TACS artifact removal method, device and equipment combining multi-resolution analysis and principal component analysis and medium

Abstract

The invention discloses a tACS artifact removal method and device combining multi-resolution analysis and principal component analysis, equipment and a medium. The method comprises the steps that electroencephalogram signals are obtained; dividing the electroencephalogram signal by adopting a multi-resolution analysis method based on wavelet transformation to obtain wavelet coefficients of different scales; performing principal component analysis on the wavelet coefficient by adopting a PCA algorithm, and removing components according to an analysis result; and processing the residual PCA components by adopting a power spectrum interpolation method, and reconstructing signals to obtain electroencephalogram signals without tACS artifacts. According to the method, multi-level frequency decomposition is carried out on the signals through wavelet transform, the frequency range where tACS artifacts are located is accurately positioned and separated, then PCA analysis is carried out on data of each frequency level, components containing the strongest artifacts are further distinguished and removed, other important physiological information is reserved, and the flexibility and accuracy of artifact processing are improved. The method can be widely applied to the field of electroencephalogram signal processing.

Description

Technical Field

[0001] The present invention relates to the field of electroencephalogram (EEG) signal processing, and particularly to a tACS artifact removal method, device, equipment and medium combining multi-resolution analysis and principal component analysis. Background Art

[0002] Electroencephalogram (EEG) is a widely used brain imaging technology that records the electrical activities of large-scale neuron synchronization and can reflect rich neural activities during cognitive processes. Brain rhythms are unique patterns distributed in different frequency bands in EEG and are related to various cognitive activities. Transcranial alternating current stimulation (tACS) is an important means of brain rhythm regulation. By directly delivering continuous micro-amount alternating current to specific cerebral cortex regions through electrodes, neuron populations in the target region are then entrained to the set alternating current frequency, thereby triggering the synchronization of oscillations in specific brain regions with the input current and effectively regulating the brain rhythms in cortical regions. Recording EEG data while performing tACS regulation can well analyze and track the changes in the brain during the brain regulation process.

[0003] Electroencephalography (EEG) is a non-invasive neuroimaging technology that measures the postsynaptic potentials generated by the electrical activities of cortical neurons by placing electrodes on the scalp. Due to its relatively simple setup, EEG has become the main neuroimaging method for recording and understanding the effects of tACS on the brain and its underlying mechanisms. However, there are artifacts in EEG signals caused by tACS stimulation. The amplitudes of these artifacts are much larger than the signals of interest. And due to hardware limitations and physiological interferences (such as head movement, heartbeat, etc.), this current is non-linearly converted and mixed with normal brain activities. Artifacts not only appear at the basic stimulation frequency but also show high power at several of its harmonics. Therefore, tACS artifacts overlap with normal EEG activities in the frequency domain, and it is impossible to use frequency domain filtering and methods that do not remove some signals of interest for denoising. Summary of the Invention

[0004] To solve at least one of the technical problems existing in the prior art to a certain extent, an object of the present invention is to provide a tACS artifact removal method, device, equipment and medium combining multi-resolution analysis and principal component analysis.

[0005] The first technical solution adopted by the present invention is:

[0006] A tACS artifact removal method combining multi-resolution analysis and principal component analysis, comprising the following steps:

[0007] Obtain EEG signals;

[0008] The multi - resolution analysis method based on wavelet transform is adopted to partition the electroencephalogram (EEG) signal, and wavelet coefficients of different scales are obtained;

[0009] The principal component analysis (PCA) algorithm is used to perform principal component analysis on the wavelet coefficients, and components are removed according to the analysis results;

[0010] The power spectrum interpolation method is used to process the remaining PCA components and reconstruct the signal, and the EEG signal with tACS artifacts removed is obtained.

[0011] Furthermore, the multi - resolution analysis method based on wavelet transform is adopted to partition the EEG signal, and wavelet coefficients of different scales are obtained, including:

[0012] For the original single - channel EEG signal x(t), wavelet decomposition is applied to generate wavelet coefficients of different scales:

[0013]

[0014] where d k (t) is the wavelet coefficient of the k - th layer, a L (t) is the approximation coefficient of the L - th layer, and L is the decomposition level.

[0015] Furthermore, the application of wavelet decomposition includes:

[0016] Decomposition is performed using the symlets wavelet function with a vanishing moment of 5.

