EEG data processing method and system based on sub-resolution quantization to eliminate electrosurgical unit interference

Abstract

The present invention belongs to the field of electroencephalogram (EEG) data acquisition and processing technology, and specifically relates to an EEG data processing method and system for eliminating electrocautery interference based on sub-resolution quantization, comprising: S1, linearly superimposing the interference signal and the EEG signal to obtain EEG signal data based on their independence; S2, decomposing the interference signal based on the offset of the EEG interference signal data to obtain the high-order binary position of the EEG signal data; S3, setting the sub-resolution and determining the sub-resolution magnitude of the EEG data based on the offset of the EEG interference signal data; S4, using the sub-resolution quantized interference data obtained by linear interpolation, and performing inverse linear interpolation to obtain the canceled interference data; S5, obtaining the EEG data with the electrocautery interference removed by point-by-point cancellation based on the corrected second interference data and the collected EEG signal data. The present invention identifies electrocautery noise by sub-resolution quantizing the pulse signal, and obtains the EEG data by linear fitting of the original signal and the interpolation-corrected signal.

Description

Technical Field

[0001] The present invention belongs to the technical field of electroencephalogram (EEG) data acquisition and processing, and in particular relates to an EEG data processing method and system based on sub-resolution quantization to eliminate electrosurgery interference. Background Art

[0002] During EEG signal acquisition and processing, sudden, strong external interference can cause large-amplitude pulses or step noise to appear in the signal. In these cases, the corresponding channel is typically designated as a bad conductor and its data removed. In rehabilitation treatment scenarios such as magnetic or electrical stimulation, or during patient motor rehabilitation, while various hardware solutions exist to minimize this noise, the detection process inevitably includes this strong interference noise. This interference can manifest on all channels, making simple channel removal impractical. Therefore, devices designed for rehabilitation applications inevitably need to address this issue.

[0003] Since motion artifact-like electric knife and sudden step noise signals are expressed as sudden changes at a certain time in the time domain, the larger the amplitude of the sudden change, the steeper the change edge. This results in the need for infinite harmonics to fit this position when mapping to the frequency domain. The power of the fitted component is much greater than that of the normal signal, resulting in an upward shift of energy across the entire frequency band on the spectrum, making it difficult for traditional frequency domain filters to handle this situation by distinguishing the signal bandwidth.

[0004] On the other hand, this type of noise is easily distinguished visually, as it exhibits more distinct characteristics in the time domain. Therefore, many methods exploit the statistical characteristics of the signal in the time domain to iteratively correct for this noise through interpolation or wavelet decomposition. While some methods theoretically and experimentally demonstrate the ability to suppress step or impulse noise, large amplitude changes significantly increase the number of algorithm iterations, requiring correction of more signals near the location of the change. This can be particularly problematic in the presence of frequent impulse interference. While amplitude limiting can shorten the iteration process, it can also cause distortion in the interpolation phase due to the loss of signals exceeding the limit, rendering it ineffective in the presence of frequent impulse noise.

[0005] To address these issues, the present invention uses real-time sub-resolution quantization to identify the primary components of noise and utilizes natural interpolation to fit the baseline drift caused by strong noise. This allows for real-time removal of sudden baseline changes from the acquired data, thereby suppressing strong interfering pulses. Furthermore, the algorithm can achieve adaptive signal processing by controlling the degree of sub-resolution quantization, thus avoiding the impact on data during normal acquisition. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention provides an EEG data processing method and system for eliminating electrosurgical interference based on sub-resolution quantization. The method decomposes data containing electrosurgical signals through sub-resolution quantization, identifies electrosurgical noise in the time domain, accurately identifies electrosurgical noise in the data, and corrects strong noise signals through interpolation. Finally, the original signal data is obtained through linear fitting of the original signal and the interpolated corrected signal, effectively improving the removal of electrosurgical signals.

