Brain-computer interface device and method for relieving pain based on VR music

Abstract

The invention discloses a brain-computer interface device and method for relieving pain based on VR music. The method comprises the steps that a user electroencephalogram signal set is acquired; the user electroencephalogram signal set comprises a user electroencephalogram signal sequence; performing classification identification processing on the user electroencephalogram signal set to obtain user feature state information; and generating corresponding auditory information and visual information according to the user feature state information. The VR technology and the brain-computer interface technology are combined, an innovative method and device are provided for pain relief, and the pain relief effect is improved; the VR music is recommended in a personalized manner according to the real-time electroencephalogram signal of the user, so that the requirements of the user can be better met, and the user experience is improved. A non-drug relieving mode is adopted, and the risk of side effects of drugs is reduced.

Description

Technical Field

[0001] The present invention relates to the field of brain-computer interface, and in particular to a brain-computer interface device and method for alleviating pain based on VR music. Background Art

[0002] The human brain contains a wealth of information about various types of human sensations. With the development of brain-computer interface technology, non-implantation methods can be used to continuously collect human EEG signals. How to accurately analyze the user's current brain state and body state in real time based on the collected human EEG signals in order to accurately analyze the user's physical signs is an urgent problem that needs to be solved.

[0003] Pain is a common clinical symptom, which brings great pain and discomfort to users. At present, the methods of relieving pain mainly include drug methods, physical methods and psychological methods. However, drug methods may bring side effects, physical methods have limited effects, and psychological methods require high cooperation from users and professional therapists. With the development of virtual reality (VR) technology and brain-computer interface technology, new ways of pain relief have been provided. Brain-computer interface technology can monitor the user's brain activity in real time and provide a basis for personalized pain relief. How to create an immersive virtual environment based on VR technology, distract the user's attention and reduce pain perception is also a problem that this patent needs to solve. Summary of the invention

[0004] The main problems solved by the present invention include: first, based on the collected human EEG signals, real-time and accurate analysis of the user's current brain state and body state is performed to extract specific signals (such as pain signals); second, an immersive virtual environment is created based on VR technology to distract the user's attention and reduce pain perception. The present invention discloses a brain-computer interface method and device for relieving pain based on VR music.

[0005] In a first aspect of the embodiment of the present application, a brain-computer interface method for relieving pain based on VR music is disclosed, comprising:

[0006] S1, collecting and obtaining a user's EEG signal set; the user's EEG signal set includes a user's EEG signal sequence;

[0007] S2, classifying and identifying the user's EEG signal set to obtain user characteristic state information;

[0008] S3, generating corresponding auditory information and visual information according to the user characteristic state information.

[0009] The collecting and obtaining of the user's electroencephalogram signal set includes:

[0010] S11, using the EEG acquisition module to acquire an initial EEG signal set of the user;

[0011] S12, preprocessing the user's initial EEG signal set to obtain a user's EEG signal set.

[0012] The preprocessing of the user's initial EEG signal set to obtain the user's EEG signal set includes:

[0013] S121, performing data cleaning processing on the user's initial EEG signal set to obtain a first signal set;

[0014] S122, normalizing the first signal set to obtain a second signal set;

[0015] S123, performing pattern monitoring processing on the second signal set to obtain a user electroencephalogram signal set.

[0016] The classifying and identifying the user's EEG signal set to obtain user characteristic state information includes:

[0017] S21, obtaining a user standard EEG signal set; the user standard EEG signal set includes a standard EEG signal sequence corresponding to each user feature;

[0018] S22, performing difference discrimination processing on the user's EEG signal set and the user's standard EEG signal set to obtain a difference vector;

[0019] S23, determining the user characteristic information corresponding to the sequence number of the element with the smallest value in the difference vector as the user characteristic state information.

