Mathematical problem solving ability improving method based on attention closed-loop visual regulation

Abstract

According to the mathematical problem solving ability improving method based on attention closed-loop visual regulation and control, the power ratio of the beta wave to the theta wave in the forehead lobe electroencephalogram signals is collected in real time, visual flicker and auditory prompt synchronous with the electroencephalogram frequency is dynamically triggered, passive attention regulation and control without active intervention of learners are achieved, and the mathematical problem solving ability improving method based on attention closed-loop visual regulation and control is achieved. And the mathematical problem solving efficiency and accuracy are improved.

Description

Technical Field

[0001] The present invention relates to electroencephalogram (EEG) signal processing and cognitive ability enhancement technology, and in particular to a method for improving mathematical problem-solving ability based on attention closed-loop visual regulation. Background Art

[0002] Currently, improving math problem-solving ability is highly dependent on learners' attention levels. Traditional EEG-based attention regulation technologies often rely on neurofeedback with active participation from learners, or use physical stimulation (such as tDCS and TMS), which poses safety risks and high costs, making it difficult to widely apply in educational scenarios. Existing technologies lack a closed-loop regulation solution that combines real-time monitoring of specific EEG characteristics for math problem-solving tasks with non-physical stimulation, making it impossible to accurately adapt to individual attention fluctuations, resulting in limited regulation effects. Summary of the Invention

[0003] In view of the above situation, it is necessary to provide a method for improving mathematical problem-solving ability based on attention closed-loop visual regulation that solves at least one of the above problems, characterized by comprising the following steps:

[0004] Through the head-mounted EEG signal acquisition device, the user's frontal EEG signals are collected in real time to obtain the power of beta waves (12-30Hz) and theta waves (4-7Hz);

[0005] Calculating the ratio of the beta wave power to the theta wave power as an attention index;

[0006] Setting a normal attention threshold interval, when the attention index is lower than the preset threshold, automatically triggering visual flicker stimulation, the visual flicker frequency is synchronized with the current beta wave frequency;

[0007] At the same time, a high-frequency auditory cue is triggered, and the frequency and volume of the auditory cue are dynamically adjusted according to the degree of decline in the attention index;

[0008] According to the changes in real-time attention indicators, the visual flicker frequency and auditory cue intensity are dynamically adjusted until the attention indicators return to the normal threshold range, and then the stimulation intensity is gradually reduced until it stops;

[0009] Implementing the above closed-loop control process in the math problem-solving task interface can achieve passive and dynamic adjustment of user attention, thereby improving the efficiency and accuracy of math problem-solving.

[0010] Preferably, the visual flicker stimulation is achieved by flickering blue light in the edge area of the math problem-solving interface screen, the flickering frequency is dynamically adjusted according to the real-time beta wave frequency, and the flickering duration is 5 to 10 seconds.

[0011] Preferably, the auditory prompt is achieved by playing high-frequency sound with a frequency of 800 to 1500 Hz through headphones, with a volume range of 50 to 60 dB, and the specific parameters are adjusted in stages according to the degree of decline in the attention index.

[0012] Preferably, the EEG signal is collected using at least two frontal electrodes, located at the Fp1 and Fp2 positions respectively, and the collected signals are processed by fast Fourier transform (FFT) to calculate the beta wave and theta wave power.

[0013] Preferably, the normal attention threshold interval is set to a β / θ ratio of 2.0 to 5.0, mild attention impairment is determined as a ratio below 2.0, and severe attention impairment is determined as a ratio below 1.6, and the visual flicker frequency and auditory prompt parameters are adjusted based on the judgment results.

[0014] Preferably, the closed-loop control process includes: initializing the EEG device and the mathematical problem-solving interface; cyclically collecting EEG signals; calculating attention indicators in real time; triggering or adjusting visual and auditory stimulation according to the indicators; and gradually reducing the stimulation until it stops when the indicators return to normal.

[0015] Preferably, the visual flicker stimulation is achieved by alternately flickering the areas around the edge of the math problem-solving interface screen. The flickering area includes rectangular areas at the upper, lower, left and right edges of the screen, and the flickering sequence and frequency are dynamically adjusted according to the real-time beta wave frequency to enhance the spatial coverage and attention activation effect of the visual stimulation.