[0007] To achieve the above object, the present invention provides an EEG data processing method based on sub-resolution quantization to eliminate electrosurgical interference, which comprises:

[0008] S1: The collected EEG signal data is obtained by linearly superimposing the interference signal and the EEG signal during acquisition;

[0009] The interference signal and the EEG signal are independent of each other during the signal transmission process. They are linearly superimposed during acquisition, and the acquired EEG signal data is:

[0010] Y(t)=S(t)+N(t)

[0011] Among them, Y(t) is the collected EEG signal data; S(t) is the real EEG signal data; N(t) is the interference signal data; t is the time parameter;

[0012] According to the superposition characteristics of the real EEG signal data S(t) and the interference signal data N(t), the range of the real EEG signal data S(t) is [-2 n ,2 n ], set the collected EEG signal data Y(t)∈[-2 m+n ,2 m+n ], then the deviation of the electric knife interference signal data is m;

[0013] S2: Decompose the interference signal data according to the deviation of the electrosurgical interference signal data to obtain the binary high bit of the EEG signal data; obtain the binary high bit N in the interference signal data N(t) according to the deviation m of the electrosurgical interference signal data obtained in step S1 H (t), the interference signal data N(t) is obtained as:

[0014] N(t)=N H (t)×2 n +N L (t)

[0015] Among them, N H (t) is the high binary bit of the interference signal data N(t); N L (t) is the low binary bit of the interference signal data N(t); n is the offset of the low pulse interference signal data:

[0016] The EEG signal data collected in step S1 is converted into:

[0017] Y(t)=N H (t)×2 n +N L (t)+S(t)=Y H (t)×2 n +Y L (t)

[0018] Among them, Y H (t) is the high bit of the binary data of the collected EEG signal; Y L (t) is the low bit of collected EEG signal data;

[0019] S3: Set the sub-resolution to N according to the electrophysiological characteristics of the brain, and determine the magnitude of the sub-resolution SR of the EEG data according to the deviation m of the electrosurgery interference signal data:

[0020] SR=(2 m modN)×N

[0021] Wherein, SR is the magnitude of the sub-resolution of the interference data; mod is the sub-resolution symbol; m is the offset of the electric knife interference signal data; N is the sub-resolution;

[0022] Constraining the collected EEG signal data Y(t) in step S2 according to the sub-resolution magnitude SR of the interference data to obtain sub-resolution quantized interference data M(t);

[0023] S4: using a linear interpolation method to process the sub-resolution quantized interference data obtained in step S3, and performing inverse linear interpolation to obtain interference-cancelling data;

[0024] S41: Using the interpolation method to make the sub-resolution quantized interference data M(t) continuous at the original accuracy, setting the interference signal data before and after the interpolation time point position t to be (t i ,y i ) and (t i+1 ,y i+1 ), the interference data M at the interpolation time point position i (t) is:

[0025]

[0026] Among them, M i (t) is the interference data at the interpolation time point t; t i is the previous data position of the interpolation time point data; y i t i The interference signal amplitude at the position; t i+1 The next data position of the interpolated time point data; yi+1 t i+1 The interference signal amplitude at the location;

[0027] S42: performing inverse linear interpolation on the first interference data M1(t) at the interpolation time point t according to the interpolation operation in step S41 to correct the obtained second interference data M2(t) at the interpolation time point t, so that the sampling rate of the sub-resolution quantized interference data is consistent with the original interference data, so as to achieve point-by-point cancellation of the electrosurgical interference data;

[0028] S5: Based on the second interference data M2(t) at the interpolation time point t corrected in step S4 and the EEG signal data Y(t) collected in step S1, the corrected EEG data with the electrosurgical interference removed is obtained by point-by-point cancellation:

[0029] R(t)=Y(t)-M2(t)

[0030] Among them, R(t) is the EEG data after removing the interference of the electrosurgical knife.

[0031] Preferably, the high bit Y of the collected EEG signal data in step S2 H (t) When there is an amplitude-exceeding pulse or step noise, the high m bits of the EEG signal data are collected. At this time, the collected EEG signal data is equivalent to the electrosurgical knife interference signal data.

