阅读说明：本技术 专注力自动侦测方法和系统 (Automatic attention detecting method and system ) 是由 郭家宏 巢佳莉 许明勋 詹惟雯 张耀宗 于 2019-08-29 设计创作，主要内容包括：本发明提供一种专注力自动侦测方法,接收用户的脑电波信号；对所述脑电波信号进行预处理,所述预处理包括加窗处理、快速傅立叶变换、归一化和滤波；对预处理后的脑电波信号进行特征值运算得到特征参数；利用所述特征参数进行专注力指数运算得到运算结果；及根据所述运算结果判断使用者是否专注。本发明还提供一种专注力自动侦测系统。本发明可更加准确的识别使用者的专注力状态,并对所述专注力状态进行标注,提高识别使用者的专注力状态。(The invention provides a concentration automatic detection method, which receives brain wave signals of a user; preprocessing the brain wave signals, wherein the preprocessing comprises windowing processing, fast Fourier transformation, normalization and filtering; performing characteristic value operation on the preprocessed brain wave signals to obtain characteristic parameters; carrying out concentration index operation by using the characteristic parameters to obtain an operation result; and judging whether the user is attentive or not according to the operation result. The invention also provides an automatic concentration detection system. The invention can more accurately identify the concentration state of the user, label the concentration state and improve the identification of the concentration state of the user.)

1. A method for automatic concentration detection, the method comprising:

receiving brain wave signals of a user;

preprocessing the brain wave signals, wherein the preprocessing comprises windowing processing, fast Fourier transformation, normalization and filtering;

performing characteristic value operation on the preprocessed brain wave signals to obtain characteristic parameters;

carrying out concentration index operation by using the characteristic parameters to obtain an operation result; and

and judging whether the user is attentive or not according to the operation result.

2. The method as claimed in claim 1, wherein the characteristic parameters include energy of each frequency band signal in the brain wave signal, ratio of frequency band signals, and fractal dimension of the brain wave signal.

3. The automatic concentration detection method as claimed in claim 2, wherein the concentration index is calculated by the following formula:

wherein, C_ch(n) is the attention index, θ_ch(t)、α_ch(t)、β_ch(t) and γ_ch(t) separately determining the energy of each frequency band signal in the electroencephalogram signal,toAs weight value of each characteristic parameter, CL_chAnd (n) is a fractal dimension of the brain wave signal.

4. The method as claimed in claim 1, wherein the determining whether the user is attentive according to the operation result comprises:

comparing the operation result with a threshold value;

confirming that the user is attentive when the operation result is greater than or equal to the threshold value; and

and when the operation result is smaller than the threshold value, confirming that the user is not attentive.

5. The method of claim 4, wherein the method further comprises:

marking the concentration result of the user;

when the operation result is greater than or equal to the threshold value, confirming that the user is attentive, and marking the brain wave signal in the corresponding time period as 1;

and when the operation result is smaller than the threshold value, confirming that the user is not attentive, and marking the brain wave signal in the corresponding time period as 0.

6. The automatic concentration detection method as claimed in claim 5, wherein the method further comprises:

for a short-time non-concentration event in two adjacent concentration events, changing the non-concentration event smaller than a fluctuation time allowable threshold into a concentration state, and labeling a brain wave signal corresponding to the short-time non-concentration event as 1;

for a short time attention event in two adjacent non-attention events, the attention event smaller than the fluctuation time allowable threshold is changed into a non-attention state, and the electroencephalogram signal corresponding to the short time attention event is marked as 0.

7. An automatic concentration detection system, comprising:

the signal receiving module is used for receiving brain wave signals of a user;

the signal processing module is used for preprocessing the brain wave signals, and the preprocessing comprises windowing processing, fast Fourier transform, normalization and filtering;

the characteristic value operation module is used for performing characteristic value operation on the preprocessed brain wave signals to obtain characteristic parameters;

the attention-specific index operation module is used for carrying out attention-specific index operation by utilizing the characteristic parameters to obtain an operation result; and

and the judging module is used for judging whether the user is concentrated according to the operation result.

8. The system of claim 7, wherein the characteristic parameters include energy of each frequency band signal in the brain wave signal, ratio of frequency band signals, and fractal dimension of the brain wave signal.

9. The automatic concentration detection system of claim 8, wherein the concentration index is calculated by the following formula:

wherein, C_ch(n) is the attention index, θ_ch(t)、α_ch(t)、β_ch(t) and γ_ch(t) separately determining the energy of each frequency band signal in the electroencephalogram signal,toAs weight value of each characteristic parameter, CL_ch(n) Is the fractal dimension of the brain wave signal.

10. The system of claim 7, wherein the determining module is further configured to compare the operation result with a threshold; confirming that the user is attentive when the operation result is greater than or equal to the threshold value; and when the operation result is smaller than the threshold value, confirming that the user is not attentive.

