Control work efficiency closed-loop enhancement method and device based on brain cognitive load

Abstract

The invention relates to a control work efficiency closed-loop enhancement method and device based on brain cognitive load, and belongs to the technical field of electroencephalogram monitoring and vagus nerve stimulation, and the method comprises the steps: collecting original electroencephalogram signals of a forehead area FP1 / FP2 in real time, and carrying out band-pass filtering and segmentation processing to obtain electroencephalogram data in a data frame form; calculating a phase amplitude coupling index of two brain wave frequency bands according to a theta brain wave and gamma brain wave instantaneous amplitude sequence calculated by Hilbert transform; calculating a brain cognitive load index for evaluating a brain cognitive load state based on the phase amplitude coupling index; according to the brain cognitive load index, dynamically adjusting stimulation pulse parameters to perform vagus nerve stimulation; the brain cognition load is reduced, and the control work efficiency is enhanced; the silent period window with the set duration is reserved after the stimulation pulse is emitted, electroencephalogram data collection is only carried out outside the window, and interference of stimulation artifacts on electroencephalogram signals is avoided. According to the invention, nerve connection can be efficiently improved, and the control efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of electroencephalogram (EEG) monitoring and vagus nerve stimulation technology, and in particular to a method and device for enhancing manipulative ergonomics based on cognitive load. Background Technology

[0002] Currently, in sectors including modern industry and transportation, operators often face long hours and high-intensity tasks. However, prolonged task operation increases the cognitive load on operators, thereby reducing reaction speed, increasing the probability of errors, and even threatening operational safety.

[0003] Vagus nerve electrical stimulation may reduce intrinsic and extrinsic cognitive load and optimize resource allocation of associated load by regulating neurotransmitters, inhibiting inflammation, and enhancing network connectivity. It has potential application value in high cognitive demand tasks (such as learning, decision-making, and rehabilitation training). Simultaneous electrical stimulation in manipulative ergonomic tasks can improve the operator's manipulative ergonomics.

[0004] However, using the traditional open-loop stimulation mode (fixed parameter output), due to the lack of dynamic physiological feedback regulation in vagal nerve electrical stimulation, there is a problem of mismatch between stimulation parameters and individual real-time cognitive load state, resulting in unstable and inefficient improvement of control efficiency, and there is also the risk of overstimulation or understimulation.

[0005] Therefore, this invention proposes a closed-loop method to enhance the operational efficiency of vagus nerve stimulation parameters by dynamically adjusting the brain's cognitive load in real time, in order to efficiently regulate brain load and enhance operational efficiency and safety. Summary of the Invention

[0006] Based on the above analysis, the present invention aims to disclose a closed-loop enhancement method and device for control ergonomics based on brain cognitive load; by detecting the state of brain cognitive load online or in real time, adjusting neural stimulation parameters in a timely manner and enabling vagus nerve stimulation to output, the present invention effectively improves neural connectivity and obtains control ergonomic enhancement function.

[0007] This invention discloses a closed-loop enhancement method for manipulation ergonomics based on brain cognitive load, comprising:

[0008] EEG data acquisition steps: Real-time acquisition of the frontal region FP1 / FP2 The raw EEG signals are bandpass filtered and segmented to obtain EEG data in the form of data frames; Brain cognitive load assessment steps: Calculations are performed by applying Hilbert transform to the EEG data in data frame format. theta brainwaves and gammaThe instantaneous amplitude sequence of EEG was used to calculate the phase amplitude coupling index of two EEG frequency bands; and based on the phase amplitude coupling index, a brain cognitive load index was calculated to assess the state of brain cognitive load. Closed-loop control steps: Dynamically adjust the stimulation pulse parameters, including frequency and amplitude, according to the brain cognitive load index to stimulate the vagus nerve, reduce brain cognitive load, and enhance control efficiency; During the closed-loop control process, a time-division multiplexing architecture is adopted. After the stimulation pulse is emitted, a silent period window of a set duration is reserved. EEG data is collected only outside the window to avoid interference of stimulation artifacts on EEG signals.

[0009] Furthermore, in the brain cognitive load state assessment steps, In the theta brainwaves and gamma After selecting frequency points at equal frequency intervals within the frequency range of the electroencephalogram (EEG), then... theta brainwaves and gamma The combination of brainwave frequencies yields N M One frequency pair; among them N for theta Number of frequency points of brain waves, M for gamma Number of frequency points of brain waves; Calculate the phase-amplitude coupling index for each frequency pair. PAC ,get N M of PAC matrix; Calculate N M's PAC The mean of the matrix yields the brain cognitive load index. WLI .

