Method and system for removing artifacts and reducing noise of electroencephalogram signals

Abstract

The invention discloses an electroencephalogram signal artifact removal and noise reduction method and system, and relates to the technical field of electroencephalogram signal processing, and the method comprises the steps: receiving an electroencephalogram signal, carrying out the adaptive multi-scale decomposition of the received electroencephalogram signal, obtaining decomposition layers and a mode function, carrying out the artifact marking at each decomposition layer, and recognizing and positioning an artifact source. According to an artifact source positioning result, artifacts are removed through a threshold filtering algorithm, time-frequency characteristics of the electroencephalogram signals are extracted, noise reduction processing is conducted on the extracted time-frequency characteristics through an intelligent noise reduction filter, the quality of the processed electroencephalogram signals is monitored in real time, and processing parameters are fed back and adjusted according to a monitoring result. And outputting the electroencephalogram signals subjected to artifact removal and noise reduction processing, performing quality evaluation on the output signals, and optimizing the processing flow according to the evaluation result. The method solves the problems that in the prior art, the accuracy of signal distortion and artifact recognition and removal is limited, and the flexibility and adaptability of the processing flow are insufficient.

Description

Technical Field

[0001] The present invention relates to the technical field of electroencephalogram (EEG) signal processing, and in particular to an EEG signal artifact removal and noise reduction method, system, computer equipment and medium. Background Art

[0002] In the field of biomedical engineering, the acquisition and analysis of EEG signals has always been an important means of neuroscience research and clinical diagnosis. With the development of signal processing technology, the removal of artifacts and noise reduction of EEG signals have become key steps to improve the quality of EEG data. Traditional EEG signal processing methods mainly include simple filtering, artificially marked artifacts, and empirical mode decomposition. Although these methods are effective within a certain range, their processing effects are often unsatisfactory in the face of complex and changeable environmental noise and physiological artifacts.

[0003] The existing related technologies have the following shortcomings: First, traditional artifact removal methods often rely on the operator's experience and are highly subjective, resulting in limited accuracy in artifact identification and removal. Secondly, it is difficult for existing noise reduction technologies to simultaneously maintain the time-frequency characteristics of the signal and reduce noise, especially when processing non-stationary EEG signals, which can easily cause signal distortion. In addition, the existing methods lack the ability to monitor and dynamically adjust processing parameters in real time, resulting in insufficient flexibility and adaptability of the processing flow. In response to these problems, the present invention proposes a method and system for artifact removal and noise reduction of EEG signals. Through technical innovations such as adaptive multi-scale decomposition, intelligent noise reduction filter groups, and real-time monitoring and process optimization, it is expected to bring significant beneficial effects in terms of accuracy of artifact identification, processing efficiency of noise reduction effects, and flexibility of processing flows; the technical solution of the present invention addresses the deficiencies of the prior art and proposes a more efficient, accurate and adaptive EEG signal processing method, which can not only improve the quality of EEG signal analysis, but also provide more reliable data support for neuroscience research and clinical applications. Summary of the invention

[0004] In view of the above existing problems, the present invention provides a method and system for removing artifacts and reducing noise of EEG signals, so as to solve the problems of signal distortion, limited accuracy of artifact identification and removal, and insufficient flexibility and adaptability of processing flow in the prior art.

[0005] In order to solve the above technical problems, a method for removing artifacts and reducing noise of EEG signals is proposed, including:

[0006] Receive EEG signals, perform adaptive multi-scale decomposition on the received EEG signals, obtain decomposition layers and modal functions, mark artifacts at each decomposition layer, identify and locate artifact sources; based on the artifact source location results, use a threshold filtering algorithm to remove artifacts, extract the time-frequency features of the EEG signals, and use an intelligent noise reduction filter group to perform noise reduction on the extracted time-frequency features; monitor the quality of the processed EEG signals in real time, adjust the processing parameters based on the monitoring result feedback, output the EEG signals that have been processed with artifacts removed and noise reduced, evaluate the quality of the output signals, and optimize the processing flow based on the evaluation results.

[0007] As a preferred solution of the method for removing artifacts and reducing noise of EEG signals described in the present invention, the adaptive multi-scale decomposition includes receiving EEG signals, performing an adaptive multi-scale decomposition and reconstruction algorithm on the received EEG signals, and obtaining a decomposition layer.