11. The automatic concentration detection system as claimed in claim 10, further comprising a labeling module for labeling the concentration result of the user;

when the operation result is greater than or equal to the threshold value, confirming that the user is attentive, and marking the brain wave signal in the corresponding time period as 1 by the marking module;

and when the operation result is smaller than the threshold value, the user is confirmed not to be attentive, and the marking module marks the brain wave signals in the corresponding time period as 0.

12. The system of claim 11, wherein for a short non-concentration event of two adjacent concentration events, the non-concentration event smaller than the allowable threshold of fluctuation time is changed to a concentration state, and the electroencephalogram signal corresponding to the short non-concentration event is labeled as 1;

for a short time attention event in two adjacent non-attention events, the attention event smaller than the fluctuation time allowable threshold is changed into a non-attention state, and the electroencephalogram signal corresponding to the short time attention event is marked as 0.

Technical Field

The invention relates to a concentration automatic detection method and system.

Background

The wearable brain wave detection device provides the opportunity for people to know the brain activities of the people and also provides the possibility of quantifying the learning index. Many assistive learning tools or specialized applications exist that mainly use electroencephalography to provide focused assessment or training aids. The traditional brain wave concentration algorithm only depends on brain wave signals of a single frequency band, or the brain wave signals of a few specific frequency bands, or the time domain energy threshold value without normalization calculation is adopted for detection. Since individual differences of brain wave signals are large, even the same individual has energy differences in different time periods on the same day, such algorithms not only need to frequently adjust the threshold values according to the characteristics of users, but also usually have highly fluctuating detection results.

Disclosure of Invention

In view of the above problems, the present application provides an automatic concentration detection method for more accurately identifying the concentration status of a user.

In one aspect, the present application provides a concentration automatic detection method, including:

receiving brain wave signals of a user;

preprocessing the brain wave signals, wherein the preprocessing comprises windowing processing, fast Fourier transformation, normalization and filtering;

performing characteristic value operation on the preprocessed brain wave signals to obtain characteristic parameters;

carrying out concentration index operation by using the characteristic parameters to obtain an operation result; and

and judging whether the user is attentive or not according to the operation result.

Preferably, the characteristic parameters include energy of each frequency band signal in the brain wave signals, a frequency band signal ratio, and a fractal dimension of the brain wave signals.

Preferably, the attention index is calculated by the following formula:

wherein, C_ch(n) is the attention index, θ_ch(t)、α_ch(t)、β_ch(t) and γ_ch(t) separately determining the energy of each frequency band signal in the electroencephalogram signal,toAs weight value of each characteristic parameter, CL_chAnd (n) is a fractal dimension of the brain wave signal.

Preferably, the determining whether the user is attentive according to the operation result includes:

comparing the operation result with a threshold value;

confirming that the user is attentive when the operation result is greater than or equal to the threshold value; and

and when the operation result is smaller than the threshold value, confirming that the user is not attentive.

Preferably, the method further comprises:

marking the concentration result of the user;

when the operation result is greater than or equal to the threshold value, confirming that the user is attentive, and marking the brain wave signal in the corresponding time period as 1;

and when the operation result is smaller than the threshold value, confirming that the user is not attentive, and marking the brain wave signal in the corresponding time period as 0.

Preferably, the method further comprises:

for a short-time non-concentration event in two adjacent concentration events, changing the non-concentration event smaller than a fluctuation time allowable threshold into a concentration state, and labeling a brain wave signal corresponding to the short-time non-concentration event as 1;

for a short time attention event in two adjacent non-attention events, the attention event smaller than the fluctuation time allowable threshold is changed into a non-attention state, and the electroencephalogram signal corresponding to the short time attention event is marked as 0.

In another aspect, the present application provides a concentration auto-detection system, the system comprising:

the signal receiving module is used for receiving brain wave signals of a user;

the signal processing module is used for preprocessing the brain wave signals, and the preprocessing comprises windowing processing, fast Fourier transform, normalization and filtering;

the characteristic value operation module is used for performing characteristic value operation on the preprocessed brain wave signals to obtain characteristic parameters;

the attention-specific index operation module is used for carrying out attention-specific index operation by utilizing the characteristic parameters to obtain an operation result; and

and the judging module is used for judging whether the user is concentrated according to the operation result.

Preferably, the characteristic parameters include energy of each frequency band signal in the brain wave signals, a frequency band signal ratio, and a fractal dimension of the brain wave signals.

Preferably, the attention index is calculated by the following formula:

wherein, C_ch(n) is the attention index, θ_ch(t)、α_ch(t)、β_ch(t) and γ_ch(t) separately determining the energy of each frequency band signal in the electroencephalogram signal,toAs weight value of each characteristic parameter, CL_chAnd (n) is a fractal dimension of the brain wave signal.

Preferably, the judging module is further configured to compare the operation result with a threshold; confirming that the user is attentive when the operation result is greater than or equal to the threshold value; and when the operation result is smaller than the threshold value, confirming that the user is not attentive.

