Attention monitoring method, system and equipment based on electroencephalogram

Abstract

The invention discloses an attention monitoring method, system and device based on electroencephalogram, and relates to the technical field of attention monitoring, the method comprises the following steps: obtaining original electroencephalogram data, carrying out passband filtering and notch filtering on the original electroencephalogram data to filter environmental noise, and obtaining denoised electroencephalogram data; the de-noised electroencephalogram data is subjected to alpha, beta and theta filtering, alpha, beta and theta frequency band power is obtained through calculation, alpha, beta and theta frequency band power data are obtained, and the original electroencephalogram data are provided with timestamps; performing attention initialization processing on the alpha, beta and theta frequency band power data to obtain reference values of an attention parameter EI and an alpha wave desynchronization parameter ASD of each terminal user; and performing normalization calculation on the EI parameter and the ASD parameter relative to the reference value to obtain an effective attention parameter value so as to realize attention monitoring.

Description

Technical Field

[0001] The present invention relates to the field of attention monitoring technology, and specifically to an electroencephalogram-based attention monitoring method, system and device. Background Art

[0002] Brain-computer interface technology uses non-invasive EEG (Electroencephalogram) to monitor the target's attention state, thereby understanding the impact of macro-environment such as teaching methods, work content, and work methods on the target object's learning or working status, and providing a reliable reference for improving work and learning methods.

[0003] With the development of EEG technology, the correlation between various bands of EEG signals and the state of human brain activity has become increasingly clear, and EEG signals have become an important research and detection method to identify the state of human brain. The impact of macro conditions in some specific scenarios on the group is very suitable for monitoring through EEG technology. Attention state is usually an aspect that everyone pays attention to. Although these parameters can help us understand the attention trend of individuals, these parameters are relative. The absolute values ​​of parameters obtained by the same person at different times will be different between different people. Only by obtaining reliable benchmark values ​​can effective monitoring of attention be achieved. At present, the usual practice is to prepare fixed videos, pictures or text resources for the monitored objects to watch and read, and record the corresponding attention parameter values ​​as a benchmark. There is a lack of changes, network communication resources are usually consumed more, and the repeatability is poor. The attention state monitoring system requires a simple and repeatable method to obtain the attention baseline for effective monitoring. Summary of the invention

[0004] In order to solve the deficiencies mentioned in the above background technology, the purpose of the present invention is to provide an EEG-based attention monitoring method, system and device.

[0005] In the first aspect, the purpose of the present invention can be achieved by the following technical solution: a method for monitoring attention based on EEG, the method comprising the following steps:

[0006] Obtaining raw EEG data, performing passband filtering and notch filtering on the raw EEG data to filter out environmental noise, and obtaining denoised EEG data; performing α, β, and θ filtering on the denoised EEG data, and calculating α, β, and θ frequency band powers to obtain α, β, and θ frequency band power data, wherein the raw EEG data carries a timestamp;

[0007] The α, β, and θ frequency band power data are processed for attention initialization to obtain the baseline values ​​of the attention parameter EI and the α wave desynchronization parameter ASD for each terminal user; the EI parameter and the ASD parameter are normalized and calculated relative to the baseline value to obtain the effective attention parameter value, thereby realizing attention monitoring.

[0008] In combination with the first aspect, in some implementations of the first aspect, the method further includes: the process of acquiring the raw EEG data includes:

[0009] The brain's electrical activity signals are collected by placing electrodes on the scalp. They need to be placed in locations with strong alpha, beta, and theta wave activities, including frontal and parietal electrode areas. Each electrode is connected to an EEG amplifier via a wire for signal amplification. The amplified EEG signal is collected and stored via a computer or recording device. EEG signals are collected continuously at high frequency with sampling points within a preset number of points per second. The sampling process is time-stamped by the system to obtain time-stamped raw EEG data.

[0010] In combination with the first aspect, in some implementations of the first aspect, the method further includes: the process of performing passband filtering and notch filtering on the raw EEG data with the timestamp:

[0011] Use infinite impulse response IIR filter for filtering. Suppose the input signal is x[n] and the output signal is y[n]. The filter formula is:

[0012]

[0013] where b k is the feedforward coefficient of the non-recursive part, a m is the feedback coefficient of the recursive part, and M and N are the number of feedforward and feedback coefficients, respectively.

