non-removable media.

The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. The computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, the storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (commonly referred to as "hard drives"). Although not shown in FIG. 7, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules 42, with the program modules 42 configured to carry out the functions of embodiments of the invention.

Program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules 42, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.

Computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, camera, etc.), with one or more devices that enable a user to interact with computer device 12, and/or with any devices (e.g., network card, modem, etc.) that enable computer device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Also, computer device 12 may communicate with one or more networks (e.g., a Local Area Network (LAN)), a Wide Area Network (WAN), and/or a public network (e.g., the Internet) via network adapter 20. As shown, the network adapter 20 communicates with the other modules of the computer device 12 via the bus 18. It should be appreciated that although not shown in FIG. 7, other hardware and/or software modules may be used in connection with computer device 12, including but not limited to: microcode, device drives, redundant processing units 16, external disk drive arrays, RAID systems, tape drives, and data backup storage systems 34, and the like.

The processing unit 16 executes various functional applications and data processing by executing programs stored in the system memory 28, for example, implementing a method for reconstructing a visual image using electroencephalogram signals provided by an embodiment of the present invention.

That is, the processing unit 16 implements, when executing the program,: establishing a corresponding relation between a feature vector of the electroencephalogram signal and an image feature of an image generated according to the electroencephalogram signal by utilizing the self-learning capability of the artificial neural network; wherein the feature image features comprise pixel point values, and pixel point positions; acquiring a current feature vector of a current electroencephalogram of a subject; determining the current image characteristic corresponding to the current characteristic vector according to the corresponding relation; specifically, determining a current image feature corresponding to the current feature vector includes: and determining the image features corresponding to the feature vectors which are the same as the current feature vectors in the corresponding relationship as the current image features.

In an embodiment of the present invention, the present invention further provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements a method for reconstructing a visual image using brain electrical signals as provided in all embodiments of the present application:

that is, the program when executed by the processor implements: establishing a corresponding relation between the feature vector of the electroencephalogram signal and the image feature of the image generated according to the electroencephalogram signal by utilizing the self-learning capability of the artificial neural network; wherein the feature image features comprise pixel point values, and pixel point positions; acquiring a current feature vector of a current electroencephalogram of a subject; determining the current image feature corresponding to the current feature vector according to the corresponding relation; specifically, determining a current image feature corresponding to the current feature vector includes: and determining the image characteristics corresponding to the characteristic vector which is the same as the current characteristic vector in the corresponding relation as the current image characteristics.

Any combination of one or more computer-readable media may be employed. The computer readable medium may be a computer-readable storage medium or a computer-readable signal medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPOM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.

While preferred embodiments of the present application have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the true scope of the embodiments of the application.

Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or terminal apparatus that comprises the element.

The method and the device for reconstructing a visual image by using an electroencephalogram signal provided by the present application are described in detail above, and specific examples are applied herein to illustrate the principles and embodiments of the present application, and the description of the above embodiments is only used to help understand the method and the core ideas of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.