EEG signal acquisition equipment evaluation system and evaluation method

Abstract

The present application discloses an evaluation system and method for an electroencephalogram (EEG) signal acquisition device. The system includes a standard control signal generation module for generating a standard control signal; an evaluation module for acquiring an EEG acquisition signal obtained by the EEG signal acquisition device, processing the EEG acquisition signal, displaying the EEG acquisition signal and the standard control signal, and comparing the waveforms and parameters of the EEG acquisition signal and the standard control signal, as well as determining the percentage of samples whose relative amplitude difference between the EEG acquisition signal and the labeled control signal exceeds a set threshold among all sample points. The present application evaluates the degree of signal distortion by calculating the normalized amplitude difference percentage, comprehensively considering the difference between the acquisition signal and the standard control signal, quantifying the distortion, and providing a reliable indicator for signal quality evaluation.

Description

Technical Field

[0001] The present application relates to the technical field of electroencephalogram (EEG) signal processing, and in particular to an EEG signal acquisition equipment evaluation system and evaluation method. Background Art

[0002] Electroencephalography (EEG) is an important method for monitoring brain function by recording electrical activity on the scalp. EEG is widely used in medical diagnosis, neuroscience research, brain-computer interfaces (BCIs), and other fields. While EEG signal analysis has high temporal resolution, it often suffers from noise, distortion, and phase deviation due to factors such as external interference, poor electrode contact, and signal processing algorithms.

[0003] In the early stages of EEG device production, quality inspectors frequently manually operated each channel to confirm the errors in pilot EEG acquisition equipment. This required assessing the frequency domain error, phase error, and amplitude error of the equipment under inspection, enabling production staff to conduct timely calibration, inspection, and quality control. For example, production staff often needed to directly compare the difference between a standard reference signal and the acquired signal, but there was no integrated detection system for this.

[0004] Traditionally, EEG acquisition equipment is tested by production testers through simple manual comparison of amplitude differences. There is a lack of supporting quantitative accuracy testing methods and system solutions to assist production testers in their work. Summary of the Invention

[0005] The technical purpose of this application is to provide an EEG signal acquisition equipment evaluation system and evaluation method, which mainly relies on manual testing of each channel of the EEG signal acquisition equipment and lacks an evaluation system that can directly and automatically compare the differences between standard control signals and acquisition signals in batches.

[0006] In order to achieve the above technical objectives, this application adopts the following technical solutions.

[0007] In a first aspect, an embodiment of the present application provides an EEG signal acquisition device evaluation system, wherein the evaluation system is used to evaluate an EEG signal acquisition device, and the evaluation system includes:

[0008] A standard control signal generating module, used for generating a standard control signal;

[0009] An evaluation module is used to obtain the EEG signal obtained by the EEG signal acquisition device, display the EEG signal and the standard control signal, compare the waveforms and parameters of the EEG signal and the standard control signal, and determine the percentage of samples whose relative amplitude of the difference between the EEG signal and the labeled control signal exceeds a set threshold among all sample points in the total number of samples.

[0010] Furthermore, the system also includes a signal correction module;

[0011] The signal correction module is used to perform Fourier transform on the EEG acquisition signal and the standard control signal, calculate the phase difference between the transformed EEG acquisition signal and the standard control signal, and use a sampling moment scale factor sequence to phase-align the EEG acquisition signal with the standard control signal according to the phase difference to obtain a corrected EEG acquisition signal.

[0012] Furthermore, the system also includes: an EEG characteristic waveform generation engine, which has a built-in international standard EEG waveform signal template library, which is used to select the corresponding template signal from the template library based on the type of required signal and related physiological parameters. The template signal can be used to generate a standard reference signal, so that the standard control signal generation module generates the standard control signal based on the standard reference signal.

[0013] Furthermore, the EEG characteristic waveform generation engine also has a built-in intelligent parameter mapping rule library, which is used to match the intelligent parameter mapping rule library according to the input physiological indicators to obtain key characteristic parameters; and use the key characteristic parameters to adjust the parameters of the template signal to generate the template signal that meets the target characteristics.

[0014] Furthermore, the system further comprises a standard reference signal generating module for generating a standard reference signal, so that the standard control signal generating module generates the standard control signal according to the standard reference signal;

[0015] The standard reference signal generation module includes FPGA, DSP, DAC, low-noise operational amplifier signal chain module, power management module and physical isolation module;

[0016] The FPGA is used to generate waveform data;

[0017] The DSP is communicatively connected to the FPGA, and the DSP is used to pre-process and optimize the quality of waveform data by controlling the PGA gain of the analog front end;

[0018] The FPGA transmits the waveform data to the DAC, and the DAC is used to convert the waveform data into an analog signal;

[0019] The low-noise operational amplifier signal chain module is used to amplify the analog signal;

[0020] The power management module is used to power the FPGA, DSP, DAC and low-noise op amp signal chain modules;

[0021] The physical isolation module adopts a three-layer PCB layout, with the top layer transmitting analog signals, the middle layer being the ground plane, and the bottom layer being the digital circuit. The analog ground and digital ground are connected at a single point via a 0Ω resistor. The exterior uses an aluminum alloy shell and is sprayed with conductive paint.

[0022] Furthermore, the system also includes an improved average value filter, which is used to filter the EEG acquisition signal using an average value.

[0023] In a second aspect, an embodiment of the present application provides an evaluation method for an EEG signal acquisition device evaluation system as provided in any possible implementation of the first aspect, including:

[0024] For each sample point n , determine the absolute value of the difference between the EEG acquisition signal of the sample point and the standard control signal, relative to the maximum absolute value of the ratio of the two ;

[0025] in, x gen [ n ] represents the sample point n The EEG signal is collected. x temp [ n ] represents the sample point n The standard control signal, max () is the maximum value function;

[0026] Traverse all sample points. If the ratio is greater than or equal to the preset value, the sample point meets the distortion condition. Count the sample points that meet the distortion condition to obtain the counting result. Count Δp ;

[0027] The counting results Count Δp The ratio to the number of all sample points is the percentage.

[0028] Furthermore, when the system further includes a signal correction module, the method further includes:

[0029] Converting the EEG acquisition signal and the standard control signal into frequency domain signals using a signal correction module;

[0030] Comparing the phase values ​​of the EEG acquisition signal and the standard control signal at the same frequency, and calculating the phase difference between the two;

[0031] The sampling moment scale factor sequence is used to align the phases of the EEG acquisition signal and the standard control signal according to the phase difference to obtain a corrected EEG acquisition signal.

[0032] Furthermore, when the system further includes an improved average filter;

[0033] The method further includes: assigning different weights to sample points in a neighborhood using a Gaussian weighting strategy optimization and a frequency band parameter dynamic adaptation method based on the physical characteristics of different frequency bands of the EEG acquisition signal;

[0034] The improved average filter is used to perform weighted averaging calculation on the sample points in the neighborhood according to the assigned weights to obtain the filtered EEG signal value.

