WO2019214445A1 - 提高注意力的教学方法、装置及计算机可读存储介质 - Google Patents

提高注意力的教学方法、装置及计算机可读存储介质 Download PDF

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Publication number
WO2019214445A1
WO2019214445A1 PCT/CN2019/084247 CN2019084247W WO2019214445A1 WO 2019214445 A1 WO2019214445 A1 WO 2019214445A1 CN 2019084247 W CN2019084247 W CN 2019084247W WO 2019214445 A1 WO2019214445 A1 WO 2019214445A1
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Prior art keywords
attention
wave
user
training
teaching
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PCT/CN2019/084247
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English (en)
French (fr)
Inventor
韩璧丞
郑辉
阿迪斯
孙东圣
杨钊祎
于翔
孙越
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深圳市心流科技有限公司
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Publication of WO2019214445A1 publication Critical patent/WO2019214445A1/zh
Priority to US17/036,084 priority Critical patent/US20210012675A1/en

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/70—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A61B5/168—Evaluating attention deficit, hyperactivity
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/372—Analysis of electroencephalograms
    • A61B5/374—Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
    • G06F3/015—Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/017—Gesture based interaction, e.g. based on a set of recognized hand gestures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/03—Arrangements for converting the position or the displacement of a member into a coded form
    • G06F3/033—Pointing devices displaced or positioned by the user, e.g. mice, trackballs, pens or joysticks; Accessories therefor
    • G06F3/0346—Pointing devices displaced or positioned by the user, e.g. mice, trackballs, pens or joysticks; Accessories therefor with detection of the device orientation or free movement in a 3D space, e.g. 3D mice, 6-DOF [six degrees of freedom] pointers using gyroscopes, accelerometers or tilt-sensors
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B5/00—Electrically-operated educational appliances
    • G09B5/02—Electrically-operated educational appliances with visual presentation of the material to be studied, e.g. using film strip
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H15/00—ICT specially adapted for medical reports, e.g. generation or transmission thereof
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2503/00—Evaluating a particular growth phase or type of persons or animals
    • A61B2503/12—Healthy persons not otherwise provided for, e.g. subjects of a marketing survey
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/08—Feature extraction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/12—Classification; Matching

Definitions

  • the present application relates to the field of educational information technology, and in particular, to a teaching method, apparatus, and computer readable storage medium for improving attention.
  • Electroencephalograph along with our life, is the overall response of the spontaneous and rhythmic electrical activity of the brain cell population in the cerebral cortex and scalp. It can be detected by electrodes placed on the scalp. EEG is different. The frequency can be divided into four rhythmic waves of ⁇ , ⁇ , ⁇ , and ⁇ . Many foreign researchers and experts have found through a lot of experiments that the alpha band in human brain waves is the main activity frequency in quiet and awakened state.
  • the teaching system only includes playing rich media courseware, or monitoring students' attention level. It does not improve the attention level of students while training students to improve their attention in the classroom. Therefore, the current teaching system can not improve students' attention.
  • the main purpose of the present application is to provide a teaching method, apparatus and computer readable storage medium for improving attention, aiming at solving the problem that the current teaching system cannot improve students' attention.
  • the present application provides a teaching method for improving attention, the method comprising the following steps:
  • the step of acquiring an EEG feature during user training, outputting and displaying a corresponding animation effect based on the EEG feature to adjust a user's attention level includes:
  • the EEG features are analyzed, and the EEG features are scored according to pre-designed rules;
  • the step of analyzing the EEG feature and scoring the EEG feature according to the pre-designed sub-rule includes:
  • the EEG features are scored based on the calculation results and the pre-designed sub-rules.
  • the teaching method for improving attention further includes:
  • Filtering is performed on the data to be filtered based on a second preset function, wherein the filter is used to remove low frequency, high frequency, and power frequency interference noise, and separate rhythm waves of respective frequency bands.
  • the teaching method for improving attention further includes:
  • the step of switching to the training mode includes:
  • the prompt to switch to the training mode is sent to the management terminal.