[0010] Furthermore, the phase amplitude coupling index PAC The calculation process includes: 1) Obtaining brainwaves from EEG data in data frame format using Hilbert transform. theta brainwaves Instantaneous phase sequence at frequency points and in gamma brainwaves Instantaneous amplitude sequence of frequency points; 2) According to Frequency point instantaneous phase divides the EEG time series into There are several statistical heaps, and each statistical heap is calculated separately. Frequency point amplitude The average amplitude is expressed as , ; 3) Average amplitude Normalization is achieved by dividing the value of each statistical heap by the sum of all heaps to obtain the normalized amplitude. 4) Calculate the deviation of the normalized amplitude from the uniform distribution for each statistical pile. The KL distance is then normalized to obtain the frequency combination. , The phase amplitude coupling index of ).

[0011] Furthermore, the frequency range of 4-7Hz is further analyzed in units of 1Hz. theta EEG waves and 30-45Hz gamma The brainwaves were divided into frequencies, resulting in combinations of 4 and 16 frequency points to form 4. 16 frequency points, targeting 4 The phase-amplitude coupling index is calculated sequentially for all combinations of time series at 16 frequency points to obtain 4 16 PAC Matrix, calculate 4 16 PAC Brain cognitive load index obtained by matrix average for: ; in, Frequency combination ( , The phase amplitude coupling index; , .

[0012] Furthermore, in the closed-loop control steps, the formula for dynamically adjusting the frequency of vagal nerve stimulation pulses based on the brain cognitive load index is as follows: .

[0013] Furthermore, in the closed-loop control steps, the formula for dynamically adjusting the amplitude of the vagus nerve stimulation pulse based on the brain cognitive load index is as follows: .

[0014] This invention also discloses an apparatus for implementing the above-described method for enhancing manipulative ergonomics based on cognitive load, comprising: a stimulation acquisition host and electrodes; wherein, The acquisition and stimulation host includes an EEG acquisition module, a processing terminal, and an electrical stimulation module; the electrodes include acquisition electrodes and stimulation electrodes; among them, The EEG acquisition module is connected to the acquisition electrodes; the acquisition electrodes are used to acquire data from the frontal region in real time. FP1 / FP2 The original EEG signal; the EEG acquisition module performs analog-to-digital conversion on the original EEG signal to obtain the digitized original EEG signal; The electrical stimulation module is connected to the stimulation electrode; the stimulation electrode is placed on the vagus nerve in the neck, and the electrical stimulation module generates current pulses which are output to the stimulation electrode to stimulate the vagus nerve in the neck. The processing terminal is used to perform bandpass filtering and segmentation on the digitized raw EEG signals to obtain EEG data in the form of data frames; to assess the brain cognitive load state based on the EEG data in the form of data frames to obtain the brain cognitive load index; and to dynamically control the frequency and amplitude of the current pulses generated by the electrical stimulation module based on the brain cognitive load index.

[0015] Furthermore, the processing terminal includes a preprocessing module, a brain cognitive load state assessment module, a closed-loop control module, and a time-sharing control module; wherein, The preprocessing module is used to receive the digitized raw EEG signals from the EEG acquisition module, perform bandpass filtering and segmentation processing, and obtain EEG data in the form of data frames. The brain cognitive load assessment module is used to calculate the cognitive load state by performing Hilbert transform on EEG data in data frame format. theta brainwaves and gamma The phase amplitude coupling index of brain waves; and the brain cognitive load index is calculated based on the phase amplitude coupling index to assess the brain cognitive load status; A closed-loop control module is used to dynamically adjust, including stimulation frequency and, based on the brain cognitive load index. The stimulation pulse parameters are output to the vagus nerve stimulator to control the frequency and amplitude of the emitted pulses; The time-sharing control module is used to control the acquisition of EEG data from the front end and the stimulation of the vagus nerve stimulator in a time-sharing manner. After the stimulation pulse is emitted, a silent period window of a set duration is reserved, and EEG data is acquired only outside the window to avoid interference of stimulation artifacts on the EEG signal.