[0035] Furthermore, the expression of the improved average filter is as follows:

[0036] ;

[0037] in y [ n ] indicates that at the sample point n The signal value after filtering is x [ n + k ] indicates the sample point n As the center, the neighborhood k The input signal value of sample points, k The value range is from - N / 2 arrive N / 2 , N Indicates the sample points n As the center, the number of sample points extending to both sides, N Determines the size of the neighborhood; ω [ k ] indicates that at the sample point n At the neighborhood k The weight of the sample points.

[0038] Compared with the prior art, the EEG signal acquisition equipment evaluation system and evaluation method provided in the embodiments of the present application achieve the following beneficial technical effects: the degree of signal distortion is evaluated by calculating the percentage interval (i.e., the normalized amplitude difference percentage) of the total number of samples between the EEG acquisition signal and the labeled reference signal at all sample points, comprehensively considering the difference between the EEG acquisition signal and the standard reference signal, quantifying the distortion, and providing a reliable indicator for signal quality evaluation of the EEG signal acquisition device. The Fourier transform and phase difference calculation and correction module uses Fourier transform to obtain signal phase information, calculates the phase difference, and corrects the received signal (i.e., the EEG acquisition signal) accordingly, aligning the phase of the received signal with the standard reference signal, synchronizing it in the time domain, and improving signal accuracy. The improved averaging filter dynamically allocates neighborhood weights based on the characteristics of different frequency bands of EEG signals. For example, it expands the high-weight area of ​​low-frequency δ waves to smooth baseline drift, and quickly attenuates the edge weights of high-frequency γ waves to suppress high-frequency noise, effectively suppressing noise spikes and improving signal purity. It can process EEG signals in different frequency bands and different physiological states, and can effectively evaluate whether they are low-frequency slow waves or high-frequency fast waves, as well as normal and abnormal physiological state signals, and has a wide range of applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings described herein are for illustrative purposes only and are not intended to limit the scope of the present application in any way. In addition, the shapes and proportional dimensions of the components in the drawings are only schematic and are used to help understand the present application. They do not specifically limit the shapes and proportional dimensions of the components of the present application. Those skilled in the art can select various possible shapes and proportional dimensions to implement the present application according to the specific circumstances under the guidance of the present application. In the drawings:

[0040] Figure 1 A schematic diagram of the structure of the EEG signal acquisition device evaluation system provided in the embodiment;

[0041] Figure 2 A schematic diagram of a template signal generated by an EEG characteristic waveform generation engine in an EEG signal acquisition device evaluation system provided in an embodiment;

[0042] Figure 3 A hardware flow chart of the standard reference signal generation module in the EEG signal acquisition device evaluation system provided in the application embodiment;

[0043] Figure 4 Schematic diagram of the appearance of the standard reference signal generating module in the embodiment;

[0044] Figure 5 This is a schematic diagram of the amplifier portion of the EEG signal acquisition device in the embodiment;

[0045] Figure 6The EEG cap part of the EEG signal acquisition device in the embodiment;

[0046] Figure 7 This is a schematic diagram of the appearance of the standard control signal generating module in the embodiment;

[0047] Figure 8 A flowchart of an evaluation method for an EEG signal acquisition device evaluation system provided in an embodiment;

[0048] Figure 9 This is a schematic diagram showing the result of the phase and amplitude of the outputs of the evaluation module being substantially aligned in the embodiment;

[0049] Figure 10 Schematic diagram of the phase misalignment and amplitude alignment results output by the evaluation module in the embodiment;

[0050] Figure 11 Schematic diagram of the phase alignment and amplitude misalignment results output by the evaluation module in the embodiment;

[0051] Figure 12 This is a schematic diagram showing a result in which the phase alignment amplitude output by the evaluation module in the embodiment is slightly deviated;

[0052] Figure 13 Schematic diagram of the output interface of the evaluation module in the embodiment. DETAILED DESCRIPTION

[0053] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0054] like Figure 1 As shown, an embodiment of the present application provides an EEG signal acquisition device evaluation system for evaluating EEG signal acquisition devices, the evaluation system comprising a standard control signal generation module and an evaluation module. The standard control signal generation module is used to generate a standard control signal.

[0055] The evaluation module is used to obtain the EEG signal obtained by the EEG signal acquisition device, display the EEG signal obtained by the EEG signal acquisition device and the standard control signal, compare the waveforms and parameters of the EEG signal and the standard control signal, and determine the percentage of samples whose relative amplitude of the difference between the EEG signal and the labeled control signal exceeds the set threshold among all sample points (that is, the normalized amplitude difference percentage) in the total number of samples.

[0056] The EEG signal acquisition device is responsible for collecting EEG signals from the brain of the test subject. These signals reflect the electrical activity of brain neurons and are of great significance for studying brain function and diagnosing neurological diseases. The EEG signal acquisition device may include an EEG cap (such as Figure 6 ), the EEG cap contains electrodes, which are made of high conductivity materials and have good biocompatibility and conductivity, such as Figure 6 The gray electrode part of the EEG cap is as follows Figure 6 The hat part.

[0057] EEG signal acquisition equipment also includes EEG cap amplifiers (such as Figure 5 The amplifier is connected to the EEG cap to amplify weak EEG signals and transmit them to the PC while suppressing noise. The accuracy of the EEG signals collected by the EEG signal acquisition device can be evaluated using an evaluation module configured on the PC. Optionally, the EEG signal acquisition device also includes filters, such as low-pass, high-pass, and band-pass filters, to remove unwanted frequency components.

[0058] During use, electrodes are placed on the subject's scalp and connected to an EEG cap, which is then connected to an amplifier via wires. The amplifier amplifies the weak EEG signals and filters them to remove noise and interference. The amplifier then converts the analog signals into digital signals, which are then transmitted to a data processing unit via a data interface.

[0059] Since the acquisition accuracy (frequency, amplitude, phase) of the EEG signal acquisition equipment is unknown before evaluation before leaving the factory, the equipment needs to acquire a standard reference signal (which can be based on the standard reference signal generated by the standard reference signal generation module) and compare the acquired EEG acquisition signal with the standard control signal for subsequent processing (calibration or maintenance) of the acquired signal.

[0060] Before receiving the EEG acquisition signal, the standard control signal generation module generates a standard control signal. Figure 7 (as shown) is a signal generating instrument running on specific high-performance computing equipment. It can create standard control signals for testing through arithmetic methods. These standard control signals can be generated based on standard reference signals. Standard control signals are arithmetically ideal signals used for comparison during evaluation, such as absolutely pure square waves, sine waves, and triangle waves. These standard control signals can help verify the effectiveness and accuracy of subsequent processing algorithms.

[0061] In one embodiment, the standard control signal generation module may include a scientific calculation module and a delay adjustment module. The scientific calculation module is used for scientific calculation to generate a dedicated standard control signal for the standard signal. The delay adjustment module allows the user to adjust the signal delay to match the temporal characteristics of the EEG signal acquisition.

[0062] The standard reference signal generated by the standard reference signal generation module can be copied to the standard control signal generation module to generate a standard control signal. The user can adjust the signal delay as needed to ensure that the standard control signal is synchronized with the collected EEG signal. The adjusted standard control signal is transmitted to the evaluation module (such as EEG signal acquisition accuracy assessment software) for comparison and evaluation.