[0014] In combination with the first aspect, in some implementations of the first aspect, the method further includes: performing α, β, and θ filtering on the denoised EEG data, and calculating the α, β, and θ frequency band powers, and the calculation process follows the following formula, as follows:

[0015]

[0016] Where N is the window size of the moving average, x[nk] is the signal value of the corresponding brain wave at the nk time point, and the calculation process of the α, β, and θ frequency band power follows the above formula.

[0017] In combination with the first aspect, in some implementations of the first aspect, the method further includes: the process of performing attention initialization processing on the data to be monitored includes: an image feedback process, a sound feedback process, and an event synchronization process;

[0018] The image feedback process changes the parameters of the graphics generation function by calculating the changes in EI parameters and ASD parameters, generating different forms of patterns that change between random and orderly, thereby attracting attention. The sound feedback process uses a music programming language to generate sound feedback and provides sound feedback interactive capabilities. The event synchronization process synchronizes external audio and video files by providing timestamps to the outside.

[0019] In combination with the first aspect, in some implementations of the first aspect, the method further includes: an attention initialization process of the image feedback process is as follows:

[0020] After the pattern is generated, the attention parameters are calculated and stored, the maximum value of the attention parameters is selected as the initial benchmark, the data is normalized with the initial benchmark, and an attention competition is performed, and the maximum value is re-selected as the final benchmark.

[0021] In combination with the first aspect, in some implementations of the first aspect, the method further includes: the process of the attention competition is as follows:

[0022] A pattern consisting of line outlines will appear within the preset parameter range of the generated pattern. The line outline pattern will be maintained by concentrating attention on the picture. The attention parameter is calculated based on the initial value benchmark. If the parameter is higher than the overall preset average value, the line outline pattern will be maintained, and if it is lower than the overall average value, the line outline pattern will disappear. The maximum value of attention obtained during the competition will become the final attention benchmark.

[0023] The calculation process of pattern generation is as follows:

[0024] Reference rotation function: reference angular frequency ω, rotation function e jωt ;

[0025] Amplitude modulation function:

[0026] Given a time range t, the generating function f(ωt) = A(ωt)e jωt

[0027] Graph drawing: Given a time range t, calculate the function f(ωt), and draw a graph with the real part of the function f(ωt) as the x-coordinate and the imaginary part as the y-coordinate;

[0028] Adjustable parameters: ω,k i Slow change effect: fixed ω, adjusted k i ; In the adjustment parameter k i When , the new amplitude value is imported through the queue to obtain the slow change effect.

[0029] In combination with the first aspect, in some implementations of the first aspect, the method further includes: a calculation formula for the attention parameter EI is as follows:

[0030]

[0031] Where β and θ represent the frequency band power of β wave and θ wave respectively.

[0032] The calculation process of the alpha wave desynchronization parameter ASD is as follows:

[0033]

[0034] Among them, α 基准 is the alpha band power in the baseline state, α 任务 is the alpha wave band power in the task state.

[0035] In a second aspect, in order to achieve the above-mentioned purpose, the present invention discloses an EEG-based attention monitoring system, comprising:

[0036] A filtering calculation module obtains raw EEG data, performs passband filtering and notch filtering on the raw EEG data to filter out environmental noise, and obtains denoised EEG data; performs α, β, and θ filtering on the denoised EEG data, and calculates α, β, and θ frequency band powers to obtain α, β, and θ frequency band power data, wherein the raw EEG data carries a timestamp;

[0037] The attention monitoring module performs attention initialization processing on the α, β, and θ frequency band power data to obtain the baseline values ​​of the attention parameter EI and the α wave desynchronization parameter ASD for each terminal user; the EI parameter and the ASD parameter are normalized relative to the baseline value to obtain the effective attention parameter value to realize attention monitoring.

[0038] In another aspect of the present invention, in order to achieve the above-mentioned purpose, a terminal device is disclosed, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the memory stores a computer program capable of running on the processor, and when the processor loads and executes the computer program, an EEG-based attention monitoring method as described above is adopted.