[0016] Furthermore, in the preprocessing module, the digitized raw EEG signal is bandpass filtered from 0.5 to 40 Hz. The continuous EEG signal after bandpass filtering is then segmented without overlap using a 5-second time window to obtain EEG data in the form of data frames.

[0017] Furthermore, both the acquisition electrode and the stimulation electrode are integrated acquisition and stimulation electrodes; in conjunction with the integrated acquisition electrode, the acquisition and stimulation host also includes an analog switch K1 and a voltage follower U4; The integrated stimulation acquisition electrode includes a snap electrode, an analog switch K2, and an operational amplifier U3. The integrated stimulation acquisition electrode is connected to the stimulation acquisition host via a 5-core cable. The snap electrode is connected to the non-inverting input of the operational amplifier U3. The first and second moving terminals of the analog switch K2 are connected to the non-inverting and inverting inputs of the operational amplifier U3, respectively, and the stationary terminal is connected to the stationary terminal of the analog switch K1 in the stimulation acquisition host via one of the core wires of the 5-core cable. The first moving terminal of the analog switch K1 is connected to the output of the voltage follower U4, and the input of the voltage follower U4 is connected to the input of the EEG acquisition module. The second moving terminal is connected to the output of the electrical stimulation module. The control terminal of analog switch K2 and the positive terminal and ground of the electrode power supply are all connected to the stimulation acquisition host through the corresponding core wires in the 5-core cable.

[0018] This invention can achieve one of the following beneficial effects: This invention discloses a closed-loop enhancement method and device for manipulative ergonomics based on cognitive load. Based on real-time EEG feedback technology, it constructs a closed-loop vagal stimulation technique to achieve real-time monitoring of cognitive load. In contrast, traditional open-loop systems, due to fixed parameters, struggle to match the dynamic fluctuations of cognitive load, leading to intervention lag or overstimulation. This invention dynamically adjusts the frequency and intensity of vagal nerve stimulation through amplitude-phase coupling index, achieving precise matching of "cognitive load - stimulation intensity." This precise dynamic control improves manipulative ergonomics and efficiency. Attached Figure Description

[0019] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. Figure 1 This is a flowchart of the closed-loop enhancement method for manipulation ergonomics based on brain cognitive load in an embodiment of the present invention. Figure 2 This is a schematic diagram of the device components and connections for implementing the closed-loop enhancement method for control efficiency in an embodiment of the present invention. Figure 3 This is a schematic diagram showing the connection of the electrical stimulation module in an embodiment of the present invention; Figure 4 This is a schematic diagram showing the connection relationship between the integrated stimulation acquisition electrode and the stimulation acquisition host in an embodiment of the present invention. Detailed Implementation

[0020] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and, together with the embodiments of the present invention, serve to illustrate the principles of the present invention.

[0021] Example 1 One embodiment of the present invention discloses a closed-loop enhancement method for manipulation ergonomics based on brain cognitive load, such as...Figure 1 As shown, it includes: S1. EEG Data Acquisition Steps: Real-time acquisition of data from the frontal region. FP1 / FP2 The raw EEG signals are bandpass filtered and segmented to obtain EEG data in the form of data frames; S2. Brain cognitive load assessment steps: Calculate the results based on the Hilbert transform of the EEG data in data frame format. theta brainwaves and gamma The instantaneous amplitude sequence of EEG was used to calculate the phase amplitude coupling index of two EEG frequency bands; and based on the phase amplitude coupling index, a brain cognitive load index was calculated to assess the state of brain cognitive load. S3. Closed-loop control steps: Dynamically adjust the stimulation pulse parameters, including frequency and amplitude, according to the brain cognitive load index to stimulate the vagus nerve, reduce brain cognitive load, and enhance control efficiency. During the closed-loop control process, a time-division multiplexing architecture is adopted. After the stimulation pulse is emitted, a silent period window of a set duration is reserved. EEG data is collected only outside the window to avoid interference of stimulation artifacts on EEG signals.

[0022] Specifically, in the S1 EEG data acquisition, the real-time prefrontal cortex... FP1 / FP2 Raw EEG signals were acquired at a sampling rate of ≥500Hz and a resolution of 24 bits. The raw EEG signals were then bandpass filtered from 0.5 to 40Hz to remove high-frequency noise (such as electromyographic interference) and low-frequency drift. The continuous EEG signals were then segmented into non-overlapping 5-second time windows (sampling rate 500Hz, 2500 points per window) for subsequent feature extraction.

