A method, system, device and medium for removing signal noise in virtual reality

Abstract

The present invention provides a method, system, device and medium for removing signal noise in virtual reality. The method includes the following steps: obtaining an EEG signal of a target object in virtual reality and resting-state data of the target object in a resting state; preprocessing the EEG signal to obtain a first intermediate-state signal; performing principal component analysis on the resting-state data to obtain a noise threshold; removing noise from the first intermediate-state signal according to the noise threshold to obtain a second intermediate-state signal; performing principal component analysis on the second intermediate-state signal to obtain first principal component data, and constructing a characteristic signal corresponding to the EEG signal according to the first principal component data. The method is highly efficient, does not require manual processing, can effectively solve the problem of large EEG signal noise during VR use, performs immediate processing on real-time monitored EEG signals, and can be widely applied to the field of signal processing technology.

Description

Technical Field

[0001] The present invention relates to the field of signal processing technology, and in particular to a method, system, device and medium for removing signal noise in virtual reality. Background Art

[0002] Virtual Reality (VR) is a simulated experience that uses gesture tracking and 3D near-eye display to immerse users in a virtual world. VR technology is currently widely used in scientific research, education, entertainment, healthcare, and other fields, and has broad application prospects.

[0003] Electroencephalogram (EEG) is a method for recording the brain's spontaneous electrical activity. The biological signals detected by EEG represent the postsynaptic potentials of pyramidal neurons in the neocortex and paleocortex. It is generally non-invasive and uses the international 10-20 system or its variants to place EEG electrodes along the scalp, commonly known as scalp EEG. The frequency of EEG signals mainly ranges from 0.5 to 100 Hz, with an amplitude of 5 to 300 uV. Because EEG signals are very weak, they are easily affected by noise, such as noise from physiological activities during the acquisition process and noise from the acquisition equipment.

[0004] With the widespread adoption of VR, the use of EEG to assess brain activity during VR use, such as real-time monitoring of visual fatigue, emotion recognition, and VR brain-computer interfaces, has gradually gained traction. However, during real-time EEG assessment of VR users, factors such as the need for user interaction with VR, prolonged viewing time, and discomfort with VR equipment can easily lead to serious noise issues such as head movement, sweating, and muscle tension. Summary of the Invention

[0005] In view of this, in order to at least partially solve the above-mentioned technical problems or one of the defects, the purpose of an embodiment of the present invention is to provide a virtual reality signal noise removal method, which can quickly and automatically remove noise from the real-time monitored EEG signal; the technical solution of this application also provides a system, equipment and medium corresponding to the method.

[0006] On the one hand, the technical solution of the present application provides a method for removing signal noise in virtual reality, comprising the following steps:

[0007] Acquiring an EEG signal of a target object in virtual reality and resting-state data of the target object in a resting state;

[0008] Preprocessing the EEG signal to obtain a first intermediate state signal;

[0009] performing principal component analysis on the resting-state data to obtain a noise threshold;

[0010] removing noise from the first intermediate state signal according to the noise threshold to obtain a second intermediate state signal;

[0011] A principal component analysis is performed on the second intermediate state signal to obtain first principal component data, and a characteristic signal corresponding to the EEG signal is constructed based on the first principal component data.

[0012] In a feasible embodiment of the present application, preprocessing the EEG signal to obtain a first intermediate state signal includes:

[0013] Downsampling the EEG signal to obtain a downsampled signal at a target frequency;

[0014] Bandpass filtering is performed on the downsampled signal to obtain the first intermediate state signal.

[0015] In a feasible embodiment of the solution of the present application, removing noise from the first intermediate state signal according to the noise threshold to obtain the second intermediate state signal includes:

[0016] Performing principal component analysis on the first intermediate state signal to obtain a principal component score matrix;

[0017] When the eigenvalue in the principal component score matrix is greater than the noise threshold, the second principal component data corresponding to the eigenvalue is removed from the first intermediate state signal to obtain the second intermediate state signal.