[0020] The step of performing difference discrimination processing on the user's EEG signal set and the user's standard EEG signal set to obtain a difference vector includes:

[0021] S221, using the user EEG signal sequence of the user EEG signal set as a row vector, constructing a user EEG matrix; representing the user standard EEG signal set as a standard EEG matrix; the row vector of the standard EEG matrix is ​​the standard EEG signal sequence corresponding to each user feature; the dimension of the standard EEG matrix is ​​the same as the dimension of the user EEG matrix;

[0022] S222, subtracting the user's EEG matrix from the standard EEG matrix to obtain a difference matrix;

[0023] S223, performing feature modeling processing on the difference matrix to obtain a feature approximation model and a feature vector;

[0024] S224: Utilize the feature approximation model to calculate and process the feature vector to obtain a difference vector.

[0025] The performing feature modeling processing on the difference matrix to obtain a feature approximation model includes:

[0026] S2231, decomposing the difference matrix to obtain a left decomposition matrix, a characteristic matrix and a right decomposition matrix;

[0027] The decomposition process is calculated as follows:

[0028] Y=UAV,

[0029] Among them, U is the left decomposition matrix, Y is the difference matrix, A is the feature matrix, V is the right decomposition matrix, U and V are both orthogonal matrices, and A is a diagonal matrix;

[0030] S2232, extracting the diagonal elements of the feature matrix to obtain a feature vector;

[0031] S2233, performing linear fitting processing on the elements and element sequence numbers of the feature vector to obtain a feature approximation model.

[0032] In a second aspect of an embodiment of the present invention, a brain-computer interface device for relieving pain based on VR music is disclosed, which is used to implement the brain-computer interface method for relieving pain based on VR music, including: an electroencephalogram acquisition module, a classification and recognition module, and a virtual reality display module;

[0033] The EEG acquisition module is used to acquire a set of EEG signals of the user;

[0034] The classification and recognition module is used to perform classification and recognition processing on the user's EEG signal set to obtain user feature state information;

[0035] The virtual reality display module is used to generate corresponding auditory information and visual information according to the user characteristic state information; the virtual reality display module stores the auditory information and visual information corresponding to each user characteristic information.

[0036] In a third aspect of an embodiment of the present invention, a brain-computer interface device for relieving pain based on VR music is disclosed, the device comprising:

[0037] A memory storing executable program code;

[0038] a processor coupled to the memory;

[0039] The processor calls the executable program code stored in the memory to execute the brain-computer interface method for relieving pain based on VR music.

[0040] In a fourth aspect of an embodiment of the present invention, a computer-storable medium is disclosed, wherein the computer-storable medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the brain-computer interface method for relieving pain based on VR music.

[0041] In a fifth aspect of an embodiment of the present invention, an information data processing terminal is disclosed, which is used to implement the brain-computer interface method for relieving pain based on VR music.

[0042] The beneficial effects of the present invention are:

[0043] Compared with the prior art, the present invention uses the combination of VR technology and brain-computer interface technology to provide an innovative method and device for pain relief, which improves the effect of pain relief; personalized recommendation of VR music based on the user's real-time EEG signal can better meet the needs of users and improve the user experience. The present invention adopts a non-drug relief method to reduce the risk of drug side effects.

[0044] The present invention aims to extract specific feature information from the user's EEG signal, performs difference discrimination processing on the user's EEG signal set and the user's standard EEG signal set to obtain a difference vector; and determines the user feature information corresponding to the sequence number of the element with the smallest value in the difference vector as the user feature state information. In this way, signal discrimination and classification errors are effectively reduced, and the accuracy and efficiency of feature discrimination are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a flow chart for implementing the method of the present invention. DETAILED DESCRIPTION

[0046] In order to better understand the content of the present invention, two embodiments are given here.

[0048] Embodiment 1:

[0049] In order to solve the problem of how to accurately analyze the user's current brain state and body state in real time based on the collected human electroencephalogram signals, the present invention discloses a brain-computer interface method and device for relieving pain based on VR music.