[0023] Specifically, the S2 brain cognitive load status assessment includes: S201, in response to theta brainwaves and gamma After selecting frequency points at equal frequency intervals within the frequency range of the electroencephalogram (EEG), then... theta brainwaves and gamma The combination of brainwave frequencies yields N M One frequency pair; among them N for theta Number of frequency points of brain waves, M for gamma Number of frequency points of brain waves; Preferably, the frequency range of 4-7Hz is specified in 1Hz increments. theta EEG waves and 30-45Hz gamma The brainwaves were divided into frequencies, resulting in combinations of 4 and 16 frequency points to form 4. 16 frequency points.

[0024] S202. Calculate the phase amplitude coupling index for each frequency pair. PAC ,get N M of PAC matrix; The calculation process for the phase amplitude coupling index (PAC) includes: 1) Obtaining brainwaves from EEG data in data frame format using Hilbert transform. theta brainwaves Instantaneous phase sequence at frequency points and in gamma brainwaves Instantaneous amplitude sequence of frequency points; The frequency point is one of four frequencies: {4, 5, 6, 7} Hz. The frequency point is one of 16 frequency points {31, 32, 32, ..., 45} Hz.

[0025] 2) According to Frequency point instantaneous phase divides the EEG time series into There are several statistical heaps, and each statistical heap is calculated separately. Frequency point amplitude The average amplitude is expressed as , ; Preferred, according to Frequency point instantaneous phase divides the EEG time series into The statistical piles (each statistical pile corresponds to a change of 20 degrees).

[0026] 3) Average amplitude Normalization is achieved by dividing the value of each statistical heap by the sum of all heaps to obtain the normalized amplitude. Normalized amplitude for: ; 4) Calculate the deviation of the normalized amplitude from the uniform distribution for each statistical pile. The KL distance is then normalized to obtain the frequency combination. , The phase amplitude coupling index of ).

[0027] Since the normalized amplitude satisfies the criterion of the discrete probability density function, its distribution is uniform in the absence of coupling. Therefore, the deviation of the normalized amplitude from the uniform distribution can represent the coupling strength. That is, the coupling strength is quantified by calculating the KL distance of the normalized amplitude from the uniform distribution. The formula for calculating the KL distance is as follows: ; In the formula, uniform distribution The formula is defined as follows: ; use right Normalization yields the modulation index for: ; Frequency combination ( , The phase amplitude coupling index of ) is: ; S203, Calculate N The mean of the PAC matrix of M is used to obtain the brain cognitive load index. WLI .

[0028] For 4-7Hz, use 1Hz as the unit. theta EEG waves and 30-45Hz gamma The brainwaves were divided into frequencies, resulting in combinations of 4 and 16 frequency points to form 4. 16 frequency points, targeting 4 The phase-amplitude coupling index is calculated sequentially for all combinations of time series at 16 frequency points to obtain 4 16 PAC Matrix, calculate 4 16 PAC Brain cognitive load index obtained by matrix average for: ; in, Frequency combination ( , The phase amplitude coupling index; , .

[0029] Practice has shown that the brain cognitive load index defined in this embodiment is effective. It is positively proportional to cognitive load (cognitive load index) The higher the value, the greater the corresponding cognitive load, and the method can accurately measure the state of cognitive load in real time. Compared with traditional methods that use changes in the relative power of brain waves to measure the state of cognitive load, the method of measuring the state of cognitive load in this embodiment is more stable and accurate.

[0030] Therefore, through the brain cognitive load index By dynamically adjusting the vagus nerve stimulation parameters, a stable and precise closed-loop adjustment of the brain's cognitive load can be achieved.

[0031] In the closed-loop control of S3, vagal nerve stimulation is performed by dynamically adjusting the stimulation pulse parameters, including frequency and amplitude, according to the brain cognitive load index; this reduces brain cognitive load and enhances control efficiency. Specifically, in the closed-loop control steps, the formula for dynamically adjusting the frequency of vagal nerve stimulation pulses based on the brain cognitive load index is as follows: .

[0032] Specifically, in the closed-loop control steps, the formula for dynamically adjusting the amplitude of the vagus nerve stimulation pulse based on the brain cognitive load index is as follows: .

[0033] The solution in this embodiment uses... theta - gamma The phase amplitude coupling index enables real-time and accurate monitoring of cognitive load. Then, through closed-loop control, the stimulation frequency and amplitude of vagal nerve stimulation pulses are adjusted to reduce brain cognitive load, thereby achieving the technical goal of enhancing control efficiency.