[0008] The adaptive multi-scale decomposition and reconstruction algorithm includes receiving the original EEG signal, converting the original EEG signal into a multi-scale decomposition format, performing variational modal decomposition on the EEG signal, analyzing the modal function obtained by the decomposition, identifying and marking the mode, removing artifacts from the marked mode using a threshold filtering algorithm, reconstructing the processed modal function, and outputting the processed EEG signal.

[0009] The variational modal decomposition of the EEG signal includes initializing the center frequency and the number of modes, setting an initial center frequency for each mode, constructing the variation by iterative solution, obtaining the modal function with the center frequency, and obtaining the variational optimal solution, which is expressed as follows:

[0010]

[0011] Where * is the convolution operation, δ(t) is the Dirac function, j is the imaginary unit, is the derivative with respect to time t, u k (t) is the kth mode function, ω k is the center frequency of the kth mode.

[0012] The iterative process of the iterative solution method includes updating each modal function, obtaining an analytical signal, calculating a spectrum, adjusting the center frequency of each modal function, and calculating a new center frequency by a weighted average of the spectrum, obtaining the bandwidth of each modal function by the gradient of the spectrum, and updating the spectrum of each modal function by projecting it onto the spectrum.

[0013] The formula for calculating the new center frequency is expressed as:

[0014]

[0015] Where n represents the number of iterations, ^ represents Fourier transform, |〃| represents modulus, and u k (t) is the kth mode function, is the center frequency of the kth mode at the n+1th iteration, ω is the angular frequency, t is the time, is the kth mode function obtained by Fourier transform.

[0016] As a preferred solution of the method for removing artifacts and reducing noise of EEG signals described in the present invention, the artifact removal includes marking artifacts at each decomposition layer, identifying and locating the artifact source, and removing the artifacts using a threshold filtering algorithm based on the artifact source positioning result.

[0017] The threshold filtering algorithm removes artifacts as follows:

[0018]

[0019] Among them, ^ represents Fourier transform, |〃| represents modulus, is the processed spectrum, λ is the threshold value set to 0.024, is the kth mode function obtained by Fourier transform.

[0020] As a preferred solution of the method for removing artifacts and reducing noise of EEG signals described in the present invention, the extraction of time-frequency characteristics of EEG signals includes preprocessing the EEG signals, segmenting and windowing them, performing short-time Fourier transform to obtain a two-dimensional matrix, and extracting the time-frequency characteristics of the EEG signals.

[0021] The preprocessing includes denoising and averaging. The denoising includes removing 50-60 Hz power frequency interference noise in the EEG signal by using a bandpass filter. The segmentation includes dividing the signal into small segments, each of which has a length of a power of 2.

[0022] As a preferred solution of the method for removing artifacts and reducing noise of EEG signals described in the present invention, the noise reduction processing includes designing an intelligent noise reduction filter, selecting an adaptive filter as the intelligent filter, and using the filter to perform inverse time-frequency conversion to obtain the noise-reduced EEG signal.

[0023] The designing of the intelligent noise reduction filter includes designing the filter bandwidth and center frequency according to the time-frequency characteristics.

[0024] The adaptive filter update formula is:

[0025] q n+1 =q n +μx n y n

[0026] Among them, q n+1is the filter weight vector after the n+1th iteration, q n is the filter weight vector at the nth iteration, μ is the learning rate, x n is the input signal vector at the nth iteration, y n is the error signal at the nth iteration.

[0027] The inverse time-frequency conversion formula is:

[0028]

[0029] Among them, x(t a )′ is the EEG time domain signal after noise reduction, X(f,τ) is the EEG time-frequency signal obtained by short-time Fourier transform, t a is the time integral variable, τ is the central time point of the window function, j is the imaginary unit, and f is the frequency variable.

[0030] As a preferred solution of the method for removing artifacts and reducing noise of EEG signals described in the present invention, the real-time monitoring of the quality of the processed EEG signals includes evaluating the quality of the processed EEG signals using signal quality indicators, and setting thresholds for comparison. When the signal does not meet the requirements, parameter adjustment is required, and the EEG signals are re-processed with artifacts and noise reduction using the adjusted parameters, and the processed EEG signals are output.

