CN112686158A - Emotion recognition system and method based on electroencephalogram signals and storage medium - Google Patents
Emotion recognition system and method based on electroencephalogram signals and storage medium Download PDFInfo
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Abstract
The invention discloses an emotion recognition system and method based on electroencephalogram signals and a storage medium, relates to the technical field of computers, and aims to solve the problems of effectively collecting the electroencephalogram signals and using the collected electroencephalogram signals in the research of electroencephalogram control technology. The emotion recognition system based on electroencephalogram signals comprises: the electroencephalogram acquisition equipment and the processing equipment; the electroencephalogram acquisition equipment is head-worn acquisition equipment; the head-wearing type acquisition equipment comprises a head wearing layer, a plurality of electroencephalogram electrodes and light-emitting components, wherein the electroencephalogram electrodes and the light-emitting components are respectively arranged on the head wearing layer, and are used for displaying different colors; the processing equipment is used for processing the electroencephalogram signals sent by the electroencephalogram acquisition equipment by utilizing an emotion classification model, determining emotion types corresponding to the electroencephalogram signals, and controlling the light-emitting colors of the light-emitting components according to the emotion types corresponding to the electroencephalogram signals.
Description
Technical Field
The invention relates to the technical field of computers, in particular to an emotion recognition system and method based on electroencephalogram signals and a storage medium.
Background
In the related technology, the electroencephalogram acquisition technology is to directly contact an electrode with the scalp, and a colloidal conductive medium is coated on the contact part of the electrode and the scalp, so as to acquire an electroencephalogram signal. However, the above scheme for acquiring the electroencephalogram signals can cause discomfort to the object to be measured; and the acquired electroencephalogram signals are not further researched.
Disclosure of Invention
The invention aims to provide an emotion recognition system, method and storage medium based on electroencephalogram signals, and aims to solve the problems of effectively acquiring the electroencephalogram signals and applying the acquired electroencephalogram signals to the research of electroencephalogram control technology.
In a first aspect, the present invention provides an emotion recognition system based on electroencephalogram signals, including: the electroencephalogram acquisition equipment and the processing equipment; the electroencephalogram acquisition equipment is head-worn acquisition equipment;
the head-wearing type acquisition equipment comprises a head wearing layer, a plurality of electroencephalogram electrodes and light-emitting components, wherein the electroencephalogram electrodes and the light-emitting components are respectively arranged on the head wearing layer, and are used for displaying different colors;
the processing equipment is used for processing the electroencephalogram signals sent by the electroencephalogram acquisition equipment by utilizing an emotion classification model, determining emotion types corresponding to the electroencephalogram signals, and controlling the light-emitting colors of the light-emitting components according to the emotion types corresponding to the electroencephalogram signals.
Compared with the prior art, the emotion recognition system based on the electroencephalogram signals comprises: the electroencephalogram acquisition equipment and the processing equipment; the electroencephalogram acquisition equipment is head-worn acquisition equipment; the head-wearing type acquisition equipment comprises a head wearing layer, a plurality of electroencephalogram electrodes and light-emitting components, wherein the electroencephalogram electrodes and the light-emitting components are respectively arranged on the head wearing layer, and the light-emitting components are used for displaying different colors; therefore, the head-wearing acquisition equipment is worn on the head of the object to be tested, so that the comfort of the object to be tested is improved, the user experience is improved, and the electroencephalogram signal is more effectively extracted; the processing equipment is used for processing the electroencephalogram signals sent by the electroencephalogram acquisition equipment by using the emotion classification model, determining emotion categories corresponding to the electroencephalogram signals, and controlling the light-emitting colors of the light-emitting components according to the emotion categories corresponding to the electroencephalogram signals; therefore, the acquired electroencephalogram signals can be used for demonstration, research, interesting performance and the like of the electroencephalogram control technology.
In a second aspect, the invention further provides an emotion recognition method based on the electroencephalogram signal, which is applied to an emotion recognition system with electroencephalogram acquisition equipment, wherein the electroencephalogram acquisition equipment is head-worn acquisition equipment; the head-wearing type acquisition equipment comprises a head wearing layer, a plurality of electroencephalogram electrodes and light-emitting components, wherein the electroencephalogram electrodes and the light-emitting components are respectively arranged on the head wearing layer, and the light-emitting components are used for displaying different colors; the method comprises the following steps:
receiving an electroencephalogram signal of a tested object sent by an electroencephalogram acquisition device;
determining emotion types corresponding to the electroencephalogram signals according to the electroencephalogram signals and the emotion classification models;
and controlling the light-emitting component to display the corresponding color of the emotion category according to the emotion category.
