Emotion synchronous monitoring device and method based on ECOG electroencephalogram signals

Abstract

The invention relates to an emotion synchronous monitoring method and device based on ECOG electroencephalogram signals, electronic equipment and a storage medium. The method comprises the following steps: acquiring an electroencephalogram signal of a user, and sending the electroencephalogram signal to a signal processing module; identifying the electroencephalogram characteristic mode based on the electroencephalogram signal, and judging the emotion type and emotion intensity of the user based on an identification result; when a start control signal sent by the signal processing module is received, shooting and recording are started, and recorded content is sent to the storage sending module in real time; data sent by the signal processing module and the wearable camera are marked and stored according to time, and when it is detected that the device is in a wireless network environment, the stored content is automatically sent to the cloud for storage. The invention provides an effective solution for the problem that the reason causing the emotion cannot be accurately positioned, the reason causing the emotion can be accurately known, and the emotion time point can be accurately recognized.

Description

Background Art

[0002] Depression is a serious and complex mental disorder. For some patients with severe depression who do not respond well to conventional treatments, craniotomy may be a treatment option. During treatment and rehabilitation, the patient's emotional state plays a crucial role in recovery outcomes. However, current methods for monitoring the emotional state of these patients are significantly inadequate.

[0003] Traditional emotion monitoring methods mainly rely on the subjective expression of patients and the observation of medical staff, making it difficult to achieve real-time, continuous and accurate grasp of patients' emotions. For example, patients may not be able to accurately express their emotions due to various reasons (such as the impact of the disease, psychological pressure, etc.), and the observation of medical staff is subjective and limited, and it is impossible to fully understand the details and underlying reasons of patients' emotional changes. This is not only not conducive to a comprehensive assessment of the patient's condition, but also hinders the timely adjustment of treatment and nursing strategies. Inaccurate monitoring may lead to misjudgment of patients' emotional changes, thereby affecting the treatment effect and recovery process. Therefore, there is an urgent need for a more accurate and effective technology that can synchronously monitor the emotions of patients undergoing craniotomy for depression, assist medical staff in better understanding changes in patients' emotional state, thereby improving treatment effects and promoting patient recovery.

[0004] Existing technical solutions have the following defects: they are unable to accurately determine the causes of mood changes in patients undergoing craniotomy for depression, and provide medical staff with a basis for targeted intervention; they are unable to accurately identify the time points of mood changes, and accurately identify the time points and mood types of patients' mood changes, laying the foundation for in-depth analysis of the patterns of changes in patients' emotional states.

[0005] Therefore, one or more methods are needed to solve the above problems.

[0006] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the Invention

[0007] The purpose of the present disclosure is to provide a method, device, electronic device and computer-readable storage medium for synchronous monitoring of emotions based on ECOG electroencephalogram (ECOG) signals, thereby overcoming one or more problems caused by the limitations and defects of related technologies to at least a certain extent.

[0008] According to one aspect of the present disclosure, a method for synchronously monitoring emotions based on ECOG electroencephalogram (ECOG) signals is provided, comprising:

[0009] An EEG acquisition step, collecting the user's EEG signals and sending the EEG signals to a signal processing module;

[0010] A signal processing step, which identifies an EEG feature pattern based on an EEG signal, and determines the user's emotion type and emotion intensity based on the identification result;

[0011] A detection control step. The wearable camera is in the standby mode when it is powered on and when it receives a stop control signal sent by the signal processing module, without taking pictures or recording. When it receives a start control signal sent by the signal processing module, it starts taking pictures and recording, and sends the recorded content to the storage and sending module in real time;

[0012] A storage step, which marks and stores the data sent by the signal processing module and the wearable camera according to time. When it detects that it is in a wireless network environment, it automatically sends the stored content to cloud storage.

[0013] In an exemplary embodiment of the present disclosure, the EEG data acquisition step of the method further includes:

[0014] The EEG acquisition module respectively acquires the first EEG signal of the frequency electrode and the second EEG signal of the reference;

[0015] Based on the first EEG signal and the second EEG signal, calculate the EEG differential signal.