[0018] In a feasible embodiment of the solution of the present application, performing principal component analysis on the first intermediate state signal to obtain a principal component score matrix includes:

[0019] Normalizing a first signal matrix corresponding to the first intermediate state signal to obtain a normalization index, and constructing a normalization matrix according to the normalization index;

[0020] Determining a covariance matrix corresponding to the standardized matrix;

[0021] Obtaining eigenvalues of the covariance matrix and determining eigenvectors corresponding to the eigenvalues;

[0022] A factor loading matrix is constructed based on the eigenvectors, and a principal component score matrix is determined based on the factor loading matrix.

[0023] In a feasible embodiment of the solution of the present application, when the eigenvalue in the principal component score matrix is greater than the noise threshold, removing the second principal component data corresponding to the eigenvalue from the first intermediate state signal to obtain the second intermediate state signal includes:

[0024] Obtaining other principal component data after removing the second principal component to construct a principal component matrix;

[0025] The second intermediate state signal is calculated based on the principal component matrix and the factor loading matrix.

[0026] In a feasible embodiment of the present application, performing principal component analysis on the resting-state data to obtain a noise threshold includes:

[0027] performing principal component analysis on the resting-state data to obtain third principal component data, and constructing a component activation matrix based on the third principal component data;

[0028] The mean and standard deviation of the elements in the component activation matrix are calculated, a threshold matrix is constructed based on the mean and the standard deviation, and the noise threshold is determined based on the square value of the second norm of the threshold matrix.

[0029] In a feasible embodiment of the solution of the present application, principal component analysis is performed on the second intermediate state signal to obtain first principal component data, and a characteristic signal corresponding to the EEG signal is constructed based on the first principal component data, including:

[0030] Performing principal component analysis on the second intermediate state signal to obtain a plurality of first principal component data;

[0031] When the sum of the variances of the first principal component data is greater than a first preset value, determining the number of features corresponding to the first principal component data;

[0032] A characteristic signal matrix is constructed according to the number of features and the first principal component data.

[0033] On the other hand, the technical solution of the present application also provides a virtual reality signal noise removal system, which includes:

[0034] a signal acquisition unit, configured to acquire an EEG signal of a target object in virtual reality and resting-state data of the target object in a resting state;

[0035] a preprocessing unit, configured to preprocess the EEG signal to obtain a first intermediate state signal;

[0036] a threshold calculation unit, configured to perform principal component analysis on the resting-state data to obtain a noise threshold;

[0037] a data denoising unit, configured to remove noise from the first intermediate state signal according to the noise threshold to obtain a second intermediate state signal;

[0038] The feature output unit is used to perform principal component analysis on the second intermediate state signal to obtain first principal component data, and construct a feature signal corresponding to the EEG signal based on the first principal component data.

[0039] On the other hand, the technical solution of the present application also provides a virtual reality signal noise removal device, which includes at least one processor; at least one memory for storing at least one program; when the at least one program is executed by the at least one processor, the at least one processor runs a virtual reality signal noise removal method as described in the first aspect.

[0040] On the other hand, the technical solution of the present application further provides a storage medium storing a program executable by a processor, wherein the program executable by the processor is used to execute a virtual reality signal noise removal method as described in any one of the first aspects when executed by the processor.

[0041] The advantages and beneficial effects of the present invention will be partially given in the following description, and the rest can be understood through the specific embodiments of the present invention:

[0042] The technical solution of the present application, based on obtaining the EEG signals and resting-state data of the target object in virtual reality, first performs principal component analysis on the resting-state data to obtain a noise threshold, then removes the noise data in the EEG signal based on the noise threshold, and after completing the denoising, performs principal component analysis on the EEG signal to obtain the corresponding characteristic signal; the method is highly efficient, does not require manual processing, can effectively solve the problem of large EEG signal noise during VR use, and performs immediate processing of real-time monitored EEG signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0044] Figure 1 A flowchart of the steps of a method for removing signal noise in virtual reality provided by the technical solution of this application;

[0045] Figure 2 This is a flowchart of the process of collecting EEG signals in the technical solution of this application;

[0046] Figure 3 Schematic diagram of the principle of noise removal through principal component analysis in the technical solution of this application;