[0050] In a first aspect of the embodiment of the present application, a brain-computer interface method for relieving pain based on VR music is disclosed, comprising:

[0051] S1, collecting and obtaining a user's EEG signal set; the user's EEG signal set includes a user's EEG signal sequence;

[0052] S2, classifying and identifying the user's EEG signal set to obtain user characteristic state information;

[0053] S3, generating corresponding auditory information and visual information according to the user characteristic state information;

[0054] The collecting and obtaining of the user's electroencephalogram signal set includes:

[0055] S11, using the EEG acquisition module to acquire an initial EEG signal set of the user;

[0056] S12, preprocessing the user's initial EEG signal set to obtain a user's EEG signal set;

[0057] The preprocessing of the user's initial EEG signal set to obtain the user's EEG signal set includes:

[0058] S121, performing data cleaning processing on the user's initial EEG signal set to obtain a first signal set;

[0059] S122, normalizing the first signal set to obtain a second signal set;

[0060] S123, performing pattern monitoring processing on the second signal set to obtain a user electroencephalogram signal set;

[0061] The classifying and identifying the user's EEG signal set to obtain user characteristic state information includes:

[0062] S21, obtaining a user standard EEG signal set; the user standard EEG signal set includes a standard EEG signal sequence corresponding to each user feature;

[0063] S22, performing difference discrimination processing on the user's EEG signal set and the user's standard EEG signal set to obtain a difference vector;

[0064] S23, determining the user characteristic information corresponding to the sequence number of the element with the smallest value in the difference vector as the user characteristic state information.

[0065] The step of performing difference discrimination processing on the user's EEG signal set and the user's standard EEG signal set to obtain a difference vector includes:

[0066] S221, using the user EEG signal sequence of the user EEG signal set as a row vector, constructing a user EEG matrix; representing the user standard EEG signal set as a standard EEG matrix; the row vector of the standard EEG matrix is ​​the standard EEG signal sequence corresponding to each user feature; the dimension of the standard EEG matrix is ​​the same as the dimension of the user EEG matrix;

[0067] S222, subtracting the user's EEG matrix from the standard EEG matrix to obtain a difference matrix;

[0068] S223, performing feature modeling processing on the difference matrix to obtain a feature approximation model and a feature vector;

[0069] S224, using the feature approximation model to calculate the feature vector to obtain a difference vector;

[0070] The performing feature modeling processing on the difference matrix to obtain a feature approximation model includes:

[0071] S2231, decomposing the difference matrix to obtain a left decomposition matrix, a characteristic matrix and a right decomposition matrix;

[0072] The decomposition process is calculated as follows:

[0073] Y=UAV,

[0074] Among them, U is the left decomposition matrix, Y is the difference matrix, A is the feature matrix, V is the right decomposition matrix, U and V are both orthogonal matrices, and A is a diagonal matrix;

[0075] S2232, extracting the diagonal elements of the feature matrix to obtain a feature vector;

[0076] S2233, performing linear fitting processing on the elements and element sequence values ​​of the feature vector to obtain a feature approximation model;

[0077] In a second aspect of the embodiment of the present application, a brain-computer interface device for relieving pain based on VR music is disclosed, which is used to implement the brain-computer interface method for relieving pain based on VR music, including: an EEG acquisition module, a classification and recognition module, and a virtual reality display module;

[0078] The EEG acquisition module is used to acquire a set of EEG signals of the user;

[0079] The classification and recognition module is connected to the EEG acquisition module and the virtual reality display module respectively, and is used to classify and recognize the user's EEG signal set to obtain user feature state information;

[0080] The virtual reality display module is used to generate corresponding auditory information and visual information according to the user characteristic state information; the virtual reality display module stores the auditory information and visual information corresponding to each user characteristic information;

[0081] The virtual reality display module can be implemented by Pico or Huawei VR Class; the auditory information can be songs; and the visual information can be movies.