[0063] The evaluation module is responsible for analyzing and evaluating the collected EEG signals. This module compares the collected EEG signals with standard control signals and calculates indicators such as error to evaluate the accuracy of the collected signals.

[0064] As examples, the software modules included in the evaluation module are:

[0065] Signal comparison panel: This software panel is used to select the signal of the channel to be tested. It can compare the waveforms of the EEG acquisition signal and the standard control signal in real-time graphics, and intuitively display various core parameters.

[0066] Error calculation unit: calculates the difference between the EEG acquisition signal and the standard control signal, such as amplitude error, phase error, etc., and performs error correction on the waiting comparison signal to facilitate calculation.

[0067] Correlation analysis unit: evaluates the correlation between signals to determine the similarity of the signals, such as by adopting a normalized amplitude difference interval percentage method in some embodiments.

[0068] The evaluation module feeds the collected EEG signal and a standard control signal into the signal comparison panel. The comparator analyzes the waveforms and parameters of the two signals, and the error calculation unit calculates differences between the signals, such as amplitude error and phase error. After error correction, the signals undergo correlation analysis. Finally, the evaluation results are reported in graphical form for user reference.

[0069] In an embodiment, the evaluation module may include using several software libraries to write several modules, respectively implementing a data loading module, a data cleaning module, and a data conversion module. First, load the signal data from the CSV file and convert the first to the second n -1 column is defined as received n -1 channel EEG acquisition signal, n The column is defined as the reference standard control signal, which is the control signal calculated by a function based on the standard reference signal for evaluation.

[0070] In some embodiments, the evaluation system further includes a signal correction module; the signal correction module is configured to perform a Fourier transform on the EEG acquisition signal and the standard control signal, calculate the phase difference between the EEG acquisition signal and the standard control signal, and use a sampling time scale factor sequence (the sampling time scale factor sequence is a time-linear sequence related to the sampling rate) to phase-align the EEG acquisition signal with the standard control signal based on the phase difference to obtain a corrected EEG acquisition signal. The phase difference calculation is accomplished by comparing the dominant frequencies of the signals. The core of this step lies in extracting useful phase information from the frequency domain information. Based on the calculated phase difference, the received signal is corrected using a specific sampling time scale factor sequence. This step converts the phase difference into an angular offset and applies it to the signal to generate a corrected EEG acquisition signal.

[0071] As an example, the generated corrected EEG signal is saved as a new CSV file and further analyzed using the evaluation module for differences compared to a standard control signal. Specifically, the signal ratio is calculated within a certain ± range. The corrected signal is then compared with the original EEG signal (received signal) and the standard control signal, and a graph is generated and saved as a PNG file.

[0072] In the fields of scientific research and medicine, generating accurate EEG waveforms that meet international standards is a long-standing challenge. Traditional methods require manual adjustment of waveform parameters, and it is difficult to simulate EEG signals that meet specific physiological conditions. This is not only time-consuming and labor-intensive, but may also lead to experimental errors. In addition, accurately simulating abnormal EEG signals (such as spikes and sharp waves in epileptic patients) is particularly important for testing medical equipment and training anti-interference algorithms, but existing simulation technologies often cannot achieve the required accuracy and stability. In response to these difficulties, such as Figure 1 and Figure 2 As shown, in some embodiments, the system also includes an EEG characteristic waveform generation engine, which has a built-in international standard EEG waveform signal template library, which is used to select the corresponding template signal from the template library based on the type of required signal and related physiological parameters. The template signal can be used as a standard reference signal, so that the standard control signal generation module generates a standard control signal based on the standard reference signal.

[0073] In some embodiments, the EEG characteristic waveform generation engine also has a built-in intelligent parameter mapping rule library, which is used to match the intelligent parameter mapping rule library according to the input physiological indicators to obtain key characteristic parameters; and use the key characteristic parameters to adjust the parameters of the template signal to generate a template signal that meets the target characteristics.

[0074] The EEG characteristic waveform generation engine is actually middleware software running on a PC. It has a built-in international standard EEG waveform template library, supports user-defined physiological parameters, and can automatically match and adjust the amplitude, frequency, and noise floor of the EEG waveform to simulate EEG signals in real environments. Through the collaborative optimization of FPGA and DSP, the EEG characteristic waveform generation engine can calculate and adjust the waveform in real time, achieve precise phase jitter and tiny amplitude adjustment, and input it into a standard signal generator to generate EEG signals with specified characteristics. This intelligent generation method not only significantly shortens the experimental preparation time, but also improves the accuracy and stability of the signal. In addition, the EEG characteristic waveform generation engine can simulate abnormal EEG signals in specific scenarios, such as the discharge activities of epileptic patients, which is crucial for training and verifying the anti-interference performance of medical equipment.

[0075] In some embodiments, the system further includes an improved average filter, which is used to filter the EEG acquisition signal using an average value.

[0076] In some embodiments, the improved average filter and the signal correction module can be built into the evaluation module.

[0077] In some embodiments, the system further comprises a standard reference signal generating module, so that the standard control signal generating module generates a standard control signal block according to the standard reference signal, for generating a standard reference signal. Figure 3 and Figure 4 As shown, in the embodiment, the standard reference signal generating module is a standard reference signal generating system coordinated by FPGA+DSP. The standard reference signal generating device can generate electrical signals of several channels at a micro-amplitude level with regular periods, such as standard reference signals such as square waves, sine waves, and triangle waves. The generated signals can be input into the standard control signal generating module to generate standard control signals for evaluating and calibrating the internal parameters of the EEG data acquisition device; at the same time, it can accept the template library and intelligent parameter mapping rules built into the EEG characteristic waveform generation engine on the PC to send the specified waveform.

[0078] The design of the standard signal generation module requires the ability to provide high stability and high precision signal output to ensure the accuracy of subsequent signal acquisition and comparison. Figure 7 As shown, the standard signal generation module is a signal generating instrument responsible for generating a series of standard reference signals with known characteristics. These signals typically include sine waves, square waves, triangle waves, etc., with precise frequency, amplitude, and phase characteristics. This standard signal generation module achieves frequency accuracy and multi-mode through the collaborative processing module of FPGA + DSP, achieves nanovolt-level accuracy and low-noise output through the signal chain of 20-bit DAC + low-noise op amp, and achieves anti-interference through multi-stage power supply filtering + electromagnetic shielding. It meets the three core requirements of EEG signal generation:

[0079] 1. High precision: 20-bit DAC + segmented calibration, amplitude error <±0.1% (nanovolt level).

[0080] 2. High purity: noise floor <500pVrms, distortion <0.001% (EEG frequency band).

[0081] 3. High adaptability: high-impedance output / battery power supply / multi-mode waveform reception, suitable for EEG research and clinical scenarios.

[0082] The hardware modules included in the standard signal generation module are:

[0083] 1. FPGA+DSP collaborative processing module:

[0084] DSP+FPGA ensures low-frequency signal phase truncation error is small and frequency stability is high. The DSP integrated core supports real-time noise generation and modulation operations to meet complex waveform requirements.