In a third aspect, the present invention further provides a storage medium, where instructions are stored, and when the instructions are executed, the method for recognizing emotion based on electroencephalogram signals is implemented.
Compared with the prior art, the emotion recognition method and the storage medium based on the electroencephalogram signals have the same beneficial effects as the emotion recognition system based on the electroencephalogram signals in the technical scheme, and are not repeated herein.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic structural diagram of an emotion recognition system based on electroencephalogram signals according to an embodiment of the present invention;
FIG. 2 is a schematic view of a structure of an artificial wig according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating the effect of the emotion recognition system based on electroencephalogram signals according to the embodiment of the present invention;
FIG. 4 is a schematic flow chart of an emotion recognition method based on electroencephalogram signals according to an embodiment of the present invention;
FIG. 5 is a schematic diagram illustrating an effect of the emotion recognition method based on electroencephalogram signals according to the embodiment of the present invention;
fig. 6 is a block diagram of a structure of an emotion recognition apparatus based on electroencephalogram signals according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an emotion recognition device based on electroencephalogram signals according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of a chip according to an embodiment of the present invention.
Detailed Description
In order to facilitate clear description of technical solutions of the embodiments of the present invention, in the embodiments of the present invention, terms such as "first" and "second" are used to distinguish the same items or similar items having substantially the same functions and actions. For example, the first threshold and the second threshold are only used for distinguishing different thresholds, and the sequence order of the thresholds is not limited. Those skilled in the art will appreciate that the terms "first," "second," etc. do not denote any order or quantity, nor do the terms "first," "second," etc. denote any order or importance.
It is to be understood that the terms "exemplary" or "such as" are used herein to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "e.g.," is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion.
In the present invention, "at least one" means one or more, "a plurality" means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone, wherein A and B can be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of the singular or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, a and b combination, a and c combination, b and c combination, or a, b and c combination, wherein a, b and c can be single or multiple.
In the related technology, the electroencephalogram acquisition technology is to directly contact an electrode with the scalp, and a colloidal conductive medium is coated on the contact part of the electrode and the scalp, so as to acquire an electroencephalogram signal. However, the above scheme for acquiring the electroencephalogram signals can cause discomfort to the object to be measured; and the acquired electroencephalogram signals are not further researched.
Aiming at the technical problems, the embodiment of the invention provides an emotion recognition system based on electroencephalogram signals, which can solve the problems of effectively acquiring electroencephalogram signals and using the acquired electroencephalogram signals for researching an electroencephalogram control technology, and a tested object wears a head-worn acquisition device on the head, so that the comfort of the tested object is improved, the user experience is improved, and the electroencephalogram signals are more effectively extracted; and the acquired electroencephalogram signals can be used for demonstration, research, interesting performance and the like of an electroencephalogram control technology.
As shown in fig. 1, an emotion recognition system 10 based on an electroencephalogram signal according to an embodiment of the present invention includes: the brain electricity acquisition device 11 and the processing device 12.
The electroencephalogram acquisition equipment 11 is head-worn acquisition equipment; the head-wearing type acquisition equipment comprises a head-wearing layer, a plurality of electroencephalogram electrodes and a light-emitting component; wherein, a plurality of brain electricity electrodes and light-emitting component are established respectively on the head wearing layer. The plurality of electroencephalogram electrodes are arranged on the head contact surface of the head wearing layer and are used for contacting with the head of the object to be tested and collecting electroencephalogram signals of the object to be tested. The light emitting assembly may display different colors of light, such as: red, blue, etc.; the light emitting assembly is used to display different colors of light according to the control of the processing device 12.
The processing device 12 is configured to process the electroencephalogram signal sent by the electroencephalogram acquisition device 11 by using the emotion classification model, determine an emotion category corresponding to the electroencephalogram signal, and control a light emitting color of the light emitting component according to the emotion category corresponding to the electroencephalogram signal. Wherein, the emotion classification model comprises: neural network models, schrodinger's equation, etc.; the mood categories may include: mood categories such as happy, sad, fear, calm, etc.
In practical application, the electroencephalogram acquisition device 11 acquires electroencephalogram signals of a detected object, sends the acquired electroencephalogram signals to the processing device 12, and the processing device 12 processes the acquired electroencephalogram signals by using the emotion classification model, determines emotion categories corresponding to the electroencephalogram signals, and controls the light-emitting component to display light-emitting colors corresponding to the emotion categories according to the emotion categories.