[0016] In an exemplary embodiment of the present disclosure, the method further includes:

[0017] Determine abnormal data for the EEG differential signal based on a first preset threshold and a second preset threshold;

[0018] If the EEG differential signal is greater than the first preset threshold or less than the second preset threshold, it is determined that the EEG differential signal is abnormal data, and the EEG differential signal is excluded;

[0019] If the EEG differential signal is less than the first preset threshold and greater than the second preset threshold, it is determined that the EEG differential signal is valid data, and the EEG differential signal is stored.

[0020] In an exemplary embodiment of the present disclosure, the method further includes:

[0021] When the storage quantity of the EEG differential signals is greater than a preset quantity, notch processing is performed on the preset quantity of the EEG differential signals;

[0022] Update the EEG differential signal and perform notch processing on the updated preset quantity of the EEG differential signals;

[0023] Perform abnormal data determination on the EEG differential signals subjected to notch processing. If the EEG differential signals subjected to notch processing are abnormal data for a continuous preset duration, an un-worn signal is generated and sent to the signal processing module.

[0024] In an exemplary embodiment of the present disclosure, the signal processing step of the method further includes:

[0025] When the EEG signal sent by the EEG acquisition module is valid data, perform frequency domain analysis on the EEG signal and identify β waves and α waves;

[0026] Calculate the concentration value Z = β * K / α + U, where K and U are correction values;

[0027] Send the EEG signal and the concentration value to the storage and sending module for storage.

[0028] In an exemplary embodiment of the present disclosure, the method further includes:

[0029] When the concentration value is lower than the preset emotion threshold, the signal processing module sends a start control signal to the wearable camera;

[0030] When the concentration value is higher than the preset emotion threshold, the signal processing module sends a stop control signal to the wearable camera.

[0031] In an exemplary embodiment of the present disclosure, the method further includes:

[0032] The user realizes the reading of the scene and sound matching the time tag by searching for the time tag in the cloud or the memory of the storage and sending module.

[0033] In one aspect of the present disclosure, there is provided an emotion synchronization monitoring device based on ECOG EEG signals, including:

[0034] An EEG acquisition module, configured to acquire the EEG signal of the user and send the EEG signal to the signal processing module;

[0035] A signal processing module, configured to identify the EEG feature pattern based on the EEG signal, and judge the emotion type and emotion intensity of the user based on the identification result;

[0036] A wearable camera, configured to be in the standby mode when powered on and when receiving the stop control signal sent by the signal processing module, without shooting or recording, and start shooting and recording when receiving the start control signal sent by the signal processing module, and send the recorded content to the storage and sending module in real time;

[0037] A storage module, configured to mark and store the data sent by the signal processing module and the wearable camera according to time, and automatically send the stored content to the cloud storage when detecting that it is in a wireless network environment.

[0038] In one aspect of the present disclosure, there is provided an electronic device, including:

[0039] a processor; and

[0040] a memory storing computer-readable instructions, which, when executed by the processor, implement the method according to any one of the above.

[0041] In one aspect of the present disclosure, there is provided a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method according to any one of the above.

[0042] An emotion synchronization monitoring method based on ECOG brain electrical signals in an exemplary embodiment of the present disclosure, wherein the method includes: an electroencephalogram acquisition step of acquiring the electroencephalogram signals of a user and sending the electroencephalogram signals to a signal processing module; a signal processing step of identifying an electroencephalogram feature pattern based on the electroencephalogram signals and judging the emotion type and emotion intensity of the user based on the identification result; a detection control step that a wearable camera is in a standby mode when it is powered on and when receiving a stop control signal sent by the signal processing module, without taking pictures or recording, and when receiving a start control signal sent by the signal processing module, starts taking pictures and recording and sends the recorded content to a storage and sending module in real time; a storage step of storing the data sent by the signal processing module and the wearable camera marked by time, and automatically sending the stored content to cloud storage when detecting that it is in a wireless network environment. The present disclosure proposes an effective solution to the problem that the cause of emotion cannot be accurately located, and can accurately know the cause of emotion and accurately identify the emotion time point.