[0039] Beneficial effects of the present invention:

[0040] The present invention can directly generate a pattern with mathematical beauty, changeability and unpredictability through a simple mathematical formula, complete the initialization of the attention baseline value, and thus form effective attention monitoring. Compared with the prior art, it does not require additional video and image resources, does not increase the network communication load, and has good consistency. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 It is a schematic flow chart of the method of the present invention;

[0043] Figure 2 is the attention initialization flow chart of the present invention;

[0044] Figure 3 It is a pattern effect diagram of the present invention;

[0045] Figure 4 is a schematic diagram of the attention baseline values ​​obtained under different batches of the present invention;

[0046] Figure 5 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] Embodiment 1:

[0049] like Figure 1 As shown, a method for monitoring attention based on EEG includes the following steps:

[0050] S101: Acquire raw EEG data, stamp the raw EEG data with a timestamp, and obtain raw EEG data with a timestamp. In this embodiment, publish the data to a communication agent through a local area network; subscribe to the raw EEG data from the communication agent, perform passband filtering and notch filtering on the raw EEG data for denoising, and perform α, β, and θ filtering on the denoised EEG data;

[0051] Specifically, the IIR filter is also used to select appropriate filter parameters, pass-band filter the corresponding band, and calculate the α, β, and θ band powers;

[0052] The process of acquiring the raw EEG data includes: collecting the brain's electrical activity signals by placing electrodes on the scalp, usually placed at locations where α, β, and θ wave activities are relatively strong, such as the frontal lobe electrode and parietal lobe electrode areas. Each electrode is connected to an EEG amplifier through a wire for signal amplification, and the amplified EEG signal is collected and stored by a computer or recording device. EEG signals can be collected continuously, usually at a high frequency of 200 to 1000 sampling points per second to ensure the accuracy of the signal. The sampling process obtains the raw EEG data with a timestamp by the system.

[0053] The process of performing passband filtering and notch filtering on the raw EEG data: using an IIR (infinite impulse response) filter for filtering. Assuming the input signal is x[n] and the output signal is y[n], the filter formula is:

[0054]

[0055] where b k is the feedforward coefficient (non-recursive part), usually related to x[n] of the filter; a m is the feedback coefficient (recursive part, usually related to the output signal y[n] of the filter; M and N are the number of feedforward and feedback coefficients, respectively.

[0056] As long as the appropriate b k and a m It is also possible to implement passband and notch filtering at once. An example is:

[0057] a m =[1,-5.23226662,13.1412423,-21.3162115,24.68953123,-21.07860679,

[0058] 13.36043405,-6.1932289,1.98264742,-0.39154442,0.03825309]

[0059] b k =[0.03137679,-0.01939192,-0.09413036,0.07756769,0.06275358,

[0060] -0.11635153,0.06275358,0.07756769,-0.09413036,-0.01939192,0.03137679]

[0061] The calculation process of the α, β, and θ band powers is as follows:

[0062]

[0063] Where N is the window size of the moving average, and x[nk] is the signal value of the corresponding EEG at time point nk. The calculation process of α, β, and θ band power all follows the above formula.

[0064] S102: In this embodiment, the α, β, and θ band power data are subscribed from the communication agent to perform the attention initialization process. First, the first stage of attention initialization is performed on the subscribed data to obtain the initial reference values ​​of the attention parameter EI and the α wave desynchronization parameter ASD of each terminal user; after the first stage of initialization process is completed, the attention detection module will perform normalization calculations on the calculations of the EI parameter and ASD parameter of each terminal based on their respective initial reference values, and then conduct an attention competition game to obtain and save the final reference values ​​of EI and ASD. The initialization process is completed, and subsequent attention monitoring will use the final reference values ​​for normalization calculations.

[0065] The process of attention initialization processing includes: image feedback process, sound feedback process and event synchronization process;

[0066] The image feedback process changes the parameters of the graphics generation function by calculating the changes in the EI parameters and the ASD parameters, generating different forms of patterns that change between random and ordered. This graphics generation method is simple to calculate and novel in form, and can effectively attract the user's attention and complete the initialization of the attention parameters.