[0017] Furthermore, the decomposition level L is calculated by the following formula:

[0018] L = Floor(log2(F s )) - 1

[0019] where F s is the sampling frequency of the signal, and Floor represents the floor function.

[0020] Furthermore, the use of the PCA algorithm to perform principal component analysis on the wavelet coefficients and removing components according to the analysis results includes:

[0021] The wavelet coefficient d k (t) of each layer is used as the input of the PCA algorithm for principal component analysis:

[0022]

[0023] where U k and ∑ k 、 represent the left singular vector, the singular value matrix, and the right singular vector respectively, and the principal components are arranged according to the magnitudes of the singular values;

[0024] According to the contribution rate of singular values, the first component is removed to achieve the removal of artifact components.

[0025] Further, the remaining PCA components are processed using the power spectrum interpolation method, and the signal is reconstructed to obtain the EEG signal with tACS artifacts removed, including:

[0026] For the remaining PCA components, calculate the power spectrum of each component;

[0027] Detect the frequency points of tACS artifacts according to the power spectrum, delete these frequency points, and use interpolation to correct the power spectrum to eliminate the spikes at these frequency points;

[0028] Use the corrected power spectrum and the corresponding phase to reconstruct the time series through inverse Fourier transform;

[0029] Use all the processed wavelet coefficients d' k '(t) and the approximation coefficient a L (t) to reconstruct the final signal as the final EEG signal with tACS artifacts removed.

[0030] Further, the calculation formula for the power spectrum of each component is as follows:

[0031] P(f) = |FFT(d' k (t))| 2

[0032] In the formula, FFT represents the fast Fourier transform, and d' k (t) is the wavelet coefficient after PCA processing;

[0033] The formula for correcting the power spectrum is:

[0034] P 修正 (f) = spectrum_interp(P(f), f tACS )

[0035] In the formula, f tACS is the tACS artifact frequency detected at the abnormal point;

[0036] The formula for reconstructing the time series is:

[0037]

[0038] In the formula, is the phase information of the original signal;

[0039] The formula for reconstructing the final signal is:

[0040]

[0041] Where x'(t) is the EEG signal with tACS artifacts removed.

[0042] The second technical solution adopted by the present invention is:

[0043] A tACS artifact removal device combining multi - resolution analysis and principal component analysis, comprising:

[0044] A signal input module for acquiring EEG signals;

[0045] A signal decomposition module for partitioning the EEG signal using a multi - resolution analysis method based on wavelet transform to obtain wavelet coefficients of different scales;

[0046] A component analysis module for performing principal component analysis on the wavelet coefficients using the PCA algorithm and removing components according to the analysis results;

[0047] A power spectrum interpolation module for processing the remaining PCA components using the power spectrum interpolation method and reconstructing the signal to obtain the EEG signal with tACS artifacts removed.

[0048] The third technical solution adopted by the present invention is:

[0049] An electronic device, the electronic device includes a processor and a memory, and at least one instruction, at least one program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement a tACS artifact removal method combining multi - resolution analysis and principal component analysis as described above.

[0050] The fourth technical solution adopted by the present invention is:

[0051] A computer - readable storage medium, in which at least one instruction, at least one program, a code set or an instruction set is stored, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by a processor to implement a tACS artifact removal method combining multi - resolution analysis and principal component analysis as described above.

[0052] The fifth technical solution adopted by the present invention is:

[0053] A computer program product or a computer program, the computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer - readable storage medium. The processor of the computer device can read the computer instructions from the computer - readable storage medium, and the processor executes the computer instructions, so that the computer device executes the above - mentioned method.

[0054] The beneficial effects of the present invention are as follows: The present invention uses wavelet transform to perform multi-level frequency decomposition on the signal, accurately locates and separates the frequency range where the tACS artifact is located, and then performs PCA analysis on the data at each frequency level to further distinguish and remove the components containing the strongest artifacts while retaining other important physiological information. This method improves the flexibility and accuracy of artifact processing. Description of the Drawings

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following introduces the relevant technical solution drawings in the embodiments of the present invention or the prior art. It should be understood that the drawings below are only for conveniently and clearly presenting some embodiments of the technical solutions in the present invention. For those skilled in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0056] Figure 1 is a schematic flowchart of a tACS artifact removal method combining multi-resolution analysis and principal component analysis in an embodiment of the present invention;

[0057] Figure 2 is a schematic flowchart of tACS artifact removal based on power spectrum interpolation. Detailed Embodiments

[0058] The following details the embodiments of the present invention. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below by referring to the drawings are exemplary and are only used to explain the present invention and should not be construed as a limitation to the present invention. For the step numbers in the following embodiments, they are only set for the convenience of explanation and illustration, and no limitation is imposed on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0059] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as up, down, front, back, left, right, etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention.