[0031] The signal quality index includes multi-dimensional calculations based on the power, frequency components and waveform characteristics of the signal, and the formula is expressed as follows:

[0032]

[0033] Among them, SQI is the signal quality index, P signal is the signal power, P noise is the noise power.

[0034] The setting threshold includes that when SQI≥0.7, the signal quality meets the normal level and the current parameters are maintained to remove artifacts and reduce noise; when SQI<0.7, the signal quality does not meet the requirements and the parameters need to be adjusted.

[0035] The formula for adjusting the parameters is:

[0036] θ n+1 =θ n +α(SQI target -SQI n )

[0037] Among them, θ n+1 is the adjusted parameter, θ n is the processing parameter, α is the adjustment step size, SQI targetis the target signal quality index, SQI n is the current signal quality index.

[0038] As a preferred solution of the method for removing artifacts and reducing noise of EEG signals described in the present invention, the optimization of the processing flow according to the evaluation results includes: performing SQI calculation on the processed EEG signal to verify whether the effect of parameter adjustment meets the normal level; when SQI≥0.7, the processing flow is determined to be valid and the current parameters continue to be applied; when SQI<0.7, the processing flow is determined to be invalid, the parameters are readjusted, and the SQI calculation continues until the requirements are met.

[0039] Another object of the present invention is to provide an artifact removal and noise reduction system for EEG signals, which solves the problems of signal distortion, limited accuracy of artifact identification and removal, and insufficient flexibility and adaptability of the processing flow in the prior art; the system of the present invention accurately identifies and locates the artifact source in the EEG signal through an adaptive multi-scale decomposition and reconstruction algorithm, effectively reduces noise interference through an intelligent noise reduction filter group, while retaining useful information, and maintains the signal time-frequency characteristics through real-time monitoring and dynamic optimization mechanisms, thereby greatly improving the signal processing efficiency and the accuracy of clinical diagnosis.

[0040] As a preferred solution of the EEG signal artifact removal and noise reduction system described in the present invention, it includes a signal receiving and processing module, a multi-scale decomposition and artifact recognition module, an artifact removal and signal reconstruction module, a time-frequency feature extraction and intelligent noise reduction module, and a real-time monitoring and process optimization module.

[0041] The signal receiving and processing module is used to ensure that the input EEG signal is suitable for subsequent processing and remove obvious noise and interference.

[0042] The multi-scale decomposition and artifact recognition module is used to decompose the EEG signal into different frequency components or modes to facilitate a more detailed analysis of the signal characteristics and provide a basis for the recognition of artifacts. At each level after decomposition, artifacts generated by non-brain source activities are recognized and marked.

[0043] The artifact removal and signal reconstruction module is used to remove the influence of artifacts on the signal in a targeted manner according to the artifact positioning result and adopt a threshold filtering algorithm. After removing the artifacts, the processed modal functions are recombined to restore the signal of the real EEG activity.

[0044] The time-frequency feature extraction and intelligent noise reduction module is used to extract the time-frequency features of the EEG signal through short-time Fourier transform, and use an adaptive filter to perform noise reduction processing on the extracted time-frequency features to reduce noise and improve the signal-to-noise ratio of the signal.

[0045] The real-time monitoring and process optimization module is used to monitor the signal quality in real time at each stage of signal processing, dynamically adjust the processing parameters according to the results of real-time monitoring, and optimize the entire signal processing process.

[0046] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of a method for removing artifacts and reducing noise of electroencephalogram signals when executing the computer program.

[0047] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for removing artifacts and reducing noise of an electroencephalogram signal.

[0048] The beneficial effects of the present invention are as follows: the present invention improves the accuracy and efficiency of artifact recognition through adaptive multi-scale decomposition, and provides a basis for accurate removal of artifacts; the use of threshold filtering algorithm and intelligent noise reduction filter group effectively removes the interference of non-brain source activities and significantly improves the signal-to-noise ratio of the signal; the real-time monitoring and dynamic adjustment of processing parameters ensure the optimization of the signal processing process and the continuous high level of signal quality; the design of the intelligent filter and the integration of the real-time monitoring module realize the automation of signal processing and the optimization of the process, and greatly improve the performance and efficiency of the system; through the implementation of computer equipment and the provision of storage media, the method of the present invention becomes more convenient and faster, and can be easily extended to a wider range of application scenarios, providing strong technical support for the clinical application and research of EEG signal analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0050] Figure 1 An overall flow chart of a method for removing artifacts and reducing noise of an EEG signal provided by an embodiment of the present invention.

