CN110989843A - Image virtual presentation circuit, method, system and wearable device - Google Patents

Abstract

The invention discloses a virtual image presentation circuit, a virtual image presentation method, a virtual image presentation system and wearable equipment, and belongs to the technical field of image processing. According to the method, an image processor acquires an image to be processed, current image features are extracted from the image to be processed, then the image processor generates a current electroencephalogram signal corresponding to the current image features through a preset neural network model, the current electroencephalogram information is transmitted to the electroencephalogram sensor, and finally the current electroencephalogram signal is sent out through the electroencephalogram sensor, so that a user receiving the current electroencephalogram signal can virtually present the image to be processed in the brain, the user does not acquire the image to be processed through eyes any more, but acquires the image to be processed through a brain virtual presentation mode without staring at the screen, the user does not need to hold electronic equipment for a long time, hands can be released, and visual fatigue is avoided.

Description

Image virtual presentation circuit, method, system and wearable device

technical field

The present invention relates to the technical field of image processing, and in particular, to a circuit, method, system and wearable device for virtual image presentation.

Background technique

With the development of the Internet and image processing technology, there are more and more videos such as variety shows, movies and TV series. People usually watch videos to decompress and relax after work. in daily life.

When video viewers watch videos, the terminal devices usually used are electronic devices such as smartphones and tablet computers. Usually, video viewers watch videos for a long time. Holding a smartphone or tablet to watch a video, you cannot release your hands, you need to maintain a certain posture, your body will get tired after a long time, and you are also prone to visual fatigue.

SUMMARY OF THE INVENTION

The main purpose of the present invention is to provide an image virtual presentation circuit, method, system and wearable device, which aims to solve the technical problem of physical fatigue and visual fatigue in the prior art when users watch videos.

In order to achieve the above object, the present invention provides an image virtual rendering circuit, the image virtual rendering circuit includes: an image processing module and a brain wave sensor;

the image processor, configured to acquire the image to be processed, and extract the current image feature from the image to be processed;

The image processor is further configured to generate a current EEG signal corresponding to the current image feature through a preset neural network model, and transmit the current EEG signal to the EEG sensor;

The brain wave sensor is used for sending out the current brain signal, so that the user who receives the current brain signal can virtually present the to-be-processed image in the brain.

Optionally, the image processor is also used to obtain image features of several sample images and sample EEG signals corresponding to each sample image, and the initial neural network is determined by the image features of the sample images and the corresponding sample EEG signals. The model is trained to obtain a preset neural network model.

Optionally, the image virtual presentation circuit further includes: a communication module;

The communication module is used for receiving the to-be-processed image transmitted by the terminal device.

Optionally, the image virtual presentation circuit further includes: an audio decoder and a speaker;

The communication module is also used to receive the audio to be processed transmitted by the terminal device;

The audio decoder is used to decode the audio to be processed to obtain a decoding result;

The speaker is used to play the decoding result.

Optionally, the image processor is further configured to acquire the number of pixels of the image to be processed before extracting the current image feature from the image to be processed; when the number of pixels exceeds a preset number, The image to be processed is compressed.

Optionally, the image processor is further configured to compress the to-be-processed image through a K-means algorithm.

In addition, in order to achieve the above object, the present invention also provides a method for virtual image presentation, the method for virtual image presentation is implemented based on a virtual image presentation circuit, and the virtual image presentation circuit includes: an image processing module and a brain wave sensor;

The image virtual presentation method includes the following steps:

The image processor acquires the image to be processed, and extracts the current image feature from the image to be processed;

The image processor generates a current EEG signal corresponding to the current image feature through a preset neural network model, and transmits the current EEG signal to the EEG sensor;

The brain wave sensor sends out the current brain signal, so that the user who receives the current brain signal can virtually present the to-be-processed image in the brain.

In addition, in order to achieve the above object, the present invention also provides a wearable device, the wearable device is a device for setting on the user's head, and the wearable device includes the image virtual presentation circuit.

Optionally, the wearable device is an earphone.

In addition, in order to achieve the above object, the present invention also provides an image virtual presentation system, the image virtual presentation system includes: a terminal device and the wearable device, the terminal device is used to transmit the image to be processed to the Wearable device.