[0032] Preferably, the superposition characteristics of the real EEG signal data S(t) and the interference signal data N(t) in step S1 are as follows: the real EEG signal data S(t) and the interference signal data N(t) overlap in the frequency domain, and the amplitude of the interference signal data N(t) is higher than that of the real EEG signal data S(t) in the time domain.

[0033] Preferably, in step S3, the sub-resolution is set to 500 so that the electrosurgical interference signal exhibits a large amplitude characteristic, and the EEG signal data characteristics within the electrosurgical interference signal time period can be retained.

[0034] Preferably, in step S3, the collected EEG signal data Y(t) is constrained according to the magnitude SR of the sub-resolution of the EEG data, so that data higher than the magnitude SR of the sub-resolution of the EEG data is limited to the magnitude SR of the sub-resolution of the EEG data, and data lower than the magnitude SR of the sub-resolution of the EEG data remains unchanged.

[0035] Preferably, the interpolation operation in step S4 can effectively reduce the impact of signal mutations and maintain the same sampling rate between the second EEG data M2(t) and the real EEG signal data S(t) at the interpolation time point t.

[0036] Preferably, the corrected EEG data from which the electrosurgical interference is removed obtained by point-by-point cancellation in step S5 can be used for real-time processing during the EEG data acquisition process to eliminate the influence of the electrosurgical interference signal.

[0037] Preferably, the EEG data R(t) from which the electrosurgical knife interference is removed in step S5 is used for the data acquisition process of the EEG signal real-time acquisition device, and the processed EEG data is used for diagnosis, analysis and real-time monitoring of the status.

[0038] The second aspect of the present invention provides an EEG data processing system based on a method for processing EEG data by sub-resolution quantization to eliminate electrosurgical interference, which comprises: a signal acquisition module, a signal processing module, a data transmission module, a power management module and a host computer interaction module;

[0039] The acquisition module includes electrodes, a preamplifier and an analog-to-digital converter, and is used to collect EEG signals from the scalp surface;

[0040] The signal processing module includes a digital signal processor, a noise reduction algorithm and a feature extraction algorithm, which is used to perform real-time noise reduction and feature extraction on the collected EEG signals;

[0041] The data transmission module includes a wireless transmission module and a wired transmission interface, which are used to transmit the processed EEG signals to a host computer or the cloud;

[0042] The power management module includes a battery, a power management chip and a low-power design to provide a stable power supply for the entire device and optimize power consumption;

[0043] The host computer interaction module includes a display screen, indicator lights and buttons or a touch screen, which are used to provide an interactive interface between the user and the device and display the device status and EEG signals.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] (1) The present invention decomposes the data containing electrosurgical knife signals through sub-resolution quantization, identifies electrosurgical knife noise in the time domain, accurately identifies the electrosurgical knife noise in the data, and corrects the strong noise signal through interpolation. Finally, the original signal data is obtained by linear fitting of the original signal and the interpolated corrected signal, which effectively improves the removal of electrosurgical knife signals, overcomes the limitations of previous studies on the removal of strong noise signals, and provides new ideas for data acquisition and data preprocessing.

[0046] (2) The present invention decomposes and corrects data based on time domain characteristics and can be applied to real-time acquisition. For the real-time acquisition process, during normal acquisition, it can avoid unnecessary impact on the data and ensure the authenticity and reliability of the data.

[0047] (3) The present invention removes interference signals from electrosurgery to ensure the acquisition of normal signals, and provides a new idea for existing acquisition equipment to filter out interference from electrosurgery and other factors during surgery. At the same time, the present invention is also effective in the data processing process. The present invention preprocesses data that is severely interfered with by noise, thereby further improving the utilization rate of clinical acquisition data. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of the EEG data processing method for eliminating electrosurgery interference based on sub-resolution quantization of the present invention;

[0049] Figure 2 This is a schematic diagram of the sub-resolution quantization principle of the present invention;

[0050] Figure 3 is a noise baseline quantization graph of the present invention;