[0060] In the description of the present invention, the meaning of "several" is one or more, the meaning of "multiple" is more than two, and understandings such as "greater than", "less than", "exceeding", etc. do not include the present number, while understandings such as "above", "below", "within", etc. include the present number. If there is a description of "first" and "second", it is only for the purpose of distinguishing technical features and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features or implicitly indicating the sequence relationship of the indicated technical features.

[0061] In the description of the present invention, unless otherwise clearly defined, words such as "set", "installed", "connected", etc. should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above words in the present invention in combination with the specific content of the technical solution.

[0062] tACS: Abbreviation for transcranial alternating current stimulation, transcranial alternating current stimulation.

[0063] EEG: Abbreviation for Electroencephalography, electroencephalogram.

[0064] MRA: Abbreviation for Multiple Resolution Analysis, multi-resolution analysis.

[0065] PCA: Abbreviation for Principal Component Analysis, principal component analysis.

[0066] As Figure 2 shown, based on the idea of power spectrum anomaly point detection, median filtering is performed on the spectrum and subtracted from the original power spectrum. During this process, a single tACS contamination will appear as a prominent peak in the difference spectrum and can be detected based on the median and inter-quartile range (IQR) of the difference spectrum; subsequently, amplitude and phase interpolation are performed on the spectrum within a certain bandwidth (0.25 - 0.5 Hz) near the abnormal frequency point, and finally, inverse Fourier transform is performed to obtain the EEG data with the tACS stimulation artifact of a single frequency removed. The specific steps are as follows:

[0067] 1) Fast Fourier transform. For the EEG data of a single channel, it is transformed into the frequency domain using FFT (Fast Fourier Transform) to obtain the power spectrum curve F1;

[0068] 2) Median filter the power spectrum with a width of 0.1 Hz to obtain the smoothed power spectrum F2, and subtract it from the initial power spectrum F1 - F2 to obtain the power spectrum difference curve F3;

[0069] 3) For all points on the difference curve F3, abnormal frequency point detection is performed, that is, the points whose spectrum values exceed the third quartile plus 1.5 to 3 times the intern quartile range (IQR) are considered spectrum abnormal points;

[0070] 4) For each abnormal point, a certain bandwidth on its left and right sides is considered to be the point where the tACS artifact is located and deleted from the spectrum. The bandwidth is generally 0.25-0.5Hz;

[0071] 5) Based on the frequency points of the remaining spectrum, the phase and amplitude of the deleted frequency points are interpolated by cubic spline respectively to complete the power spectrum and obtain the complete power spectrum F4;

[0072] 6) Perform inverse fast Fourier transform on F4, transform the signal into the time domain, obtain the single-channel EEG data after removing the artifacts, and complete the artifact removal.

[0073] This method can only perform a simple spectrum interpolation once. In actual situations where more spectrum leakage and high-order harmonics are generated, it cannot effectively remove tACS artifacts. For example, under the influence of the properties of the electrochemical interface of the conductive medium, the change of the contact area during stimulation, the dynamic range of the EEG acquisition amplifier, and the frequency response characteristics, strong spectrum leakage and high-order harmonics are often generated, resulting in a single simple spectrum interpolation that cannot effectively remove tACS artifacts. Therefore, other signal processing processes need to be introduced.

[0074] In view of the fact that the intensity of tACS artifacts is large, the performance on multiple EEG channels is relatively consistent, and the frequency component is single and the correlation with the EEG component is low, in order to remove tACS artifacts and retain EEG activity to the greatest extent, the present invention provides an algorithm (MRA-PCA) of multi-resolution analysis (Multiple Resolution Analysis, MRA) and principal component analysis (Principal Component Analysis, PCA) based on wavelet transform. Using the method of multi-resolution analysis based on wavelet transform, the data is further divided into sub-band time series, and then principal component analysis is performed, and then each principal component is subdivided to further increase the number of components extracted. The MRA-PCA process of the above process can effectively remove the tACS artifacts with the highest intensity in multi-channel EEG data, and the remaining components still contain strong tACS artifacts, so it is necessary to perform power spectrum interpolation on the remaining components one by one to obtain the components after removing the artifacts. Then the PCA components are reconstructed to the signal space to complete the removal of tACS artifacts.