[0051] Figure 2 A system solution flow chart of an artifact removal and noise reduction system for EEG signals provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0053] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0054] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is mutually exclusive with other embodiments, either individually or selectively.

[0055] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0056] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are 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 direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0057] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0058] Example 1, reference Figure 1 , which is the first embodiment of the present invention, provides a method for removing artifacts and reducing noise of an EEG signal, comprising:

[0059] S1: Receive EEG signals, perform adaptive multi-scale decomposition on the received EEG signals, obtain decomposition layers and modal functions, mark artifacts at each decomposition layer, and identify and locate artifact sources.

[0060] It should be noted that the adaptive multi-scale decomposition includes receiving an EEG signal, performing an adaptive multi-scale decomposition and reconstruction algorithm on the received EEG signal, and obtaining a decomposition layer.

[0061] The adaptive multi-scale decomposition and reconstruction algorithm includes receiving the original EEG signal, converting the original EEG signal into a multi-scale decomposition format, performing variational modal decomposition on the EEG signal, analyzing the modal function obtained by the decomposition, identifying and marking the mode, removing artifacts from the marked mode using a threshold filtering algorithm, reconstructing the processed modal function, and outputting the processed EEG signal.

[0062] Furthermore, the variational modal decomposition of the EEG signal includes initializing the center frequency and the number of modes, setting an initial center frequency for each mode, constructing the variation by iterative solution, obtaining the modal function with the center frequency, and obtaining the variational optimal solution, which is expressed as follows:

[0063]

[0064] Where * is the convolution operation, δ(t) is the Dirac function, j is the imaginary unit, is the derivative with respect to time t, u k (t) is the kth mode function, ω k is the center frequency of the kth mode.

[0065] The δ(t) Dirac function formula is:

[0066]

[0067] Where t is the time and must satisfy

[0068] The iterative process includes updating each modal function, obtaining an analytical signal, and calculating a spectrum, adjusting the center frequency of each modal function, and calculating a new center frequency by a weighted average of the spectrum, obtaining the bandwidth of each modal function by the gradient of the spectrum, and updating the spectrum of each modal function by projecting it onto the spectrum.

[0069] The formula for calculating the new center frequency is expressed as:

[0070]

[0071] Where n represents the number of iterations, ^ represents Fourier transform, |〃| represents modulus, and u k (t) is the kth mode function, is the center frequency of the kth mode at the n+1th iteration, ω is the angular frequency, t is the time, is the kth mode function obtained by Fourier transform.

[0072] It should be further explained that the artifact removal includes marking artifacts at each decomposition layer, identifying and locating artifact sources, and removing artifacts using a threshold filtering algorithm based on the artifact source location result.

[0073] The threshold filtering algorithm removes artifacts as follows:

[0074]

[0075] Among them, ^ represents Fourier transform, |〃| represents modulus, is the processed spectrum, λ is the threshold value set to 0.024, is the kth mode function obtained by Fourier transform.

[0076] The reconstruction includes converting the processed spectrum of each modal function back to the time domain through an inverse Hilbert transform and adding the processed modal functions to obtain an artifact-free EEG signal, which is expressed as follows:

[0077]

[0078] Among them, x′(t) is the EEG signal obtained after artifact removal, u k (t)′ is the mode function obtained by inverse Hilbert transform.

[0079] S2: According to the artifact source localization results, the threshold filtering algorithm is used to remove the artifacts, the time-frequency features of the EEG signal are extracted, and the extracted time-frequency features are denoised using an intelligent denoising filter group.

[0080] Furthermore, the extraction of time-frequency features of the EEG signal includes preprocessing the EEG signal, segmenting and windowing the signal, performing a short-time Fourier transform to obtain a two-dimensional matrix, and extracting the time-frequency features of the EEG signal.

[0081] The preprocessing includes denoising and averaging. The denoising includes removing 50-60 Hz power frequency interference noise in the EEG signal by using a bandpass filter. The segmentation includes dividing the signal into small segments, each of which has a length of a power of 2.