[0082] The step of performing difference discrimination processing on the user's EEG signal set and the user's standard EEG signal set to obtain a difference vector includes:

[0083] S2201, using the user EEG signal sequence of the user EEG signal set as a row vector, constructing a user EEG matrix; representing the user standard EEG signal set as a standard EEG matrix; the row vector of the standard EEG matrix is ​​the standard EEG signal sequence corresponding to each user feature; the dimension of the standard EEG matrix is ​​the same as the dimension of the user EEG matrix;

[0084] S2202, performing subtraction processing on the user's EEG matrix and the standard EEG matrix to obtain a difference matrix;

[0085] S2203, performing time-frequency transformation on each row vector of the difference matrix to obtain a corresponding time-frequency vector;

[0086] S2204, performing logarithmic weighted summation processing on all time-frequency vectors to obtain a difference vector;

[0087] The expression of the logarithmic weighted summation process is:

[0088]

[0089] Among them, H k is the kth element value of the difference vector, p k is the median of the time-frequency vector corresponding to the kth row vector, r k,i is the i-th element value of the time-frequency vector corresponding to the k-th row vector, and N is the column dimension of the difference matrix;

[0090] The decomposition process can be implemented by using a matrix singular value decomposition algorithm.

[0091] The linear fitting process uses the characteristic vector element serial number value Ix as a known independent variable and the characteristic vector element value as a known dependent variable, and uses the known independent variable and the known dependent variable to construct a curve to be approximated, and uses the function approximation method to perform curve fitting on the curve to be approximated to obtain a characteristic approximation model.

[0092] The feature vector is represented as I a , I a =[λ 1 ,λ 2 ,…,λ N1 ], N1 is the number of elements contained in the characteristic vector; the curve fitting of the curve to be approximated by the function approximation method can adopt the best consistent linear approximation method. The characteristic approximation model f(Ix) is expressed as:

[0093] f(Ix)=αP1 (Ix) P1 +α P1-1 (Ix) P1-1 +…]+α 2 (Ix) 2 +α 1 (Ix)+α 0 ,

[0094] Where P1 is the order of the feature approximation model f(Ix), α0, α1, α2, …, α P1 is the coefficient of the feature approximation model f(Ix);

[0095] The step of performing difference discrimination processing on the user's EEG signal set and the user's standard EEG signal set to obtain a difference vector includes:

[0096] S221, using the user EEG signal sequence of the user EEG signal set as a row vector, constructing a user EEG matrix; representing the user standard EEG signal set as a standard EEG matrix; the row vector of the standard EEG matrix is ​​the standard EEG signal sequence corresponding to each user feature; the dimension of the standard EEG matrix is ​​the same as the dimension of the user EEG matrix;

[0097] S222, multiplying the standard EEG matrix and the user EEG matrix to obtain a cross-correlation matrix;

[0098] S223, performing matrix decomposition processing on the mutual correlation matrix to obtain an eigenvalue sequence;

[0099] S224, determining the eigenvalue sequence as a difference vector.

[0100] The user characteristic information corresponding to the sequence number of the element with the smallest value in the difference vector is determined as the user characteristic state information, which is determined according to the sequence number of the element with the smallest value in the difference vector, and the row vector of the standard EEG matrix with the corresponding sequence number is determined, and the user characteristic state information corresponding to the corresponding standard EEG signal sequence is determined according to the row vector;

[0101] The value of the user characteristic information may be pain, happiness, tension, sadness, etc.

[0102] The user EEG signal sequence of the user EEG signal set is used as a row vector to construct a user EEG matrix, which is obtained by duplicating the row vector according to the number of the standard EEG signal sequence and then superimposing them in the column direction to obtain the user EEG matrix;

[0103] The data cleaning process includes filling missing values, smoothing noise data, and smoothing or deleting outliers; the smoothed noise data is obtained by first determining the noise data, and then smoothing the noise data according to the data before and after the noise data; the noise data is a value whose value is less than the detection sensitivity of the sensor of the observed data, or greater than the measurement upper limit of the sensor of the observed data. The outlier point can be determined by the Kalman filter method. The filling value of the missing value can be determined by averaging the measured values ​​within a certain sampling interval before and after the missing value.