[0075] Example 1

[0076] like Figure 1As shown in the figure, this embodiment provides a tACS artifact removal method combining multi-resolution analysis and principal component analysis, including the following steps:

[0077] S1. Obtain the electroencephalogram (EEG) signal.

[0078] S2. Use the multi-resolution analysis method based on wavelet transform to partition the EEG signal and obtain wavelet coefficients at different scales.

[0079] For the original single-channel EEG signal x(t), apply wavelet decomposition to generate wavelet coefficients at different scales:

[0080]

[0081] where d k (t) is the wavelet coefficient at the k-th layer, and a L (t) is the approximation coefficient at the L-th layer, and L is the number of decomposition layers.

[0082] Specifically, the selection of the wavelet function is very important. Considering the spectral characteristics of the tACS artifact, symlets with a vanishing moment of 5 are selected as the wavelet function, which has good time-frequency localization characteristics. In addition, the decomposition layer number L is determined in the following way:

[0083] L = Floor(log2(F s )) - 1

[0084] That is, subtract 1 after rounding down the logarithm to the base 2 at this sampling frequency, ensuring that the lowest-frequency sub-band component is around 0 - 4 Hz, that is, the EEG delta rhythm. Among them, F s is the sampling frequency of the signal. This setting ensures that the lowest-frequency sub-band component is mainly concentrated in the range of 0 - 4 Hz.

[0085] S3. Use the PCA algorithm to perform principal component analysis on the wavelet coefficients and remove components according to the analysis results.

[0086] Take the wavelet coefficient d k (t) at each layer as the input of the PCA algorithm for principal component analysis:

[0087]

[0088] where U k and ∑ k 、 represent the left singular vector, the singular value matrix, and the right singular vector respectively, and the principal components are arranged according to the magnitude of the singular values;

[0089] According to the contribution rate of the singular values, remove the first component to achieve the removal of the artifact component.

[0090] S4. Use the power spectrum interpolation method to process the remaining PCA components and reconstruct the signal to obtain the EEG signal with tACS artifacts removed.

[0091] As an alternative implementation, step S4 specifically includes the following steps:

[0092] S41. Power spectrum calculation: For the remaining PCA components, calculate the power spectrum of each component:

[0093] P(f) = |FFT(d' k (t))| 2

[0094] where FFT represents the fast Fourier transform, and d' k (t) is the detail coefficient after PCA processing.

[0095] S42. Power spectrum interpolation: For the frequency points of tACS artifacts detected by outlier detection, use interpolation to correct the power spectrum and eliminate the spikes at these frequency points:

[0096] P 修正 (f) = spectrum_interp(P(f), f tACS )

[0097] where f tACS is the frequency of tACS artifacts detected by outlier detection.

[0098] S43. Inverse transformation: Use the corrected power spectrum and the corresponding phase to reconstruct the time series through inverse Fourier transform:

[0099]

[0100] where is the phase information of the original signal.

[0101] S44. Reconstruction into the signal space: Use all the processed detail coefficients d' k '(t) and the approximation coefficient a L (t) to reconstruct the final signal:

[0102]

[0103] The obtained x'(t) is the EEG signal with tACS artifacts removed.

[0104] Generally speaking, the MRA-PCA method proposed by the present invention uses wavelet transform to perform multi-level frequency decomposition on signals, enabling precise localization and separation of the frequency range where tACS artifacts are located. This decomposition strategy is superior to traditional band-pass filtering because it allows for personalized processing at different frequency levels and more refined manipulation of signals. Then, PCA analyzes the data at each frequency level, further differentiating and removing components containing the strongest artifacts while retaining other important physiological information. This method improves the flexibility and precision of artifact processing. The MRA-PCA method preserves the integrity of most of the original EEG signals by only removing the tACS artifact components that most significantly affect data quality, avoiding the loss of useful signals caused by overprocessing.

[0105] In summary, compared with the prior art, the method of the present invention has at least the following advantages and beneficial effects:

[0106] The present invention realizes multi-resolution analysis by using wavelet transform, performs multi-level decomposition on signals, and each level corresponds to a different frequency range. This is different from traditional single-frequency band processing or simple Fourier transform methods, which usually cannot provide local information in both time and frequency simultaneously. By precisely controlling the processing strategy of each layer, it is possible to more specifically lock in tACS artifacts within a specific frequency range without affecting the signal quality in other frequency ranges.