In the present invention, the image to be processed is acquired by the image processor, the current image feature is extracted from the to-be-processed image, and then the image processor generates the current EEG signal corresponding to the current image feature through a preset neural network model, and then uses the preset neural network model to generate the current EEG signal. The current EEG information is transmitted to the EEG sensor, and finally the current EEG signal is sent out through the EEG sensor, so that the user who receives the current EEG signal can virtually present the image to be processed in the brain. The image to be processed is no longer obtained through the eyes, but obtained through the virtual presentation of the brain. There is no need to stare at the screen, so that the user does not need to hold the electronic device for a long time, which can liberate the hands and avoid visual fatigue.

Description of drawings

In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention, and for those of ordinary skill in the art, other drawings can also be obtained according to the structures shown in these drawings without creative efforts.

1 is a functional block diagram of an embodiment of an image virtual presentation circuit of the present invention;

FIG. 2 is a functional block diagram of a second embodiment of an image virtual presentation circuit according to the present invention;

FIG. 3 is a schematic flowchart of an embodiment of an image virtual presentation method according to the present invention;

FIG. 4 is a schematic structural diagram of an embodiment of an image virtual presentation system of the present invention.

Description of reference numbers:

label name label name 100 image processor 200 brain wave sensor 300 Communication module 1 Bluetooth earphone 11 Communication module 12 Communication module 13 brain wave sensor 14 brain wave sensor 15 power module 16 power module 2 Terminal Equipment twenty one wireless communication module twenty two power module

The realization, functional characteristics and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments.

Detailed ways

It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relationship between various components under a certain posture (as shown in the accompanying drawings). The relative positional relationship, the movement situation, etc., if the specific posture changes, the directional indication also changes accordingly.

In addition, the descriptions involving "first", "second", etc. in the present invention are only for descriptive purposes, and should not be understood as indicating or implying their relative importance or implying the number of indicated technical features. Thus, a feature delimited with "first", "second" may expressly or implicitly include at least one of that feature. In addition, the technical solutions between the various embodiments can be combined with each other, but must be based on the realization by those of ordinary skill in the art. When the combination of technical solutions is contradictory or cannot be realized, it should be considered that the combination of such technical solutions does not exists, and it is not within the protection scope of the present invention.

The invention provides an image virtual rendering circuit.

Referring to FIG. 1, FIG. 1 is a functional block diagram of an embodiment of an image virtual rendering circuit of the present invention. In the embodiment of the present invention, the image virtual rendering circuit includes: an image processing module 100 and a brain wave sensor 200; wherein,

The image processor 100 is configured to acquire an image to be processed, and extract current image features from the image to be processed.

It should be noted that there is usually a large amount of information in the image, and the virtual presentation of the image in the user's brain is similar to that of the user forming a recall picture in the brain. The recall picture is generally abstract. That is, in this embodiment, the corresponding current image feature can be extracted from the to-be-processed image.

It is understandable that, the image feature may be information such as the outline and tone of the to-be-processed image, which is not limited in this embodiment.

For example, the to-be-processed image corresponds to a screen of a TV drama at 1 hour and 11 minutes. At this time, the current image feature can be extracted from the to-be-processed image.

In a specific implementation, since the number of pixels of the image to be processed is not fixed when the current image feature is extracted from the image to be processed, there may be a situation where the number of pixels of the image to be processed is very large, However, for this embodiment, the final image is to be virtually presented in the user's brain. This process is similar to the user forming a memory image in the brain. Even if the number of pixels is very high, for the user, the virtual presented image is not It will become clearer with the increase of the number of pixels, but the increase of the number of pixels will prolong the time for extracting the current image features, and also increase the processing load. Therefore, in this embodiment, the image processor 100 is also used for Before extracting the current image feature from the image to be processed, the number of pixels of the image to be processed is obtained; when the number of pixels exceeds a preset number, the image to be processed is compressed.

Assuming that the preset number is 640*480, if the number of pixels of the digital signal is 1600*1200, at this time, the digital signal needs to be compressed.

Specifically, when compressing the digital signal, the compression can be performed by means of mean value compression, that is, taking every N*N in the digital signal as an object, and averaging the color values of the pixels in each object Calculate and use the mean calculation result as the color value of the object, and finally use each object as a pixel. At this time, the digital signal can be compressed N*N times, assuming that N*N is 4*4, the compressed digital signal The number of pixels is 400*300.

Of course, in order to ensure the fidelity of the compressed image, in this embodiment, the image processor 100 is further configured to compress the to-be-processed image by using the K-means algorithm.