[0082] Furthermore, the short-time Fourier transform formula is:

[0083]

[0084] Among them, X(f,τ) is the EEG time-frequency signal obtained by short-time Fourier transform, x(t a ) is the EEG signal, w(t a -τ) is the window function, j is the imaginary unit, f is the frequency variable, t a is the time-integrated variable, and τ is the central time point of the window function.

[0085] It should be noted that the noise reduction processing includes designing an intelligent noise reduction filter, selecting an adaptive filter as the intelligent filter, and using the filter to perform inverse time-frequency conversion to obtain a noise-reduced EEG signal.

[0086] The designing of the intelligent noise reduction filter includes designing the filter bandwidth and center frequency according to the time-frequency characteristics.

[0087] The adaptive filter update formula is:

[0088] q n+1 =q n +μx n y n

[0089] Among them, q n+1 is the filter weight vector after the n+1th iteration, q n is the filter weight vector at the nth iteration, μ is the learning rate, x n is the input signal vector at the nth iteration, y n is the error signal at the nth iteration.

[0090] It should be further explained that the inverse time-frequency conversion formula is:

[0091]

[0092] Among them, x(t a ) ′ is the EEG time domain signal after noise reduction, X(f,τ) is the EEG time-frequency signal obtained by short-time Fourier transform, t a is the time integral variable, τ is the central time point of the window function, j is the imaginary unit, and f is the frequency variable.

[0093] S3: Monitor the quality of the processed EEG signal in real time, adjust the processing parameters according to the monitoring result feedback, output the EEG signal after artifact removal and noise reduction, evaluate the quality of the output signal, and optimize the processing flow according to the evaluation result.

[0094] Furthermore, the real-time monitoring of the quality of the processed EEG signal includes using signal quality indicators to evaluate the quality of the processed EEG signal and setting thresholds for comparison. When the signal does not meet the requirements, parameter adjustment is required, and the EEG signal is re-processed with artifact removal and noise reduction using the adjusted parameters to output the processed EEG signal.

[0095] The signal quality index includes multi-dimensional calculations based on the power, frequency components and waveform characteristics of the signal, and the formula is expressed as follows:

[0096]

[0097] Among them, SQI is the signal quality index, P signal is the signal power, P noise is the noise power.

[0098] Furthermore, the threshold setting includes that when SQI≥0.7, the signal quality meets the normal level and the current parameters are maintained to remove artifacts and reduce noise; when SQI<0.7, the signal quality does not meet the requirements and the parameters need to be adjusted.

[0099] The formula for adjusting the parameters is:

[0100] θ n+1 =θ n +α(SQI target -SQI n )

[0101] Among them, θ n+1 is the adjusted parameter, θ n is the processing parameter, α is the adjustment step size, SQI target is the target signal quality index, SQI n is the current signal quality index.

[0102] The optimization process according to the evaluation results includes: performing SQI calculation on the processed EEG signal to verify whether the effect of parameter adjustment meets the normal level. When SQI≥0.7, the processing process is determined to be valid and the current parameters continue to be applied; when SQI<0.7, the processing process is determined to be invalid, the parameters are readjusted, and the SQI calculation continues until the requirements are met.

[0103] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0104] Example 2, reference Figure 2 , which is the second embodiment of the present invention, provides an artifact removal and noise reduction system for EEG signals, including a signal receiving and processing module 100, a multi-scale decomposition and artifact recognition module 200, an artifact removal and signal reconstruction module 300, a time-frequency feature extraction and intelligent noise reduction module 400, and a real-time monitoring and process optimization module 500.

[0105] The signal receiving and processing module 100 is used to ensure that the input EEG signal is suitable for subsequent processing and remove obvious noise and interference.

[0106] The multi-scale decomposition and artifact recognition module 200 is used to decompose the EEG signal into different frequency components or modes to facilitate a more detailed analysis of the signal characteristics and provide a basis for the recognition of artifacts. At each level after decomposition, artifacts generated by non-brain source activities are recognized and marked.

[0107] The artifact removal and signal reconstruction module 300 is used to remove the influence of artifacts on the signal in a targeted manner according to the artifact positioning result by using a threshold filtering algorithm. After removing the artifacts, the processed modal functions are recombined to restore the real EEG activity signal.

