Electroencephalogram data processing method and system for eliminating electrotome interference based on sub-resolution quantification

Abstract

The invention belongs to the technical field of electroencephalogram data acquisition and processing, and particularly relates to an electroencephalogram data processing method and system for eliminating electrotome interference based on sub-resolution quantification, and the method comprises the steps: S1, obtaining electroencephalogram data through linear superposition according to the independence of interference signals and electroencephalogram signals; s2, decomposing the interference signal according to the offset degree of the electrotome interference signal data to obtain a binary high order of the electroencephalogram signal data; s3, setting a sub-resolution, and determining the sub-resolution magnitude of the electroencephalogram data according to the offset degree of the electrotome interference signal data; s4, obtaining interference data quantized by sub-resolution by using a linear interpolation method, and performing inverse linear interpolation to obtain offset interference data; and S5, according to the corrected second interference data and the acquired electroencephalogram signal data, performing point-by-point cancellation to obtain electroencephalogram data without electrotome interference. According to the method, the pulse signal is quantized through the sub-resolution, the electrotome noise is recognized, and the electroencephalogram data is obtained through linear fitting of the original signal and the interpolation correction 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 electrosurgical unit interference. Background Art

[0002] During the EEG signal acquisition and processing, sudden strong interference from the outside world can cause pulses or step noises with huge amplitudes to appear in the signal. At this time, the corresponding channel is generally set as a bad conductor and the data of the corresponding channel is removed. In rehabilitation treatment scenarios such as magnetic stimulation or electrical stimulation or during patient motor rehabilitation, although there are various hardware solutions to minimize this noise, the detection process will inevitably include this strong interference noise, and this interference may appear on all channels. Simply removing the channel may not be feasible. Equipment for rehabilitation applications inevitably needs to solve this problem.

[0003] Since the electric knife and sudden step noise signals similar to motion artifacts are represented 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 the position when mapping to the frequency domain, and the power of the fitted component is much larger than the normal signal, which shows an upward shift of energy in the entire frequency band on the spectrum, making it difficult to handle this situation by distinguishing the signal bandwidth using traditional frequency domain filters.

[0004] On the other hand, this type of noise can be easily distinguished visually, that is, it has more obvious characteristics in the time domain, so many methods use the statistical characteristics of the signal in the time domain through interpolation or wavelet decomposition, and iteratively implement the method to correct this noise. Although some methods can indeed suppress step or impulse noise in theory and testing, when the amplitude mutation is too large, the number of algorithm iterations will also increase significantly, and more signals near the mutation position will also be corrected, especially when there is frequent pulse signal interference, the effect is not rational. Although the amplitude limit can be used to shorten the iteration process, the interpolation part may be distorted due to the loss of the over-limit signal, and it is powerless in the case of frequent impulse noise.

[0005] In view of the above problems, the present invention uses real-time sub-resolution quantization of the main components of noise, and uses natural interpolation to fit the baseline drift caused by strong noise, and removes the sudden baseline value from the collected data in real time to suppress strong interference pulses. Secondly, the algorithm can realize adaptive processing of the signal by controlling the sub-resolution degree of quantization, avoiding the impact on the data during normal collection. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides an EEG data processing method and system for eliminating electrosurgical interference based on sub-resolution quantization. The data containing electrosurgical signals are decomposed through sub-resolution quantization, the electrosurgical noise is identified in the time domain, the electrosurgical noise in the data is accurately identified, and the strong noise signal is corrected through interpolation. Finally, the original signal data is obtained through linear fitting of the original signal and the interpolated corrected signal, which effectively improves the removal of the electrosurgical signal.

[0007] To achieve the above object, the present invention provides an EEG data processing method based on sub-resolution quantization to eliminate electrosurgical unit 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 obtained as follows:

[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 electrosurgical 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 binary bit of the collected EEG signal data; Y L (t) is the low bit of collected EEG signal data;

[0019] S3: According to the electrophysiological characteristics of the brain, the sub-resolution is set to N, and the magnitude SR of the sub-resolution of the electrosurgical interference signal data is determined according to the deviation m:

[0020] SR=(2 m mod N)×N

[0021] Among them, 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 magnitude SR of the sub-resolution 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 an 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 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 is the next data position of the interpolated time point data; yi+1 t i+1 The interference signal amplitude at the location;

[0027] S42: According to the interpolation operation in step S41, the first interference data M1(t) at the interpolation time point t is inversely interpolated 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 electric knife 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 knife 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 a pulse or step noise with an amplitude exceeding the limit, 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, in step S1, the superposition characteristics of the real EEG signal data S(t) and the interference signal data N(t) 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 unit interference signal exhibits a large amplitude characteristic, and the EEG signal data characteristics within the electrosurgical unit interference signal time period can be retained.

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

[0035] Preferably, the interpolation operation in step S4 can effectively reduce the impact of signal mutations 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).

[0036] Preferably, the corrected EEG data without electrosurgical interference obtained by point-by-point cancellation in step S5 can be used for real-time processing during EEG data acquisition to eliminate the influence of electrosurgical interference signals.