The image processor 100 is further configured to generate a current EEG signal corresponding to the current image feature by using a preset neural network model, and transmit the current EEG signal to the EEG sensor.

In the specific implementation, a technology for reproducing images seen by the human eye based on brain waves has recently appeared in the prior art. Therefore, in this embodiment, a preset neural network model can be established in advance, and the preset neural network model can reflect the corresponding relationship between EEG signals and image features. It is assumed that the neural network model generates a current EEG signal corresponding to the current image feature, and transmits the current EEG signal to the EEG sensor 200 .

In order to facilitate the generation of the preset neural network model, in this embodiment, the image processor 100 is further configured to obtain image features of several sample images and sample EEG signals corresponding to each sample image, and obtain the image features of the sample images through the image features of the sample images. The initial neural network model is trained with the corresponding sample EEG signals to obtain a preset neural network model.

It is understandable that, in order to ensure the correspondence between the image features of the sample image and the sample EEG signal, in this embodiment, for the sample EEG signal corresponding to the sample image, it may be generated when viewing the sample image. The EEG signal, that is, the EEG signal can be collected when viewing the sample image, and the collected EEG signal can be used as the sample EEG signal corresponding to the sample image.

Since the EEG signal is usually within a certain frequency range, that is to say, the EEG signal that the human brain can receive has a certain range. Generally speaking, the EEG signal can be divided into four bands, namely δ(1- 3Hz), θ (4-7Hz), α (8-13Hz) and β (14-30Hz). Among them, the delta wave, the frequency is 1-3 times per second, when people are in infancy or immature intellectual development, adults are extremely fatigued and lethargic, this wave band can appear. Theta waves, with a frequency of 4-7 times per second, are extremely pronounced in adults when their will is frustrated and depressed, as well as in mentally ill patients. However, this wave is the main component in the EEG of teenagers (10-17 years old). Alpha wave, the frequency is 8-13 times per second, the average number is about 10 times, it is the basic rhythm of normal human brain waves, if there is no external stimulus, its frequency is quite constant. The rhythm is most obvious when a person is awake, quiet, and eyes closed, and the alpha wave disappears immediately when eyes are opened or when they receive other stimuli. Beta wave, the frequency is 14-30 times per second, this wave appears when mental tension and emotional excitement or excitement, when people wake up from sleep, the original slow wave rhythm can be replaced by this rhythm immediately.

In a specific implementation, since there may be individual differences in the EEG signals between different users, in order to ensure that the generated EEG signals can conform to the user's EEG signals, in this embodiment, the user's EEG signals can be collected when the user watches the sample image. EEG signals, and the collected EEG signals are used as the sample EEG signals corresponding to the sample images, that is, for different users, there are different preset neural network models respectively.

The brain wave sensor 200 is configured to send out the current brain electrical signal, so that the user who receives the current brain electrical signal can virtually present the image to be processed in the brain.

In this embodiment, an image to be processed is acquired by an image processor, and current image features are extracted from the to-be-processed image, and then the image processor generates a current EEG signal corresponding to the current image feature through a preset neural network model, and uses The current EEG information is transmitted to the EEG sensor, and finally the current EEG signal is sent out through the EEG sensor, so that the user who receives the current EEG signal can virtually present the image to be processed in the brain, The user no longer obtains the image to be processed through the eyes, but obtains it through the virtual presentation of the brain. There is no need to stare at the screen, so that the user does not need to hold the electronic device for a long time. While freeing hands, it also avoids visual fatigue.

Further, as shown in FIG. 2 , based on the first embodiment, a second embodiment of the image virtual rendering circuit of the present invention is proposed.

In this embodiment, the image virtual presentation circuit further includes: a communication module 300;

The communication module 300 is used for receiving the to-be-processed image transmitted by the terminal device.

It should be noted that the terminal device is a device that can be used to process video, such as a notebook computer, a personal computer, a tablet computer, or a smart phone, which is not limited in this embodiment.

In a specific implementation, the communication module 300 can be a wireless communication module, that is, it can be wirelessly paired with the terminal device to realize a wireless communication connection, so as to receive the data transmitted by the terminal device through the Bluetooth communication protocol. The to-be-processed image, of course, can also receive the to-be-processed image transmitted by the terminal device through a 3G, 4G, or 5G communication protocol, which is not limited in this embodiment.

Assuming that the image virtual presentation circuit is in the Bluetooth headset, at this time, after the communication module 300 is paired with the terminal device through wireless Bluetooth, the to-be-processed image transmitted by the terminal device can be received through the 5G communication protocol.