Such as: the electroencephalogram acquisition device 11 acquires an electroencephalogram signal 1 of a detected object, sends the acquired electroencephalogram signal 1 to the processing device 12, the processing device 12 processes the acquired electroencephalogram signal 1 by using the emotion classification model, determines that an emotion category corresponding to the electroencephalogram signal 1 is a sad category, and controls the light emitting component to display blue corresponding to the sad category according to the sad category by the processing device 12.
In one implementation, the head-wearing layer is artificial skin or artificial wig.
In the case where the head-worn layer is artificial skin, the electroencephalogram acquisition device 11 includes: artificial skin, a plurality of electroencephalogram electrodes and a light-emitting component; the electroencephalogram electrodes and the light-emitting component are respectively arranged on the artificial skin, and the electroencephalogram electrodes are arranged on the head contact surface of the artificial skin.
In practical application, the tested object places the artificial skin on the head, the electroencephalogram signals are collected by utilizing the electroencephalogram electrodes on the head contact surface of the artificial skin, the collected electroencephalogram signals are processed by the processing equipment 12, the emotion types corresponding to the electroencephalogram signals are determined, and the light-emitting components are controlled to display light with corresponding colors according to the emotion types.
Under the condition that the head wearing layer is the artificial wig, the electroencephalogram acquisition device 11 includes: the artificial wig, a plurality of electroencephalogram electrodes and a light-emitting component; the electroencephalogram electrodes and the light-emitting component are respectively arranged on the artificial wig, and the electroencephalogram electrodes are arranged on the head contact surface of the artificial wig.
In practical application, the artificial wig is worn on the head of the tested object, electroencephalogram signals are collected by utilizing a plurality of electroencephalogram electrodes on the head contact surface of the artificial wig, the collected electroencephalogram signals are processed by the processing equipment 12, emotion categories corresponding to the electroencephalogram signals are determined, and the light emitting components are controlled to display light with corresponding colors according to the emotion categories.
In one implementation mode, the artificial wig comprises a hair bottom layer and artificial hair formed on the hair bottom layer; the hair bottom layer is provided with a light-emitting component and a plurality of brain electrical electrodes.
The artificial wig includes: bottom layer of hair and artificial hair; the artificial hair is formed on the hair bottom layer, a plurality of brain electrodes which are electrically connected are arranged on the hair bottom layer, and a plurality of light-emitting components are uniformly distributed on the hair bottom layer; the light-emitting component is a light-emitting diode, the electroencephalogram electrode is a dry electrode, the dry electrode has larger input impedance, and electroencephalogram signals can be collected by directly attaching the dry electrode to the scalp.
As shown in FIG. 2, the subject wears the artificial hair piece 21 on the head, the artificial hair piece 21 includes a hair substrate 22 and artificial hair 23, the artificial hair 23 is formed on the hair substrate 22, the hair substrate 22 has a light emitting assembly 24 and a plurality of brain electrodes 25, and the light emitting assembly 24 can be located at the root of the artificial hair 23.
As shown in fig. 3, the effect schematic diagram of the emotion recognition system based on electroencephalogram signals of the embodiment of the present invention is that a subject wears an artificial wig 21, and an emotion stimulation video 31 is played for the subject wearing the artificial wig 21, and the subject induces various emotions due to stimulation of the emotion stimulation video, including: mood of happy, sad, fear, calm, etc.; the method comprises the following steps that a plurality of electroencephalogram electrodes on an artificial wig 21 collect electroencephalogram signals 32 of a tested object, the collected electroencephalogram signals 32 are sent to a processing device 12, the processing device 12 searches electroencephalogram signal samples matched with the electroencephalogram signals through a cloud database, and emotion types corresponding to the electroencephalogram signals are determined according to the electroencephalogram signal samples and an emotion classification model; after obtaining the emotion type corresponding to the electroencephalogram signal, the processing device 12 controls the light emitting component 24 of the artificial wig 21 to display the color corresponding to the emotion type.
The emotion recognition system based on the electroencephalogram signals comprises: the electroencephalogram acquisition equipment and the processing equipment; the electroencephalogram acquisition equipment is head-worn acquisition equipment; the head-wearing type acquisition equipment comprises a head wearing layer, a plurality of electroencephalogram electrodes and light-emitting components, wherein the electroencephalogram electrodes and the light-emitting components are respectively arranged on the head wearing layer, and the light-emitting components are used for displaying different colors; therefore, the head-wearing acquisition equipment is worn on the head of the object to be tested, so that the comfort of the object to be tested is improved, the user experience is improved, and the electroencephalogram signal is more effectively extracted; the processing equipment is used for processing the electroencephalogram signals sent by the electroencephalogram acquisition equipment by using the emotion classification model, determining emotion categories corresponding to the electroencephalogram signals, and controlling the light-emitting colors of the light-emitting components according to the emotion categories corresponding to the electroencephalogram signals; therefore, the acquired electroencephalogram signals can be used for demonstration, research, interesting performance and the like of the electroencephalogram control technology.