[0067] The sound feedback process generates sound feedback using a music programming language and provides interactive sound feedback capabilities. The event synchronization process synchronizes external audio and video files by providing timestamps to the outside, providing a convenient interface for multimodal data collaboration and data set accumulation.

[0068] The first stage of attention initialization process is as follows:

[0069] By allowing users to observe randomly generated patterns to enter a relatively focused attention state, the absolute value of the attention parameter is calculated and stored, the maximum value of the attention parameter is selected as the initial benchmark, the data is normalized based on the initial benchmark, and an attention competition is conducted.

[0070] The process of the attention contest is as follows:

[0071] A pattern consisting of line outlines will appear within the preset parameter range of the generated pattern. The line outline pattern will be maintained by concentrating attention on the picture. The attention parameter is calculated based on the initial value benchmark. If the parameter is higher than the overall preset average value, the line outline pattern will be maintained, and if it is lower than the overall average value, the line outline pattern will disappear. The maximum value of attention obtained during the competition will become the final attention benchmark.

[0072] The calculation process of pattern generation is as follows:

[0073] Reference rotation function: reference angular frequency ω, rotation function e jωt ;

[0074] Amplitude modulation function:

[0075]

[0076] Given a time range t, the generating function f(ωt) = A(ωt)e jωt

[0077] Graph drawing: Given a time range t, calculate the function f(ωt), and draw a graph with the real part of the function f(ωt) as the x-coordinate and the imaginary part as the y-coordinate;

[0078] Adjustable parameters: ω,k i Slow change effect: fixed ω, adjusted k i ; In the adjustment parameter k i When the new amplitude value is imported by queue, the slow change effect is obtained. Usually the parameter k i At least one of them must be an irrational number, such as the natural number e, pi, or some other form of irrational number.

[0079] The calculation formula of the attention parameter EI is as follows:

[0080]

[0081] Where β and θ represent the frequency band power of β wave and θ wave respectively.

[0082] The calculation process of the alpha wave desynchronization parameter ASD is as follows:

[0083]

[0084] Among them, α 基准 is the alpha band power in the baseline state, α 任务 is the alpha wave band power in the task state.

[0085] Specifically, alpha wave desynchronization - refers to the reduction in alpha wave (8–13 Hz) activity when a person is concentrating on a task or thinking. Alpha waves are often associated with states of relaxation (such as resting with eyes closed), and when the brain is in a highly engaged state, alpha waves are usually reduced or desynchronized, characterized by ASD parameters.

[0086] Both ASD and EI parameters reflect brain engagement, but they focus on different aspects:

[0087] The ASD parameters focus on a reduction in alpha wave power, which typically occurs when the brain is engaged in cognitive tasks or focusing. A reduction in alpha waves typically indicates that the individual is in an active state of thinking.

[0088] The EI parameter looks at the relative balance of beta waves, which indicate alertness and active thinking, and theta waves, which are often associated with relaxation, light meditation, or lack of concentration. Increased EI values ​​often indicate increased concentration or cognitive load.

[0089] Both ASD parameters and EI parameters are related to individuals. The relative changes in parameters can reflect changes in the individual's brain state, but the absolute values ​​depend on the individual situation and vary greatly.

[0090] Specifically, the present invention is further described below by way of embodiments:

[0091] like Figure 3 As shown, at a specific reference angular frequency ω, the formula Take n = 2, k1 = 2k2 = kπ, and the function generates patterns when k is 3.12, 3.13, 3.14, 3.142, 3.15, and 3.16. It can be seen that the graphics are linear only in a very small range, and in other cases, two-dimensional dot patterns with mathematical beauty are generated. In the attention initialization stage, the terminal user is first allowed to observe the changes in the generated pattern to generate an initial attention benchmark, and then the attention parameters are calculated based on this benchmark to enter the game competition stage. In the game competition stage, the terminal user must concentrate as much as possible to observe the graphics. People with attention parameters higher than the group average will have linear patterns, while people with attention parameters lower than the average will have two-dimensional dot patterns. The game is a dynamic equilibrium state with unpredictability, which can increase the fun and obtain the maximum attention benchmark value as much as possible in the second stage.