[0051] Figure 4 This is a time-frequency diagram before correction of a first example of the effect of suppressing electrosurgery interference of the present invention;

[0052] Figure 5 This is a time-frequency diagram after correction of a first example of the effect of suppressing electrosurgery interference according to the present invention;

[0053] Figure 6 This is a time-frequency diagram before correction of a second example of the effect of suppressing electrosurgery interference of the present invention;

[0054] Figure 7 This is a time-frequency diagram after correction of a second example of the suppression effect of the present invention on electrosurgery interference. DETAILED DESCRIPTION

[0055] The exemplary embodiments, features, and aspects of the present invention will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

[0056] The present invention provides an EEG data processing method based on sub-resolution quantization to eliminate electrotome interference. As an EEG data processing method to eliminate strong pulse interference, it solves the problem that the use of electrotome will interfere with the acquisition of EEG data. Figure 1As shown, according to the independence of the interference signal and the EEG signal, the EEG signal data is obtained by linear superposition; the interference signal is decomposed according to the offset of the electrosurgical interference signal data to obtain the binary high bit of the EEG signal data; the sub-resolution is set, and the sub-resolution magnitude of the EEG data is determined according to the offset of the electrosurgical interference signal data; the sub-resolution quantized interference data is obtained using a linear interpolation method, and inverse linear interpolation is performed to obtain the interference-cancelling data; based on the corrected second interference data and the collected EEG signal data, the EEG data with the electrosurgical interference removed is obtained by point-by-point cancellation; which includes:

[0057] Step S1: Based on the independence of the interference signal and the EEG signal, the EEG signal data is obtained by linear superposition.

[0058] The external interference signal and the EEG signal are independent of each other during the signal transmission process. They are linearly superimposed during acquisition, and the acquired EEG signal data is:

[0059] Y(t)=S(t)+N(t);

[0060] Among them, Y(t) is the collected EEG signal data; S(t) is the real EEG signal data; N(t) is the interference signal data; and t is the time parameter.

[0061] According to the superposition characteristics of the real EEG signal data S(t) and the interference signal data N(t), specifically: the real EEG signal data S(t) and the interference signal data N(t) overlap and cannot be distinguished in the frequency domain, and the amplitude of the interference signal data N(t) is higher than that of the real EEG signal data S(t) in the time domain; the range of the real EEG signal data S(t) is [-2 n ,2 n ], set the collected EEG signal data Y(t)∈[-2 m+n ,2 m+n ], the deviation of the electric knife interference signal data is m.

[0062] Step S2: Decompose the interference signal data according to the deviation of the electrosurgery interference signal data to obtain the binary high bit of the EEG signal data; obtain the binary high bit N in the interference signal data N(t) according to the deviation m of the electrosurgery interference signal data obtained in step S1. H (t), the interference signal data N(t) is obtained as:

[0063] N(t)=N H (t)×2 n +N L (t);

[0064] Among them, N H (t) is the high binary bit of the interference signal data N(t); N L(t) is the low binary bit of the interference signal data N(t); n is the offset of the low pulse interference signal data.

[0065] The EEG signal data collected in step S1 is converted into:

[0066] Y(t)=N H (t)×2 n +N L (t)+S(t)=Y H (t)×2 n +Y L (t);

[0067] Among them, Y H (t) is the high bit of the binary data of the collected EEG signal; Y L (t) is the low bit of collected EEG signal data.

[0068] Collect EEG signal data binary high bit Y H (t) When there is an amplitude-exceeding pulse or step noise, the high m bits of the EEG signal data are collected. like Figure 3 The figure shows the noise baseline quantization diagram of the present invention; at this time, the collected EEG signal data is equivalent to the electrosurgical knife interference signal data.

[0069] Step S3: The sub-resolution N is set to 500 according to the physiological characteristics of the EEG signal, so that the electrosurgical interference signal exhibits a large amplitude characteristic and can retain the EEG signal data characteristics within the time period of the electrosurgical interference signal.