[0043] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] By referring to the drawings to describe its exemplary embodiments in detail, the above and other features and advantages of the present disclosure will become more obvious.

[0045] Figure 1 shows a flowchart of an emotion synchronization monitoring method based on ECOG brain electrical signals according to an exemplary embodiment of the present disclosure;

[0046] Figure 2 shows a system structure diagram of an emotion synchronization monitoring method based on ECOG brain electrical signals according to an exemplary embodiment of the present disclosure;

[0047] Figure 3 shows a functional block diagram of an emotion synchronization monitoring device based on ECOG brain electrical signals according to an exemplary embodiment of the present disclosure;

[0048] Figure 4 Schematic block diagram of an emotion synchronization monitoring device based on ECOG brain electrical signals according to an exemplary embodiment of the present disclosure is shown;

[0049] Figure 5 Block diagram of an electronic device according to an exemplary embodiment of the present disclosure is schematically shown;

[0050] Figure 6 Schematic diagram of a computer-readable storage medium according to an exemplary embodiment of the present disclosure is schematically shown. Detailed implementation manners

[0051] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. Like reference numerals in the figures denote like or similar parts, and thus their repetitive description will be omitted.

[0052] In addition, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present disclosure. However, those skilled in the art will realize that the technical solutions of the present disclosure can be practiced without one or more of the specific details, or other methods, components, materials, devices, steps, etc. can be adopted. In other cases, well-known structures, methods, devices, implementations, materials, or operations are not shown or described in detail to avoid obscuring aspects of the present disclosure.

[0053] The block diagrams shown in the drawings are only functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or in one or more software-hardened modules, or in different networks and / or processor devices and / or microcontroller devices.

[0054] In the present exemplary embodiment, first, an emotion synchronization monitoring method based on ECOG brain electrical signals is provided; as shown in Figure 1 , the emotion synchronization monitoring method based on ECOG brain electrical signals may include the following steps:

[0055] EEG acquisition step S110, acquiring the brain electrical signals of the user and sending the brain electrical signals to the signal processing module;

[0056] Signal processing step S120, identifying the brain electrical feature pattern based on the brain electrical signals, and judging the emotion type and emotion intensity of the user based on the identification result;

[0057] The detection and control step S130: The wearable camera is in the standby mode when it is powered on and when it receives the stop control signal sent by the signal processing module. It does not perform video recording or audio recording. When it receives the start control signal sent by the signal processing module, it starts video recording and audio recording, and sends the recorded content to the storage and sending module in real time.

[0058] The storage step S140: Mark and store the data sent by the signal processing module and the wearable camera according to time. When it detects that it is in a wireless network environment, it automatically sends the stored content to cloud storage.

[0059] An emotion synchronization monitoring method based on ECOG brain electrical signals in an exemplary embodiment of the present disclosure. The method includes: an EEG acquisition step of acquiring the user's EEG signals and sending the EEG signals to the signal processing module; a signal processing step of identifying the EEG feature pattern based on the EEG signals and judging the user's emotion type and emotion intensity based on the identification result; a detection and control step: The wearable camera is in the standby mode when it is powered on and when it receives the stop control signal sent by the signal processing module. It does not perform video recording or audio recording. When it receives the start control signal sent by the signal processing module, it starts video recording and audio recording, and sends the recorded content to the storage and sending module in real time; a storage step of marking and storing the data sent by the signal processing module and the wearable camera according to time. When it detects that it is in a wireless network environment, it automatically sends the stored content to cloud storage. The present disclosure proposes an effective solution to the problem that the cause of emotion cannot be accurately located, can accurately know the cause of emotion, and accurately identify the emotion time point.

[0060] Next, a method for emotion synchronization monitoring based on ECOG brain electrical signals in this exemplary embodiment will be further described.