[0034] In this embodiment, the stimulation frequency of the vagus nerve electrical stimulation pulse (10-20Hz) partially overlaps with the EEG frequency band (δ / θ / α), resulting in signal contamination.

[0035] To avoid signal contamination, a time-division multiplexing architecture is adopted in the closed-loop control process. A silent period window of a set duration (20ms) is reserved after the stimulation pulse is emitted. EEG data is collected only outside the window to avoid interference of stimulation artifacts on the EEG signal.

[0036] In summary, this embodiment's closed-loop enhancement method for manipulatory ergonomics based on cognitive load constructs a closed-loop vagal stimulation technique based on real-time EEG feedback technology, achieving real-time monitoring of cognitive load status. In contrast, traditional open-loop systems, due to fixed parameters, struggle to match the dynamic fluctuations of cognitive load, leading to intervention lag or overstimulation. This embodiment's method dynamically adjusts the frequency and intensity of vagal nerve stimulation through amplitude-phase coupling index, achieving precise matching of "cognitive load - stimulation intensity." This precise dynamic control improves manipulatory ergonomics and efficiency.

[0037] Example 2 This invention discloses an apparatus employing the closed-loop enhancement method for manipulation ergonomics based on brain cognitive load as described in Embodiment 1. Figure 2 As shown, it includes: a stimulation acquisition unit and electrodes; wherein, The acquisition and stimulation host includes an EEG acquisition module, a processing terminal, and an electrical stimulation module; the electrodes include acquisition electrodes and stimulation electrodes; among them, The EEG acquisition module is connected to the acquisition electrodes; the acquisition electrodes are used to acquire data from the frontal region in real time. FP1 / FP2The original EEG signal; the EEG acquisition module performs analog-to-digital conversion on the original EEG signal to obtain the digitized original EEG signal; The electrical stimulation module is connected to the stimulation electrode; the stimulation electrode is placed on the vagus nerve in the neck, and the electrical stimulation module generates current pulses which are output to the stimulation electrode to stimulate the vagus nerve in the neck. The processing terminal is used to perform bandpass filtering and segmentation on the digitized raw EEG signals to obtain EEG data in the form of data frames; to assess the brain cognitive load state based on the EEG data in the form of data frames to obtain the brain cognitive load index; and to dynamically control the frequency and amplitude of the current pulses generated by the electrical stimulation module based on the brain cognitive load index.

[0038] Specifically, the core of the EEG acquisition module is the ADS131E08S high-performance analog-to-digital converter, which features: 1) a 24-bit high-precision Δ-Σ architecture; 2) 8-channel synchronous sampling capability; 3) a single-channel power consumption of only 2mW; 4) a maximum sampling rate of 64kSPS; and 5) an SPI digital interface. The chip uses a 64-pin TQFP package, offering high integration and a simple peripheral circuit. In the circuit design, the analog input INxP is connected to the output of the pre-amplifier, and a 330nF capacitor is placed between the reference voltage pins VREEP and VREEN to optimize system startup time while ensuring low noise characteristics. Regarding the clock scheme selection, although the chip's built-in oscillator is suitable for low-power applications, considering that the device in this embodiment needs to acquire data synchronously with external events, an external 2.048MHz crystal oscillator is ultimately used to ensure timing accuracy. This design ensures signal acquisition quality while balancing system stability and response speed.

[0039] Specifically, the electrical stimulation module includes a voltage-to-current conversion circuit, such as... Figure 3 As shown in the diagram, U1 uses an AD8426 instrumentation amplifier with dual power supply and wide power supply voltage range designed by Analog Devices (ADI). Its inverting input is connected to a 0V reference voltage, and its non-inverting input is connected to the voltage output of the pre-amplifier DAC. The feedback loop consists of a voltage follower formed by operational amplifier U2. Its high input impedance reduces the influence of load impedance and improves circuit stability. Resistor R1 is selected with an accuracy of 0.1% and a resistance of 500Ω (Rload). When the DAC output voltage Vin ranges from 0 to 2.5V, a voltage of 0 to 2.5V will be generated across R1, and the current output Iout will range from 0mA to 5mA.