  • the step of switching to the training mode further includes:
  • the teaching method for improving attention further includes:
  • the classification stores the EEG feature, the attention value, and the training result when the user is training
  • the EEG feature, the attention value, and the training result are compression-encrypted, and an attention analysis report is generated.
  • the present application further provides an instruction device for improving attention
  • a teaching device for improving attention includes: a memory, a processor, and an improvement stored on the memory and operable on the processor
  • a teaching program of attention the step of implementing the above-described teaching method for improving attention when the instructional program for improving attention is executed by the processor.
  • the present application further provides a computer readable storage medium, wherein the computer readable storage medium stores an instruction program for improving attention, and the instruction program for improving attention is implemented by a processor. Any of the steps of any of the above teaching methods for improving attention.
  • the present application obtains the brain wave data collected by the EEG collecting device, calculates the attention value based on the brain wave data, and then switches to the training mode if the attention value is less than the first preset threshold, and finally acquires the user.
  • the EEG feature during training is based on the EEG feature output and displays the corresponding animation effect to adjust the user's attention; thus, the user's attention is monitored while monitoring the user's attention, thereby improving the user's attention. force.
  • FIG. 1 is a schematic structural diagram of a terminal to which a teaching apparatus for improving attention is in a hardware operating environment according to an embodiment of the present application;
  • FIG. 2 is a schematic flow chart of a first embodiment of a teaching method for improving attention according to the present application
  • FIG. 3 is a schematic diagram of the method for improving attention in the second embodiment of the present application, in which the EEG feature is acquired during user training, and the corresponding animation effect is output based on the EEG feature to adjust the attention level of the user. Schematic diagram of the process;
  • FIG. 4 is a schematic diagram showing a refinement process of the step of scoring the electroencephalographic features according to the pre-designed sub-rule according to the third embodiment of the present teaching method for improving attention;
  • FIG. 5 is a schematic flowchart diagram of a fourth embodiment of a teaching method for improving attention according to the present application.
  • FIG. 6 is a schematic flow chart of a fifth embodiment of a teaching method for improving attention according to the present application.
  • FIG. 7 is a schematic flowchart of a step of switching to a training mode according to a seventh embodiment of the teaching method for improving attention according to the seventh embodiment of the present invention
  • FIG. 8 is a schematic flow chart of an eighth embodiment of a teaching method for improving attention according to the present application.
  • FIG. 1 is a schematic structural diagram of a terminal to which a device belongs in a hardware operating environment according to an embodiment of the present application.
  • the terminal in this embodiment of the present application may be a PC.
  • the terminal may include a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002.
  • the communication bus 1002 is used to implement connection communication between these components.
  • the user interface 1003 can include a display, an input unit such as a keyboard, and the optional user interface 1003 can also include a standard wired interface, a wireless interface.
  • the network interface 1004 can optionally include a standard wired interface, a wireless interface (such as a WI-FI interface).
  • the memory 1005 may be a high speed RAM memory or a stable memory (non-volatile) Memory), such as disk storage.
  • the memory 1005 can also optionally be a storage device independent of the aforementioned processor 1001.
  • the terminal may further include a camera, RF (Radio) Frequency, RF) circuits, sensors, audio circuits, WiFi modules, and more.
  • sensors such as light sensors, motion sensors, and other sensors.
  • the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display according to the brightness of the ambient light, and the proximity sensor may turn off the display and/or when the mobile terminal moves to the ear.
  • the gravity acceleration sensor can detect the magnitude of the acceleration in the direction (usually three axes). When it is stationary, it can detect the magnitude and direction of gravity.
  • the mobile terminal can be used to identify the attitude of the mobile terminal (such as horizontal and vertical screen switching, related Game, magnetometer attitude calibration), vibration recognition related functions (such as pedometer, tapping), etc.; of course, the mobile terminal can also be equipped with other sensors such as gyroscope, barometer, hygrometer, thermometer, infrared sensor, etc. Let me repeat.
  • terminal structure shown in FIG. 1 does not constitute a limitation to the terminal, and may include more or less components than those illustrated, or a combination of certain components, or different component arrangements.