The embodiment of the invention also provides an emotion recognition method based on the electroencephalogram signal, which is applied to an emotion recognition system with electroencephalogram acquisition equipment, wherein the electroencephalogram acquisition equipment is head-worn acquisition equipment; the head-wearing type acquisition equipment comprises a head wearing layer, a plurality of electroencephalogram electrodes and light-emitting components, wherein the electroencephalogram electrodes and the light-emitting components are respectively arranged on the head wearing layer, and the light-emitting components are used for displaying different colors; as shown in fig. 4, the method includes:
step 401: receiving the EEG signal of the object to be tested sent by the EEG acquisition equipment.
The electroencephalogram acquisition equipment acquires an electroencephalogram signal of a measured object and sends the acquired electroencephalogram signal to the processing equipment; the processing equipment receives the electroencephalogram signals of the tested object sent by the electroencephalogram acquisition equipment. Wherein, the tested object can be human, animal, etc.
It should be noted that the electroencephalogram acquisition device and the processing device in the embodiment of the present invention are the same as those in the above embodiment, and details are not repeated in this embodiment.
Before the electroencephalogram acquisition equipment acquires the electroencephalogram signal of a detected object, the detected object wears the electroencephalogram acquisition equipment, and the electroencephalogram acquisition equipment comprises: the head wearing layer, the electroencephalogram electrode and the light-emitting component, and electrical elements such as the electroencephalogram electrode and the light-emitting component can be powered by the flexible battery pack arranged on the electroencephalogram acquisition equipment. In addition to the above power supply method, other power supply methods may be used, such as: a spectacle battery, a hair band battery, a graphene battery, a polysilicon battery, and the like.
After the electroencephalogram acquisition equipment is worn by the tested object, the emotional stimulation material is played for the tested object, so that the electroencephalogram acquisition equipment acquires the electroencephalogram signal of the tested object. Here, various stimulation materials are made for emotion category calibration, and are mainly presented in a picture or video manner, and are used for inducing an emotional state of a measured object, such as: positive emotions, negative emotions. Wherein, the segments reflecting positive emotions mainly select scenes which are funny in popular movies, such as: video clips such as Taijiong of Jiong as man and Yuekeng Baoben to induce joyful and cheerful emotion of the tested object; the segments reflecting the negative emotions are mainly selected from films describing disaster scenes such as Tangshan earthquake and Yinyangyi, etc., so as to induce the emotions of sadness and pain of the tested object. A prompt of 10 seconds is provided before each video, and the name of the film and the reflected emotional state of the tested object are informed. According to clinical tests, the stimulation material is substantially capable of eliciting both positive and negative emotions.
After the object wears the brain electricity collection equipment, before the brain electricity collection equipment gathers the brain electricity signal of object, still include: and detecting whether the wearing of the electroencephalogram acquisition equipment is normal or not, wherein the detecting comprises electroencephalogram signal acquisition commissioning and light-emitting indication. Such as: and (5) running each electroencephalogram electrode for 1 minute in a test mode, and testing whether the color of the light-emitting diode is normal or not.
Step 402: and determining the emotion category corresponding to the electroencephalogram signal according to the electroencephalogram signal and the emotion classification model.
The emotion classification model can be a neural network model, a Schrodinger equation and the like, and represents the corresponding relation between different electroencephalogram signal samples and emotion classification; the mood categories may include: happy, sad, frightened, calm, etc. Such as: the emotion corresponding to the electroencephalogram signal sample 1 is classified as happy, the emotion corresponding to the electroencephalogram signal sample 2 is classified as sad, the emotion corresponding to the electroencephalogram signal sample 3 is classified as fear, and the emotion corresponding to the electroencephalogram signal sample 4 is classified as calm.
After the processing equipment receives the electroencephalogram signal of the tested object sent by the electroencephalogram acquisition equipment, the processing equipment matches the electroencephalogram signal sample in the emotion classification model according to the electroencephalogram signal, and therefore the emotion category corresponding to the electroencephalogram signal is determined.
Such as: and matching the electroencephalogram signal 1 with the electroencephalogram signal sample 1, so that the emotion type corresponding to the electroencephalogram signal 1 can be determined to be happy.