[0047] Figure 4 This is a schematic diagram of the principle of feature extraction based on noise-removed data in the technical solution of this application. DETAILED DESCRIPTION

[0048] The embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention and are not to be construed as limiting the present invention. The step numbers in the following embodiments are provided for ease of explanation only and do not limit the order of the steps. The order of execution of the steps in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0049] It should be noted that although the device schematics illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the device or the sequence in the flowcharts. The terms "first," "second," and so forth, in the specification, claims, and accompanying drawings, are used to distinguish similar items and are not necessarily intended to describe a specific sequence or precedence.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0051] As pointed out in the background technology, the EEG signal denoising technology in the related technical solutions usually adopts the independent component analysis method, which has slow operation speed and low efficiency. Moreover, it usually requires manual denoising and cannot perform real-time denoising on the EEG signal during VR use. In view of the technical problems or defects in the related technical solutions, such as the problem of real-time noise removal during VR EEG evaluation,

[0052] First, as Figure 1 As shown, the technical solution of the present application provides a method for removing signal noise in virtual reality, and the method includes steps S100-S500:

[0053] S100: Acquire an EEG signal of a target object in virtual reality and resting-state data of the target object in a resting state.

[0054] Specifically in the embodiment, Figure 2As shown, first, prepare for signal collection by having the target subject (subject) wear VR equipment, such as VR glasses or AR helmets. Adjust the device parameters and prepare for use. Then, have the subject wear the EEG device. Have the subject close their eyes and rest, remaining calm and relaxed, but not falling asleep. Keep the subject still while collecting their signals for 5 minutes. During the collection process, observe the EEG signals to ensure they are stable, clean, and free from excessive noise. Finally, collect data during VR usage. Have the subject use the VR device, such as watching videos or playing games, while recording their EEG signals.

[0055] S200: Preprocess the EEG signal to obtain a first intermediate state signal.

[0056] Specifically in the embodiment, preliminary preprocessing is performed on the collected EEG signal to roughly remove the noise signal in the EEG signal.

[0057] In some feasible implementations, the step S200 of preprocessing the EEG signal to obtain the first intermediate state signal may further include steps S210-S220:

[0058] S210 , performing bandpass filtering on the EEG signal to obtain the first intermediate state signal.

[0059] The target frequency may refer to a preset downsampling frequency in the embodiment. Specifically, in the embodiment, the collected raw EEG data is downsampled to 100 Hz. The EEG signal data is then subjected to a 0.5-45 Hz bandpass filtering operation to remove the corresponding noise components in the EEG signal.

[0060] S300 , performing principal component analysis on the resting-state data to obtain a noise threshold.

[0061] Specifically, in this embodiment, the artifact subspace reconstruction (ASR) algorithm is used to estimate the noise threshold. Y is a q*m matrix, representing the reference signal—that is, resting-state data collected before viewing VR. Q is the number of time points, and m is the number of electrode channels. X is an n*m matrix, representing the target data collected during VR viewing. N is the number of time points, and m is the number of electrode channels.

[0062] More specifically, in some feasible implementations, the step S300 of performing principal component analysis on the resting-state data to obtain a noise threshold may include steps S310-S320:

[0063] S310, performing principal component analysis on the resting-state data to obtain third principal component data, and constructing a component activation matrix based on the third principal component data;

[0064] S320. Calculate the mean and standard deviation of the elements in the component activation matrix, construct a threshold matrix based on the mean and the standard deviation, and determine the noise threshold based on the square value of the second norm of the threshold matrix.

[0065] The third principal component data mainly includes the principal component data obtained by performing principal component analysis on the reference signal Y, i.e., the resting state data. Specifically, in the embodiment, principal component analysis is performed on the reference signal Y to obtain principal component V=[v1, ..., v m ]. Component activation matrix A = YV T For each component a k , estimate its mean m k and standard deviation s k For its mean m=[m1,...,m m ] and s=[s1,...,s m ], estimate a principal component threshold vector z = m + bs, and a threshold matrix Z = diag (z) V T In this embodiment, b can be set to a constant of 6. The noise threshold t(w) is the square of the 2-norm of Z*w.