[0070] The magnitude of the sub-resolution SR of the EEG data is determined based on the deviation m of the electrosurgery interference signal data:

[0071] SR=(2 m mod500)×500;

[0072] Wherein, SR is the magnitude of the sub-resolution of the interference data; mod is the sub-resolution symbol; and m is the offset of the electric knife interference signal data.

[0073] like Figure 2 The figure shows the principle diagram of sub-resolution quantization of the present invention; the collected EEG signal data Y(t) in step S2 is constrained according to the sub-resolution magnitude SR of the interference data to obtain sub-resolution quantized interference data M(t); the data higher than the sub-resolution magnitude SR of the EEG data is restricted to the sub-resolution magnitude SR of the EEG data, and the data lower than the sub-resolution magnitude SR of the EEG data remains unchanged.

[0074] Step S4: using a linear interpolation method to process the sub-resolution quantized interference data obtained in step S3, and performing inverse linear interpolation to obtain interference-cancelling data.

[0075] Step S41: Using the interpolation method to make the sub-resolution quantized interference data M(t) continuous at the original precision, setting the interference signal data before and after the interpolation time point position t to be (t i ,y i ) and (t i+1 ,y i+1 ), the interference data M at the interpolation time point position i (t) is:

[0076]

[0077] Among them, M i (t) is the interference data at the interpolation time point t; t i is the previous data position of the interpolation time point data; y i t i The interference signal amplitude at the position; t i+1 The next data position of the interpolated time point data; y i+1 t i+1 The interference signal amplitude at the location.

[0078] The overall data after interpolation is now:

[0079]

[0080] Step S42: According to the interpolation operation in S41, the first interference data M1(t) at the interpolation time point t is subjected to inverse linear interpolation to correct the second interference data M2(t) at the interpolation time point t, and the overall data after interpolation is: M1(t)+M2(t)={(t1',y1'),(t1",y1"),(t'2,y'2),(t″2,y″2),(t'3,y'3),(t″3,y″3),......}. Among them, M2(t)={(t1",y1"),(t″2,y″2),(t″3,y″3),......}, so that the sampling rate of the sub-resolution quantized interference data is consistent with that of the original interference data, so as to achieve point-by-point cancellation of the electrosurgical interference data; the interpolation operation can effectively reduce the influence of signal mutation and keep the sampling rate of the second EEG data M2(t) at the interpolation time point t the same as that of the real EEG signal data S(t).

[0081] Step S5: Based on the second interference data M2(t) at the interpolation time point t corrected in S4 and the EEG signal data Y(t) collected in step S1, the corrected EEG data with the electrosurgical interference removed is obtained by point-by-point cancellation:

[0082] R(t)=Y(t)-M2(t);

[0083] Among them, R(t) is the EEG data after removing the interference of the electrosurgical knife.

[0084] like Figure 4 The figure shows the time-frequency diagram before correction of the first example of the effect of suppressing the interference of the electric knife of the present invention. It can be seen that the impulse noise interference is concentrated on the right side of the figure. Figure 6 The figure shows a time-frequency diagram before correction of a second example of the effect of suppressing electric knife interference of the present invention. It can be seen that the impulse noise interference is concentrated in the middle position of the figure and is relatively dispersed.

[0085] like Figure 5 The figure shows the first example of the effect of the present invention on suppressing the interference of the electric knife after correction. It can be seen that the impulse noise interference on the right side of the figure is effectively suppressed and the noise interference cannot be clearly seen. Figure 7 The figure shows the time-frequency diagram after correction of the second example of the suppression effect of the present invention on the electrosurgical knife interference. The pulse interference with strong dispersion distribution in the figure has been effectively eliminated, and the interference caused by the electrosurgical knife interference signal to the original neural signal S(t) has been greatly removed. It can be seen that the advantages and actual effects can be clearly seen based on the time-frequency spectrum data.