[0016] Preferably, the calculation of the attention index further includes:

[0017] In addition to the beta wave (12-30 Hz) and theta wave (4-7 Hz) power ratios, the alpha wave (8-12 Hz) power was also collected;

[0018] By weighted calculation of the comprehensive ratio of beta wave power to theta wave and alpha wave power, a multi-band comprehensive attention index is formed;

[0019] The attention state is judged based on this comprehensive indicator, which triggers visual flashes and auditory cue stimulation to improve the accuracy of attention recognition and the regulation effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a flowchart of a method for improving mathematical problem-solving ability based on attention closed-loop visual regulation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the following is a further detailed description of the method for improving mathematical problem-solving ability based on attention closed-loop visual regulation of the present invention in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0022] In the description of the present invention, unless otherwise specified, "plurality" means two or more; the terms "center", "longitudinal", "lateral", "upper", "lower", "left", "right", "inner", "outer", "front end", "rear end", "head", "tail", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore should not be understood as limiting the present invention. In addition, the terms "first", "second", "third", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance.

[0023] In the description of this utility, it should be noted that, unless otherwise specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; mechanical connections, electrical connections; direct connections, indirect connections through an intermediate medium, and internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in this utility based on specific circumstances.

[0024] See Figure 1 This invention aims to provide a non-invasive, closed-loop control method based on visual and auditory stimulation. This method monitors the prefrontal beta / theta wave ratio during math problem-solving in real time, dynamically adjusts visual flicker frequency and auditory cue parameters, and passively maintains the activation state of the attention network. This method achieves efficient attention control without active intervention from the learner, thereby improving math problem-solving efficiency and accuracy.

[0025] The present invention provides a method for improving mathematical problem-solving ability based on closed-loop visual control of attention. The core of the method is to precisely control the individual's attention state by monitoring the individual's EEG activity in real time during the mathematical problem-solving process and dynamically adjusting visual and auditory stimulation based on EEG characteristics. The method achieves closed-loop control through the following steps:

[0026] EEG signal acquisition and preprocessing: A head-mounted EEG signal acquisition device is used to acquire the user's beta wave (12-30 Hz) and theta wave (4-7 Hz) signals from the prefrontal cortex, and the beta / theta power ratio (R) is calculated as an attention indicator [9]. Specifically, the device uses at least two electrodes placed in the prefrontal region (Fp1 and Fp2 positions) to acquire EEG signals. To improve signal quality, the original signal needs to undergo preprocessing steps such as filtering and noise reduction.

[0027]

[0028] Among them, P β represents the beta wave power, P θ Represents theta wave power.

[0029] Dynamic Threshold Judgment: Set the attention threshold interval [Rmin, Rmax] and determine in real time whether the current β / θ ratio R is lower than the attention loss threshold Rmin. The threshold setting can be adaptively adjusted based on individual baseline EEG data to improve judgment accuracy.

[0030] Non-physical stimulation intervention: When the β / θ ratio R falls below the threshold Rmin, visual flickering stimulation synchronized with the current β wave frequency and high-frequency auditory cues are triggered to activate the prefrontal attention network. The visual stimulation is achieved by flashing blue light at the edge of the math problem-solving screen, and the auditory cue is achieved by playing high-frequency tones between 800 and 1500 Hz through headphones.

[0031] (2) key technical details;

[0032] 1. EEG signal processing algorithm

[0033] Calculation of β / θ power ratio: Perform fast Fourier transform (FFT) on the collected EEG signal to obtain the β band power P β and theta band power P θ , calculate the ratio R.

[0034]

[0035] Set the normal attention threshold interval to

[0036] [Rmin,Rmax]

[0037] When R <R min It is judged as decreased attention.

[0038] Multi-band comprehensive attention index: To improve the accuracy of attention state recognition, α wave (8-12Hz) power P can be further introduced α By weighted calculation of the comprehensive ratio of β wave power to θ wave and α wave power, a multi-band comprehensive attention index R is formed.c .