[0066] S400: Remove noise from the first intermediate state signal according to the noise threshold to obtain a second intermediate state signal.

[0067] In the embodiment, X is an n*m matrix, which is the target data collected when watching VR, where n is the number of time points and m is the number of electrode channels. First, principal component analysis (PCA) is performed on X to obtain the principal component W = [w1, w2, ..., w m ]. On the basis of obtaining all principal component data, the noise principal component is removed to obtain the principal component data after noise removal, and the noise removal data is obtained by operation with the factor loading matrix, that is, the second intermediate state signal.

[0068] In some feasible implementations, in the method, step S400 of removing noise from the first intermediate state signal according to the noise threshold to obtain the second intermediate state signal may include steps S410-S420:

[0069] S410, performing principal component analysis on the first intermediate state signal to obtain a principal component score matrix;

[0070] S420. When the eigenvalue in the principal component score matrix is greater than the noise threshold, remove the second principal component data corresponding to the eigenvalue from the first intermediate state signal to obtain the second intermediate state signal.

[0071] Specifically in the embodiment, Figure 3 As shown, the EEG signal matrix A is an n*m matrix, that is, the signal data has n time points and m electrode channels. In the process of principal component analysis of A, the electrical signal matrix A is first standardized to obtain the standardized matrix Z; then the covariance matrix of the matrix Z is calculated, and the eigenvalues and corresponding eigenvectors of the covariance matrix Z are further calculated. If the principal component variance of the EEG signal, that is, the eigenvalue σ corresponding to the covariance matrix of A, is i , which satisfies σ i >t(w i ), then the principal component w i Eliminate the data to obtain the principal component data after elimination, and perform operations on it with the factor loading matrix to obtain the data after noise removal.

[0072] In some feasible implementations, step S410 of performing principal component analysis on the first intermediate state signal to obtain a principal component score matrix, i.e., the principal component analysis process, may include steps S411-S414:

[0073] S411 . Normalize a first signal matrix corresponding to the first intermediate state signal to obtain a normalization index, and construct a normalization matrix according to the normalization index.

[0074] Specifically in the embodiment, for the matrix element x in the EEG signal matrix A, ij , that is, the value of the jth electrode at the i-th time point, which is converted into a standardized index z ij :

[0075]

[0076] in, Right now and s j are the mean and standard deviation of the jth electrode respectively. ij The matrix Z is the normalized matrix of A.

[0077] S412: Determine a covariance matrix corresponding to the standardized matrix.

[0078] Specifically, in the embodiment, the covariance matrix of Z is calculated as C=cov(X); C=E((XE(X))(XE(X)) T ).

[0079] S413: Obtain the eigenvalues of the covariance matrix, and determine the eigenvectors corresponding to the eigenvalues.

[0080] Specifically, in the embodiment, it is assumed that λ and x are the eigenvalues and eigenvectors of C, respectively. Then, the characteristic polynomial |C-λI| is first calculated, and all roots of |C-λI|=0 are obtained. These roots are all the eigenvalues of C. For each eigenvalue λi, the basic solution system v1, v2...v is solved for the linear equation system (C-λiI) x=0. t Then x=k1v1+k2v2+L+…+k t v t is the eigenvector corresponding to the eigenvalue, where k1, k2, L..., k t is an arbitrary constant that is not all zero.

[0081] S414. Construct a factor loading matrix based on the eigenvectors, and determine a principal component score matrix based on the factor loading matrix.

[0082] Specifically in the embodiment, the eigenvectors are sorted row by row from top to bottom according to their corresponding eigenvalues to obtain a matrix H (factor loading matrix). Then the principal component score matrix is F=HX T .

[0083] In some feasible implementations, when the eigenvalue in the principal component score matrix is greater than the noise threshold, the method removes the second principal component data corresponding to the eigenvalue from the first intermediate state signal to obtain the second intermediate state signal in step S420, which may include steps S421-S422:

[0084] S421, obtaining other principal component data after removing the second principal component to construct a principal component matrix;

[0085] S422. Obtain the second intermediate state signal by calculation according to the principal component matrix and the factor loading matrix.