[0108] The time-frequency feature extraction and intelligent noise reduction module 400 is used to extract the time-frequency features of the EEG signal through short-time Fourier transform, and apply an adaptive filter to perform noise reduction processing on the extracted time-frequency features to reduce noise and improve the signal-to-noise ratio of the signal.

[0109] The real-time monitoring and process optimization module 500 is used to monitor the signal quality in real time at each stage of signal processing, dynamically adjust the processing parameters according to the results of real-time monitoring, and optimize the entire signal processing process.

[0110] Embodiment 3, the third embodiment of the present invention, is different from the first two embodiments in that:

[0111] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0112] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0113] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0114] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of 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, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

Claims

1. A method for removing artifacts and reducing noise of EEG signals, characterized in that: include, Receive EEG signals, perform adaptive multi-scale decomposition on the received EEG signals, obtain decomposition layers and modal functions, mark artifacts at each decomposition layer, and identify and locate artifact sources; According to the artifact source localization results, the threshold filtering algorithm is used to remove the artifacts, the time-frequency characteristics of the EEG signal are extracted, and the extracted time-frequency characteristics are denoised using an intelligent denoising filter group; Monitor the quality of processed EEG signals in real time, adjust processing parameters based on monitoring feedback, output EEG signals that have been processed for artifact removal and noise reduction, evaluate the quality of the output signals, and optimize the processing flow based on the evaluation results.

2. The method for removing artifacts and reducing noise of an electroencephalogram signal according to claim 1, characterized in that: The adaptive multi-scale decomposition includes receiving an EEG signal, performing an adaptive multi-scale decomposition and reconstruction algorithm on the received EEG signal, and obtaining a decomposition layer; The adaptive multi-scale decomposition and reconstruction algorithm includes receiving the original EEG signal, converting the original EEG signal into a multi-scale decomposition format, performing variational modal decomposition on the EEG signal, analyzing the modal function obtained by the decomposition, identifying and marking the mode, removing artifacts from the marked mode using a threshold filtering algorithm, reconstructing the processed modal function, and outputting the processed EEG signal; Variational modal decomposition of EEG signals includes initializing the center frequency and the number of modes, setting an initial center frequency for each mode, constructing variations through iterative solution, obtaining modal functions with center frequencies, and obtaining the optimal solution of variations. The formula is expressed as follows: Where * is the convolution operation, δ(t) is the Dirac function, j is the imaginary unit, is the derivative with respect to time t, u k (t) is the kth mode function, ω k is the center frequency of the kth mode; The iterative process of the iterative solution method includes updating each modal function, obtaining an analytical signal, calculating a spectrum, adjusting the center frequency of each modal function, and calculating a new center frequency by a weighted average of the spectrum, obtaining the bandwidth of each modal function by a gradient of the spectrum, and updating the spectrum of each modal function by projecting it onto the spectrum; The formula for calculating the new center frequency is expressed as: Where n represents the number of iterations, ^ represents Fourier transform, |〃| represents modulus, and u k (t) is the kth mode function, is the center frequency of the kth mode at the n+1th iteration, ω is the angular frequency, t is the time, is the kth mode function obtained by Fourier transform.

3. The method for removing artifacts and reducing noise of an EEG signal as claimed in claim 2, characterized in that: The artifact removal includes marking artifacts at each decomposition layer, identifying and locating the artifact source, and removing the artifacts using a threshold filtering algorithm based on the artifact source location result; The threshold filtering algorithm removes artifacts as follows: Among them, ^ represents Fourier transform, |〃| represents modulus, is the processed spectrum, λ is the threshold value set to 0.024, is the kth mode function obtained by Fourier transform.

4. The method for removing artifacts and reducing noise of an EEG signal as claimed in claim 3, characterized in that: The extracting of the time-frequency characteristics of the EEG signal includes preprocessing the EEG signal, segmenting and windowing the signal, performing short-time Fourier transform to obtain a two-dimensional matrix, and extracting the time-frequency characteristics of the EEG signal; The preprocessing includes denoising and averaging, wherein the denoising includes removing the 50-60 Hz power frequency interference noise in the EEG signal by using a bandpass filter; the segmentation includes dividing the signal into small segments, each of which has a length of a power of 2; The short-time Fourier transform formula is: Among them, X(f,τ) is the EEG time-frequency signal obtained by short-time Fourier transform, x(t a ) is the EEG signal, w(t a -τ) is the window function, j is the imaginary unit, f is the frequency variable, t a is the time-integrated variable, and τ is the central time point of the window function.