[0085] The 20-bit high-precision DAC (digital-to-analog converter) is used in combination with dynamic range extension technology to achieve an ultra-wide amplitude output of 1nV-10mV (6 orders of magnitude dynamic range). The segmented calibration algorithm (automatic calibration of offset / gain every 100mV range) is used in conjunction with programmable gain amplifier (PGA) technology to achieve a 1nV resolution (full scale 10mV / 2 20 The DAC's nonlinear distortion is ≤0.001%. Combined with a precision reference (temperature coefficient <1ppm / °C) and an error compensation algorithm, the amplitude error is controlled to ±0.1% (+1nV reference error). The signal chain design utilizes a multi-stage amplification strategy: nanovolt-level small signal preprocessing is achieved through a PGA, followed by power delivery through a low-noise op amp, preventing small signals from being overwhelmed by noise during transmission.

[0086] Among them, the FPGA part is responsible for:

[0087] Real-time waveform generation: Based on DDS technology, it has a built-in 32-bit phase accumulator, high frequency resolution, supports μHz-level stepping, and covers the entire EEG frequency band.

[0088] High-speed data preprocessing: caching waveform data, supporting dual-channel independent phase adjustment, and meeting the requirements of coherent / incoherent EEG signal simulation.

[0089] Multi-mode control: Communicates with the DSP via the AXI bus to achieve AM / FM modulation, noise superposition, and simple multi-waveform synthesis (such as sine, square, and triangle waves).

[0090] Among them, the DSP part is responsible for:

[0091] Algorithm processing: Run digital filtering and distortion compensation algorithms to ensure waveform quality.

[0092] Human-computer interaction: Connect to middleware, parse PC commands, update waveform parameters in real time, and control the PGA gain of the analog front end.

[0093] 2. DAC and low-noise op amp signal chain module:

[0094] Among them, the DAC module is part of the signal chain design and is used for nanovolt-level precision and low-noise core.

[0095] It uses a 20-bit high-precision DAC to support a wide dynamic range of 1nV–10mV. It has a built-in automatic range calibration algorithm (every 100mV segment), and the offset / gain compensation coefficients are stored in EEPROM. The amplitude error is less than ±0.1% (+1nV reference error). The programmable gain amplifier (PGA) technology (1–1000 times) enables nanovolt-level small signal preprocessing to prevent op amp noise from drowning out small signals. The 20-bit DAC+PGA combination has a resolution of 1nV (10mV / 2 20 ), which meets the requirements for accurate generation of EEG signals (μV–nV level).

[0096] The analog front end is part of the signal chain design, responsible for low-noise amplification and filtering. It is equipped with a precision reference source (temperature coefficient <1ppm / °C).

[0097] Buffered by a low-noise op amp with a drive capability of 20mA, the DAC reference stability is ensured.

[0098] The secondary is equipped with a low noise operational amplifier circuit.

[0099] The differential-to-single-ended module constructs a high-input impedance (>10GΩ) differential amplifier circuit to match the high-impedance characteristics of EEG electrodes and suppress common-mode noise.

[0100] The final stage is equipped with a three-stage filtering system:

[0101] ① Pre-filtering: LC low-pass (100kHz cutoff) filters out DAC switching noise;

[0102] ② Active filtering: Second-order Sallen-Key low-pass (200Hz cutoff), combined with digital filtering to achieve -80dB / decade roll-off;

[0103] ③ Noise cancellation: Active noise cancellation circuit (50 / 60Hz notch depth > 60dB) suppresses power supply ripple.

[0104] 3. Power Management Module

[0105] The three-stage voltage regulation and battery-redundant power architecture provides power via an AC adapter or lithium-ion battery. The isolated power supply utilizes digital isolation and voltage reduction technology, achieving an isolation noise level exceeding 150dB. The precision voltage regulation section includes analog power supplies for op amps and digital power supplies for FPGAs / DSPs, both of which feature extremely low noise. Ripple suppression is achieved by connecting tantalum and ceramic capacitors in parallel with each chip power pin, resulting in a ripple of less than 1μVrms (20MHz bandwidth). The low-noise design, in battery-powered mode, uses multi-stage filtering to ensure clean power for the reference and op amps, reducing 1 / f noise.

[0106] 4. Physical isolation module:

[0107] Physical isolation is achieved using a three-layer PCB layout: the top layer for analog signals, the middle layer as a ground plane, and the bottom layer for digital circuitry. The analog and digital grounds are connected at a single point via a 0Ω resistor. The device's exterior is constructed of a 2mm thick aluminum alloy casing, and the interior is sprayed with conductive paint, achieving a shielding effectiveness exceeding 80dB (10kHz–1GHz). For signal isolation, digital control signals are isolated using optocouplers to prevent digital noise from coupling into the analog circuitry. The Ethernet interface utilizes pulse transformer isolation to isolate common ground noise. This anti-interference design, through shielding, isolation, and a layered layout, comprehensively suppresses EMI / RFI interference, ensuring that nanovolt-level signals are protected from environmental noise.

[0108] The present application also provides an evaluation method for the EEG signal acquisition device evaluation system provided in the above embodiment, such as Figure 8 shown.

[0109] In some embodiments, the evaluation module can perform data processing on the EEG signals acquired by the EEG signal acquisition device, including data loading, data cleaning, and data conversion. This module is used to process CSV files and provides powerful database framework operations, making it very convenient to read data from files, extract columns and rows, and perform simple data cleaning operations.

[0110] As an example, first, the evaluation module extracts EEG signal data from the file. Assuming that each signal is sampled as equally spaced time series data, the signal can be regarded as a discrete function in the time domain. After reading the data, the first thing to do is to ensure that the EEG signal is clean, such as removing duplicate rows, missing values, noise, etc. Generally speaking, commonly used denoising methods include moving average filters, median filters, etc. If there are obvious outliers in the data, they can be identified and removed by calculating the deviation of each point relative to its neighboring points. Here, an improved average filter and median filter are proposed, which can better handle noise.

[0111] The read_csv() function directly reads the CSV file and extracts the desired columns from the data by locating the columns. received_data contains n columns of EEG signals collected from the EEG device, while true_data is the standard control signal used as a reference value. The entire process is simple and efficient, demonstrating the convenience of this module in data processing.

[0112] In an embodiment, the percentage of samples whose relative amplitude difference between the EEG acquisition signal and the labeled control signal exceeds a set threshold among all sample points (i.e., the interval-normalized amplitude difference percentage) is determined, and the signal correlation is calculated using the interval-normalized amplitude difference percentage.

[0113] Calculating the amplitude difference is the simplest method for evaluating the difference in amplitude between two sets of signals. Traditionally, this calculation is typically performed through a simple difference comparison. In the evaluation module of this embodiment, an interval-normalized amplitude difference percentage metric is determined. In production practice, this metric can more accurately and quickly measure the amplitude and phase differences between two signals over a period of time, greatly facilitating the automated detection of faulty or defective EEG acquisition devices.