Step 403: and controlling the light-emitting component to display the corresponding color of the emotion category according to the emotion category.
In practical applications, the correspondence between the emotion category and the color may be set in advance, such as: happy corresponds to red, sad corresponds to blue, fear corresponds to black, calm corresponds to yellow.
After the processing equipment determines the emotion category corresponding to the electroencephalogram signal, the processing equipment generates a control instruction, and the light-emitting component displays the color corresponding to the emotion category based on the control instruction generated by the processing equipment.
Such as: the processing equipment determines that the emotion category corresponding to the electroencephalogram signal 1 is sad, the processing equipment generates a control instruction, and the light-emitting component displays the color corresponding to the sad on the basis of the control instruction generated by the processing equipment: blue in color.
In an implementation manner, after receiving an electroencephalogram signal of a tested object sent by an electroencephalogram acquisition device, before determining an emotion category corresponding to the electroencephalogram signal according to the electroencephalogram signal and an emotion classification model, the method further includes: and carrying out filtering and denoising processing on the electroencephalogram signals, and removing artifact signals in the electroencephalogram signals to obtain the processed electroencephalogram signals.
Here, the processing equipment receives the electroencephalogram signal of the measured object sent by the electroencephalogram acquisition equipment, the electroencephalogram signal is an original electroencephalogram signal acquired through an electroencephalogram electrode, the original electroencephalogram signal contains an artifact signal, and the emotion category corresponding to the electroencephalogram signal can be influenced, so that the electroencephalogram signal needs to be filtered and denoised before the emotion category corresponding to the electroencephalogram signal is determined, and the artifact signal in the electroencephalogram signal is removed, such as: and the eye electrical, electrocardio, myoelectric, power frequency interference and other artifact signals can obtain a purer processed electroencephalogram signal.
In an implementation manner, after receiving an electroencephalogram signal of a tested object sent by an electroencephalogram acquisition device, before determining an emotion category corresponding to the electroencephalogram signal according to the electroencephalogram signal and an emotion classification model, the method further includes: acquiring an electroencephalogram signal sample and an emotion type sample corresponding to the electroencephalogram signal sample; and training the emotion classification model according to the electroencephalogram signal sample and the emotion category sample so as to update parameters of the emotion classification model.
Before determining the emotion category corresponding to the electroencephalogram signal according to the electroencephalogram signal and the emotion classification model, carrying out multiple acquisition on the object to be tested to obtain electroencephalogram signal samples which are acquired for multiple times and emotion category samples corresponding to the electroencephalogram signal samples, inputting the electroencephalogram signal samples and the emotion category samples into the emotion classification model, training the emotion classification model, and updating parameters of the emotion classification model.
Such as: the emotion classification model is Y ═ AX + B, where Y, A, X, B is a tensor; collecting the object to be tested for multiple times to obtain electroencephalogram signal samples collected for multiple times: s1, S2, S3 and S4, and emotion category samples M1, M2, M3 and M4 corresponding to the electroencephalogram signal samples. Sampling the electroencephalogram signals: s1, S2, S3 and S4, emotion classification samples M1, M2, M3 and M4 are input into an emotion classification model Y which is AX + B for training, and parameters A1 and B1 of the updated emotion classification model are obtained.
In one implementation, training the emotion classification model according to the electroencephalogram signal sample and the emotion classification sample to update parameters of the emotion classification model includes: taking the electroencephalogram signal sample as the input of an emotion classification model, and taking the emotion category sample as the output of the emotion classification model; parameters of the emotion classification model are updated.
The method comprises the steps of collecting a tested object for multiple times to obtain electroencephalogram signal samples collected for multiple times and emotion category samples corresponding to the electroencephalogram signal samples, using the electroencephalogram signal samples as input of an emotion classification model, and using the emotion category samples as output of the emotion classification model, so that updated parameters are obtained.
Such as: the emotion classification model is Y ═ CX + D, where Y, C, X, D is a tensor; collecting the object to be tested for multiple times to obtain electroencephalogram signal samples collected for multiple times: s5, S6, S7 and S8, and emotion category samples M5, M6, M7 and M8 corresponding to the electroencephalogram signal samples. Sampling the electroencephalogram signals: s5, S6, S7, and S8 are used as input X of the emotion classification model Y — CX + D, and the emotion classification samples M5, M6, M7, and M8 are used as output Y of the emotion classification model Y — CX + D, and the emotion classification model Y — CX + D is trained to obtain parameters C1 and D1 of the updated emotion classification model.
It should be noted that, the object to be tested is collected for a plurality of times, and a plurality of electroencephalogram signal samples and a plurality of emotion type samples obtained can be stored in the cloud database.