[0040] Specifically, the processing terminal includes a preprocessing module, a brain cognitive load state assessment module, a closed-loop control module, and a time-sharing control module; wherein, The preprocessing module is used to receive the digitized raw EEG signals from the EEG acquisition module, perform bandpass filtering and segmentation processing, and obtain EEG data in the form of data frames. In the preprocessing module, the raw digitized EEG signals are bandpass filtered from 0.5 to 40 Hz. The continuous EEG signals after bandpass filtering are then segmented without overlap using a 5-second time window to obtain EEG data in the form of data frames.

[0041] The brain cognitive load assessment module is used to calculate the cognitive load state by performing Hilbert transform on EEG data in data frame format. theta brainwaves and gamma The phase amplitude coupling index of brain waves; and the brain cognitive load index is calculated based on the phase amplitude coupling index to assess the brain cognitive load status; For 4-7Hz, use 1Hz as the unit. theta EEG waves and 30-45Hz gamma The brainwaves were divided into frequencies, resulting in combinations of 4 and 16 frequency points to form 4. 16 frequency points, targeting 4 The phase-amplitude coupling index is calculated sequentially for all combinations of time series at 16 frequency points to obtain 4 16 PAC Matrix, calculate 4 16 PAC Brain cognitive load index obtained by matrix average for: ; in, Frequency combination ( , The phase amplitude coupling index; , .

[0042] The technical method for phase amplitude coupling index is described in Example 1.

[0043] A closed-loop control module is used to dynamically adjust, including stimulation frequency and, based on the brain cognitive load index. The stimulation pulse parameters are output to the vagus nerve stimulator to control the frequency and amplitude of the emitted pulses; The formula for adjusting the stimulation frequency is: .

[0044] The formula for adjusting the stimulus amplitude is: .

[0045] The time-sharing control module is used to control the acquisition of EEG data from the front end and the stimulation of the vagus nerve stimulator in a time-sharing manner. After the stimulation pulse is emitted, a silent period window of a set duration (20ms) is reserved. EEG data is only acquired outside the window to avoid interference of stimulation artifacts on the EEG signal.

[0046] In the preferred embodiment, both the acquisition electrode and the stimulation electrode are integrated stimulation and acquisition electrodes; to achieve the switching between stimulation and acquisition modes of the electrodes, such as Figure 4 As shown, the stimulus acquisition host also includes an analog switch K1 and a voltage follower U4; The integrated stimulation acquisition electrode includes a snap electrode, an analog switch K2, and an operational amplifier U3. The integrated stimulation acquisition electrode is connected to the stimulation acquisition host via a 5-core cable. The snap electrode is connected to the non-inverting input of the operational amplifier U3. The first and second moving terminals of the analog switch K2 are connected to the non-inverting and inverting inputs of the operational amplifier U3, respectively, and the stationary terminal is connected to the stationary terminal of the analog switch K1 in the stimulation acquisition host via one of the core wires of the 5-core cable. The first moving terminal of the analog switch K1 is connected to the output of the voltage follower U4, and the input of the voltage follower U4 is connected to the input of the EEG acquisition module. The second moving terminal is connected to the output of the electrical stimulation module. The control terminal of analog switch K2 and the positive terminal and ground of the electrode power supply are all connected to the stimulation acquisition host through the corresponding core wires in the 5-core cable.

[0047] When operating in acquisition mode, the first moving terminals of analog switch K1 and module switch K2 are connected; The EEG signal acquisition path is as follows: press the electrode → non-inverting input of operational amplifier U3 → output of operational amplifier U3 → input of EEG acquisition module to obtain raw EEG signal; The signal feedback path is: output of op-amp U3 → voltage follower → inverting input of op-amp U3.

[0048] It forms a unity-gain negative feedback buffer, which increases the input impedance, reduces the output impedance, and stabilizes the signal; it enables the acquisition of high-impedance EEG signals, reduces interference, and improves the signal-to-noise ratio.

[0049] When operating in stimulation mode, the second moving terminals of analog switch K1 and module switch K2 are connected; The stimulation pulse emission path is: output of the electrical stimulation module → snap electrode → vagus nerve in the neck.

[0050] The integrated stimulation and acquisition electrode in this embodiment significantly improves signal coupling efficiency, effectively suppresses common-mode interference (CMRR > 120dB), and greatly reduces the impact of motion artifacts. It supports rapid replacement of various conductive media (gel / Ag-AgCl / conductive paste), meeting the requirement for uniform distribution of stimulation currents in the 10-100μA range while overcoming the limitations of traditional electrodes in installation, debugging, and stimulation-acquisition coordination. Real-world testing data shows that this design maintains a signal-to-noise ratio improvement of >30dB even at 50Hz power frequency.