[0114] The traditional amplitude difference calculation only relies on the simple subtraction of signal values, which does not take into account the amplitude of the signal phase change at different times. For example, when the amplitudes of two signals are at different levels, only a simple difference calculation cannot effectively reflect the similarity of the two signals. Affected by the period and amplitude, the random simple difference may be large or small. Therefore, the traditional method is easy to mislead in the comparison of signals of different magnitudes. In order to overcome this limitation, the embodiment adopts an improved interval normalized amplitude difference percentage calculation, including: for each sample point n , determine the absolute value of the difference between the EEG acquisition signal of the sample point and the standard control signal, relative to the maximum absolute value ratio of the two, traverse all sample points, if the ratio is greater than or equal to the preset value P, then the sample point meets the distortion condition, the sample points that meet the distortion condition are counted to obtain the counting result Count Δp ; The counting results Count Δp The ratio of the number of all sample points is the percentage (interval normalized amplitude difference percentage). Its expression is as follows:

[0115] ;

[0116] in, x gen [ n ] represents the sample point n The EEG signal is collected. x temp [n ] represents the sample point n The standard control signal, max () is the maximum value function; | x gen [ n ]- x temp [ n ]|represent the received signal (i.e. EEG sampling signal) and the standard control signal at the sampling point n (time n ), Count Δp The number of signals limited by the preset value P amplitude range is counted. The preset value P amplitude is a limited amplitude, which represents the signal accuracy range that should be achieved after acquisition. Count all The total number of signals received is counted. max(| x gen [ n ]|,| x temp [ n ]|) represents the maximum amplitude of the two signals at that moment, which is used to calculate the normalized intra-group difference.

[0117] One of the benefits of this method is that it clarifies the concept of time intervals. It requires long-term collection and recording of standard control signals and EEG acquisition signals. It can perform overall large-interval analysis or key interval analysis according to different needs. It is particularly suitable for long-term data sets such as EEG.

[0118] Secondly, normalizing within-group differences eliminates errors caused by inconsistent amplitudes when comparing multiple groups. This is particularly useful for processing EEG signals, which have a wide range of amplitude variations. (You might need to compare 1mV or 40uV, avoiding interference from range configuration.)

[0119] Finally, by calculating relative percentages, we can better determine the signal's compliance probability. In the underlying signal verification scenario of EEG signal acquisition equipment, this can intuitively and accurately demonstrate many physical meanings, such as whether the discrete digital signals obtained after the standard signal passes through the acquisition system are missing in number, the total number of qualified signals within a specified time interval and within the allowable fluctuation range, and their proportion of the total number of signals. This reflects the qualification level of the EEG acquisition system.

[0120] In EEG signal analysis scenarios, calculating the percentage difference in amplitude between intervals is also valuable, revealing subtle changes in brain activity under different conditions. For example, when comparing pathological EEG signals with healthy EEG signals, this percentage difference can provide quantitative support for clinical diagnosis. Combined with the spatial location of the channels, it can also help doctors better identify lesions or abnormal activity in specific brain regions.

[0121] In some embodiments, the evaluation system includes a signal correction module, and the evaluation method of the evaluation system also includes the following method: using the signal correction module to convert the EEG acquisition signal and the standard control signal into frequency domain signals; comparing the phase values ​​of the EEG acquisition signal and the standard control signal at the same frequency, and calculating the phase difference between the two; using the sampling moment proportional factor sequence, the EEG acquisition signal and the standard control signal are phase-aligned according to the phase difference, thereby achieving phase compensation and obtaining a corrected EEG acquisition signal.

[0122] Calculating phase difference relies on the Fourier transform. The Fourier transform converts a time-domain signal into a frequency-domain signal, facilitating analysis of the signal's frequency components and phase differences. The fft function directly computes the fast Fourier transform of a signal, returning an array of complex numbers in the frequency domain. Phase information can be obtained using the angle (phase angle) of the complex number using the angle function.

[0123] ;

[0124] Here, phase information is extracted from the first frequency component of the Fourier transform result (ignoring the DC component), and the phase difference between the two is calculated. This phase difference is used to measure the frequency asynchrony of the signal and provide a basis for subsequent phase correction.

[0125] Specifically, Fourier transform is a common tool for converting signals from the time domain to the frequency domain. It can decompose a time-varying signal into a series of sine waves of different frequencies. The formula for Fourier transform is as follows:

[0126] ;

[0127] in: x [ n ] is the original time series signal, N is the total length of the signal. F ( f ) is the frequency domain representation of the signal, indicating different frequencies f The amplitude and phase information on . is a complex exponential that represents the weighting of each frequency component. The result of the Fourier transform is a complex number, where the real and imaginary parts represent the amplitude and phase of the signal at a specific frequency, respectively. By converting the complex number to polar coordinates, the amplitude and phase information can be obtained.

[0128] The amplitude can be calculated using the following formula:

[0129] ;

[0130] The phase can be calculated using the following formula:

[0131] ;

[0132] The atan2 function here is a two-parameter inverse tangent function that is used to ensure that the calculated phase angle is in the correct quadrant.

[0133] After obtaining the Fourier transform results of the received signal (i.e., EEG acquisition signal) and the standard control signal, the next step is to calculate the phase difference between them. The calculation of the phase difference is based on the difference in the phase values ​​of the two signals at the same frequency:

[0134] ;

[0135] This phase difference is usually calculated at the dominant frequency of the signal, that is, the frequency component with the largest amplitude. The dominant frequency can be determined by finding the maximum value in the Fourier transform result.

[0136] The goal of phase correction is to align the phase of the received signal with the phase of the standard signal. The idea of ​​phase correction is to adjust the received signal based on the calculated phase difference so that it is synchronized with the standard signal in the time domain. The correction method is to apply a corresponding phase offset to the signal. Assume that the phase difference is Δθ , then the signal can be corrected in the following ways:

[0137] ;

[0138] The intuitive meaning of this formula is that by adjusting the phase of the received signal, it is aligned with the reference signal. The corrected signal and the standard signal have the same phase in time, thus reducing the phase error between the two.

[0139] The phase difference is usually an angle value, which can be converted into a time offset. If the sampling frequency is f s , the time length of the signal is T , then the phase difference Δθ Can be converted into time difference Δt :

[0140] ;

[0141] in f is the main frequency. This time difference Δ t Indicates the shift in time that the signal needs to undergo to align with the reference control signal.

[0142] As an example, the sampling time scale factor sequence in this embodiment can be expressed as: t = np.arange(N) / fs;

[0143] where np.arange(N) is used to create a N An array of integers from 0 to N-1 representing the index of each sampling point. Dividing this array by the sampling frequency fs yields the signal time vector t in seconds. This time vector t represents the instant at each sampling point. If each element in the sampling time scale factor sequence t is considered a sampling time scale factor, the sampling time scale factor sequence t represents the time from the start of the signal to each sampling point. In other words, t is a time series that increases linearly with the sampling rate and accurately reflects the sampling instants of the signal.