[0107] Combined with PCA, principal component analysis is performed on the signals decomposed at each frequency level, which not only simplifies the data structure but also accurately identifies and removes those components that mainly contribute to tACS artifacts. This method can maximize the retention of useful EEG signal components while removing tACS artifacts, improving the purification efficiency and quality of the data.

[0108] This fine control and selection mechanism is not available in traditional tACS artifact removal methods, and those methods usually may lead to the loss of valuable information.

[0109] Example 2

[0110] This embodiment provides a tACS artifact removal device combining multi-resolution analysis and principal component analysis, including:

[0111] A signal input module for acquiring EEG signals;

[0112] A signal decomposition module for performing partitioning processing on the EEG signals by using a multi-resolution analysis method based on wavelet transform to obtain wavelet coefficients at different scales;

[0113] A component analysis module for performing principal component analysis on the wavelet coefficients by using the PCA algorithm and removing components according to the analysis results;

[0114] A power spectrum interpolation module is used to process the remaining PCA components by using the power spectrum interpolation method, reconstruct the signal, and obtain the EEG signal with tACS artifacts removed.

[0115] Since this device is a tACS artifact removal device combining multi-resolution analysis and principal component analysis in an embodiment of the present invention, and the principle of solving problems by this device is similar to that of this method, the implementation of this device can refer to the implementation process of the above method embodiment, and the repeated parts will not be described again.

[0116] Embodiment 3

[0117] An embodiment of the present invention further provides an electronic device, which includes a processor and a memory. At least one instruction, at least one program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement Figure 1 a tACS artifact removal method combining multi-resolution analysis and principal component analysis as shown.

[0118] It can be understood that the memory may include a random access memory (RAM), or may also include a read-only memory (ROM). Optionally, the memory includes a non-transitory computer-readable storage medium. The memory can be used to store instructions, programs, codes, code sets or instruction sets. The memory may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function, instructions for implementing the above method embodiments, etc.; the data storage area may store data created according to the use of the server, etc.

[0119] The processor may include one or more processing cores. The processor connects various parts within the entire server using various interfaces and circuits. By running or executing instructions, programs, code sets, or instruction sets stored in the memory, and by invoking data stored in the memory, it performs various functions of the server and processes data. Optionally, the processor may be implemented in at least one of the following hardware forms: Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor may integrate a combination of one or several of the Central Processing Unit (CPU) and modem, etc. Among them, the CPU mainly processes the operating system, application programs, etc.; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor and may be implemented separately through a single chip.

[0120] Since this electronic device is the electronic device corresponding to a tACS artifact removal method combining multi-resolution analysis and principal component analysis in an embodiment of the present invention, and the principle of how this electronic device solves problems is similar to that of this method, the implementation of this electronic device can refer to the implementation process of the above method embodiment, and repeated parts will not be elaborated again.

[0121] Embodiment 4

[0122] An embodiment of the present invention further provides a computer-readable storage medium, in which at least one instruction, at least one segment of program, code set, or instruction set is stored, and the at least one instruction, the at least one segment of program, the code set, or the instruction set is loaded and executed by a processor to implement Figure 1 a tACS artifact removal method combining multi-resolution analysis and principal component analysis as shown.

[0123] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by a program instructing related hardware. This program can be stored in a computer-readable storage medium, which includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM), or other optical disc memories, magnetic disk memories, tape memories, or any other computer-readable medium capable of carrying or storing data.

[0124] Since this storage medium is the storage medium corresponding to a tACS artifact removal method combining multi-resolution analysis and principal component analysis in the embodiments of the present invention, and the principle of solving problems by this storage medium is similar to that of this method, the implementation of this storage medium can refer to the implementation process of the above method embodiments, and the repeated parts will not be described again.

[0125] Embodiment 5

[0126] In some possible implementation manners, various aspects of the method of the embodiments of the present invention can also be implemented in the form of a program product, which includes program code. When the program product runs on a computer device, the program code is used to cause the computer device to execute the steps of a tACS artifact removal method combining multi-resolution analysis and principal component analysis according to various exemplary implementation manners described above in this specification. Among them, the executable computer program code or "code" for executing each embodiment can be written in a high-level programming language such as C, C++, C#, Smalltalk, Java, JavaScript, Visual Basic, structured query language (e.g., Transact-SQL), Perl, or in various other programming languages.