[0086] Specifically in the embodiment, the principal component matrix remaining after removing the noise principal component is V m The corresponding factor loading matrix is represented by H m Indicates. Then Xclean is the data after noise removal, that is, the second intermediate state signal.

[0087] S500: Perform principal component analysis on the second intermediate state signal to obtain first principal component data, and construct a characteristic signal corresponding to the EEG signal based on the first principal component data.

[0088] In the embodiment, principal component analysis is also used to extract characteristic signals. More specifically, step S500 in the embodiment may include steps S510-S530:

[0089] S510: Perform principal component analysis on the second intermediate state signal to obtain a plurality of first principal component data;

[0090] S520: When the sum of the variances of the first principal component data is greater than a first preset value, determine the number of features corresponding to the first principal component data;

[0091] S530: Construct a characteristic signal matrix according to the number of features and the first principal component data.

[0092] Specifically in the embodiment, Figure 4 As shown, the principal component analysis is performed on Xclean to obtain the principal component data F = [f1, ..., f m ], where f1, ..., f m Arrange them from large to small according to their corresponding eigenvalues. The embodiment is to find the smallest p so that the principal component variance σ1+σ2+…+σ p >85%. p refers to the number of principal components, that is, the number of features to be extracted. The principal component variance is the eigenvalue of Xclean. Sort the principal component variances from large to small σ1, σ2, ..., σ m , add them one by one until their sum is greater than 85% and then stop adding, that is, σ1+σ2+…+σ p >85%, and σ1+σ2+...+σ p-1 ≤85%; p is the number of principal components to be extracted in the embodiment. Then F m =[f1,…,f m ] is the characteristic signal extracted by the embodiment.

[0093] On the other hand, the technical solution of the present application also provides a virtual reality signal noise removal system, which includes:

[0094] a signal acquisition unit, configured to acquire an EEG signal of a target object in virtual reality and resting-state data of the target object in a resting state;

[0095] a preprocessing unit, configured to preprocess the EEG signal to obtain a first intermediate state signal;

[0096] a threshold calculation unit, configured to perform principal component analysis on the resting-state data to obtain a noise threshold;

[0097] a data denoising unit, configured to remove noise from the first intermediate state signal according to the noise threshold to obtain a second intermediate state signal;

[0098] The feature output unit is used to perform principal component analysis on the second intermediate state signal to obtain first principal component data, and construct a feature signal corresponding to the EEG signal based on the first principal component data.

[0099] On the other hand, the technical solution of the present application also provides a virtual reality signal noise removal device, which includes: at least one processor; at least one memory for storing at least one program; when the at least one program is executed by the at least one processor, the at least one processor runs a virtual reality signal noise removal method as described in the second aspect.

[0100] An embodiment of the present invention further provides a storage medium storing a corresponding execution program, which is executed by a processor to implement a virtual reality signal noise removal method in the first aspect.

[0101] From the above specific implementation process, it can be concluded that the technical solution provided by the present invention has the following advantages or strengths compared with the existing technology:

[0102] The virtual reality signal noise removal method proposed in the technical solution of this application has high computational efficiency and does not require manual operation. It can effectively solve the noise problem of VR real-time monitoring of EEG signals, thereby obtaining clean EEG signal data.

[0103] Furthermore, although the present invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It will also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the module will be understood within the ordinary skill of an engineer. Therefore, a person skilled in the art will be able to implement the present invention as set forth in the claims using ordinary skill without undue experimentation. It will also be understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0104] The logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing the logical functions, which may be embodied in any computer-readable medium for use by or in conjunction with an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other system that can fetch instructions from and execute instructions on an instruction execution system, apparatus, or device).