Preferably, the system further comprises a labeling module for labeling the concentration result of the user;

when the operation result is greater than or equal to the threshold value, confirming that the user is attentive, and marking the brain wave signal in the corresponding time period as 1 by the marking module;

and when the operation result is smaller than the threshold value, the user is confirmed not to be attentive, and the marking module marks the brain wave signals in the corresponding time period as 0.

Preferably, for a short-time non-concentration event of two adjacent concentration events, the non-concentration event smaller than the allowable threshold of fluctuation time is changed into a concentration state, and the electroencephalogram signal corresponding to the short-time non-concentration event is labeled as 1;

for a short time attention event in two adjacent non-attention events, the attention event smaller than the fluctuation time allowable threshold is changed into a non-attention state, and the electroencephalogram signal corresponding to the short time attention event is marked as 0.

Bearing the above, the automatic detection method and system for concentration force provided by the application can analyze and judge whether the user concentrates on the collected brain wave signals of the user, and can mark concentration force results in the brain wave signals displayed graphically. The user can conveniently have objective progress indexes when performing the autonomous training and the objective progress indexes are used as reference basis for adjusting the training content. The state can be absorbed in but there is the restraining power to transient state fluctuation ability to effective the detection to avoid the influence of frequent interference, can provide continuity event special attention and judge accurately and stably.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.

Fig. 1 is a block diagram of an automatic concentration detection system in an embodiment of the present application.

Fig. 2 shows a waveform diagram corresponding to the characteristic parameter and concentration index in the present application.

FIG. 3A is a graph showing the labeling results after the concentration index is compared with the threshold.

FIG. 3B shows a labeled result graph resulting from eliminating the short non-attentive event of the two attentive events.

FIG. 3C shows a labeled result graph obtained for eliminating a short duration of attention event of two non-attention events.

Fig. 4 illustrates a flow chart of a method of automatic detection of concentration in some embodiments of the present application.

Description of the main elements

Electronic device 1

Wearable measuring device 2

Metrology module 210

Transmission module 211

Communication unit 11

Display unit 12

Concentration automatic detection system 10

Signal receiving module 101

Signal processing module 102

Eigenvalue calculation module 103

Special attention index operation module 104

Judging module 105

Annotating module 106

The following detailed description will further illustrate the invention in conjunction with the above-described figures.

Detailed Description

Fig. 1 is a block diagram of an automatic concentration detection system according to an embodiment of the present invention.

The automatic concentration detection system provided in the embodiment of the present invention is applied to an electronic device 1, where the electronic device 1 includes, but is not limited to, a memory, at least one processor, a communication unit 11, and a display unit 12. The memory stores the automatic concentration detection system 10 and associated information that can be run on the processor.

In the present embodiment, the electronic device 1 further includes other hardware, for example, the electronic device 1 may further include a chip set, a sensing device, and the like, and since other hardware of the electronic device 1 is part of the conventional technology, a description thereof is omitted here.

The communication unit 11 may be an electronic module containing the necessary hardware, software, or firmware configured to establish data exchange with other communication devices via a communication network. For example, in some embodiments, the communication unit 11 may establish a communication connection with the wearable measuring device 2, and receive brain wave signals of the user from the wearable measuring device 2. In other embodiments, the communication unit 11 may also establish a connection with a server, from which the brain wave signals of the user are acquired. The communication network may include at least one of a wired network and a wireless network. The wireless network and the wired network may be any networks existing in the prior art and appearing in the future suitable for the electronic device 1 to communicate with the wearable measurement device 2.

The display unit 12 is used for displaying the electroencephalogram signal and/or the automatic detection result of concentration after labeling. In the present embodiment, the Display unit 12 may have a touch function, such as a Liquid Crystal Display (LCD) Display screen or an Organic Light-Emitting Diode (OLED) Display screen.

The memory stores the concentration auto-detection system 10 and various data, such as physiological parameter data. The Memory may be, but is not limited to, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), and the like.

In one embodiment, the electronic device 1 further includes a training database (not shown) disposed in the memory. Wherein the training database is used for receiving and storing brain wave signals transmitted from the wearable measuring device 2.

The brain wave signal may be acquired in the following manner. The training database may store brain wave signals collected by the wearable measuring device 2 at the time of the tester's standardized concentration testing event. For example, the wearable measurement device 2 collects brain wave signals of 20 testers in total during a standardized concentration experiment. The attention-specific experimental events include reading, reading aloud, digital Russian dice games, shooting games, driving, event interruption, sitting still, eyes closed, spirit raising, Go/Nogo tests of children examination tests, and the like. During the experiment event program, the tester can record the face, eyes and field environment information of the tester by synchronously recording through a plurality of lenses. In addition, in order to confirm the reproducibility of the experimental event and the frequency band signal correlation corresponding to the brain wave signal, a plurality of experimental events of the same type can be performed on the testers, and the brain wave signals of the testers during each experimental event can be respectively collected.

The processor may be an electronic module, such as a server, that includes one or more of hardware, software, or firmware. The servers may be in a centralized configuration or in a distributed cluster arrangement. In other embodiments, the Processor may be a single computer or a Central Processing Unit (CPU) in the computer, or a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or the like.