[0144] During phase compensation, this embodiment uses complex multiplication to rotate the signal phase. Because the real and imaginary parts of a signal are composed of cosine and sine components, respectively, using complex multiplication for phase adjustment not only effectively achieves signal alignment but also avoids direct manipulation of the signal phase, ensuring simplicity and efficiency of the signal alignment operation.

[0145] The specific phase alignment method in the embodiment is implemented by the following formula:

[0146] aligned_signal = signal * np.cos(-phase_diff) - np.imag(signal *np.sin(-phase_diff));

[0147] This formula uses a complex phase compensation method to achieve alignment by rotating the signal (i.e., adjusting the phase). In Python's NumPy library, np.sin() and np.cos() are functions used to calculate sine and cosine values, respectively. np.sin(-phase_diff) calculates the sine of the negative phase difference -phase_diff, while np.cos(-phase_diff) calculates the cosine of the negative phase difference -phase_diff.

[0148] Sin(-phase_diff) and cos(-phase_diff) use the negative phase difference -phase_diff to adjust the signal phase. Cos(-phase_diff) and sin(-phase_diff) correspond to the real and imaginary parts of the phase difference, respectively. Based on the calculation results of these functions, the signal phase is rotated in the negative direction by phase_diff, thereby achieving signal alignment.

[0149] The operation signal * np.cos(-phase_diff) adjusts the phase of the real part of the signal, that is, the amplitude of the signal is multiplied by np.cos(-phase_diff).

[0150] np.imag(signal * np.sin(-phase_diff)) uses the np.imag() function to obtain the imaginary part of the signal. This effectively performs a phase adjustment on the imaginary part of the signal, similar to adjusting the real part, but requires extracting the imaginary part and then multiplying it by np.sin(-phase_diff).

[0151] In EEG signal processing, various noise interferences are unavoidable. Traditional averaging filters smooth the signal by calculating the simple mean of the data points, but this method is prone to losing valuable details when processing high-noise or complex signals. Therefore, the evaluation system of the EEG signal acquisition device provided in the embodiment of the present application includes an improved averaging filter to improve filtering accuracy.

[0152] In some embodiments, the evaluation method also includes: an improved average filter uses Gaussian weighting strategy optimization and frequency band parameter dynamic adaptation method based on the physical characteristics of different frequency bands of the EEG acquisition signal to assign different weights to sample points in the neighborhood; and uses the improved average filter to perform weighted averaging calculation on the sample points in the neighborhood according to the assigned weights to obtain the filtered EEG acquisition signal value.

[0153] In EEG signal processing, traditional averaging filters, due to their "simple averaging" nature, can easily blur key signal details. The improved weighted averaging filter effectively alleviates this problem by assigning weights to each EEG frequency band, prioritizing the bands of interest. The following explains this using formulas and EEG scenarios.

[0154] The formula of the improved average filter is as follows:

[0155] ;

[0156] in, x [ n + k] is the original EEG sampling signal, such as the voltage value (μV level) of the sleep EEG delta wave (0.1–4 Hz) and the wakefulness alpha wave (8–13 Hz). w [ k ] is a weighting factor, generated based on a Gaussian distribution. For example, when filtering a segment of alpha wave signals, the closer the sampling points are to the center of the window (representing continuous fluctuations of the same rhythm), the higher the weight is, thus emphasizing the continuity of normal EEG rhythms. N is the window size, which needs to match the EEG frequency band characteristics. When processing delta waves (low frequency), N Can be set to 200 (corresponding to 2 seconds sampling, 0.1Hz signal period 10 seconds, covering the complete fluctuation); when processing gamma waves (30-100Hz) N Set to 50 (to adapt to high-frequency rapid changes). y [ n ] is the filtered EEG signal, which retains key rhythms (such as sleep slow waves) and suppresses noise spikes. In the improved average filter, Gaussian weighting is calculated by formula Implementation (σ controls the weight decay rate):

[0157] - Low-frequency signals (such as delta waves): Set σ to a larger value (such as σ = 10) to expand the area with high weight, smooth long-term baseline drift, and retain the slow wave trend.

[0158] - High-frequency signals (such as gamma waves): Set σ to a small value (such as σ=3) to quickly attenuate edge weights and suppress high-frequency noise (such as electromyographic interference).

[0159] For example, when processing an α wave signal containing electromyographic noise, the α wave sampling points near the center of the window are given high weights, while the edge noise points are given low weights. After filtering, the α wave rhythm is clearer.

[0160] Dynamic adaptation of frequency band parameters: Dynamically adjust the filter window size according to the physical characteristics (such as frequency and period) of different frequency bands (δ wave, α wave, γ wave) of EEG signals ( N ) and the Gaussian weight parameter ( σ ), solves the signal blurring problem caused by the "one-size-fits-all" approach of traditional averaging filtering in EEG processing. This frequency-band adaptive parameter design significantly improves the filtering's targetedness and represents a substantial improvement over existing technologies.

[0161] Gaussian weighting strategy optimization: Differentiated settings for different noise characteristics of low-frequency and high-frequency signals σ Values ​​(such as low frequency delta waves using σ= 15 to expand the smoothing range, and σ = 3 for high-frequency gamma waves to quickly attenuate noise weights), optimizing the weight distribution to achieve efficient noise reduction while preserving signal characteristics. This strategy is innovative in the application of EEG.

[0162] According to the application examples, the improved method can improve the noise suppression rate in δ wave extraction while retaining the corresponding amplitude, which has certain advantages over the traditional method. Weighted average filtering itself is a conventional method in signal processing, but its application in combining it with EEG frequency band characteristics and dynamically adjusting parameters has not been widely disclosed. Among the existing EEG filtering methods, fixed window or fixed weight design is more common, while dynamic adjustment according to frequency band is more common. N The strategy of and σ is novel, especially for the long window of low-frequency δ waves ( N= 200) and a short window of high-frequency gamma waves ( N= 50) design, possibly the first proposed. This method specifically optimizes the characteristics of EEG signals (low amplitude, high noise, and multi-frequency bands). Compared with general signal processing solutions, its adaptability to the EEG field enhances the technical innovation.

[0163] The embodiment of the present application can implement a weighted average filtering method that dynamically allocates neighborhood weights based on the main effective frequency band of the EEG signal, as well as its corresponding EEG signal phase extraction and correction method based on Fourier transform. Combined with the steps of phase information extraction, phase difference calculation and correction, it can effectively reduce the phase error in EEG signal acquisition to improve the stability and accuracy of the EEG signal.

[0164] In an embodiment, the evaluation module can compare the corrected EEG signal with the standard control signal, and is usually concerned with the deviation of the corrected signal within a certain range. For example, suppose it is desired that the corrected signal and the standard signal are within a certain amplitude range of ±. The number of points in the corrected signal that meet this condition can be counted to calculate their proportion. The calculation steps are as follows:

[0165] (1) Calculate the deviation: Calculate the difference between the corrected signal and the standard signal at each time point:

[0166] ;

[0167] (2) Range statistics: Based on a given deviation range, count the number of points where the signal falls within the range. For example, for a given range of ±10, calculate whether each time point satisfies: | δ ( t )|≤10;

[0168] (3) Ratio calculation: Finally, the number of points that meet the conditions is divided by the total number of points in the signal to obtain the compliance percentage of the corrected signal:

[0169] ;

[0170] The results of this calculation can be used to measure the effectiveness of the signal correction. If the deviations at most points are within the given range, the phase correction is effective and the signal has been successfully corrected. After signal correction, the code calculates the deviations of the corrected signal. The goal of the statistics is to determine the proportion of corrected signals that fall within the given range.