  • an operation server may be included in the memory 1005 as a computer storage medium.
  • the network interface 1004 is mainly used to connect to the background server and perform data communication with the background server;
  • the user interface 1003 is mainly used to connect the client (user end), and perform data communication with the client;
  • the processor 1001 can be used to call a program stored in the memory 1005.
  • the device includes: a memory 1005, a processor 1001, and a program stored on the memory 1005 and executable on the processor 1001, wherein when the processor 1001 calls a program stored in the memory 1005, Do the following:
  • processor 1001 can call the attention-enhancing teaching program stored in the memory 1005, and also perform the following operations:
  • the EEG features are analyzed, and the EEG features are scored according to pre-designed rules;
  • processor 1001 can call the attention-enhancing teaching program stored in the memory 1005, and also perform the following operations:
  • the EEG features are scored based on the calculation results and the pre-designed sub-rules.
  • processor 1001 can call the attention-enhancing teaching program stored in the memory 1005, and also perform the following operations:
  • Filtering is performed on the data to be filtered based on a second preset function, wherein the filter is used to remove low frequency, high frequency, and power frequency interference noise, and separate rhythm waves of respective frequency bands.
  • processor 1001 can call the attention-enhancing teaching program stored in the memory 1005, and also perform the following operations:
  • processor 1001 can call the attention-enhancing teaching program stored in the memory 1005, and also perform the following operations:
  • the prompt to switch to the training mode is sent to the management terminal.
  • processor 1001 can call the attention-enhancing teaching program stored in the memory 1005, and also perform the following operations:
  • processor 1001 can call the attention-enhancing teaching program stored in the memory 1005, and also perform the following operations:
  • the classification stores the EEG feature, the attention value, and the training result when the user is training
  • the EEG feature, the attention value, and the training result are compression-encrypted, and an attention analysis report is generated.
  • FIG. 2 is a schematic flowchart diagram of a first embodiment of teaching for improving attention of the present application.
  • the teaching of increasing attention includes the following steps:
  • Step S10 acquiring brain wave data collected by the brain electrical collection device and calculating an attention value based on the brain wave data
  • the brain electrical collection device includes an EEG (electroencephalo-graph)
  • EEG electroencephalo-graph
  • the head ring of the brain wave which can collect the brain wave data of the user in real time, and the brain wave data includes the values corresponding to the Alpha wave, the Beta wave, the Delta wave, the Gamma wave and the Theta wave, and the values of the different frequency waves can reflect the current human body.
  • the state of the brain for example, when people concentrate on learning and concentration, the brain frequency is in the Alpha wave (frequency range 8-13 Hz). At this time, the brain wave is relatively stable, which is the best brain wave state for people to learn and think.
  • Beta wave frequency range above 14Hz
  • the brain wave frequency becomes faster and the amplitude increases, and the appropriate Beta wave has a positive effect on the improvement of attention and the development of cognitive behavior. , but the duration is short and fatigue.
  • the brain frequency is in Theta wave.
  • the collected brain wave data is sent to the attention training system, for example, when the data acquisition frequency is set to 160 Hz, 80 original EEG data is sent to the attention training system as a data packet every 0.5 seconds, attention
  • the system will calculate the current attention value of the user based on the brain wave data, and the predicted attention value can be calculated by the machine learning training model, the attention value is sent to the display terminal, and the attention value is monitored in real time.
  • Step S20 if the attention value is less than the first preset threshold, switching to the training mode
  • the first preset threshold is set by a technician.
  • the attention system When in the normal teaching mode, the attention system will detect the user's attention value in the normal teaching mode, and when the detected attention value is less than the first When the threshold is preset, the training mode is switched. Further, if the user's attention value is detected to be lower than a certain lower limit value, it is determined that the user's attention is not concentrated, and if the user's attention value is detected. When the value is higher than a certain upper limit, it is determined that the user's attention is highly concentrated. If the user's attention value is detected at the lower limit value and the upper limit value, the user's attention is determined.