The processor implements the method steps in the attention detection method embodiments described below by operating or executing the attention detection system 10 stored in the memory.

In the present embodiment, the electronic device 1 may be a handheld computing device such as a tablet computer, a smart phone, and a notebook computer.

In this embodiment, the wearable measurement device 2 includes a memory and a processor. The memory includes a measurement module 210 and a transmission module 211. The measurement module 210 is used for measuring physiological information of the user, for example, brain wave information. The metrology module 210 may include six-axis or nine-axis acceleration, a gyroscope, a geomagnetic sensor, and a galvanic detector, which may measure a reference signal. The transmission module 211 is configured to transmit the measured physiological information to the electronic device 1. The transmission module 211 may transmit the physiological information through a wireless network (e.g., bluetooth, WI-FI) or a wired network.

In some embodiments, the concentration auto-detection system 10 operates in the electronic device 1. The automatic concentration detection system 10 may include a plurality of functional modules composed of program code segments. The program codes of the various program segments of the attention detection system 10 may be stored in the memory of the electronic device 1 and executed by at least one processor to implement the attention detection and/or labeling functions.

In this embodiment, the attention-focusing automatic detection system 10 can be divided into a plurality of functional modules according to the functions performed by the system. The functional module may include: a signal receiving module 101, a signal processing module 102, a feature value operation module 103, a concentration index operation module 104, a judgment module 105, and a labeling module 106.

The signal receiving module 101 is configured to receive brain wave signals of a user.

In this embodiment, the electroencephalogram signal is detected by the wearable measurement device 2 and then transmitted to the electronic device 1, and the signal receiving module 101 receives the electroencephalogram signal of the user. The wearable measuring device 2 may be a brain wave-traceable smart headband made of an elastic material and capable of contacting the head of the user. For example, the smart headband is worn on the forehead to behind the ears. The intelligent headband is provided with seven electrodes which are a first electrode, a second electrode, a third electrode, a fourth electrode, a fifth electrode, a sixth electrode and a seventh electrode respectively. The first and seventh electrodes are in contact with the upper left and right ears of the user, respectively, and the second, third, fourth, fifth, and sixth electrodes may be in contact with the forehead of the user. The third electrode, the fourth electrode and the fifth electrode positioned in the middle are reference electrodes, and the first electrode, the second electrode, the sixth point electrode and the seventh electrode are measuring electrodes and used for measuring brain wave signals of a user.

In some embodiments, the wearable measuring device 2 can periodically capture the brain wave signals of the user during a time interval. For example, the wearable measurement device 2 may capture the brain waves of the user once per second, each for 10 seconds. However, it should be noted that the time interval and the brain wave acquisition frequency in the embodiment are only one implementation manner, and the invention is not limited thereto.

The frequency of the brain wave signals is subdivided into the following according to the classification of electroencephalographs and the international union of clinical physiology society: delta waves (0.5-4 Hz), theta waves (4-7 Hz), alpha waves (8-13 Hz), beta waves (14-30 Hz), and gamma waves (30 Hz-100 + Hz), wherein the alpha waves represent the most clear, quiet, stable and concentrated state of a person; the beta wave represents the emotional involvement working state and is the brain wave in high thinking such as tension, anxiety or excitement, uneasiness and the like; theta waves (4-7 Hz) represent that a person is in a light sleep and doze state, and the theta waves rarely appear in a normal adult in an awake state; the delta wave represents the sleep state of a person, with normal adults without delta waves in the awake state; gamma waves are associated with consciousness perception.

In this embodiment, the wearable measuring device 2 may further output a reference signal, where the reference signal includes acceleration, contact quality, forehead touching, signal quality, chewing characteristic brain wave, blinking characteristic brain wave, battery power, concentration index, and relaxation index.

When electroencephalogram is collected, the wearable measuring device 2 is in contact with the forehead of a user, the eyes are close to the forehead, and cornea or eyelid muscles can carry a trace amount of charges when moving. Therefore, in the brain wave acquisition process, the blink characteristic brain waves caused by blinking are often acquired at the same time. Also, since the wearable measuring device 2 is in contact with the upper part of the user's ear, the mastication characteristic brain waves caused by mastication are collected during the brain wave collection process due to the muscular movement of the user during mastication.

In this embodiment, the wearable measuring device 2 transmits the electroencephalogram signal and the reference signal to the electronic device 1 through bluetooth or other wireless network transmission methods.

The signal processing module 102 is configured to perform preprocessing on the brain wave signal, where the preprocessing includes windowing, fast fourier transform, normalization, and filtering. In order to make the extracted frequency band signal more reliable, the brain wave signal is preprocessed after being received. The preprocessing mainly comprises windowing, fast Fourier transformation, normalization and filtering.

(1) Windowing treatment: before the fast fourier transform is performed on the brain wave signal, the brain wave signal needs to be divided into a plurality of data segments through a windowing process. And the windowing processing is to perform windowing function processing on the brain wave signals to obtain a plurality of data segments. In the present embodiment, the brain wave signal may be processed by using a Hamming window as an observation window.