[0039]

[0040] Among them, w β 、w θ 、w α The preset weights can be optimized and adjusted according to the user's EEG characteristics.

[0041] Dynamic stimulation parameter generation rules

[0042] The system dynamically adjusts the visual flicker frequency, auditory cue frequency, and stimulation intensity based on real-time EEG data. Table 1 shows an example of a dynamic stimulation parameter generation rule:

[0043] Table 1

[0044]

[0045] Visual flicker frequency: synchronized with the current beta wave frequency, equal to the beta wave frequency when it decreases slightly, and f when it decreases severely β ×1.2.

[0046] Auditory cue frequency: Dynamically adjusted within the range of 800-1500Hz according to the degree of attention loss.

[0047] Stimulation intensity: divided into three levels: high, medium and low, and adjusted according to the degree of attention loss.

[0048] (3) Detailed description of the technical solution

[0049] 1. The hardware system of the present invention mainly includes the following components

[0050] Head-mounted EEG acquisition device: Used to collect EEG signals from the user's frontal lobe. This device must have at least two dry electrodes, located at positions Fp1 and Fp2, capable of collecting EEG signals in real time and transmitting them to the control system. The device should provide good wearing comfort and signal stability.

[0051] Visual stimulation display: This is used to present the math problem-solving task interface and visual flicker stimulation. The display should have a high refresh rate and color reproduction to ensure the quality of visual stimulation.

[0052] Auditory stimulation headphones: Used to play auditory prompts and white noise. The headphones should have good sound quality and sound insulation to ensure the effectiveness of auditory stimulation.

[0053] Control system: Consists of a computer and related software, and is used to implement functions such as EEG signal processing, dynamic threshold determination, stimulation parameter generation, and closed-loop process control. The control system should have real-time data processing capabilities and stable operating performance.

[0054] 2. The software system of the present invention mainly includes the following modules

[0055] EEG signal acquisition module: responsible for obtaining raw EEG signals from the EEG acquisition device and performing preprocessing, including filtering and noise reduction.

[0056] EEG signal processing module: responsible for analyzing the preprocessed EEG signals, calculating the power of β waves, θ waves and α waves, and calculating the β / θ ratio and multi-band comprehensive attention index.

[0057] Dynamic threshold judgment module: responsible for setting and adjusting the attention threshold interval [Rmin, Rmax] based on the user's individual baseline data and real-time EEG data, and judging the current attention state.

[0058] Stimulus parameter generation module: responsible for generating parameters such as visual flicker frequency, auditory cue frequency and stimulation intensity based on attention state and beta wave frequency.

[0059] Closed-loop control module: responsible for controlling the visual stimulation display and auditory stimulation headphones based on real-time EEG data and stimulation parameters to achieve a closed-loop control process.

[0060] Data recording module: responsible for recording EEG data, stimulation parameters and user behavior data for subsequent analysis and optimization.

[0061] 3. The workflow of the present invention is as follows

[0062] The user wears a head-mounted EEG acquisition device and starts the math problem-solving task interface.

[0063] Initialize the system, connect the EEG acquisition equipment, calibrate the electrode position, and load the problem.

[0064] The system enters the cyclic acquisition mode and collects the user's frontal lobe EEG signals in real time.

[0065] The EEG signal processing module preprocesses and analyzes the signals and calculates the β / θ ratio R and the multi-band comprehensive attention index Rc.

[0066] The dynamic threshold judgment module judges the current attention state based on the R value and Rc value.

[0067] If the R value is lower than the threshold Rmin, the stimulation parameter generation module generates parameters such as visual flicker frequency, auditory cue frequency and stimulation intensity according to the current β wave frequency and attention state.

[0068] The closed-loop control module controls the visual stimulus display and auditory stimulus headphones, triggering the visual flashes and auditory cues.

[0069] During the stimulation period, the system continuously monitors the changes in R and Rc values and dynamically adjusts the stimulation parameters.

[0070] If the R value returns to the normal range [Rmin, Rmax], the system gradually reduces the stimulation intensity until it stops.