[0086] The second aspect of the embodiment of the present invention proposes an EEG data processing system based on a sub-resolution quantization method for eliminating electrosurgical unit interference, which includes: a signal acquisition module, a signal processing module, a data transmission module, a power management module and a host computer interaction module.

[0087] The acquisition module includes electrodes, preamplifiers and analog-to-digital converters, which are used to collect EEG signals from the scalp surface.

[0088] The signal processing module includes a digital signal processor, a noise reduction algorithm and a feature extraction algorithm, which are used to perform real-time noise reduction and feature extraction on the collected EEG signals.

[0089] The data transmission module includes a wireless transmission module and a wired transmission interface, which is used to transmit the processed EEG signals to a host computer or the cloud.

[0090] The power management module includes a battery, a power management chip, and a low-power design to provide a stable power supply for the entire device and optimize power consumption.

[0091] The host computer interaction module includes a display screen, indicator lights and buttons or a touch screen, which is used to provide an interactive interface between the user and the device and display the device status and EEG signals.

[0092] The beneficial effects of the present invention are as follows: the present invention provides an EEG data processing method based on sub-resolution quantization to eliminate electrosurgical interference. The present invention decomposes the electrosurgical signal data through sub-resolution quantization, accurately identifies electrosurgical noise at the time domain level, and corrects strong noise signals through interpolation. The original signal data is obtained by linear fitting of the original signal and the interpolated corrected signal, which effectively improves the removal of electrosurgical signals, overcomes the limitations of previous studies on the removal of strong noise signals, and provides new ideas for data acquisition and data preprocessing. The present invention decomposes and corrects data based on time domain characteristics and can be applied to real-time acquisition; for the real-time acquisition process, during normal acquisition, it can avoid unnecessary influence on the data and ensure the authenticity and reliability of the data; through verification and analysis of two groups of actual cases, it is proved that the present invention can effectively eliminate the interference caused by electrosurgical interference signals to the original neural signals, and the advantages and effects of the present invention can be clearly seen.

[0093] The embodiments described above are merely descriptions of preferred implementations of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should fall within the scope of protection determined by the claims of the present invention.

Claims

1. A method for processing EEG data based on sub-resolution quantization to eliminate electrosurgical interference, characterized in that: It includes: S1: The collected EEG signal data is obtained by linearly superimposing the interference signal and the EEG signal during acquisition; The interference signal and the EEG signal are independent of each other during the signal transmission process. They are linearly superimposed during acquisition, and the acquired EEG signal data is: ； in, To collect EEG signal data; is the real EEG signal data; is the interference signal data; is the time parameter; Based on real EEG signal data and interference signal data Superposition characteristics to obtain real EEG signal data The scope is , set up to collect EEG signal data , then the deviation of the electric knife interference signal data is ; S2: Decompose the interference signal data according to the deviation of the electrosurgical interference signal data to obtain the high-order binary bit of the EEG signal data; , obtain interference signal data Medium binary high bit , get the interference signal data for: ； in, Interference signal data Medium binary high bit; Interference signal data Medium binary low bit; The deviation of the low pulse interference signal data is: The EEG signal data collected in step S1 is converted into: ； in, To collect the binary high bit of EEG signal data; To collect the low bit of EEG signal data; S3: Set the sub-resolution to N according to the electrophysiological characteristics of the brain, and the deviation of the electrosurgery interference signal data Determining the magnitude of sub-resolution in EEG data for: ； in, is the order of magnitude of the sub-resolution of the interference data; is a sub-resolution symbol; is the deviation of the electric knife interference signal data; N is the sub-resolution; According to the sub-resolution level of interference data For the collected EEG signal data in step S2 Constraints are applied to obtain sub-resolution quantized interference data ; S4: using a linear interpolation method to process the sub-resolution quantized interference data obtained in step S3, and performing inverse linear interpolation to obtain interference-cancelling data; S41: Interpolation method is used to make sub-resolution quantization of interference data Continuous at original precision, setting the interpolation time point position The interference signal data before and after are and , the interference data of the interpolation time point position for: ； in, Interpolation time point Interference data at is the previous data position of the interpolation time point data; for The interference signal amplitude at the location; The next data position of the interpolated time point data; for The interference signal amplitude at the location; S42: interpolating the time points according to the interpolation operation in step S41 The first interference data at Perform inverse linear interpolation to correct the interpolation time point The second interference data at , so that the sampling rate of the sub-resolution quantized interference data is consistent with the original interference data, so as to achieve point-by-point cancellation of the electrosurgical interference data; S5: interpolation time point corrected in step S4 The second interference data at And the EEG signal data collected in step S1 , the corrected EEG data with electrocautery interference removed is obtained by point-by-point cancellation: ； in, To remove EEG data from electrocautery interference.