(2) Fast Fourier transform: and performing fast Fourier transform on the data in each window to obtain a frequency domain brain wave signal, and calculating the power spectrum of the brain wave signal to obtain the energy distribution of the brain wave signal.

(3) Normalization: in the present embodiment, normalization processing is performed on the electroencephalogram signals of the respective frequency bands by dividing the sum of the electroencephalogram signal energies of the different characteristic wave bands by the sum of the electroencephalogram signal energies of all of the electroencephalogram signals within the sampling frequency. Because the brain wave signal intensity of users of different age groups is not consistent, even the brain wave signals collected by the same user in different time periods are not consistent. Therefore, the brain wave signals are processed by normalization so that the brain wave signals of all users are within a percentage range. Thereby being convenient for the automatic concentration marking system is suitable for users of different age groups.

(4) Filtering: and filtering the normalized brain wave signals. The filtering comprises filtering the normalized brain wave signals through a band-pass filter and a wave trap. For example, the delta wave, the theta wave, the alpha wave, the beta wave and the gamma wave in the brain wave signal are retained by band-pass filtering, signals of other frequency bands are removed, and then the interference signals in the delta wave, the theta wave, the alpha wave, the beta wave and the gamma wave are removed by an averaging filter (for example).

It is understood that, in other embodiments, the above-mentioned preprocessing of the brain wave signals may be performed in the wearable measuring device 2, and the wearable measuring device 2 then transmits the preprocessed brain wave signals to the electronic device 1.

The feature value operation module 103 is configured to perform feature value operation on the preprocessed brain wave signals to obtain feature parameters.

In this embodiment, the characteristic parameters include energy of each frequency band signal in the brain wave signal, a frequency band signal ratio, and a fractal dimension of the brain wave signal.

In this embodiment, the frequency band signal ratio includes a ratio β/θ between a β wave and a θ wave, a ratio γ/α between a γ wave and an α wave, and a ratio β/α between a β wave and an α wave.

The fractal dimension of the brain wave signal can be calculated by a method for calculating the fractal dimension for waveform data, which is proposed by Katz. The specific calculation method is the prior art and is not described herein again.

It is understood that the method for calculating the fractal dimension of the brain wave signal may also be arc theory Curve-length (cl) combined with Line length theory Line-length (ll) calculation method proposed by Estellerl et al. Although the logarithmic relationship employed by the calculation method provided by Katz may provide more significant calculation results that are easier to distinguish between events. To reduce the high power consumption operation of the electronic device 1 and speed up the identification speed, Esteller et al further adopts an algorithm of removing the simplified logarithmic relationship. In the present embodiment, the fractal dimension cl (n) of the brain wave signal is calculated using the calculation method proposed by Esteller. The calculation method proposed by Esteller et al is also prior art and will not be described herein.

In the present embodiment, it is necessary to perform windowing processing and filtering processing again on the waveform corresponding to the characteristic parameter. The windowing process is consistent with the windowing process when the brain wave signal is preprocessed. Wherein the size of the time window and the repetition rate in the windowing process are adjusted according to the balance of the collected training database and the recognition success rate. Although the effect of short-time noise can be further reduced by using a long time window, the resolution of short-time events may be lost. For example, a 10 second rectangular window is used after test adjustment with test data from five different testers. And the filtering process can adopt a moving average filter, and the moving average filter can effectively reduce the interference of outliers and noise on the general trend of the waveform corresponding to the characteristic parameters.

In this embodiment, in order to visually compare the curve changes of the signals of the brain waves in each frequency band and each characteristic value, so as to check the relevance between the signals of the brain waves in each frequency band and each characteristic value and an event to be detected, after the characteristic value operation is performed on the preprocessed brain wave signals to obtain the characteristic parameters, the characteristic parameters are graphically displayed. In the present embodiment, the electroencephalogram signals and the characteristic values are graphically displayed by using the Matlab program of MathWorks corporation, and the result is shown in fig. 2. Fig. 2 shows δ waves, θ waves, α waves, β waves, γ waves, and energy curves thereof, respectively. For example, Gamma, Beta, Alpha, Theta, and Delta in FIG. 2 describe the Gamma, Beta, Alpha, Theta, and Delta waves, respectively. Gamma abs in fig. 2 describes the energy curve of the gamma wave, BetaAbs describes the energy curve of the beta wave, AlphaAbs describes the energy curve of the alpha wave, and theta-wave. BetaAbs describes the energy curve of the beta wave, AlphaAbs describes the energy curve of the alpha wave, and theta-wave. The frequency band signal ratio curve is also shown in fig. 2. For example, in fig. 2, B/T represents a ratio β/θ between the β wave and the θ wave, G/a represents a ratio γ/α between the γ wave and the α wave, and B/a represents a ratio β/α between the β wave and the α wave.

The concentration index operation module 104 is configured to perform concentration index operation by using the characteristic parameters to obtain an operation result.