In one implementation, determining an emotion category corresponding to the electroencephalogram signal according to the electroencephalogram signal and the emotion classification model includes: searching an electroencephalogram signal sample matched with the electroencephalogram signal through a cloud database; and determining the emotion type corresponding to the electroencephalogram signal according to the electroencephalogram signal sample and the emotion classification model.
The processing device receives an electroencephalogram signal of a tested object sent by the electroencephalogram acquisition device, compares a plurality of electroencephalogram signal samples stored in the cloud database with the electroencephalogram signal, and obtains an electroencephalogram signal sample matched with the electroencephalogram signal; and inputting the electroencephalogram signal sample into an emotion classification model, and calculating by using the electroencephalogram signal sample and parameters in the emotion classification model to obtain an emotion category corresponding to the electroencephalogram signal.
Such as: a plurality of brain electrical signal samples stored in a cloud database: comparing an electroencephalogram signal sample 1, an electroencephalogram signal sample 2, an electroencephalogram signal sample 3 and an electroencephalogram signal sample 4 with the electroencephalogram signal 1 to obtain an electroencephalogram signal sample 2 matched with the electroencephalogram signal 1; and calculating by using the electroencephalogram signal sample 2 and parameters in the emotion classification model to obtain the calmness of the emotion type corresponding to the electroencephalogram signal 1.
It should be noted that the cloud database is an accumulation database established according to electroencephalogram signals and emotion categories on the basis of extracting a large number of electroencephalogram signals, and emotion classification is more accurate with increase of accumulated data volume.
In practical application, as shown in fig. 5, the electroencephalogram 51 is input into an emotion classification model, an emotion classifier 52 in the emotion classification model classifies the electroencephalogram 51 to obtain an emotion category 53 corresponding to the electroencephalogram 51, and the emotion category 53 may include: happy, sad, frightened, calm. Wherein, the emotion classifier is essentially some algorithmic functions.
In one implementation, the mood classification model includes the schrodinger equation.
The schrodinger equation can be expressed by equation (1):
wherein H is a Hamiltonian matrix and psi is a probability amplitude function.
The expression of the probability amplitude function ψ can be expressed by equation (2):
wherein the content of the first and second substances,
a probability amplitude function representing that the estimated value of the measured object in the negative emotional state is lower than the standard value,
and the probability amplitude function representing that the estimated value of the tested object in the positive emotional state is lower than the standard value. PsiLL、ψLH、ψHLAnd psiHHThe first letter in (a) represents the positive and negative orientation of the emotion, L represents a negative emotion, and H represents a positive emotion; the second letter represents the result of comparing the emotional arousal level of the subject with the standard value, L represents lower than the standard value, and H represents higher than the standard value.
According to the formula (1), the emotion classification function of the measured object at the time t is obtained, and can be represented by the formula (3):
ψ1(t)＝e-i·t·Hψ0equation (3).
The embodiment of the invention can achieve the following technical effects: the electroencephalogram acquisition equipment is used as a carrier to acquire electroencephalogram signals of a detected object, corresponding emotion categories are established according to an emotion classification model, and the corresponding emotion categories are reflected through the light-emitting component, so that a whole set of emotion recognition method based on the electroencephalogram signals is formed, and the method can be used for demonstration, research and interesting games of electroencephalogram control technology.
Fig. 6 is a block diagram illustrating a structure of an emotion recognition apparatus based on electroencephalogram signals according to an embodiment of the present invention, in a case where functional modules are divided according to functions. As shown in fig. 6, the emotion recognition apparatus 60 based on electroencephalogram signals includes: a communication module 601 and a processing module 602.
The communication module 601 is used for acquiring the electroencephalogram signals acquired by the electroencephalogram acquisition equipment.
A processing module 602, configured to support the emotion recognition apparatus based on electroencephalogram signals to perform steps 101 to 102 in the foregoing embodiments.
All relevant contents of each step related to the above method embodiment may be referred to the functional description of the corresponding functional module, and are not described herein again.
In some possible implementations, the emotion recognition apparatus based on electroencephalogram signal may further include a storage module 603 for storing program codes and data of the base station.
The Processing module may be a Processor or a controller, and may be, for example, a Central Processing Unit (CPU), a general-purpose Processor, a Digital Signal Processor (DSP), an Application-Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a transistor logic device, a hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. The processor may also be a combination of computing functions, e.g., comprising one or more microprocessors, DSPs, and microprocessors, among others. The communication module may be a transceiver, a transceiving circuit or a communication interface, etc. The storage module may be a memory.