Of course, the communication module 300 can also be a wired communication module, that is, it can be a communication interface, through which the to-be-processed image transmitted by the terminal device is received.

Further, in this embodiment, the image virtual presentation circuit further includes: an audio decoder (not shown) and a speaker (not shown).

The communication module 300 is further configured to receive the audio to be processed transmitted by the terminal device.

The audio decoder is configured to decode the to-be-processed audio to obtain a decoding result.

The speaker is used to play the decoding result.

It should be noted that, generally speaking, the virtual presentation of the image to be processed in the brain is a continuous process, but if the time is too long, it will appear very monotonous. In this embodiment, the communication module 300 receives the audio to be processed transmitted by the terminal device, the audio decoder decodes the audio to be processed, and obtains a decoding result, and the speaker decodes the decoding result. to play.

It is also assumed that the virtual image presentation circuit is in a Bluetooth headset. When a movie is being played in the terminal device, the content (image and audio) played in the terminal device can be transmitted to the Bluetooth headset for signal processing.

In addition, to achieve the above purpose, an embodiment of the present invention further provides a method for virtual image presentation. Referring to FIG. 3 , FIG. 3 is a schematic flowchart of the first embodiment of the method for virtual image presentation of the present invention.

In the first embodiment, the image virtual rendering method is implemented based on an image virtual rendering circuit, and the image virtual rendering circuit includes: an image processing module and a brain wave sensor;

The image virtual presentation method includes the following steps:

S10: The image processor acquires the to-be-processed image, and extracts the current image feature from the to-be-processed image.

It should be noted that there is usually a large amount of information in the image, and the virtual presentation of the image in the user's brain is similar to that of the user forming a recall picture in the brain. The recall picture is generally abstract. That is, in this embodiment, the corresponding current image feature can be extracted from the to-be-processed image.

It is understandable that, the image feature may be information such as the outline and tone of the to-be-processed image, which is not limited in this embodiment.

For example, the to-be-processed image corresponds to a screen of a TV drama at 1 hour and 11 minutes. At this time, the current image feature can be extracted from the to-be-processed image.

In a specific implementation, since the number of pixels of the image to be processed is not fixed when the current image feature is extracted from the image to be processed, there may be a situation where the number of pixels of the image to be processed is very large, However, for this embodiment, the final image is to be virtually presented in the user's brain. This process is similar to the user forming a memory image in the brain. Even if the number of pixels is very high, for the user, the virtual presented image is not It will become clearer with the increase of the number of pixels, but the increase of the number of pixels will prolong the time for extracting the current image features, and also increase the processing load. Therefore, in this embodiment, the image processor 100 is also used for Before extracting the current image feature from the image to be processed, the number of pixels of the image to be processed is obtained; when the number of pixels exceeds a preset number, the image to be processed is compressed.

Assuming that the preset number is 640*480, if the number of pixels of the digital signal is 1600*1200, at this time, the digital signal needs to be compressed.

Specifically, when compressing the digital signal, the compression can be performed by means of mean value compression, that is, taking every N*N in the digital signal as an object, and averaging the color values of the pixels in each object Calculate and use the mean calculation result as the color value of the object, and finally use each object as a pixel. At this time, the digital signal can be compressed N*N times, assuming that N*N is 4*4, the compressed digital signal The number of pixels is 400*300.

Of course, in order to ensure the fidelity of the compressed image, in this embodiment, the image processor 100 is further configured to compress the to-be-processed image by using the K-means algorithm.

S20: The image processor generates a current EEG signal corresponding to the current image feature by using a preset neural network model, and transmits the current EEG signal to the EEG sensor.

In the specific implementation, a technology for reproducing images seen by the human eye based on brain waves has recently appeared in the prior art. Therefore, in this embodiment, a preset neural network model can be established in advance, and the preset neural network model can reflect the corresponding relationship between EEG signals and image features. It is assumed that the neural network model generates a current EEG signal corresponding to the current image feature, and transmits the current EEG signal to the EEG sensor 200 .

In order to facilitate the generation of the preset neural network model, in this embodiment, the image processor 100 is further configured to obtain image features of several sample images and sample EEG signals corresponding to each sample image, and obtain the image features of the sample images through the image features of the sample images. The initial neural network model is trained with the corresponding sample EEG signals to obtain a preset neural network model.