[0171] First, calculate whether the absolute value of each value in the corrected data array is within the specified range, and store the result in withinrange Then, use sum The function counts the number of signals that meet the range conditions and saves the results in counwithirange Finally, by dividing the number of signals that meet the conditions by the total number of signals totalpoints , calculate the proportion of these signals in the total signal and multiply it by 100 to get the percentage, and store the result in percentagwithirange The purpose of this process is to evaluate the accuracy and reliability of the corrected signal.

[0172] withinrange First, calculate whether each correction signal is within the specified range, and then sum The function counts the number of signals that meet the criteria and finally calculates the ratio of these signals. This step can provide an intuitive value to evaluate the matching degree of the corrected signal with the standard signal.

[0173] In some embodiments, the evaluation module can also realize data visualization. The data visualization part is achieved by matplotlib The code is completed by the library, which provides flexible plotting functions. The code creates multiple graphs representing the corrected EEG signal, the original EEG signal, the phase difference, and the standard control signal, and plots them on the same chart.

[0174] The final step in signal processing is visual analysis. By plotting a comparison chart of the corrected signal (corrected EEG signal), the original signal (original EEG signal), and the standard signal (standard control signal), you can visually see the effect of the correction. Typically, the following information needs to be displayed in the chart:

[0175] Original vs. Corrected Signal: Plot the original received signal and the phase-corrected signal in the time domain to observe the phase alignment of the signals.

[0176] Difference between signal and reference signal: The difference between the corrected signal and the standard signal can be plotted to show the deviation distribution.

[0177] Visualization of statistical analysis results: If statistical results are available for the number of points within the deviation range, a bar chart or pie chart can be used to display the signal's conformance to the specified range. This section displays the results of plotting multiple curves, allowing you to visually visualize the signal's phase alignment. Different colored lines in the chart represent different data columns, and dotted lines indicate the allowable signal range. The resulting chart is saved as a PNG file, which is crucial for analyzing and comparing signals.

[0178] In some embodiments, the evaluation system of the EEG signal acquisition device implements a waveform template library and its intelligent parameter mapping rules in the middleware, and this component is the EEG characteristic waveform generation engine.

[0179] As an example, the International Standard EEG Waveform Signal Template Library includes international standard EEG waveforms, such as the 10-20 system α Wave templates, clinical abnormal wave (spike wave, sharp wave) models, etc. The template library uses a structured storage method, with waveform type, physiological parameter range, etc. as indexes. For user-defined physiological parameters, such as age-related α The frequency drift of the wave is realized by establishing a mathematical model. Assume α The relationship between wave frequency and age is a linear model f = f 0+ k ·( age - age 0), where f The current age α wave frequency, f 0 is the base age age 0 o'clock α wave frequency, k is the frequency drift coefficient. For example, the base age age 0 = At 30 years old α Wave frequency f 0=10Hz, frequency drift coefficient k =-0.05 Hz / year. When the user inputs an age of 40, the calculated α Wave frequency f =10-0.05·(40-30)=9.5Hz.

[0180] As an example, the intelligent parameter mapping rule base can be as follows: input physiological indicators, such as "mild anxiety β The system automatically matches the amplitude, frequency and noise floor through a predefined rule base. The rule base is based on a large amount of clinical data and research results. β Assume normal β The amplitude range is Anormal =[5,10] μV , when the anxiety is mild, the amplitude enhancement ratio is r =1.5, then the matching amplitude range is The frequency range is also determined based on clinical data, such as normal β The wave frequency range is f normal =[12,20]Hz, which may become f anxiety =[13,22]Hz. The noise floor simulates the real EEG noise distribution and adopts the 1 / f noise model. Its power spectral density formula is: ,in S 0 is the power spectrum density at low frequency, γ Usually between 0.5 - 2, for EEG noise, γ≈ 1. By adjusting S 0 to adapt to the noise level under different physiological conditions.

[0181] The EEG waveform generation engine can also use hardware collaborative optimization, such as pre-loading waveform feature code tables, such as α The phase jitter characteristics of the wave. Phase jitter can be expressed as standard deviation δ φ To describe, assume α The ideal phase of the wave is φ 0, actual phase φ 0= φ 0+δφ, where FPGA according to the predefined δ φ The phase is adjusted in real time. The DSP calculates the waveform correction in real time, and the amplitude is fine-tuned using the proportional integral (PI) control algorithm. Assume that the desired amplitude is A d , the current amplitude is A c , then the adjustment amount:

[0182] ,in K p and K i are the proportional and integral coefficients. Frequency microstepping is achieved by adjusting the phase increment of DDS (direct digital synthesis). The initial phase increment is Δ φ 0, the frequency microstepping amount is Δf , then the adjusted phase increment is:

[0183] ,in f clk is the system clock frequency, N is the number of phase accumulator bits.

[0184] By setting up an EEG characteristic waveform generation engine, the embodiment can shorten the calibration experiment preparation time and improve calibration efficiency; it can accurately simulate abnormal EEG signals during epileptic seizures, which can be used for anti-interference algorithm training and pathological exploration of EEG equipment. The scenario value of this embodiment may include: in the field of scientific research, through the built-in template library and intelligent parameter mapping, it can be equipped with a standard reference signal generating device, and researchers can generate standard waveforms that conform to their set templates with one click. For example, when conducting an EEG signal acquisition system calibration experiment, it used to take a lot of time to manually set the waveform parameters. Now, with this engine, the experimental preparation time can be shortened accordingly, greatly improving the experimental efficiency. In the medical field, it can simulate patient-specific EEG, such as abnormal discharges in epileptic foci. By adjusting the waveform parameters such as amplitude, frequency and phase, it can accurately simulate abnormal EEG signals during epileptic seizures, which can be used for anti-interference algorithm training of EEG equipment, improving the equipment's ability to recognize and process clinical EEG signals.

[0185] Figure 9 This is a schematic diagram of the result of the basic alignment of the phase and amplitude output by the evaluation module in the embodiment; first, the phase and amplitude of the three EEG acquisition signals (received signal 1, received signal 2, and received signal 3) are aligned. The yellow line represents the phase difference. At this time, the phase difference correction is relatively strong. The comparison between the obtained corrected signal and the standard control signal is calculated. By calculating the amplitude difference between the three corrected signals and the standard control signal, the three blue amplitude difference curves (amplitude difference curve 1, amplitude difference curve 2, and amplitude difference curve 3) in the above figure are further obtained, showing the corrected amplitude difference at the corresponding moment. Finally, the percentage of the three amplitude differences within the range of 10 is calculated to further evaluate the correction effect. Figure 9 It can be seen that the difference percentages of the three signals corrected by ADP all reached more than 97%, which means that the error range of 97% of the signals is within the specified accuracy, which illustrates the effectiveness of this research method.