[0061] Embodiment 1:

[0062] In the EEG acquisition step S110, the user's EEG signals can be acquired and sent to the signal processing module.

[0063] In the embodiment of this example, the EEG data acquisition step of the method further includes:

[0064] The EEG acquisition module respectively acquires the first EEG signal of the frequency electrode and the second EEG signal of the reference;

[0065] Based on the first EEG signal and the second EEG signal, calculate the EEG differential signal.

[0066] In the embodiment of this example, the method further includes:

[0067] Determine abnormal data for the EEG differential signal based on a first preset threshold and a second preset threshold;

[0068] If the EEG differential signal is greater than the first preset threshold or less than the second preset threshold, determine that the EEG differential signal is abnormal data and exclude the EEG differential signal;

[0069] If the EEG differential signal is less than the first preset threshold and greater than the second preset threshold, determine that the EEG differential signal is valid data and store the EEG differential signal.

[0070] In the embodiment of this example, the method further includes:

[0071] When the storage quantity of the EEG differential signal is greater than a preset quantity, perform notch filtering on the preset quantity of the EEG differential signals;

[0072] Update the EEG differential signal and perform notch filtering on the updated preset quantity of the EEG differential signals;

[0073] Perform abnormal data determination on the EEG differential signal after notch filtering. If the EEG differential signal after notch filtering is abnormal data for a continuous preset duration, generate an un-worn signal and send it to the signal processing module.

[0074] In signal processing step S120, the EEG feature pattern can be identified based on the EEG signal, and the user's emotion type and emotion intensity can be judged based on the identification result.

[0075] In the embodiment of this example, the signal processing step of the method further includes:

[0076] When the EEG signal sent by the EEG acquisition module is valid data, perform frequency domain analysis on the EEG signal and identify the β wave and the α wave;

[0077] Calculate the concentration value Z = β * K / α + U, where K and U are correction values;

[0078] Send the EEG signal and the concentration value to the storage and sending module for storage.

[0079] In detection control step S130, the wearable camera is in the standby mode when it is powered on and when it receives the stop control signal sent by the signal processing module, without taking pictures or recording. When it receives the start control signal sent by the signal processing module, it starts taking pictures and recording, and sends the recorded content to the storage and sending module in real time.

[0080] In the embodiment of this example, the method further includes:

[0081] When the concentration value is lower than the preset emotion threshold, the signal processing module sends a start control signal to the wearable camera;

[0082] When the concentration value is higher than the preset emotion threshold, the signal processing module sends a stop control signal to the wearable camera.

[0083] In the storage step S140, the data sent by the signal processing module and the wearable camera can be marked and stored according to time. When it is detected that the device is in a wireless network environment, the stored content is automatically sent to the cloud storage.

[0084] In the embodiment of this example, the method further includes:

[0085] The user reads the scene and sound matching the time tag by searching for the time tag in the cloud or the memory of the storage sending module.

[0086] Embodiment 2:

[0087] In the embodiment of this example, as Figure 2 shown, the emotion event synchronization monitoring method based on electroencephalogram signals of the present disclosure includes the following steps:

[0088] (1) The electroencephalogram acquisition module acquires the electroencephalogram signal V1 of the frequency electrode, and the electroencephalogram acquisition module acquires the electroencephalogram signal V2 of the reference, and obtains the differential signal V3 = V2 - V1. If the amplitude of V3 is greater than 30000 or less than -10000, it is determined as abnormal data and excluded. If the amplitude of V3 is less than 30000 and greater than -10000, V3 is stored in the stored data A1. When the number of data in A1 reaches 1024, the 1024 data are subjected to 50HZ notch filtering, and the processed 1024 data are sent to the signal processing module. Subsequently, for each newly incoming V3, the 0th data in A1 is deleted, the array sequence numbers of the other 1023 data are decreased by one in turn, and the newly incoming V3 is used as the 1024th data of A1, and the newly formed data is re-subjected to 50HZ notch filtering. When the number of newly incoming V3 reaches 1024, the 1024 data after notch filtering are sent to the signal processing module again. If the value of V3 is continuously abnormal data for more than 4 seconds, a non-worn signal is sent to the signal processing module;