2. The method for processing EEG data based on sub-resolution quantization to eliminate electrosurgical interference according to claim 1, characterized in that: The high-order binary bit of the collected EEG signal data in step S2 When there is a pulse or step noise with an amplitude exceeding the limit, the high Bit At this time, the collected EEG signal data is equivalent to the electric knife interference signal data.

3. The method for processing EEG data based on sub-resolution quantization to eliminate electrosurgical interference according to claim 1, characterized in that: Step S1: Real EEG signal data and interference signal data Superposition characteristics, specifically: real EEG signal data and interference signal data Overlap in the frequency domain and interfere with signal data in the time domain The amplitude is higher than the real EEG signal data .

4. The method for processing EEG data based on sub-resolution quantization to eliminate electrosurgical interference according to claim 1, characterized in that: In step S3, the sub-resolution is set to 500 so that the electrosurgical interference signal exhibits a large amplitude characteristic, and the EEG signal data characteristics within the electrosurgical interference signal time period can be retained.

5. The method for processing EEG data based on sub-resolution quantization to eliminate electrosurgical interference according to claim 1, characterized in that: The magnitude of the sub-resolution of the EEG data in step S3 Collecting EEG signal data Constraints are made to make the level higher than the sub-resolution of EEG data The data is limited to the sub-resolution level of EEG data. , which is orders of magnitude lower than the sub-resolution of EEG data. The data remains unchanged.

6. The method for processing EEG data based on sub-resolution quantization to eliminate electrosurgical interference according to claim 1, characterized in that: The interpolation operation in step S4 can effectively reduce the impact of signal mutation and maintain the interpolation time point Second EEG data Compared with real EEG signal data The sampling rate is the same.

7. The method for processing EEG data based on sub-resolution quantization to eliminate electrosurgical interference according to claim 1, characterized in that: The corrected EEG data with electrosurgical interference removed obtained by point-by-point cancellation in step S5 can be used for real-time processing during the EEG data acquisition process to eliminate the influence of the electrosurgical interference signal.

8. The method for processing EEG data based on sub-resolution quantization to eliminate electrosurgical interference according to claim 1, characterized in that: EEG data after removing electrosurgery interference in step S5 , used for the data acquisition process of the real-time EEG signal acquisition device, and uses the processed EEG data for diagnosis, analysis and real-time monitoring of the status.

9. An EEG data processing system for the EEG data processing method based on sub-resolution quantization to eliminate electrosurgery interference according to claim 1, characterized in that: It includes: signal acquisition module, signal processing module, data transmission module, power management module and host computer interaction module; The acquisition module includes electrodes, a preamplifier and an analog-to-digital converter, and is used to collect EEG signals from the scalp surface; The signal processing module includes a digital signal processor, a noise reduction algorithm and a feature extraction algorithm, which is used to perform real-time noise reduction and feature extraction on the collected EEG signals; The data transmission module includes a wireless transmission module and a wired transmission interface, which are used to transmit the processed EEG signals to a host computer or the cloud; The power management module includes a battery, a power management chip and a low-power design to provide a stable power supply for the entire device and optimize power consumption; The host computer interaction module includes a display screen, indicator lights and buttons or a touch screen, which are used to provide an interactive interface between the user and the device and display the device status and EEG signals.

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