It is understandable that, in order to ensure the correspondence between the image features of the sample image and the sample EEG signal, in this embodiment, for the sample EEG signal corresponding to the sample image, it may be generated when viewing the sample image. The EEG signal, that is, the EEG signal can be collected when viewing the sample image, and the collected EEG signal can be used as the sample EEG signal corresponding to the sample image.

Since the EEG signal is usually within a certain frequency range, that is to say, the EEG signal that the human brain can receive has a certain range. Generally speaking, the EEG signal can be divided into four bands, namely δ(1- 3Hz), θ (4-7Hz), α (8-13Hz) and β (14-30Hz). Among them, the delta wave, the frequency is 1-3 times per second, when people are in infancy or immature intellectual development, adults are extremely fatigued and lethargic, this wave band can appear. Theta waves, with a frequency of 4-7 times per second, are extremely prominent in adults when they are frustrated and depressed, and in mentally ill patients. However, this wave is the main component in the EEG of teenagers (10-17 years old). Alpha wave, the frequency is 8-13 times per second, the average number is about 10 times, it is the basic rhythm of normal human brain waves, if there is no external stimulus, its frequency is quite constant. The rhythm is most obvious when a person is awake, quiet, and eyes closed, and the alpha wave disappears immediately when eyes are opened or when they receive other stimuli. Beta wave, the frequency is 14-30 times per second, this wave appears when mental tension and emotional excitement or excitement, when people wake up from sleep, the original slow wave rhythm can be replaced by this rhythm immediately.

In a specific implementation, since there may be individual differences in the EEG signals between different users, in order to ensure that the generated EEG signals can conform to the user's EEG signals, in this embodiment, the user's EEG signals can be collected when the user watches the sample image. EEG signals, and the collected EEG signals are used as the sample EEG signals corresponding to the sample images, that is, for different users, there are different preset neural network models respectively.

S30: The brain wave sensor sends the current brain signal, so that the user who receives the current brain signal can virtually present the to-be-processed image in the brain.

In this embodiment, an image to be processed is acquired by an image processor, and current image features are extracted from the to-be-processed image, and then the image processor generates a current EEG signal corresponding to the current image feature through a preset neural network model, and uses The current EEG information is transmitted to the EEG sensor, and finally the current EEG signal is sent out through the EEG sensor, so that the user who receives the current EEG signal can virtually present the image to be processed in the brain, The user no longer obtains the image to be processed through the eyes, but obtains it through the virtual presentation of the brain. There is no need to stare at the screen, so that the user does not need to hold the electronic device for a long time. While freeing hands, it also avoids visual fatigue.

The method in this embodiment can also be used to implement the functions in the above-mentioned image virtual rendering circuit, which will not be repeated here.

In addition, in order to achieve the above object, an embodiment of the present invention further provides a wearable device, the wearable device is a device for setting on the user's head, and the wearable device includes the above-mentioned virtual image presentation circuit.

It should be noted that, for the wearable device, it only needs to be a device that can be installed on the user's head, such as a Bluetooth headset, an EEG cap, smart glasses and other devices, which are not limited in this embodiment. .

In addition, in order to achieve the above object, an embodiment of the present invention also provides a virtual image presentation system, where the virtual image presentation system includes: a terminal device and the above-mentioned wearable device.

It can be understood that when the wearable device is a Bluetooth headset, because of its advantages of low cost and light weight, it is convenient for users to use. 12 can be built in the bluetooth headset 1, the wireless communication module 21 is built in the terminal device 2, the communication module 11 and the communication module 12 on the bluetooth headset 1 are respectively built in the left bluetooth earphone and the right bluetooth earphone, the communication module 11 And the communication module 12 can perform wireless pairing with the wireless communication module 21 on the terminal device 2 to realize wireless communication connection, so that the Bluetooth headset device 1 can receive the to-be-processed image in the terminal device 2 through the wireless communication connection. The communication module 11, the communication module 12 and the wireless communication module 21 can realize video signal communication based on the current 5G communication protocol. For example, Bluetooth headset 1 is paired with terminal device 2 through wireless Bluetooth. After successful pairing, the content (image and audio) played in terminal device 2 can be transmitted to Bluetooth headset 1 for signal processing through the 5G communication protocol.