[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 knife 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 are 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 through linear fitting of the original signal and the interpolated corrected signal, which effectively improves the removal of electrosurgical knife signals and overcomes the limitations of previous studies on the removal of strong noise signals, providing 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 electrosurgical knives to ensure the acquisition of normal signals, and provides a new idea for existing acquisition equipment to filter out interference from electrosurgical knives during surgery. At the same time, the present invention is also effective for the data processing process. The present invention preprocesses data that is severely interfered by noise, thereby further improving the utilization rate of clinical acquisition data. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flow chart of the EEG data processing method based on sub-resolution quantization to eliminate electrosurgical unit interference 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 diagram of the present invention;

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

[0052] Figure 5 This is a time-frequency diagram after correction of a first example of the suppression effect of the present invention on the interference of an electric knife;

[0053] Figure 6 A time-frequency diagram before correction of a second example of the suppressing effect of the present invention on the interference of an electric knife;

[0054] Figure 7 This is a time-frequency diagram after correction of a second example of the suppressing effect of the present invention on the interference of an electric knife. 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 specified.

[0056] The present invention provides an EEG data processing method based on sub-resolution quantization to eliminate electrosurgical interference. As an EEG data processing method to eliminate strong pulse interference, it solves the problem that the use of electrosurgical knife 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 obtained by the linear interpolation method is used, and the offset interference data is obtained by inverse linear interpolation; according to 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; it 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 obtained as follows:

[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 ], then 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 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:

[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 in 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 binary bit of the collected EEG signal data; 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 a pulse or step noise with an amplitude exceeding the limit, 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: According to the physiological characteristics of the EEG signal, the sub-resolution N is set to 500, 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] According to the deviation m of the electrosurgical interference signal data, the magnitude SR of the sub-resolution of the EEG data is determined as follows:

[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 order SR of the sub-resolution of the interference data to obtain the sub-resolution quantized interference data M(t); the data higher than the order SR of the sub-resolution of the EEG data is limited to the order SR of the sub-resolution of the EEG data, and the data lower than the order SR of the sub-resolution of the EEG data remains unchanged.

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

[0075] Step S41: Using an 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 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 is 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:

[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 the original interference data, so as to realize the point-by-point cancellation of the electrosurgical interference data; the interpolation operation can effectively reduce the influence of the 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 knife interference removed is obtained by point-by-point elimination:

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

[0085] like Figure 5 The first example of the corrected time-frequency diagram of the present invention's suppression effect on electrosurgical interference is shown. It can be seen that the impulse noise interference on the right side of the diagram 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 unit interference. The pulse interference with strong dispersion distribution in the figure has been effectively eliminated, and the interference caused by the electrosurgical unit 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 are 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 are 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 data of electrosurgical signals 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 can ensure the authenticity and reliability of the data; through the verification and analysis of two groups of actual cases, it is proved that the present invention can effectively eliminate the interference of 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 only descriptions of the preferred implementation modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design 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 all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for processing electroencephalogram data based on sub-resolution quantization to eliminate electrosurgical unit 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 obtained as follows: Y(t)=S(t)+N(t) 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; 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 electrosurgical knife interference signal data is m; 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: N(t)=N H (t)×2 n +N L (t) 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: The EEG signal data collected in step S1 is converted into: Y(t)=N H (t)×2 n +N L (t)+S(t)=Y H (t)×2 n +Y L (t) Among them, Y H (t) is the high binary bit of the collected EEG signal data; Y L (t) is the low bit of collected EEG signal data; S3: According to the electrophysiological characteristics of the brain, the sub-resolution is set to N, and the magnitude SR of the sub-resolution of the electrosurgical interference signal data is determined according to the deviation m: SR=(2 m mod N)×N Among them, 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; Constraining the collected EEG signal data Y(t) in step S2 according to the magnitude SR of the sub-resolution of the interference data to obtain sub-resolution quantized interference data M(t); 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: Using an 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 i (t) is: 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 is the next data position of the interpolated time point data; y i+1 t i+1 The interference signal amplitude at the location; S42: According to the interpolation operation in step S41, the first interference data M1(t) at the interpolation time point t is inversely interpolated 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 electric knife interference data; 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 knife interference removed is obtained by point-by-point cancellation: R(t)=Y(t)-M2(t) Among them, R(t) is the EEG data after removing the interference of the electrosurgical knife.

2. The method for processing EEG data based on sub-resolution quantization to eliminate electrosurgical unit interference according to claim 1, characterized in that: The high bit Y of the collected EEG signal data in step S2 H (t) When there is a pulse or step noise with an amplitude exceeding the limit, 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.

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: 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 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.

4. The method for processing EEG data based on sub-resolution quantization to eliminate electrosurgical unit 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 unit interference according to claim 1, characterized in that: In step S3, the collected EEG signal data Y(t) is constrained according to the sub-resolution level SR of the EEG data, so that the data higher than the sub-resolution level SR of the EEG data is limited to the sub-resolution level SR of the EEG data, and the data lower than the sub-resolution level SR of the EEG data remains unchanged.

6. The method for processing EEG data based on sub-resolution quantization to eliminate electrosurgical unit interference according to claim 1, characterized in that: The interpolation operation in step S4 can effectively reduce the impact of signal mutations 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).

7. The method for processing EEG data based on sub-resolution quantization to eliminate electrosurgical unit interference according to claim 1, characterized in that: The corrected EEG data without electrosurgical interference 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 unit interference according to claim 1, characterized in that: 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.

9. An EEG data processing system based on a sub-resolution quantization method for eliminating electrosurgical knife interference, 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 are 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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