[0186] Figure 10 The results of amplitude normalization alone without phase alignment are shown. Comparing the corrected signal with the standard control signal does not provide a simple conclusion about the effectiveness of the correction. However, further calculation of the amplitude difference between the three correction curves and the standard signal reveals that only 36% of the amplitude differences are within 10. Therefore, simply performing amplitude correction on the received signal is not sufficient to achieve the desired effect.

[0187] The collected signal will exist Figure 11In the case shown, there are significant phase and amplitude differences between the acquired signal and the reference signal. Without correcting these signals, calculating the percentage of amplitude differences within 10 reveals that only 12% of the three signals are within 10, indicating that the difference between the received signal and the reference signal is significant and requires signal correction and alignment.

[0188] Figure 12 It shows that the phase difference between the directly received signal and the standard control signal is small. In this case, a good effect can be achieved without normalizing the amplitude difference. However, Figure 9 The results shown in reach 98%. It can be concluded that the ADP method used in this study can further reduce the difference between the received signal and the standard control signal even when the phase and amplitude differences between the received signal and the standard signal are small.

[0189] Figure 13 It shows the interface of the evaluation module (evaluation software) for the accuracy of some EEG signal acquisition.

[0190] In view of the current system acquisition error detection problems in the production of EEG equipment, this application provides a set of EEG signal acquisition equipment evaluation system and evaluation method, in which the evaluation module can be a host computer evaluation software that can issue an evaluation report. This evaluation system is of great significance for guiding the automation of EEG equipment production testing.

[0191] The above is a detailed introduction to the EEG signal acquisition equipment evaluation system and evaluation method provided by this application. This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the concept of this application and should not be understood as limiting the scope of protection of this application.

Claims

1. An EEG signal acquisition device evaluation system, characterized in that: The evaluation system is used to evaluate an EEG signal acquisition device, and the evaluation system includes: A standard control signal generating module, used for generating a standard control signal; an evaluation module, configured to obtain an EEG signal acquired by the EEG signal acquisition device, display the EEG signal and the standard control signal, compare the waveforms and parameters of the EEG signal and the standard control signal, and determine the percentage of samples whose relative amplitude of the difference between the EEG signal and the standard control signal exceeds a set threshold among all sample points in the total number of samples; The method for determining the percentage includes: For each sample point n , determine the absolute value of the difference between the EEG acquisition signal of the sample point and the standard control signal, relative to the maximum absolute value of the ratio of the two ; in, x gen [ n ] represents the sample point n The EEG signal is collected. x temp [ n ] represents the sample point n The standard control signal, max () is the maximum value function; Traverse all sample points. If the ratio is greater than or equal to the preset value, the sample point meets the distortion condition. Count the sample points that meet the distortion condition to obtain the counting result. Count Δp ; The counting results Count Δp The ratio to the number of all sample points is the percentage.

2. The EEG signal acquisition device evaluation system according to claim 1, characterized in that: The system also includes a signal correction module; The signal correction module is used to perform Fourier transform on the EEG acquisition signal and the standard control signal, calculate the phase difference between the transformed EEG acquisition signal and the standard control signal, and use a sampling moment scale factor sequence to phase-align the EEG acquisition signal with the standard control signal according to the phase difference to obtain a corrected EEG acquisition signal.

3. The EEG signal acquisition device evaluation system according to claim 1, characterized in that: The system further comprises: An EEG characteristic waveform generation engine has a built-in international standard EEG waveform signal template library, which is used to select corresponding template signals from the template library based on the required signal type and related physiological parameters. The template signal can be used to generate a standard reference signal, so that the standard control signal generation module generates the standard control signal based on the standard reference signal.

4. The EEG signal acquisition device evaluation system according to claim 3, characterized in that: The EEG characteristic waveform generation engine also has a built-in intelligent parameter mapping rule library, which is used to match the intelligent parameter mapping rule library according to the input physiological indicators to obtain key characteristic parameters; and use the key characteristic parameters to adjust the parameters of the template signal to generate the template signal that meets the target characteristics.

5. The EEG signal acquisition device evaluation system according to claim 1, characterized in that: The system further comprises a standard reference signal generating module for generating a standard reference signal, so that the standard control signal generating module generates the standard control signal according to the standard reference signal; The standard reference signal generation module includes FPGA, DSP, DAC, low-noise operational amplifier signal chain module, power management module and physical isolation module; The FPGA is used to generate waveform data; The DSP is communicatively connected to the FPGA, and the DSP is used to pre-process and optimize the quality of waveform data by controlling the FPGA gain of the analog front end; The FPGA transmits the waveform data to the DAC, and the DAC is used to convert the waveform data into an analog signal; The low-noise operational amplifier signal chain module is used to amplify the analog signal; The power management module is used to power the FPGA, DSP, DAC and low-noise op amp signal chain modules; The physical isolation module adopts a three-layer PCB layout, with the top layer transmitting analog signals, the middle layer being the ground plane, and the bottom layer being the digital circuit. The analog ground and digital ground are connected at a single point via a 0Ω resistor. The exterior uses an aluminum alloy shell and is sprayed with conductive paint.

6. The EEG signal acquisition device evaluation system according to claim 1, characterized in that: The system further includes an improved average filter, which is used to filter the EEG acquisition signal using an average value.

7. The method for evaluating an EEG signal acquisition device according to any one of claims 1 to 6, wherein the system further comprises a signal correction module; the method further comprises: Converting the EEG acquisition signal and the standard control signal into frequency domain signals using a signal correction module; Comparing the phase values ​​of the EEG acquisition signal and the standard control signal at the same frequency, and calculating the phase difference between the two; The sampling moment scale factor sequence is used to align the phases of the EEG acquisition signal and the standard control signal according to the phase difference to obtain a corrected EEG acquisition signal.

8. The evaluation method of the EEG signal acquisition device evaluation system according to claim 7, characterized in that: When the system further includes an improved average filter; The method further includes: assigning different weights to sample points in a neighborhood using a Gaussian weighting strategy optimization and a frequency band parameter dynamic adaptation method based on the physical characteristics of different frequency bands of the EEG acquisition signal; The improved average filter is used to perform weighted averaging calculation on the sample points in the neighborhood according to the assigned weights to obtain the filtered EEG signal value.

9. The evaluation method of the EEG signal acquisition device evaluation system according to claim 8, characterized in that: The expression of the improved average filter is as follows: ； in y [ n ] indicates that at the sample point n The signal value after filtering is x [ n + k ] indicates the sample point n As the center, the neighborhood k The input signal value of sample points, k The value range is from - N / 2 arrive N / 2 , N Indicates the sample points n As the center, the number of sample points extending to both sides, N Determines the size of the neighborhood; ω [ k ] indicates that at the sample point n At the neighborhood k The weight of the sample points.

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