[0105] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0106] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

[0107] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for removing signal noise in virtual reality, characterized in that: The following steps are involved: Acquiring an EEG signal of a target object in virtual reality and resting-state data of the target object in a resting state; Preprocessing the EEG signal to obtain a first intermediate state signal; performing principal component analysis on the resting-state data to obtain a noise threshold; removing noise from the first intermediate state signal according to the noise threshold to obtain a second intermediate state signal; A principal component analysis is performed on the second intermediate state signal to obtain first principal component data, and a characteristic signal corresponding to the EEG signal is constructed based on the first principal component data.

2. The method for removing signal noise in virtual reality according to claim 1, characterized in that: The preprocessing of the EEG signal to obtain a first intermediate state signal includes: Downsampling the EEG signal to obtain a downsampled signal at a target frequency; Bandpass filtering is performed on the downsampled signal to obtain the first intermediate state signal.

3. The method for removing signal noise in virtual reality according to claim 1, characterized in that: The removing noise from the first intermediate state signal according to the noise threshold to obtain the second intermediate state signal includes: Performing principal component analysis on the first intermediate state signal to obtain a principal component score matrix; When the eigenvalue in the principal component score matrix is greater than the noise threshold, the second principal component data corresponding to the eigenvalue is removed from the first intermediate state signal to obtain the second intermediate state signal.

4. The method for removing signal noise in virtual reality according to claim 3, characterized in that: The performing principal component analysis on the first intermediate state signal to obtain a principal component score matrix includes: Normalizing a first signal matrix corresponding to the first intermediate state signal to obtain a normalization index, and constructing a normalization matrix according to the normalization index; Determining a covariance matrix corresponding to the standardized matrix; Obtaining eigenvalues of the covariance matrix and determining eigenvectors corresponding to the eigenvalues; A factor loading matrix is constructed based on the eigenvectors, and a principal component score matrix is determined based on the factor loading matrix.

5. The method for removing signal noise in virtual reality according to claim 4, characterized in that: When an eigenvalue in the principal component score matrix is greater than the noise threshold, removing second principal component data corresponding to the eigenvalue from the first intermediate state signal to obtain the second intermediate state signal includes: Obtaining other principal component data after removing the second principal component to construct a principal component matrix; The second intermediate state signal is calculated based on the principal component matrix and the factor loading matrix.

6. The method for removing signal noise in virtual reality according to claim 1, characterized in that: The step of performing principal component analysis on the resting-state data to obtain a noise threshold comprises: performing principal component analysis on the resting-state data to obtain third principal component data, and constructing a component activation matrix based on the third principal component data; The mean and standard deviation of the elements in the component activation matrix are calculated, a threshold matrix is constructed based on the mean and the standard deviation, and the noise threshold is determined based on the square value of the second norm of the threshold matrix.

7. The method for removing signal noise in virtual reality according to claim 1, characterized in that: The performing principal component analysis on the second intermediate state signal to obtain first principal component data, and constructing a characteristic signal corresponding to the EEG signal according to the first principal component data, includes: Performing principal component analysis on the second intermediate state signal to obtain a plurality of first principal component data; When the sum of the variances of the first principal component data is greater than a first preset value, determining the number of features corresponding to the first principal component data; A characteristic signal matrix is constructed according to the number of features and the first principal component data.

8. A virtual reality signal noise removal system, characterized in that: include: a signal acquisition unit, configured to acquire an EEG signal of a target object in virtual reality and resting-state data of the target object in a resting state; a preprocessing unit, configured to preprocess the EEG signal to obtain a first intermediate state signal; a threshold calculation unit, configured to perform principal component analysis on the resting-state data to obtain a noise threshold; a data denoising unit, configured to remove noise from the first intermediate state signal according to the noise threshold to obtain a second intermediate state signal; The feature output unit is used to perform principal component analysis on the second intermediate state signal to obtain first principal component data, and construct a feature signal corresponding to the EEG signal based on the first principal component data.

9. A virtual reality signal noise removal device, characterized in that: The device comprises: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor runs the method for removing signal noise in virtual reality as described in any one of claims 1 to 7.

10. A storage medium storing a program executable by a processor, characterized in that: The processor-executable program is used to run a virtual reality signal noise removal method according to any one of claims 1 to 7 when executed by the processor.

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