[0104] The normalization process is to map data in different value ranges to a specified value range, and its mathematical expression is:

[0105]

[0106] Among them, t is a data value in the signal set, t max is the maximum value of all data in the signal set, t min is the minimum value of all data in the signal set, t 归一化 is the value of data t after normalization;

[0107] The performing pattern monitoring processing on the second signal set to obtain a user electroencephalogram signal set includes:

[0108] S1231, for each type of data attribute of the second signal set, take the data collection information of the data as the known independent variable, take the data value of the data as the known dependent variable, use the known independent variable and the known dependent variable to construct a curve to be approximated, use the function approximation method to perform curve fitting on the curve to be approximated, and obtain a characteristic approximation model f(Ix) of the type of data attribute; use the characteristic approximation model f(Ix) to calculate and process the known independent variables to obtain an approximate dependent variable; determine whether the absolute value of the difference between the approximate dependent variable and the corresponding known dependent variable is greater than a set first regression discrimination threshold; if it is greater than the first regression discrimination threshold, delete the data from the second signal set; if it is less than or equal to the first regression discrimination threshold, do not process the data;

[0109] S1232, performing fusion processing on all data of the second signal set after executing S1231 to obtain a user electroencephalogram signal set;

[0110] The data collection information is the collection time information or space information of the EEG signal data;

[0111] The curve fitting of the curve to be approximated by the function approximation method may be performed by using the best consistent linear approximation method. The characteristic approximation model f(Ix) is expressed as:

[0112] f(Ix)=α P1 (Ix) P1 +α P1-1 (Ix) P1-1 +…+α 2 (Ix) 2 +α 1 (Ix)+α 0 ,

[0113] Where P1 is the order of the feature approximation model f(Ix), α0, α1, α2, …, α P1 is the coefficient of the feature approximation model f(Ix);

[0114] In a third aspect of an embodiment of the present invention, a brain-computer interface device for relieving pain based on VR music is disclosed, the device comprising:

[0115] A memory storing executable program code;

[0116] a processor coupled to the memory;

[0117] The processor calls the executable program code stored in the memory to execute the brain-computer interface method for relieving pain based on VR music.

[0118] In a fourth aspect of an embodiment of the present invention, a computer-storable medium is disclosed, wherein the computer-storable medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the brain-computer interface method for relieving pain based on VR music.

[0119] In a fifth aspect of an embodiment of the present invention, an information data processing terminal is disclosed, which is used to implement the brain-computer interface method for relieving pain based on VR music.

[0120] Embodiment 2:

[0121] The present invention provides a brain-computer interface device for relieving pain based on VR music, comprising:

[0122] 1. VR equipment: used to provide users with an immersive virtual environment, including visual and auditory experience.

[0123] 2. EEG acquisition module: used to collect the user's EEG signals, including electrodes, amplifiers and filters, etc.

[0124] 3. Data analysis module: Analyze the collected EEG signals and extract characteristic parameters related to pain, such as EEG frequency and amplitude.

[0125] 4. Music recommendation module: Based on the results of the data analysis module, suitable VR music is recommended to users to relieve pain.

[0126] 5. Control module: used to coordinate the work of each module and realize the overall function of the device.

[0127] The present invention also provides a brain-computer interface method for relieving pain based on VR music, comprising the following steps:

[0128] 1. The user wears the VR device and EEG acquisition module.

[0129] 2. The EEG acquisition module collects the user's EEG signals in real time and transmits them to the data analysis module.

[0130] 3. The data analysis module analyzes the EEG signals and extracts characteristic parameters related to pain.

[0131] 4. The music recommendation module recommends suitable VR music to users based on feature parameters.

[0132] 5. The VR device plays recommended VR music, and users enjoy the music in a virtual environment, distracting their attention and relieving pain.

[0133] 6. The EEG acquisition module continuously collects EEG signals, and the data analysis module evaluates the pain relief effect in real time and adjusts the music recommendation if necessary.

[0134] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A brain-computer interface method for relieving pain based on VR music, characterized in that: include: S1, collect the user's EEG signal set; The user EEG signal set includes a user EEG signal sequence; S2, classifying and identifying the user's EEG signal set to obtain user characteristic state information; S3, generating corresponding auditory information and visual information according to the user characteristic state information.