[0071] The system records EEG data, stimulation parameters, and user behavior data.

[0072] Execute the loop until the problem-solving task is completed.

[0073] The following is a detailed description of the embodiments and Figure 1 , the technical solution of the present invention is further described in detail.

[0074] Example 1: Closed-loop control based on real-time EEG signals;

[0075] Hardware preparation;

[0076] EEG acquisition equipment: Muse 2 headband, including dry electrodes located at Fp1 and Fp2 positions on the frontal lobe.

[0077] Visual stimulation display: 15.6-inch laptop screen, resolution 1920x1080, refresh rate 60Hz.

[0078] Auditory stimulation headphones: Sony MDR-ZX110AP on-ear headphones.

[0079] Software preparation;

[0080] Operating system: Windows 10.

[0081] Development environment: Python 3.7, OpenBCI GUI, FFT, PyQt5.

[0082] Mathematical problem-solving interface: Written in PyQt5, the center of the interface displays the geometry proof question, and the edge area serves as a visual flashing feedback area.

[0083] Software implementation;

[0084] EEG signal acquisition: Use the OpenBCI GUI to connect to the Muse 2 headband and collect EEG signals from the Fp1 and Fp2 electrodes in real time.

[0085] Signal preprocessing: The original signal was filtered using a Butterworth bandpass filter with a frequency range of 2–45 Hz to remove power frequency interference and myoelectric noise.

[0086] Feature extraction: The filtered signal is divided into frames with a frame length of 1 second and a frame shift of 0.5 seconds. Each frame is subjected to an FFT, and the power values of the 4-7 Hz (θ wave) and 12-30 Hz (β wave) are calculated.

[0087] Threshold setting: Set the threshold according to user historical data, Rmin = 2.0, Rmax = 5.0.

[0088] Stimulation parameter generation: Dynamically adjust the stimulation parameters according to the real-time β / θ ratio R.

[0089] If R < 2.0, a visual flicker and auditory cue are triggered. The visual flicker frequency is equal to the beta wave frequency, the color is blue, and the brightness is 50% of the maximum brightness. The auditory cue frequency is 1000Hz and the volume is 50dB.

[0090] If 2.0≤R≤5.0, gradually reduce the stimulation intensity by reducing the flashing frequency and prompt volume by 10% every 2 seconds until it stops.

[0091] If R>5.0, a soothing stimulus was triggered, with a visual flicker frequency of 5 Hz and a green color, and an auditory cue of white noise at a volume of 40 dB.

[0092] Interface display: The user interface was written using PyQt5 to display EEG signals, β / θ ratio, and stimulation status in real time.

[0093] Experimental process:

[0094] The user wears the Muse 2 headband and starts the math problem-solving interface.

[0095] The system automatically connects to the EEG acquisition device and calibrates the electrode position.

[0096] The user starts solving the problem and the system monitors EEG signals in real time.

[0097] If the user's β / θ ratio drops to 1.8 due to complex reasoning, the edge of the screen flashes blue light at the current β wave frequency (such as 20Hz), and the headphones play a 1000Hz high-frequency sound.

[0098] After 5-10 seconds of continuous stimulation, if the β / θ ratio returns to 2.5, the flicker frequency is gradually reduced to 15 Hz and the volume is reduced to 40 dB until attention is stabilized, and then the stimulation is stopped.

[0099] During the entire problem-solving process, the system automatically adjusts stimulation parameters based on real-time EEG data without the need for active user operation.

[0100] Example 2: Closed-loop control based on personalized threshold optimization

[0101] Based on Example 1, a personalized threshold optimization module is added.

[0102] Before the user performs math problem-solving training, baseline EEG data is collected for 10 minutes to record the user's EEG activity in a relaxed state.

[0103] Baseline data were analyzed and user-specific beta / theta ratio range and beta wave frequency range were calculated.

[0104] Adjust the values of Rmin and Rmax according to the individual range, and optimize the mapping relationship between visual flicker frequency and auditory cue frequency.

[0105] In the subsequent closed-loop control process, the visual flicker frequency and auditory cue intensity are dynamically adjusted based on this personalized threshold.