[0089] (2) When the signal processing module receives the unactivated signal sent by the EEG acquisition module, it enters the standby mode. When it receives the valid EEG signal sent by the EEG acquisition module, it performs frequency-domain analysis on the received signal, identifies the beta wave and alpha wave, and calculates the concentration value Z = β * K / α + U, where K and U are correction values. The signal processing module sends the valid EEG signal and the concentration value Z to the storage and transmission module for storage in real time. When Z is lower than the emotion threshold F, the signal processing module sends a start control signal to the wearable camera. When Z is higher than the emotion threshold F, the signal processing module sends a stop control signal to the wearable camera;

[0090] (3) The wearable camera is in the standby mode both when it is powered on and when it receives the stop control signal sent by the signal processing module, without taking pictures or recording. When it receives the start control signal sent by the signal processing module, it starts taking pictures and recording, and sends the recorded content to the storage and transmission module in real time;

[0091] (4) The storage and transmission module marks and stores the data sent by the signal processing module and the wearable camera according to time. When it detects that it is in a wifi environment, it automatically sends the stored content to the cloud storage;

[0092] (5) When the user needs to know the change of emotion and the content corresponding to the emotion, they only need to find the corresponding time tag in the cloud or the memory of the storage and transmission module to read the scene and sound at that time.

[0093] It should be noted that although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that these steps must be executed in this specific order, or that all the steps shown must be executed to achieve the desired result. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be decomposed into multiple steps for execution, etc.

[0094] In addition, in the present exemplary embodiment, an emotion synchronization monitoring device based on ECOG EEG signals is also provided. Referring to Figure 4 As shown, the emotion synchronization monitoring device 400 based on ECOG EEG signals may include: an EEG acquisition module 410, a signal processing module 420, a wearable camera 430, and a storage module 440. Among them:

[0095] The EEG acquisition module 410 is used to acquire the EEG signals of the user and send the EEG signals to the signal processing module;

[0096] The signal processing module 420 is used to identify the EEG feature pattern based on the EEG signal and judge the emotion type and emotion intensity of the user based on the identification result;

[0097] The wearable camera 430 is in the standby mode both when it is powered on and when it receives a stop control signal sent by the signal processing module, without performing video recording or audio recording. When it receives a start control signal sent by the signal processing module, it starts video recording and audio recording, and transmits the recorded content to the storage and sending module in real time;

[0098] The storage module 440 is used to mark and store the data sent by the signal processing module and the wearable camera according to time. When it detects that it is in a wireless network environment, it automatically sends the stored content to cloud storage.

[0099] As Figure 3 shown, in the embodiment of this example, the electroencephalogram (EEG) acquisition module 410 is surgically implanted on the surface of the patient's cerebral cortex and connected to the signal processing module 420, and is used to acquire EEG signals; the signal processing module 420 is connected to the wearable camera 430 to control its video recording function; the storage module 440 is respectively connected to the signal processing module 420 and the wearable camera 430, and is used to store and send data.

[0100] The described EEG acquisition module 410, as a key component directly contacting the surface of the cerebral cortex, the ECoG electrode can acquire EEG signals with high resolution and high sensitivity. The acquired signals are transmitted to the signal processing module 420 after preliminary processing. Its advantage is that compared with traditional electrodes, it can obtain more accurate and richer EEG information related to emotions, providing strong support for accurately judging the patient's emotional condition;

[0101] The described signal processing module 420 receives the EEG data transmitted by the EEG acquisition module 410 in real time, and analyzes it using advanced signal processing algorithms and emotion recognition models. This model is trained based on a large amount of EEG data and emotion state annotations of patients with depression, and can accurately identify the EEG feature patterns corresponding to different emotion states (such as changes in EEG signals in specific frequency bands, changes in EEG rhythms, etc.), and judge the patient's current emotion type (such as depression, anxiety, calm, etc.) and emotion intensity. When it detects an obvious change in emotion, it sends a video recording signal to the wearable camera 430; if it is in the reminder mode, it can also send an alarm prompt sound so that medical staff can pay attention to the patient's condition in time;