When the processing module is a processor, the communication module is a communication interface, and the storage module is a memory, the emotion recognition device based on electroencephalogram signals according to the embodiment of the present invention may be the emotion recognition device based on electroencephalogram signals shown in fig. 7.
Fig. 7 shows a schematic structural diagram of an emotion recognition device based on electroencephalogram signals according to an embodiment of the present invention. As shown in fig. 7, the electroencephalogram signal-based emotion recognition apparatus 70 includes a processor 701 and a communication interface 702.
As shown in fig. 7, the processor may be a general processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more ics for controlling the execution of programs according to the present invention. The number of the communication interfaces may be one or more. The communication interface may use any transceiver or the like for communicating with other devices or communication networks.
As shown in fig. 7, the emotion recognition apparatus based on electroencephalogram signals may further include a communication line 703. The communication link may include a path for transmitting information between the aforementioned components.
Optionally, as shown in fig. 7, the emotion recognition apparatus based on electroencephalogram signals may further include a memory 704. The memory is used for storing computer-executable instructions for implementing the inventive arrangements and is controlled by the processor for execution. The processor is used for executing the computer execution instructions stored in the memory, thereby realizing the method provided by the embodiment of the invention.
As shown in fig. 7, the memory may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a Random Access Memory (RAM) or other types of dynamic storage devices that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to such. The memory may be separate and coupled to the processor via a communication link. The memory may also be integral to the processor.
Optionally, the computer-executable instructions in the embodiment of the present invention may also be referred to as application program codes, which is not specifically limited in this embodiment of the present invention.
In particular implementations, as one embodiment, processor 701 may include one or more CPUs, such as CPU0 and CPU1 in fig. 7, as shown in fig. 7.
In a specific implementation, as an example, as shown in fig. 7, the electroencephalogram signal-based emotion recognition apparatus may include a plurality of processors, such as the processor 701-1 and the processor 701-2 in fig. 7. Each of these processors may be a single core processor or a multi-core processor.
Fig. 8 is a schematic structural diagram of a chip according to an embodiment of the present invention. As shown in fig. 8, the chip 80 includes one or more than two (including two) processors 701 and a communication interface 702.
Optionally, as shown in FIG. 8, the chip also includes memory 704, which may include read-only memory and random access memory, and provides operating instructions and data to the processor. The portion of memory may also include non-volatile random access memory (NVRAM).
In some embodiments, as shown in FIG. 8, the memory stores elements, execution modules or data structures, or a subset thereof, or an expanded set thereof.
In the embodiment of the present invention, as shown in fig. 8, by calling an operation instruction stored in the memory (the operation instruction may be stored in the operating system), a corresponding operation is performed.
As shown in fig. 8, a processor, which may also be referred to as a Central Processing Unit (CPU), controls the processing operation of any one of the emotion recognition apparatuses based on the electroencephalogram signal.
As shown in fig. 8, the memories may include read-only memory and random access memory, and provide instructions and data to the processor. The portion of memory may also include NVRAM. For example, in applications where the memory, communication interface, and memory are coupled together by a bus system that may include a power bus, a control bus, a status signal bus, etc., in addition to a data bus. But for clarity of illustration the various busses are labeled in figure 8 as the bus system 705.
As shown in fig. 8, the method disclosed in the above embodiments of the present invention may be applied to a processor, or may be implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The processor may be a general purpose processor, a Digital Signal Processor (DSP), an ASIC, an FPGA (field-programmable gate array) or other programmable logic device, discrete gate or transistor logic device, or discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
In one possible implementation, as shown in fig. 8, the communication interface is used to acquire an electroencephalogram signal acquired by an electroencephalogram acquisition device. The processor is used for executing steps 101 to 102 of the electroencephalogram signal-based emotion recognition method in the embodiment shown in fig. 1.
In one aspect, a storage medium is provided, and may be a computer-readable storage medium, in which instructions are stored, and when executed, implement the functions performed by the electroencephalogram signal-based emotion recognition device in the above embodiments.
In one aspect, a chip is provided, where the chip is applied to an emotion recognition device based on an electroencephalogram signal, and the chip includes at least one processor and a communication interface, where the communication interface is coupled to the at least one processor, and the processor is configured to execute instructions to implement the functions performed by the emotion recognition device based on an electroencephalogram signal in the foregoing embodiments.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the procedures or functions described in the embodiments of the present invention are performed in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, a terminal, a user device, or other programmable apparatus. The computer program or instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer program or instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire or wirelessly. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that integrates one or more available media. The usable medium may be a magnetic medium, such as a floppy disk, a hard disk, a magnetic tape; or optical media such as Digital Video Disks (DVDs); it may also be a semiconductor medium, such as a Solid State Drive (SSD).