In the present embodiment, the characteristic parameters are subjected to attention-directed index operation c (n) by the following formula:

wherein, C_ch(n) is the attention index, θ_ch(t)、α_ch(t)、β_ch(t) and γ_ch(t) separately determining the energy of each frequency band signal in the electroencephalogram signal,toAs weight value of each characteristic parameter, CL_chAnd (n) is a fractal dimension of the brain wave signal.

It should be noted that, when detecting whether the user is continuously attentive, the user's original attentive state fluctuation data needs to be removed. Different types of brain wave data can be derived when different users execute different events, the brain partition application modes of the users are different, and the acquired brain wave data can be different. In order to achieve the calculation of the attention index, it is necessary to combine several different dominant frequency bands of brain wave signals and several different derived brain wave signals. Therefore, the optimal feature parameter combination and the weight coefficient of each feature parameter can be found out through experiments and by an iterative method of machine learning based on the experimental data. For example, the characteristic parameters adopted in the scheme are delta waves, theta waves, alpha waves, beta waves, and frequency band signal ratios beta/theta, gamma/alpha and beta/alpha.

In this embodiment, the advantage of listing three combinations of the frequency band signal ratios β/θ, γ/α, β/α, etc. is that the user can monitor the fading of the dominant frequency bands of the two frequency bands in a unified event at the same time for each combination. For example, the loss of the dominant band conversion in the process of carrying out the memory and decision event test by the user is collected. It should be noted that, the frequency band signal ratios β/θ, γ/α, β/α are set to have an upper limit value to avoid forming outliers due to too high exponents.

The judging module 105 is configured to judge whether the user focuses on the computing result.

In the present embodiment, whether the user is attentive is determined by comparing the calculation result with a threshold value. Confirming that the user is attentive when the operation result is greater than or equal to the threshold value; and when the operation result is smaller than the threshold value, confirming that the user is not attentive.

In another embodiment, the classification result may be obtained by inputting the feature parameters to a classifier. The classifier may include a pre-established concentration determination model. For example, a continuous concentration judgment model and a multiple event concentration judgment model. It is to be understood that the concentration determination model may also include other models.

The concentration judgment model can output a corresponding judgment result according to the input characteristic parameters. For example, when the concentration determination model is a continuous concentration determination model, the classifier may output a result of continuous concentration of the user or a result of continuous non-concentration of the user according to the input feature parameters. When the attention-focused judgment model is a multi-event attention-focused judgment model, the classifier can output a result focused by a user on multiple events according to the input characteristic parameters or output a result focused by the user on multiple events.

In the present embodiment, the concentration determination model is previously established from the collected electroencephalogram signals of the user.

The labeling module 106 is configured to label the concentration result of the user.

In this embodiment, the concentration result of the user is labeled as a time series x [ n ] composed of 0 and 1, and refer to fig. 3A, which is a schematic diagram of the labeled concentration result. When the operation result is greater than or equal to the threshold value, confirming that the user is attentive, and marking a signal in a corresponding time period as 1; and when the operation result is smaller than the threshold value, confirming that the user is not attentive, and marking the signal in the corresponding time period as 0. For example, score TH shown in fig. 2 indicates the threshold, and conc indicates the operation result curve. As shown in FIG. 2, the threshold score TH is larger than the operation result conc within the time interval range of 0-190 s. Therefore, the annotation result of 0 in the time interval of 0 to 190s shown in fig. 3A can be obtained. As shown in fig. 2, the threshold scoreTH is smaller than the operation result conc within the time interval range of 190s to 250 s. Therefore, a labeling result of 1 in the time interval range of 190s to 250s shown in FIG. 3A can be obtained. As shown in fig. 2, the threshold score TH is greater than the operation result conc in a time interval range of 250s to 260 s. Therefore, it is possible to obtain a result of 0 labeling within the time interval shown by 250s to 260s in FIG. 3A. As shown in fig. 2, the threshold score TH is smaller than the operation result conc in the time interval range of 260s to 271 s. Therefore, the result of the annotation in the time interval range of 260s to 271s in FIG. 3A is 1. As in fig. 2, at 272s, the threshold score TH is greater than the operation result conc. Thus, a result of 0 is obtained at 272s in FIG. 3A. As shown in fig. 2, the threshold score TH is smaller than the operation result conc within a time interval range of 272s to 435 s. Therefore, the result of the annotation in the time interval range of 260s to 271s in FIG. 3A is 1. By analogy, the concentration result of the user can be labeled by the threshold and the operation result curve in fig. 2, and the graph shown in fig. 3A is obtained.