[0092] The prior art usually uses stored video resources or picture resources to allow the end user to observe and enter a state of concentration, but the repeatability obtained in this way is not good. The attention baseline value generated in the first initialization stage of the present invention can be comparable to the existing technical level. The advantage is that there is no need to store additional resources, and flexible and diverse patterns can be generated using simple formulas. The entire initialization process of the present invention combines the first step initialization process and the process of the attention competition. Through the interactive feedback brought by the attention competition, the repeatability and stability of the attention baseline parameters are significantly improved. Figure 4 The attention baseline values ​​of the EI parameter after simply looking at pictures, watching videos, and executing the complete initialization process are shown. It can be seen that the initialization process significantly improves the repeatability and stability of the baseline value. Please note that the EI parameter is a ratio, without units, and the absolute value is meaningless. Only the relative trend is meaningful. Therefore, choosing a good baseline value is very important for effectively monitoring attention. Table 1 below shows the EI baseline values ​​for executing the initialization process, watching videos, and watching pictures. It can be seen that the standard deviation is minimized by adopting the linear initialization process, thereby improving the stability of the baseline value.

[0093] Table 1 Mean and standard deviation of EI benchmark values ​​generated by each method

[0094]

[0095] Embodiment 2: In the second aspect, as Figure 5 As shown, in order to achieve the above-mentioned purpose, the present invention discloses an attention monitoring system based on EEG, comprising:

[0096] The filter calculation module 11 obtains the original EEG data, performs passband filtering and notch filtering on the original EEG data to filter out environmental noise, and obtains denoised EEG data; performs α, β, and θ filtering on the denoised EEG data, and calculates the α, β, and θ frequency band powers to obtain α, β, and θ frequency band power data, wherein the original EEG data has a timestamp;

[0097] The attention monitoring module 12 performs attention initialization processing on the α, β, and θ frequency band power data to obtain the baseline values ​​of the attention parameter EI and the α wave desynchronization parameter ASD of each terminal user; normalizes the EI parameter and the ASD parameter relative to the baseline value to obtain the effective attention parameter value, thereby realizing attention monitoring.

[0098] Based on the same inventive concept, the present invention also provides a computer device, which includes: one or more processors, and a memory for storing one or more computer programs; the program includes program instructions, and the processor is used to execute the program instructions stored in the memory. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is used to implement one or more instructions, specifically for loading and executing one or more instructions in a computer storage medium to implement the above method.

[0099] It needs to be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium, on which a computer program is stored, and the computer program is executed by a processor to execute the above method. The storage medium can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0100] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0101] The above shows and describes the basic principles, main features and advantages of the present disclosure. Those skilled in the art should understand that the present disclosure is not limited by the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present disclosure. Without departing from the spirit and scope of the present disclosure, the present disclosure may have various changes and improvements, and these changes and improvements fall within the scope of the present disclosure to be protected.

Claims

1. A method for monitoring attention based on electroencephalogram, characterized in that: The method comprises the following steps: Obtaining raw EEG data, performing passband filtering and notch filtering on the raw EEG data to filter out environmental noise, and obtaining denoised EEG data; performing α, β, and θ filtering on the denoised EEG data, and calculating α, β, and θ frequency band powers to obtain α, β, and θ frequency band power data, wherein the raw EEG data carries a timestamp; The α, β, and θ frequency band power data are processed for attention initialization to obtain the baseline values ​​of the attention parameter EI and the α wave desynchronization parameter ASD for each terminal user; the EI parameter and the ASD parameter are normalized and calculated relative to the baseline value to obtain the effective attention parameter value, thereby realizing attention monitoring.

2. The method for monitoring attention based on EEG according to claim 1, characterized in that: The process of obtaining the raw EEG data includes: The brain's electrical activity signals are collected by placing electrodes on the scalp. They need to be placed in locations with strong alpha, beta, and theta wave activities, including frontal and parietal electrode areas. Each electrode is connected to an EEG amplifier via a wire for signal amplification. The amplified EEG signal is collected and stored via a computer or recording device. EEG signals are collected continuously at high frequency with sampling points within a preset number of points per second. The sampling process is time-stamped by the system to obtain time-stamped raw EEG data.