[0051] Figure 4The diagram below only illustrates the connection between a single integrated stimulation acquisition electrode and the stimulation acquisition host. When multiple integrated stimulation acquisition electrodes are required, please refer to the diagram below. Figure 4 The connection method is expanded, and the stimulation or acquisition mode is selected by controlling the switch corresponding to the control electrode.

[0052] In summary, the device in this embodiment, based on real-time EEG feedback technology, constructs a closed-loop vagal stimulation technique to achieve real-time monitoring of cognitive load. In contrast, traditional open-loop systems, with their fixed parameters, struggle to match the dynamic fluctuations of cognitive load, leading to intervention lag or overstimulation. This embodiment's method dynamically adjusts the frequency and intensity of vagal nerve stimulation through amplitude-phase coupling index, achieving precise matching between cognitive load and stimulation intensity. This precise dynamic control improves operational efficiency.

[0053] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A closed-loop enhancement method for manipulation ergonomics based on brain cognitive load, characterized in that, include: EEG data acquisition steps: Real-time acquisition of the frontal region FP1 / FP2 The raw EEG signals are bandpass filtered and segmented to obtain EEG data in the form of data frames; Brain cognitive load assessment steps: Calculations are performed by applying Hilbert transform to the EEG data in data frame format. theta brainwaves and gamma The instantaneous amplitude sequence of brain waves was used to calculate the phase amplitude coupling index of two brain wave frequency bands; And a brain cognitive load index for assessing brain cognitive load status was calculated based on the phase amplitude coupling index; Closed-loop control steps: Dynamically adjust the stimulation pulse parameters, including frequency and amplitude, according to the brain cognitive load index to stimulate the vagus nerve, reduce brain cognitive load, and enhance control efficiency; During the closed-loop control process, a time-division multiplexing architecture is adopted. After the stimulation pulse is emitted, a silent period window of a set duration is reserved. EEG data is collected only outside the window to avoid interference of stimulation artifacts on EEG signals.

2. The method for enhancing manipulative ergonomics based on cognitive load according to claim 1, characterized in that, In the steps of assessing cognitive load, In the theta brainwaves and gamma After selecting frequency points at equal frequency intervals within the frequency range of the electroencephalogram (EEG), then... theta brainwaves and gamma The combination of brainwave frequencies yields N M One frequency pair; among them N for theta Number of frequency points of brain waves, M for gamma Number of frequency points of brain waves; Calculate the phase-amplitude coupling index for each frequency pair. PAC ,get N M of PAC matrix; Calculate N M's PAC The mean of the matrix yields the brain cognitive load index. WLI .

3. The closed-loop enhancement method for manipulation ergonomics based on brain cognitive load according to claim 2, characterized in that, Phase amplitude coupling index PAC The calculation process includes: 1) Obtaining brainwaves from EEG data in data frame format using Hilbert transform. theta brainwaves Instantaneous phase sequence at frequency points and in gamma brainwaves Instantaneous amplitude sequence of frequency points; 2) According to Frequency point instantaneous phase divides the EEG time series into There are several statistical heaps, and each statistical heap is calculated separately. Frequency point amplitude The average amplitude is expressed as , ; 3) Average amplitude Normalization is achieved by dividing the value of each statistical heap by the sum of all heaps to obtain the normalized amplitude. 4) Calculate the deviation of the normalized amplitude from the uniform distribution for each statistical pile. The KL distance is then normalized to obtain the frequency combination. , The phase amplitude coupling index of ).

4. The method for enhancing manipulative ergonomics based on cognitive load according to claim 3, characterized in that, For 4-7Hz frequencies, use 1Hz as the unit. theta EEG waves and 30-45Hz gamma The brainwaves were divided into frequencies, resulting in combinations of 4 and 16 frequency points to form 4. 16 frequency points, targeting 4 The phase-amplitude coupling index is calculated sequentially for all combinations of time series at 16 frequency points to obtain 4 16 PAC Matrix, calculate 4 16 PAC Brain cognitive load index obtained by matrix average for: ； in, Frequency combination ( , The phase amplitude coupling index; , .