2. The brain-computer interface method for relieving pain based on VR music as claimed in claim 1, characterized in that: The collecting and obtaining of the user's EEG signal set includes: S11, using the EEG acquisition module to acquire an initial EEG signal set of the user; S12, preprocessing the user's initial EEG signal set to obtain a user's EEG signal set.

3. The brain-computer interface method for relieving pain based on VR music as claimed in claim 2, characterized in that: The preprocessing of the user's initial EEG signal set to obtain the user's EEG signal set includes: S121, performing data cleaning processing on the user's initial EEG signal set to obtain a first signal set; S122, normalizing the first signal set to obtain a second signal set; S123, performing pattern monitoring processing on the second signal set to obtain a user electroencephalogram signal set.

4. The brain-computer interface method for relieving pain based on VR music as claimed in claim 2, characterized in that: The classifying and identifying the user's EEG signal set to obtain user characteristic state information includes: S21, obtaining a user standard EEG signal set; the user standard EEG signal set includes a standard EEG signal sequence corresponding to each user feature; S22, performing difference discrimination processing on the user's EEG signal set and the user's standard EEG signal set to obtain a difference vector; S23, determining the user characteristic information corresponding to the sequence number of the element with the smallest value in the difference vector as the user characteristic state information.

5. The brain-computer interface method for relieving pain based on VR music as claimed in claim 4, characterized in that: The step of performing difference discrimination processing on the user's EEG signal set and the user's standard EEG signal set to obtain a difference vector includes: S221, using the user EEG signal sequence of the user EEG signal set as a row vector, constructing a user EEG matrix; representing the user standard EEG signal set as a standard EEG matrix; the row vector of the standard EEG matrix is ​​the standard EEG signal sequence corresponding to each user feature; the dimension of the standard EEG matrix is ​​the same as the dimension of the user EEG matrix; S222, subtracting the user's EEG matrix from the standard EEG matrix to obtain a difference matrix; S223, performing feature modeling processing on the difference matrix to obtain a feature approximation model and a feature vector; S224: Utilize the feature approximation model to calculate and process the feature vector to obtain a difference vector.

6. The brain-computer interface method for relieving pain based on VR music as claimed in claim 5, characterized in that: The performing feature modeling processing on the difference matrix to obtain a feature approximation model includes: S2231, decomposing the difference matrix to obtain a left decomposition matrix, a characteristic matrix and a right decomposition matrix; The decomposition process is calculated as follows: Y=UAV, Among them, U is the left decomposition matrix, Y is the difference matrix, A is the feature matrix, V is the right decomposition matrix, U and V are both orthogonal matrices, and A is a diagonal matrix; S2232, extracting the diagonal elements of the feature matrix to obtain a feature vector; S2233, performing linear fitting processing on the elements and element sequence numbers of the feature vector to obtain a feature approximation model.

7. A brain-computer interface device for relieving pain based on VR music, characterized in that: A brain-computer interface method for relieving pain based on VR music for implementing any one of claims 1 to 6, comprising: an EEG acquisition module, a classification and recognition module, and a virtual reality display module; The EEG acquisition module is used to acquire a set of EEG signals of the user; The classification and recognition module is connected to the EEG acquisition module and the virtual reality display module respectively, and is used to classify and recognize the user's EEG signal set to obtain user feature state information; The virtual reality display module is used to generate corresponding auditory information and visual information according to the user characteristic state information; the virtual reality display module stores the auditory information and visual information corresponding to each user characteristic information.

8. A brain-computer interface device for relieving pain based on VR music, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the brain-computer interface method for relieving pain based on VR music as described in any one of claims 1 to 6.

9. A computer storable medium, characterized in that: The computer storable medium stores computer instructions, which, when called by a computer, are used to execute the brain-computer interface method for relieving pain based on VR music as described in any one of claims 1 to 6.

10. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the brain-computer interface method for relieving pain based on VR music as described in any one of claims 1 to 6.

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