3. The method for monitoring attention based on EEG according to claim 1, characterized in that: The process of performing passband filtering and notch filtering on the raw EEG data: Use infinite impulse response IIR filter for filtering. Suppose the input signal is x[n] and the output signal is y[n]. The filter formula is: where b k is the feedforward coefficient of the non-recursive part, a m is the feedback coefficient of the recursive part, and M and N are the number of feedforward and feedback coefficients, respectively.

4. The method for monitoring attention based on EEG according to claim 1, characterized in that: The denoised EEG data is subjected to α, β, and θ filtering, and the α, β, and θ frequency band powers are calculated. The calculation process follows the following formula: Where N is the window size of the moving average, x[nk] is the signal value of the corresponding brain wave at the nk time point, and the calculation process of the α, β, and θ frequency band power follows the above formula.

5. The method for monitoring attention based on EEG according to claim 1, characterized in that: The process of performing attention initialization processing on the data to be monitored includes: an image feedback process, a sound feedback process and an event synchronization process; The image feedback process changes the parameters of the graphics generation function by calculating the changes in EI parameters and ASD parameters, generating different forms of patterns that change between random and orderly, thereby attracting attention. The sound feedback process uses a music programming language to generate sound feedback and provides sound feedback interactive capabilities. The event synchronization process synchronizes external audio and video files by providing timestamps to the outside.

6. The method for monitoring attention based on EEG according to claim 5, characterized in that: The attention initialization process of the image feedback process is as follows: After the pattern is generated, the attention parameters are calculated and stored, the maximum value of the attention parameters is selected as the initial benchmark, the data is normalized with the initial benchmark, and an attention competition is performed, and the maximum value is re-selected as the final benchmark.

7. The method for monitoring attention based on EEG according to claim 6, characterized in that: The process of the attention contest is as follows: A pattern consisting of line outlines will appear within the preset parameter range of the generated pattern. The line outline pattern will be maintained by concentrating attention on the picture. The attention parameter is calculated based on the initial value benchmark. If the parameter is higher than the overall preset average value, the line outline pattern will be maintained, and if it is lower than the overall average value, the line outline pattern will disappear. The maximum value of attention obtained during the competition will become the final attention benchmark. The calculation process of pattern generation is as follows: Reference rotation function: reference angular frequency ω, rotation function e jωt ; Amplitude modulation function: Given a time range t, the generating function f(ωt) = A(ωt)e jωt ; Graph drawing: Given a time range t, calculate the function f(ωt), and draw a graph with the real part of the function f(ωt) as the x-coordinate and the imaginary part as the y-coordinate; Adjustable parameters: ω,k i Slow change effect: fixed ω, adjusted k i ; In the adjustment parameter k i When , the new amplitude value is imported through the queue to obtain the slow change effect.

8. The method for monitoring attention based on EEG according to claim 1, characterized in that: The calculation formula of the attention parameter EI is as follows: In the formula, β and θ represent the frequency band power of β wave and θ wave respectively; The calculation process of the alpha wave desynchronization parameter ASD is as follows: Among them, α 基准 is the alpha band power in the baseline state, α 任务 is the alpha wave band power in the task state.

9. An EEG-based attention monitoring system, characterized in that: include: The filtering calculation module obtains the original EEG data, performs passband filtering and notch filtering on the original EEG data to filter out environmental noise, and obtains denoised EEG data; Perform α, β, and θ filtering on the denoised EEG data, and calculate the α, β, and θ frequency band power to obtain α, β, and θ frequency band power data, wherein the original EEG data has a timestamp; The attention monitoring module performs attention initialization processing on the α, β, and θ frequency band power data to obtain the baseline values ​​of the attention parameter EI and the α wave desynchronization parameter ASD for each terminal user; the EI parameter and the ASD parameter are normalized relative to the baseline value to obtain the effective attention parameter value to realize attention monitoring.

10. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: The memory stores a computer program that can be run on the processor. When the processor loads and executes the computer program, an EEG-based attention monitoring method according to any one of claims 1 to 8 is adopted.

Images