[0106] Example 3: Adjustment based on the position of the visual flickering area

[0107] Based on Example 1, the visual flicker area is adjusted.

[0108] Divides the screen edge into four areas: top, bottom, left, and right.

[0109] When visual flicker occurs, the four areas flash in turn, and the flashing order and frequency are dynamically adjusted according to the real-time beta wave frequency.

[0110] It can increase the randomness of the flashing direction to avoid visual fatigue for users.

[0111] Example 4: Multi-band comprehensive attention index combined with alpha waves

[0112] On the basis of Example 1, the calculation of α wave power is added.

[0113] The alpha wave power in the 8-12 Hz frequency band is collected and weighted together with the beta and theta wave powers to form a multi-band comprehensive attention index Rc.

[0114] The Rc value is used to judge the attention state and trigger the corresponding visual flash and auditory cue stimulation.

[0115]

[0116] Among them, w β 、w θ 、w α The preset weights are optimized and adjusted according to the user's EEG characteristics.

[0117] Technical effects of the present invention:

[0118] (1) Solving the defects of existing technologies

[0119] 1. No need for active participation, adapting to the needs of passive regulation;

[0120] This invention overcomes the drawback of traditional neurofeedback, which relies on the learner's subjective effort, by automatically monitoring and stimulating through a closed-loop system. Even in states of fatigue or low motivation, attention networks can be passively activated through visual and auditory stimulation, improving the continuity of the problem-solving process.

[0121] 2. Non-physical stimulation, high safety

[0122] This invention only uses non-invasive sensory stimulation such as visual flicker and auditory cues, avoiding the potential risks of physical stimulation such as tDCS / TMS (such as scalp damage and electromagnetic interference). It is suitable for learners of all ages and is particularly suitable for large-scale application in educational scenarios.

[0123] 3. Dynamically adapt to individual differences and precisely control

[0124] The present invention dynamically adjusts stimulation parameters based on the real-time β / θ ratio and β wave frequency (such as synchronization of flickering frequency with current EEG frequency), solving the "one-size-fits-all" problem of existing open-loop control technology, making the stimulation parameters fit the individual neural oscillation characteristics and improving the control accuracy.

[0125] (2) Specific technical advantages

[0126] 1. Real-time closed loop improves problem-solving efficiency

[0127] Maintaining a high beta wave state in the prefrontal cortex through dynamic stimulation shortens the thinking time for complex mathematical problems.

[0128] 2. Adaptive learning ability

[0129] The system can record individual EEG baseline data through multiple training sessions, automatically optimize threshold settings (such as Rmin, Rmax) and stimulation parameter mapping rules, and form a personalized control plan. Long-term use can gradually reduce dependence on external stimulation and enhance the ability to maintain autonomous attention.

[0130] 3. Low cost and portability

[0131] The present invention is based on consumer-grade EEG devices (such as hundred-dollar head-mounted sensors) and ordinary display / audio devices. It does not require professional medical-grade instruments and can be integrated into terminals such as tablets and learning machines to support use in classrooms or home scenarios.

[0132] (3) Detailed analysis of beneficial effects;

[0133] 1. The present invention achieves beneficial effects through the following structural features

[0134] Closed-loop control system: real-time monitoring of EEG signals, dynamic adjustment of stimulation parameters, and precise control of individual attention status.

[0135] Non-invasive stimulation: Use non-invasive sensory stimulation such as visual flashes and auditory cues to avoid potential risks caused by physical stimulation.

[0136] Personalized threshold optimization: Based on individual baseline EEG data, adaptively adjust threshold settings and stimulation parameter mapping rules to improve regulation effects.

[0137] 2. Theoretical explanation

[0138] The theoretical basis of this invention lies in the close relationship between EEG activity and cognitive function. Beta waves are associated with focused attention, while theta waves are associated with relaxation and meditation. By increasing the beta / theta ratio, an individual's attention level can be effectively improved. Furthermore, visual flicker and auditory cues can activate the prefrontal attention network, promoting the allocation and utilization of cognitive resources.