5. The method for removing artifacts and reducing noise of an EEG signal as claimed in claim 4, characterized in that: The noise reduction process includes designing an intelligent noise reduction filter, selecting an adaptive filter as the intelligent filter, and using the filter to perform inverse time-frequency conversion to obtain a noise-reduced EEG signal; The designing of the intelligent noise reduction filter includes designing the filter bandwidth and center frequency according to the time-frequency characteristics; The adaptive filter update formula is: q n+1 =q n +μx n y n Among them, q n+1 is the filter weight vector after the n+1th iteration, q n is the filter weight vector at the nth iteration, μ is the learning rate, x n is the input signal vector at the nth iteration, y n is the error signal at the nth iteration; The inverse time-frequency conversion formula is: Among them, x(t a ) ′ is the EEG time domain signal after noise reduction, X(f,τ) is the EEG time-frequency signal obtained by short-time Fourier transform, t a is the time integral variable, τ is the central time point of the window function, j is the imaginary unit, and f is the frequency variable.

6. The method for removing artifacts and reducing noise of an electroencephalogram signal as claimed in claim 5, characterized in that: The real-time monitoring of the quality of the processed EEG signal includes evaluating the quality of the processed EEG signal using a signal quality index, setting a threshold for comparison, adjusting parameters when the signal does not meet the requirements, and re-processing the EEG signal with artifact removal and noise reduction using the adjusted parameters, and outputting the processed EEG signal; The signal quality index includes multi-dimensional calculations based on the power, frequency components and waveform characteristics of the signal, and the formula is expressed as follows: Among them, SQI is the signal quality index, P signal is the signal power, P noise is the noise power; The setting threshold includes that when SQI≥0.7, the signal quality meets the normal level, and the current parameters are maintained to remove artifacts and reduce noise; when SQI<0.7, the signal quality does not meet the requirements and the parameters need to be adjusted; The formula for adjusting the parameters is: i n+1 =θ n +α(SQI target -SQI n ) Among them, θ n+1 is the adjusted parameter, θ n is the processing parameter, α is the adjustment step size, SQI target is the target signal quality index, SQI n is the current signal quality index.

7. The method for removing artifacts and reducing noise of an electroencephalogram signal as claimed in claim 6, characterized in that: The optimization process according to the evaluation results includes: performing SQI calculation on the processed EEG signal to verify whether the effect of parameter adjustment meets the normal level. When SQI≥0.7, the processing process is determined to be valid and the current parameters continue to be applied; when SQI<0.7, the processing process is determined to be invalid, the parameters are readjusted, and the SQI calculation continues until the requirements are met.

8. A system using the method for removing artifacts and reducing noise of an electroencephalogram signal as claimed in any one of claims 1 to 7, characterized in that: It includes signal receiving and processing module, multi-scale decomposition and artifact recognition module, artifact removal and signal reconstruction module, time-frequency feature extraction and intelligent noise reduction module, and real-time monitoring and process optimization module; The signal receiving and processing module is used to ensure that the input EEG signal is suitable for subsequent processing and remove obvious noise and interference; The multi-scale decomposition and artifact recognition module is used to decompose the EEG signal into different frequency components or modes, so as to analyze the signal characteristics more carefully and provide a basis for the recognition of artifacts. At each level after decomposition, the artifacts generated by non-brain source activities are recognized and marked; The artifact removal and signal reconstruction module is used to remove the influence of the artifact on the signal in a targeted manner according to the artifact positioning result, and after removing the artifact, the processed modal functions are recombined to restore the signal of the real brain electrical activity; The time-frequency feature extraction and intelligent noise reduction module is used to extract the time-frequency features of the EEG signal through short-time Fourier transform, and apply an adaptive filter to perform noise reduction on the extracted time-frequency features to reduce noise and improve the signal-to-noise ratio of the signal; The real-time monitoring and process optimization module is used to monitor the signal quality in real time at each stage of signal processing, dynamically adjust the processing parameters according to the results of real-time monitoring, and optimize the entire signal processing process.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for removing artifacts and reducing noise of an electroencephalogram signal according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for removing artifacts and reducing noise of an electroencephalogram signal according to any one of claims 1 to 7 are implemented.

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