  • the attention value of each student is obtained, and the average attention value is calculated, and according to the average attention value, it is determined whether the attention of the entire class is concentrated, specifically, if the average attention is concerned When the force value is less than a certain preset threshold, it is determined that the class attention is not concentrated. If the average attention value is greater than a predetermined threshold, the class is determined to be concentrated, and when the average attention value is less than a certain value When the threshold is preset, the normal teaching system is switched to the training system and into the training mode.
  • Step S30 Acquire an EEG feature during user training, and output and display a corresponding animation effect based on the EEG feature to adjust user attention.
  • the brain electrical characteristics include delta, theta, alpha, beta, high-beta, The energy value of each frequency of gamma, the mean value of each frequency energy in the frequency domain, the standard deviation, the ratio of the energy of each frequency band, the product, and of course, the full frequency domain signal in the preset frequency range and the frequency domain characteristics of each frequency wave, For example, to obtain a full frequency domain signal below 80 Hz and the Theta wave, Frequency domain features of Alpha, Beta, Gamma, and Theta bands.
  • the frequency domain features include mean value, peak value, and standard deviation corresponding to the frequency wave.
  • the brain electrical characteristics and frequency domain characteristics of each frequency wave are analyzed by the machine learning training model, and the weights of the corresponding values of the EEG features of each frequency wave are determined. The EEG features are then scored according to the weights and scoring rules.
  • the user's attention can be trained through small games, pictures, and animation effects, such as game programs such as flower opening, leaf growth, and snorkeling.
  • the human brain generates a small amount of current during its operation.
  • the attention training system detects the current brainwave activity of the trainer and combines the actual situation of the brain to use the specified computer game to help people exercise in the weak areas of the brain.
  • the brain nerve in order to achieve the purpose of improving the attention of the brain, in the training process, the user's EEG features are scored, rewarding or punishing the user through the animation effect, thereby giving the user feedback and improving the user's attention level.
  • the teaching method for improving attention obtains the brain wave data collected by the brain electricity collecting device by the brain, and calculates the attention value based on the brain wave data, and then if the attention value is less than the first preset threshold, Then switching to the training mode, finally obtaining the EEG feature of the user training, outputting and displaying the corresponding animation effect based on the EEG feature to adjust the user's attention; achieving the attention of the user while monitoring the user's attention Training, which increases the user's attention.
  • step S30 includes:
  • Step S31 analyzing the EEG feature, and scoring the EEG feature according to a pre-designed sub-rule
  • analyzing the EEG feature includes calculating a mean value, a standard deviation, a ratio, and a product of the energy values corresponding to the Alpha wave, the Beta wave, the Delta wave, the Gamma wave, and the Theta wave in the frequency domain, and Calculate the energy value corresponding to each frequency wave as a percentage of the total energy.
  • the energy value corresponding to the full frequency domain signal may also be calculated.
  • the full frequency domain signal includes Alpha wave, Beta wave, Delta wave, Gamma wave, Theta wave and other frequency bands.
  • the signal for example, calculates the total energy value of the energy values corresponding to the Alpha wave, the Beta wave, the Delta wave, the Gamma wave, and the Theta wave, and then calculates the energy corresponding to the Alpha wave, the Beta wave, the Delta wave, the Gamma wave, and the Theta wave according to the total energy value.
  • the value is a percentage of the total energy value.
  • the pre-designed sub-rule is set by a technician, and the attention training system may store a score table corresponding to the score of the brain wave feature, and the score corresponding to the brain wave feature within the range may not be the same, for example, if the calculation is performed by Alpha
  • the respective energy values of the wave, Beta wave, Delta wave, Gamma wave, and Theta wave are percentages of the total energy value, and the calculated percentage is compared with the percentage of the energy value in the score ratio to determine the final score.
  • Step S32 comparing the score with a second preset threshold to obtain a comparison result
  • Step S33 loading a file corresponding to the animation effect based on the comparison result, and playing the file content.