[0139] In summary, this invention achieves real-time, precise, and passive optimization of attention during mathematical problem-solving through non-invasive closed-loop visual control technology, providing a safe and efficient technical solution for improving learning ability. This method offers advantages such as no active participation, high security, dynamic adaptation to individual differences, real-time closed-loop improvement of problem-solving efficiency, adaptive learning capabilities, low cost, and portability, and has broad prospects for educational applications.

[0140] The above examples detail the specific structures and operating principles of each claim of the present invention, combining them with mathematical problem-solving scenarios to ensure the completeness and feasibility of the technical solution. Each example focuses on the technical features themselves, avoiding ambiguous statements, and fully demonstrates the innovation and practicality of the present invention.

[0141] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any technician familiar with the present profession can make some changes or modifications to equivalent embodiments of equivalent changes using the technical content disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A method for improving mathematical problem-solving ability based on attention closed-loop visual regulation, characterized in that: The following steps are involved: Through the head-mounted EEG signal acquisition device, the user's frontal EEG signals are collected in real time to obtain the power of beta waves (12-30Hz) and theta waves (4-7Hz); Calculating the ratio of the beta wave power to the theta wave power as an attention index; Setting a normal attention threshold interval, when the attention index falls below the preset threshold, automatically triggering visual flicker stimulation, the visual flicker frequency being synchronized with the current beta wave frequency; At the same time, a high-frequency auditory cue is triggered, and the frequency and volume of the auditory cue are dynamically adjusted according to the degree of decline in the attention index; According to the changes in real-time attention indicators, the visual flicker frequency and auditory cue intensity are dynamically adjusted until the attention indicators return to the normal threshold range, and then the stimulation intensity is gradually reduced until it stops; Implementing the above closed-loop control process in the math problem-solving task interface can achieve passive and dynamic adjustment of user attention, thereby improving the efficiency and accuracy of math problem-solving.

2. The method according to claim 1, characterized in that The visual flicker stimulation is achieved by flickering blue light in the edge area of the math problem-solving interface screen. The flickering frequency is dynamically adjusted according to the real-time beta wave frequency, and the flickering duration is 5 to 10 seconds.

3. The method according to claim 1, characterized in that The auditory prompt is achieved by playing high-frequency sounds with a frequency of 800 to 1500 Hz through headphones, with a volume range of 50 to 60dB. The specific parameters are adjusted in levels according to the degree of decline in attention indicators.

4. The method according to claim 1, wherein EEG signals were collected using at least two prefrontal electrodes, located at Fp1 and Fp2 respectively. The collected signals were processed by fast Fourier transform (FFT) to calculate the beta wave and theta wave power.

5. The method according to claim 1, wherein The normal attention threshold interval is set as a β / θ ratio of 2.0 to 5.

0. Mild attention impairment is judged as a ratio below 2.0, and severe attention impairment is judged as a ratio below 1.

6. The visual flicker frequency and auditory cue parameters are adjusted based on the judgment results.

6. The method according to claim 1, characterized in that The closed-loop control process includes: initializing the EEG device and the math problem-solving interface; cyclically collecting EEG signals; calculating attention indicators in real time; triggering or adjusting visual and auditory stimulation based on the indicators; and gradually reducing the stimulation until it stops when the indicators return to normal.

7. The method according to claim 1, characterized in that The visual flicker stimulation is achieved by alternately flickering the areas around the edges of the math problem-solving interface screen. The flickering areas include rectangular areas at the upper, lower, left, and right edges of the screen, and the flickering sequence and frequency are dynamically adjusted according to the real-time beta wave frequency to enhance the spatial coverage and attention activation effects of the visual stimulation.

8. The method according to claim 1, characterized in that The calculation of the attention index further includes: In addition to the beta wave (12-30 Hz) and theta wave (4-7 Hz) power ratios, the alpha wave (8-12 Hz) power was also collected; By weighted calculation of the comprehensive ratio of beta wave power to theta wave and alpha wave power, a multi-band comprehensive attention index is formed; The attention state is judged based on this comprehensive indicator, which triggers visual flashes and auditory cue stimulation to improve the accuracy of attention recognition and the regulation effect.

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