5. The method for enhancing manipulative ergonomics based on cognitive load according to claim 2, characterized in that, In the closed-loop control process, the formula for dynamically adjusting the frequency of vagal nerve stimulation pulses based on the brain cognitive load index is as follows: 。 6. The method for enhancing manipulative ergonomics based on cognitive load according to claim 2, characterized in that, In the closed-loop control process, the formula for dynamically adjusting the amplitude of the vagus nerve stimulation pulse based on the brain cognitive load index is as follows: 。 7. An apparatus for implementing the closed-loop enhancement method for manipulation ergonomics based on brain cognitive load as described in any one of claims 1-6, characterized in that, include: The stimulation device and electrodes were used for data acquisition; among them, The acquisition and stimulation host includes an EEG acquisition module, a processing terminal, and an electrical stimulation module; the electrodes include acquisition electrodes and stimulation electrodes; among them, The EEG acquisition module is connected to the acquisition electrodes; the acquisition electrodes are used to acquire data from the frontal region in real time. FP1 / FP2 The original EEG signal; the EEG acquisition module performs analog-to-digital conversion on the original EEG signal to obtain the digitized original EEG signal; The electrical stimulation module is connected to the stimulation electrode; the stimulation electrode is placed on the vagus nerve in the neck, and the electrical stimulation module generates current pulses which are output to the stimulation electrode to stimulate the vagus nerve in the neck. The processing terminal is used to perform bandpass filtering and segmentation on the digitized raw EEG signals to obtain EEG data in the form of data frames; to assess the brain cognitive load state based on the EEG data in the form of data frames to obtain the brain cognitive load index; and to dynamically control the frequency and amplitude of the current pulses generated by the electrical stimulation module based on the brain cognitive load index.

8. The apparatus according to claim 7, characterized in that, The processing terminal includes a preprocessing module, a brain cognitive load state assessment module, a closed-loop control module, and a time-sharing control module; wherein, The preprocessing module is used to receive the digitized raw EEG signals from the EEG acquisition module, perform bandpass filtering and segmentation processing, and obtain EEG data in the form of data frames. The brain cognitive load assessment module is used to calculate the cognitive load state by performing Hilbert transform on EEG data in data frame format. theta brainwaves and gamma The phase amplitude coupling index of brain waves; and the brain cognitive load index is calculated based on the phase amplitude coupling index to assess the brain cognitive load status; A closed-loop control module is used to dynamically adjust, including stimulation frequency and, based on the brain cognitive load index. The stimulation pulse parameters are output to the vagus nerve stimulator to control the frequency and amplitude of the emitted pulses; The time-sharing control module is used to control the acquisition of EEG data from the front end and the stimulation of the vagus nerve stimulator in a time-sharing manner. After the stimulation pulse is emitted, a silent period window of a set duration is reserved, and EEG data is acquired only outside the window to avoid interference of stimulation artifacts on the EEG signal.

9. The apparatus according to claim 8, characterized in that, In the preprocessing module, the raw digitized EEG signals are bandpass filtered from 0.5 to 40 Hz. The continuous EEG signals after bandpass filtering are then segmented without overlap using a 5-second time window to obtain EEG data in the form of data frames.

10. The apparatus according to any one of claims 7-9, characterized in that, Both the acquisition electrode and the stimulation electrode are integrated stimulation acquisition electrodes; In conjunction with the integrated acquisition electrode, the acquisition stimulation host also includes an analog switch K1 and a voltage follower U4; The integrated stimulation acquisition electrode includes a snap electrode, an analog switch K2, and an operational amplifier U3. The integrated stimulation acquisition electrode is connected to the stimulation acquisition host via a 5-core cable. The snap electrode is connected to the non-inverting input terminal of the operational amplifier U3. The first and second moving terminals of the analog switch K2 are connected to the non-inverting and inverting input terminals of the operational amplifier U3, respectively, and the stationary terminal is connected to the stationary terminal of the analog switch K1 of the stimulation acquisition host via one of the core wires of the 5-core cable. The first moving terminal of analog switch K1 is connected to the output terminal of voltage follower U4, and the input terminal of voltage follower U4 is connected to the input terminal of EEG acquisition module; the second moving terminal is connected to the output terminal of electrical stimulation module. The control terminal of analog switch K2 and the positive terminal and ground of the electrode power supply are all connected to the stimulation acquisition host through the corresponding core wires in the 5-core cable.

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