While the invention has been described in connection with various embodiments, other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a review of the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the word "a" or "an" does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.
While the invention has been described in conjunction with specific features and embodiments thereof, it will be evident that various modifications and combinations can be made thereto without departing from the spirit and scope of the invention. Accordingly, the specification and figures are merely exemplary of the invention as defined in the appended claims and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the invention. It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. An emotion recognition system based on an electroencephalogram signal, comprising: the electroencephalogram acquisition equipment and the processing equipment; the electroencephalogram acquisition equipment is head-worn acquisition equipment;
the head-wearing type acquisition equipment comprises a head wearing layer, a plurality of electroencephalogram electrodes and light-emitting components, wherein the electroencephalogram electrodes and the light-emitting components are respectively arranged on the head wearing layer, and are used for displaying different colors;
the processing equipment is used for processing the electroencephalogram signals sent by the electroencephalogram acquisition equipment by utilizing an emotion classification model, determining emotion types corresponding to the electroencephalogram signals, and controlling the light-emitting colors of the light-emitting components according to the emotion types corresponding to the electroencephalogram signals.
2. The electroencephalogram signal-based emotion recognition system of claim 1, wherein the head-worn layer is artificial skin or an artificial wig.
3. The electroencephalogram signal-based emotion recognition system of claim 2, wherein the artificial hairpiece includes a bottom layer of hair and artificial hair formed on the bottom layer of hair; the hair bottom layer is provided with a light-emitting component and a plurality of brain electrical electrodes.
4. The emotion recognition method based on the electroencephalogram signals is characterized by being applied to an emotion recognition system with electroencephalogram acquisition equipment, wherein the electroencephalogram acquisition equipment is head-worn acquisition equipment; the head-wearing type acquisition equipment comprises a head wearing layer, a plurality of electroencephalogram electrodes and light-emitting components, wherein the electroencephalogram electrodes and the light-emitting components are respectively arranged on the head wearing layer, and are used for displaying different colors; the method comprises the following steps:
receiving an electroencephalogram signal of a tested object sent by the electroencephalogram acquisition equipment;
determining the emotion type corresponding to the electroencephalogram signal according to the electroencephalogram signal and the emotion classification model;
and controlling the light-emitting component to display the color corresponding to the emotion category according to the emotion category.
5. The electroencephalogram signal based emotion recognition method according to claim 4, wherein after receiving the electroencephalogram signal of the object to be tested sent by the electroencephalogram acquisition device, before determining the emotion category corresponding to the electroencephalogram signal according to the electroencephalogram signal and the emotion classification model, the method further comprises:
and carrying out filtering and denoising processing on the electroencephalogram signals, and removing artifact signals in the electroencephalogram signals to obtain the processed electroencephalogram signals.
6. The electroencephalogram signal based emotion recognition method according to claim 4, wherein after receiving the electroencephalogram signal of the object to be tested sent by the electroencephalogram acquisition device, before determining the emotion category corresponding to the electroencephalogram signal according to the electroencephalogram signal and the emotion classification model, the method further comprises:
acquiring an electroencephalogram signal sample and an emotion type sample corresponding to the electroencephalogram signal sample;
and training the emotion classification model according to the electroencephalogram signal sample and the emotion category sample so as to update parameters of the emotion classification model.
7. The electroencephalogram signal based emotion recognition method of claim 6, wherein the training the emotion classification model according to the electroencephalogram signal sample and the emotion classification sample to update parameters of the emotion classification model comprises:
taking the electroencephalogram signal sample as the input of the emotion classification model, and taking the emotion category sample as the output of the emotion classification model;
and updating the parameters of the emotion classification model.
8. The electroencephalogram signal based emotion recognition method according to claim 4, wherein the determining of the emotion category corresponding to the electroencephalogram signal according to the electroencephalogram signal and the emotion classification model includes:
retrieving an electroencephalogram signal sample matched with the electroencephalogram signal through a cloud database;
and determining the emotion type corresponding to the electroencephalogram signal according to the electroencephalogram signal sample and the emotion classification model.
9. The electroencephalogram signal-based emotion recognition method of any one of claims 6 to 8, wherein the emotion classification model includes Schachyman's equation.
10. A storage medium, wherein instructions are stored in the storage medium, and when the instructions are executed, the method for emotion recognition based on electroencephalogram signals according to any one of claims 4 to 9 is implemented.
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