[0102] The described wearable camera 430 has the functions of real-time video recording and audio recording. When it receives a start control signal sent by the signal processing module 420 at the input end, it starts real-time video recording and audio recording, and stops video recording and audio recording until it receives a stop control signal sent by the signal processing module 420, and transmits the recorded content to the storage module 440;

[0103] The storage module 440 stores the captured data sent by the wearable camera 430. Meanwhile, it also stores in real time the EEG data and the recognition results sent by the signal processing module 420, and sends the data to the cloud.

[0104] It should be noted that although several modules or units of an emotion synchronization monitoring device 400 based on ECOG EEG signals are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0105] In addition, in the exemplary embodiments of the present disclosure, an electronic device capable of implementing the above method is also provided.

[0106] Those skilled in the art can understand that various aspects of the present invention can be implemented as a system, a method, or a program product. Therefore, various aspects of the present invention can be specifically implemented in the following forms, namely: a complete hardware embodiment, a complete software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, which can be collectively referred to as "circuit", "module", or "system" here.

[0107] The following refers to Figure 5 to describe the electronic device 500 according to this embodiment of the present invention. Figure 5 The illustrated electronic device 500 is merely an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention.

[0108] As Figure 5 shown, the electronic device 500 is presented in the form of a general-purpose computing device. The components of the electronic device 500 may include, but are not limited to: at least one of the above-mentioned processing units 510, at least one of the above-mentioned storage units 520, a bus 530 connecting different system components (including the storage unit 520 and the processing unit 510), and a display unit 540.

[0109] Among them, the storage unit stores program code, and the program code can be executed by the processing unit 510, so that the processing unit 510 executes the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of this specification. For example, the processing unit 510 can execute steps S110 to S140 as Figure 1 shown.

[0110] The storage unit 520 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 5201 and / or a cache storage unit 5202, and may further include a read-only storage unit (ROM) 5203.

[0111] The storage unit 520 may also include a program / utilities 5204 having a set (at least one) of program modules 5205. Such program modules 5205 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment.

[0112] The bus 530 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus structures.

[0113] The electronic device 500 may also communicate with one or more external devices 570 (such as a keyboard, a pointing device, a Bluetooth device, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 500, and / or may communicate with any device that enables the electronic device 500 to communicate with one or more other computing devices (such as a router, a modem, etc.). Such communication may be through an input / output (I / O) interface 550. Also, the electronic device 500 may communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 560. As shown in the figure, the network adapter 560 communicates with other modules of the electronic device 500 through the bus 530. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0114] Through the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software, or can be implemented by the way of software combined with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0115] In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium, on which a program product capable of implementing the above-mentioned method of this specification is stored. In some possible embodiments, various aspects of the present invention can also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of this specification.

[0116] Reference Figure 6 As shown, a program product 600 for implementing the above method according to an embodiment of the present invention is described. It can be a portable compact disc read-only memory (CD-ROM) and includes program code, and can run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited to this. In this document, a readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0117] The program product can adopt any combination of one or more readable media. The readable media can be a readable signal medium or a readable storage medium. A readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM 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 above.

[0118] A computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries the readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable signal medium can also be any readable medium other than the readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0119] The program code contained on the readable medium can be transmitted by any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0120] The program code for performing the operations of the present invention can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or, it can be connected to an external computing device (e.g., by using an Internet service provider to connect through the Internet).

[0121] In addition, the above-mentioned drawings are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present invention, rather than for limiting purposes. It is easy to understand that the processes shown in the above-mentioned drawings do not indicate or limit the chronological order of these processes. Additionally, it is also easy to understand that these processes can be executed, for example, synchronously or asynchronously in multiple modules.