It should be noted that, in order to ensure continuity of the cross-threshold event in the graph shown in fig. 3A, the transient fluctuation in the event is further eliminated by an adjustable post-processing fluctuation time tolerance threshold. For example, for a short-time non-concentration event of two adjacent concentration events, the labeling results in the time interval range shown in 250s to 260s and 272s in fig. 3A are both 0, and since the time interval range is smaller than the fluctuation time tolerance threshold, the labeling result in the time interval range can be set to 1, and the labeling result shown in fig. 3B can be obtained. For the short-time attention event of the two adjacent non-attention events, as shown in fig. 3A at 1030s to 1045s, the labeling result of the time interval range is 1, and since the time interval range is smaller than the allowable fluctuation time threshold, the labeling result of the time interval range may be set to 0, and the labeling result shown in fig. 3C is obtained. That is, for any continuous sequence, the correlation between all sampling points is compared, and first, for the short-time non-attention event of two adjacent attention events, the non-attention event smaller than the allowable threshold of fluctuation time is changed to the attention state, and the electroencephalogram signal corresponding to the short-time non-attention event is labeled as 1. Determining the non-focused sequence of events by:

wherein x is_d[n]T is a fluctuation time tolerance threshold value for a short non-concentration event in two adjacent concentration events.

And for a short-time concentration event in two adjacent non-concentration events, the concentration event smaller than the fluctuation time allowable threshold is changed into a non-concentration state, and the short-time concentration event is marked as 0. The concentration event sequence is determined by the following formula:

wherein x is_c[n]For the short non-attentive event of two adjacent attentive events, T is the fluctuation timeA threshold is allowed.

Please refer to fig. 4, which is a flowchart illustrating an attention automatic detection method according to some embodiments of the present invention. The order of the steps in the flow chart may be changed and some steps may be omitted according to different needs. For convenience of explanation, only portions related to the embodiments of the present invention are shown.

Step S41, receiving a brain wave signal of the user.

In the present embodiment, the electroencephalogram signal is detected by the wearable measurement apparatus 2 and then transmitted to the electronic apparatus 1. The wearable measuring device 2 may be a brain wave-traceable smart headband made of an elastic material and capable of contacting the head of the user. For example, the smart headband is worn on the forehead to behind the ears. The intelligent headband is provided with seven electrodes which are a first electrode, a second electrode, a third electrode, a fourth electrode, a fifth electrode, a sixth electrode and a seventh electrode respectively. The first and seventh electrodes are in contact with the upper left and right ears of the user, respectively, and the second, third, fourth, fifth, and sixth electrodes may be in contact with the forehead of the user. The third electrode, the fourth electrode and the fifth electrode positioned in the middle are reference electrodes, and the first electrode, the second electrode, the sixth point electrode and the seventh electrode are measuring electrodes and used for measuring brain wave signals of a user.

In some embodiments, the wearable measuring device 2 can periodically capture the brain wave signals of the user during a time interval. For example, the wearable measurement device 2 may capture the brain waves of the user once per second, each for 10 seconds. However, it should be noted that the time interval and the brain wave acquisition frequency in the embodiment are only one implementation manner, and the invention is not limited thereto.

When electroencephalogram is collected, the wearable measuring device 2 is in contact with the forehead of a user, the eyes are close to the forehead, and cornea or eyelid muscles can carry a trace amount of charges when moving. Therefore, in the brain wave acquisition process, the blink characteristic brain waves caused by blinking are often acquired at the same time. Also, since the wearable measuring device 2 is in contact with the upper part of the user's ear, the mastication characteristic brain waves caused by mastication are collected during the brain wave collection process due to the muscular movement of the user during mastication.

In this embodiment, the wearable measuring device 2 transmits the electroencephalogram signal and the reference signal to the electronic device 1 through bluetooth or other wireless network transmission methods.

And step S42, preprocessing the brain wave signals, wherein the preprocessing comprises windowing processing, fast Fourier transformation, normalization and filtering.

Because the collected brain wave signals are weak, the signals are easily interfered by other signals in the environment. In order to make the extracted frequency band signal more reliable, the brain wave signal is preprocessed after being received. The preprocessing mainly comprises windowing, fast Fourier transformation, normalization and filtering.

(1) Windowing treatment: before the fast fourier transform is performed on the brain wave signal, the brain wave signal needs to be divided into a plurality of data segments through a windowing process. And the windowing processing is to perform windowing function processing on the brain wave signals to obtain a plurality of data segments. In the present embodiment, the brain wave signal may be processed by using a Hamming window as an observation window.

(2) Fast Fourier transform: and performing fast Fourier transform on the data in each window to obtain a frequency domain brain wave signal, and calculating the power spectrum of the brain wave signal to obtain the energy distribution of the brain wave signal.

(3) Normalization: in the present embodiment, normalization processing is performed on the electroencephalogram signals of the respective frequency bands by dividing the sum of the electroencephalogram signal energies of the different characteristic wave bands by the sum of the electroencephalogram signal energies of all of the electroencephalogram signals within the sampling frequency. Because the brain wave signal intensity of users of different age groups is not consistent, even the brain wave signals collected by the same user in different time periods are not consistent. Therefore, the brain wave signals are processed by normalization so that the brain wave signals of all users are within a percentage range. Thereby being convenient for the automatic concentration marking system is suitable for users of different age groups.