  • the second preset threshold is set by a technician, and the score is compared with the second preset threshold. If the score is less than the second preset threshold, the file corresponding to the animation effect is loaded, and Playing the file content, for example, if the score is less than the second preset threshold, loading the file of the animation effect of the penalty theme, and playing the content corresponding to the file, if the score is greater than the second preset threshold, loading the animation of the bonus theme
  • the effect of the file, and play the corresponding content of the file of course, according to the EEG characteristics of the class, calculate the average value corresponding to the EEG feature, and then score according to the average.
  • the EEG feature is analyzed, and the EEG feature is scored according to a pre-designed sub-rule, and then the score is compared with a second preset threshold. To obtain a comparison result, finally loading a file corresponding to the animation effect based on the comparison result, and playing the file content; realizing the scoring of the EEG feature, and displaying the corresponding animation effect, thereby improving the student's attention .
  • step S31 includes:
  • Step S311 acquiring an Alpha wave, a Beta wave, a Delta wave, a Gamma wave, and a Theta wave corresponding to the EEG feature;
  • Step S312 calculating a mean value, a standard deviation, a ratio, and a product of the energy values corresponding to the Alpha wave, the Beta wave, the Delta wave, the Gamma wave, and the Theta wave in the frequency domain to obtain a calculation result;
  • Step S313 the EEG feature is scored based on the calculation result and the pre-designed sub-rule.
  • the brain electrical characteristics include delta, theta, alpha, beta, high-beta, gamma energy values, the mean value of each frequency energy in the frequency domain, the standard deviation, the ratio of the energy of each frequency band, and the product. You can choose the energy value of each frequency of delta, theta, alpha, beta, high-beta, gamma, the mean of each frequency energy in the frequency domain, the standard deviation, and the ratio of the energy of each frequency band as the brain d feature.
  • the percentage of the energy value corresponding to each frequency wave is also possible to calculate the percentage of the energy value corresponding to each frequency wave as a percentage of the total energy, for example, calculate the total energy value of the energy values corresponding to the Alpha wave, the Beta wave, the Delta wave, the Gamma wave, and the Theta wave, and then calculate the Alpha wave according to the total energy value, The energy values corresponding to each of the Beta wave, the Delta wave, the Gamma wave, and the Theta wave account for a percentage of the total energy value.
  • the Alpha wave, the Beta wave, the Delta wave, the gamma wave, and the Theta wave corresponding to the EEG feature are obtained, and then the Alpha wave, the Beta wave, the Delta wave, the Gamma wave, and the Theta wave are calculated.
  • a fourth embodiment of the teaching method for improving the attention of the present application is provided.
  • the method further includes:
  • Step S40 removing brainwave data center power, eye electricity, and random noise based on the first preset function to obtain data to be filtered;
  • the EEG signal is a highly random electrophysiological signal, and various emotions and mental states affect it. Variety. Therefore, EEG signals have high time-varying sensitivity and are easily contaminated by irrelevant noise, thus forming various EEG artifacts. The most influential ones are ECG and EEG artifacts, and EMG caused by blinking. Signal interference, potential changes due to friction between the head ring and the skin. The main characteristics of these noises are: special peaks in the frequency domain signal and the frequency division signal. The main function of the data preprocessing module is to detect these noises and reject them. The data cleaning includes three parts, IMU motion processing, blink detection, and peak compression.
  • the IMU data is headband physical motion data collected by the head loop built-in module, and the data includes three headbands in the three-dimensional space at the time point. Acceleration on the coordinate axis, when the acceleration is greater than a certain threshold, it is judged that the data of the time is not credible, for the untrusted data segment, directly discarded, and linear interpolation is performed in the frequency domain, and the first preset function is used to remove the brain wave data.
  • V V_ ⁇ 0 ⁇ + (t-t_ ⁇ 0 ⁇ ) * (V_ ⁇ 1 ⁇ -V_ ⁇ 0 ⁇ ) / (t_ ⁇ 1 ⁇ -t ⁇ 0 ⁇ ), where V is the interpolation at time t, and V_ ⁇ 0 ⁇ and V_ ⁇ 1 ⁇ are the start and end times of the discarded data segment, respectively, V_ ⁇ 0 ⁇ and V_ ⁇ 1 ⁇ is the voltage value at the corresponding time.