[0127] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0128] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0129] The above embodiments are only for illustrating the technical concept and features of the present invention, and the purpose is to enable those of ordinary skill in the art to understand the content of the present invention and implement it accordingly, and it cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made according to the essence of the content of the present invention should be covered within the protection scope of the present invention.

Claims

1. A tACS artifact removal method combining multi - resolution analysis and principal component analysis, characterized in that, The following steps are involved: Acquire EEG signals; The multi-resolution analysis method based on wavelet transform is used to divide the EEG signal and obtain wavelet coefficients of different scales. The PCA algorithm is used to perform principal component analysis on the wavelet coefficients, and components are eliminated based on the analysis results; The power spectrum interpolation method was used to process the remaining PCA components and reconstruct the signal to obtain the EEG signal without tACS artifacts.

2. The tACS artifact removal method combining multi-resolution analysis and principal component analysis according to claim 1, wherein The multi-resolution analysis method based on wavelet transform is used to divide and process the EEG signal to obtain wavelet coefficients of different scales, including: For the original single-channel EEG signal x(t), wavelet decomposition is applied to generate wavelet coefficients of different scales: where d k (t) is the wavelet coefficient of the k-th layer, a L (t) is the approximation coefficient of the L-th layer, and L is the number of decomposition layers.

3. A tACS artifact removal method combining multi-resolution analysis and principal component analysis according to claim 2, characterized in that, The application of wavelet decomposition comprises: The symlets wavelet function with a vanishing moment of 5 is used for decomposition.

4. A tACS artifact removal method combining multi-resolution analysis and principal component analysis according to claim 2, characterized in that The number of decomposition layers L is calculated by the following formula: L = Floor(log2(F s )) - 1 where F s is the sampling frequency of the signal, and Floor represents the floor function.

5. A tACS artifact removal method combining multi-resolution analysis and principal component analysis according to claim 1, characterized in that, The PCA algorithm is used to perform principal component analysis on the wavelet coefficients, and components are eliminated according to the analysis results, including: Take the wavelet coefficient d k (t) of each layer as the input of the PCA algorithm and perform principal component analysis: Among them, U k and Σ k , represent the left singular vector, the singular value matrix, and the right singular vector respectively, and the principal components are arranged in descending order of the singular values; According to the contribution rate of the singular value, the first component is eliminated to achieve the removal of artifact components.

6. A tACS artifact removal method combining multi - resolution analysis and principal component analysis according to claim 1, characterized in that, The method of using a power spectrum interpolation method to process the remaining PCA components and reconstruct the signal to obtain an EEG signal with tACS artifacts removed includes: For the remaining PCA components, the power spectrum of each component is calculated; The frequency points of tACS artifacts are detected based on the power spectrum, these frequency points are deleted, and the power spectrum is corrected using interpolation. Eliminate the peaks at these frequency points; Using the corrected power spectrum and the corresponding phase, the time series is reconstructed by inverse Fourier transform; Use all processed wavelet coefficients d' k '(t) and approximation coefficients a L (t) to reconstruct the final signal as the EEG signal with finally removed tACS artifacts.

7. A tACS artifact removal method combining multi - resolution analysis and principal component analysis according to claim 6, characterized in that, The power spectrum of each component is calculated as follows: P(f) = |FFT(d' k (t))| 2 where FFT represents the fast Fourier transform, and d' k (t) are the wavelet coefficients after PCA processing; The formula for correcting the power spectrum is: P 修正 (f) = spectrum_interp(P(f), f tACS ) where f tACS is the tACS artifact frequency detected at the anomaly point; The formula for reconstructing the time series is: In the formula, is the phase information of the original signal; The formula for reconstructing the final signal is: Where x'(t) is the EEG signal with tACS artifacts removed.

8. A tACS artifact removal device combining multi-resolution analysis and principal component analysis, characterized in that, include: A signal input module, used to obtain EEG signals; A signal decomposition module is used to divide and process the EEG signal using a multi-resolution analysis method based on wavelet transform to obtain wavelet coefficients of different scales; The component analysis module is used to perform principal component analysis on the wavelet coefficients using the PCA algorithm and eliminate components based on the analysis results; The power spectrum interpolation module is used to process the remaining PCA components using the power spectrum interpolation method and reconstruct the signal to obtain the EEG signal with tACS artifacts removed.

9. An electronic device, characterized in that, The electronic device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the method according to any one of claims 1 to 7.

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