(4) Filtering: and filtering the normalized brain wave signals. The filtering comprises filtering the normalized brain wave signals through a band-pass filter and a wave trap. For example, the delta wave, the theta wave, the alpha wave, the beta wave and the gamma wave in the brain wave signals are retained by band-pass filtering, signals of other frequency bands are removed, and then the interference signals in the delta wave, the theta wave, the alpha wave, the beta wave and the gamma wave are removed by the mean value filter.

It is understood that, in other embodiments, the above-mentioned preprocessing of the brain wave signals may be performed in the wearable measuring device 2, and the wearable measuring device 2 then transmits the preprocessed brain wave signals to the electronic device 1.

And step S43, performing characteristic value operation on the preprocessed brain wave signals to obtain characteristic parameters.

In this embodiment, the characteristic parameters are energy of each frequency band signal in the electroencephalogram signal, a ratio of the frequency band signals, and a fractal dimension of the electroencephalogram signal.

In this embodiment, the frequency band signal ratio includes a ratio β/θ between a β wave and a θ wave, a ratio γ/α between a γ wave and an α wave, and a ratio β/α between a β wave and an α wave.

The fractal dimension of the brain wave signal can be calculated by a method for calculating the fractal dimension for waveform data, which is proposed by Katz. The specific calculation method is the prior art and is not described herein again.

In the present embodiment, it is necessary to perform windowing processing and filtering processing again on the waveform corresponding to the characteristic parameter. The windowing process is consistent with the windowing process when the brain wave signal is preprocessed. Wherein the size of the time window and the repetition rate in the windowing process are adjusted according to the balance of the collected training database and the recognition success rate. Although the effect of short-time noise can be further reduced by using a long time window, the resolution of short-time events may be lost. For example, a 10 second rectangular window is used after test adjustment with test data from five different testers. And the filtering process can adopt a moving average filter, and the moving average filter can effectively reduce the interference of outliers and noise on the general trend of the waveform corresponding to the characteristic parameters.

In this embodiment, in order to visually compare the curve changes of the signals of the brain waves in each frequency band and each characteristic value, so as to check the relevance between the signals of the brain waves in each frequency band and each characteristic value and an event to be detected, after the characteristic value operation is performed on the preprocessed brain wave signals to obtain the characteristic parameters, the characteristic parameters are graphically displayed.

And step S44, performing concentration index operation by using the characteristic parameters to obtain an operation result.

In the present embodiment, the characteristic parameters are subjected to attention-directed index operation c (n) by the following formula:

wherein, C_ch(n) is the attention index, θ_ch(t)、α_ch(t)、β_ch(t) and γ_ch(t) separately determining the energy of each frequency band signal in the electroencephalogram signal,toAs weight value of each characteristic parameter, CL_chAnd (n) is a fractal dimension of the brain wave signal.

It should be noted that, when detecting whether the user is continuously attentive, the user's original attentive state fluctuation data needs to be removed. Different types of brain wave data can be derived when different users execute different events, the brain partition application modes of the users are different, and the acquired brain wave data can be different. In order to achieve the calculation of the attention index, it is necessary to combine several different dominant frequency bands of brain wave signals and several different derived brain wave signals. Therefore, the optimal feature parameter combination and the weight coefficient of each feature parameter can be found out through experiments and by an iterative method of machine learning based on the experimental data. For example, the characteristic parameters adopted in the scheme are delta waves, theta waves, alpha waves, beta waves, and frequency band signal ratios beta/theta, gamma/alpha and beta/alpha.

In this embodiment, the advantage of listing three combinations of the frequency band signal ratios β/θ, γ/α, β/α, etc. is that the user can monitor the fading of the dominant frequency bands of the two frequency bands in a unified event at the same time for each combination. For example, the loss of the dominant band conversion in the process of carrying out the memory and decision event test by the user is collected. It should be noted that, the frequency band signal ratios β/θ, γ/α, β/α are set to have an upper limit value to avoid forming outliers due to too high exponents.

And step S45, judging whether the user is attentive or not according to the operation result.

In the present embodiment, whether the user is attentive is determined by comparing the calculation result with a threshold value. Confirming that the user is attentive when the operation result is greater than or equal to the threshold value; and when the operation result is smaller than the threshold value, confirming that the user is not attentive.

In another embodiment, the classification result may be obtained by inputting the feature parameters to a classifier. The classifier may include a pre-established concentration determination model. For example, a continuous concentration judgment model and a multiple event concentration judgment model. It is to be understood that the concentration determination model may also include other models.

In the present embodiment, the concentration determination model is previously established from the collected electroencephalogram signals of the user.

And step S46, marking the concentration result of the user.

In the present embodiment, the collected electroencephalogram signals of the user are analyzed to determine whether the user is attentive through the above-described steps S41 to S46, and the attention result is labeled on the electroencephalogram signals displayed graphically. The user can conveniently have objective progress indexes when performing the autonomous training and the objective progress indexes are used as reference basis for adjusting the training content. The state can be absorbed in but there is the restraining power to transient state fluctuation ability to effective the detection to avoid the influence of frequent interference, can provide continuity event special attention and judge accurately and stably.