[0122] Those skilled in the art will readily think of other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include well-known common general knowledge or conventional technical means in the technical field not disclosed in the present disclosure. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of the present disclosure are pointed out by the claims.

[0123] It should be understood that the present disclosure is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims

1. An emotion synchronization monitoring method based on ECoG brain electrical signals, characterized in that, The method includes: An EEG acquisition step of acquiring the EEG signal of the user and sending the EEG signal to the signal processing module; A signal processing step of identifying the EEG feature pattern based on the EEG signal and judging the emotion type and emotion intensity of the user based on the identification result; A detection control step that the wearable camera is in the standby mode when it is powered on and when it receives the stop control signal sent by the signal processing module, without shooting or recording. When it receives the start control signal sent by the signal processing module, it starts shooting and recording, and sends the recorded content to the storage and sending module in real time; A storage step of storing the data sent by the signal processing module and the wearable camera according to time tags, and automatically sending the stored content to the cloud storage when it detects that it is in a wireless network environment.

2. The method according to claim 1, wherein The EEG data acquisition step of the method further includes: The EEG acquisition module respectively acquires the first EEG signal of the frequency electrode and the second EEG signal of the reference; 3. The method according to claim 3, wherein Calculating the EEG differential signal based on the first EEG signal and the second EEG signal. The method further includes: Judging abnormal data for the EEG differential signal based on a first preset threshold and a second preset threshold; If the EEG differential signal is greater than the first preset threshold or less than the second preset threshold, it is determined that the EEG differential signal is abnormal data, and the EEG differential signal is excluded; 4. The method according to claim 3, wherein If the EEG differential signal is less than the first preset threshold and greater than the second preset threshold, it is determined that the EEG differential signal is valid data, and the EEG differential signal is stored. The method further includes: When the storage quantity of the EEG differential signal is greater than the preset quantity, notch processing is performed on the preset quantity of the EEG differential signals; Updating the EEG differential signal and performing notch processing on the updated preset quantity of the EEG differential signals; 5. The method according to claim 3, wherein Judging abnormal data for the EEG differential signal subjected to notch processing. If the EEG differential signal subjected to notch processing is abnormal data for a continuous preset duration, an un-worn signal is generated and sent to the signal processing module. The signal processing step of the method further includes: When the EEG signal sent by the EEG acquisition module is valid data, performing frequency domain analysis on the EEG signal and identifying the β wave and the α wave; Calculating the concentration value Z = β * K / α + U, where K and U are correction values; 6. The method according to claim 5, wherein Sending the EEG signal and the concentration value to the storage and sending module for storage. The method further includes: When the concentration value is lower than the preset emotion threshold, the signal processing module sends a start control signal to the wearable camera; 7. The method according to claim 1, characterized in that, When the concentration value is higher than the preset emotion threshold, the signal processing module sends a stop control signal to the wearable camera. The method further includes:

8. An emotion synchronization monitoring device based on ECoG brain electrical signals, characterized in that, The user reads the scene and sound matching the time tag in the cloud or in the memory of the storage and sending module by searching for the time tag. The device includes: An EEG acquisition module for acquiring the EEG signal of the user and sending the EEG signal to the signal processing module; A signal processing module for identifying the EEG feature pattern based on the EEG signal and judging the emotion type and emotion intensity of the user based on the identification result; A wearable camera, which is in the standby mode both when it is powered on and when it receives a stop control signal sent by the signal processing module, without taking pictures or recording. When it receives a start control signal sent by the signal processing module, it starts taking pictures and recording, and sends the recorded content to the storage and sending module in real time; A storage module, which is used to mark and store the data sent by the signal processing module and the wearable camera according to time, and automatically sends the stored content to the cloud storage when it detects that it is in a wireless network environment.

9. An electronic device, characterized in that, Comprising A processor; and A memory, on which computer-readable instructions are stored, and when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that, A computer program is stored thereon, and when the computer program is executed by the processor, the method according to any one of claims 1 to 7 is implemented.

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