After the image to be processed is sent to the Bluetooth headset 1 through the terminal device 2, the image processor in the Bluetooth headset device 1 processes the to-be-processed image to obtain a digital signal, and then passes through the brain wave sensor in the Bluetooth headset 1. 13 and the brain wave sensor 14 convert the digital signal into a frequency signal that the user's brain can accept, and use it in the brain to present the image in the mobile phone. Since the Bluetooth headset 1 is worn close to the human skin, the brain wave sensor 13 and the brain wave sensor 14 can receive the frequency of the brain waves of the human brain, and then the digital signals can be converted into the frequency of the brain waves of the user's brain through the brain wave sensor. range, so that the brain can receive the image played by the terminal device 2.

The power supply part of the Bluetooth headset 1 can be similar to the wireless communication process, and can directly supply power to the Bluetooth headset 1 through the terminal device 2, so that the power of the power module 22 in the terminal device 2 can be supplied to the Bluetooth headset 1, or the terminal device 2. The power supply module 15 and the power supply module 16 of the Bluetooth headset 1 can be directly charged wirelessly. Of course, it is also possible to directly supply power to the Bluetooth headset 1 through the power supply module 15 and the power supply module 16 , which is not limited in this embodiment.

Of course, Bluetooth headset 1 can directly switch between audio and video. In terminal device 2, if only audio is played, there will be no image signal processing in Bluetooth headset 1. At this time, Bluetooth headset 1 plays the function of playing music and can only listen to music. ; If the terminal device 2 is playing a video (such as MV, TV series, movie, etc.), the image processing module of the Bluetooth headset 1 will work, and the image can be virtually presented at this time, and the sound in the video can also be heard. Therefore, the user can Switch freely as needed.

The above are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the description and drawings of the present invention, or directly or indirectly applied in other related technical fields , are similarly included in the scope of patent protection of the present invention.

Claims (10)

1. an image virtual presentation circuit, it is characterized in that, described image virtual presentation circuit comprises: image processing module and brain wave sensor; the image processor, configured to acquire the image to be processed, and extract the current image feature from the image to be processed; The image processor is further configured to generate a current EEG signal corresponding to the current image feature through a preset neural network model, and transmit the current EEG information to the EEG sensor; The brain wave sensor is used for sending out the current brain signal, so that the user who receives the current brain signal can virtually present the to-be-processed image in the brain. 2 . The image virtual presentation circuit according to claim 1 , wherein the image processor is further configured to acquire image features of several sample images and sample EEG signals corresponding to each sample image, and the sample images The initial neural network model is trained by the image features and corresponding sample EEG signals to obtain a preset neural network model. 3. The image virtual presentation circuit according to claim 1, wherein the image virtual presentation circuit further comprises: a communication module; The communication module is used for receiving the to-be-processed image transmitted by the terminal device. 4. The image virtual rendering circuit according to claim 3, wherein the image virtual rendering circuit further comprises: an audio decoder and a speaker; The communication module is also used to receive the audio to be processed transmitted by the terminal device; The audio decoder is used to decode the audio to be processed to obtain a decoding result; The speaker is used to play the decoding result. 5 . The virtual image presentation circuit according to claim 1 , wherein the image processor is further configured to obtain the to-be-processed image before extracting the current image feature from the to-be-processed image. 6 . The number of pixels of the image; when the number of pixels exceeds the preset number, the to-be-processed image is compressed. 6 . The image virtual rendering circuit according to claim 5 , wherein the image processor is further configured to compress the to-be-processed image through a K-means algorithm. 7 . 7. An image virtual presentation method, characterized in that the image virtual presentation method is implemented based on an image virtual presentation circuit, the image virtual presentation circuit comprising: an image processing module and a brain wave sensor; The image virtual presentation method includes the following steps: The image processor acquires the image to be processed, and extracts the current image feature from the image to be processed; The image processor generates a current EEG signal corresponding to the current image feature through a preset neural network model, and transmits the current EEG signal to the EEG sensor; The brain wave sensor sends out the current brain signal, so that the user who receives the current brain signal can virtually present the to-be-processed image in the brain. 8 . A wearable device, characterized in that, the wearable device is a device for setting on a user's head, and the wearable device comprises the image virtual presentation circuit according to any one of claims 1 to 6 . 9. The wearable device of claim 8, wherein the wearable device is an earphone. 10 . An image virtual presentation system, characterized in that the image virtual presentation system comprises: a terminal device and the wearable device according to any one of claims 8 to 9 , the terminal device is used to display the image to be processed. 11 . to the wearable device.

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