  • Step S50 filtering the data to be filtered by a filter based on a second preset function, wherein the filter is used for removing low frequency, high frequency, and power frequency interference noise, and separating rhythm waves of each frequency band.
  • the second preset function includes a butter function and a filtfilt function
  • the filter may be used to filter the filtered data, first by bandpass filtering, and then by band rejection filtering, and the low frequency interference is mainly a baseline drift. Poor contact between the electrode and the human body during measurement, temperature drift of the amplifier or breathing High frequency interference is mainly caused by radio frequency interference and myoelectric interference in the acquisition.
  • Bandpass filtering can be performed with a Butterworth filter, and the filter function and the filtfilt function are called to filter the filtered data.
  • a digital notch can be used to remove 50 Hz (and possibly 60 Hz) power frequency interference, and various rhythmic waves can be separated by an FIR digital filter.
  • the teaching method for improving attention provides a method for removing brainwave data center power, eye electricity, and random noise based on a first preset function to obtain data to be filtered, and then filtering the signal to be filtered based on a second preset function.
  • the data is filtered by a filter, wherein the filter is used for removing low frequency, high frequency and power frequency interference noise, and separating rhythm waves of each frequency band; realizing denoising and filtering of brain wave data, thereby ensuring The accuracy of the test.
  • step S30 the method further includes:
  • Step S60 if the training mode ends, switching to the normal teaching mode
  • the mode is automatically switched to the normal teaching mode, and the training result of the user in the training mode is stored and displayed in the terminal, and the training result includes the user training, Alpha wave, Beta wave, Delta.
  • the energy value of the frequency wave such as the wave, the Gamma wave, and the Theta wave, the EEG characteristic, the score corresponding to the EEG characteristic, and the attention value, etc., the user can see the change of the attention of the change reaction of the brain wave in the display terminal, and these The data is stored so that the user's attention can be analyzed.
  • the teaching method for improving attention according to the embodiment, if the training mode ends, switching to the normal teaching mode; realizing the switching between the training mode and the ordinary teaching mode, thereby further improving the user's attention,
  • step S20 includes:
  • Step S21 If the attention value is less than the first preset threshold, send a prompt to switch to the training mode to the management terminal.
  • the prompt message that is switched to the training mode is sent to the management terminal, and the manager can switch to the training mode by using the management terminal, and select the interface corresponding to the training mode.
  • a small game or an animation special effect may be selected.
  • the manager clicks on the training mode in the management terminal display interface the scene corresponding to the small game is displayed, and the user can display the game according to the small game.
  • the test is carried out, and the brain wave collecting device collects the user brain wave in real time.
  • the brain wave monitoring device monitors the changes of the user brain wave in real time, and will alert in the software interface in different forms, and the teacher can quickly see which classmates are not focused.
  • the teaching method for improving attention provides that if the attention value is less than the first preset threshold, the prompt for switching to the training mode is sent to the management terminal; and the user's attention value is smaller than the first When the threshold is preset, the manager is prompted to switch modes, thereby improving the user experience.
  • step S20 further includes:
  • Step S22 if the attention value is less than the first preset threshold, acquiring a corresponding breakpoint of the current playing content
  • Step S23 automatically switching to the training mode at the time corresponding to the breakpoint.
  • the breakpoint can be set by a technician to divide the content in the normal mode teaching into a plurality of parts, and the breakpoint refers to a critical point between the contents of the user in the normal teaching mode, and the The position of the data corresponding to the critical point is marked with a special identifier, and each part is connected by a breakpoint point, and each of the partition points corresponds to a different time. If the attention is less than the first preset threshold, the time at which the current playback content is located is identified, and Switching to the training mode at this time, for example, the courseware content is divided into five parts in the normal teaching mode. When the teacher teaches the second part, the calculated attention value of the brain wave is less than the first preset threshold, then the part is obtained. The breakpoint corresponding to the content, and obtain the time corresponding to the breakpoint, and switch to the training mode at this time.
  • the teaching method for improving attention in the stairway if the attention value is less than the first preset threshold, acquiring the corresponding break point of the current playing content, and then automatically switching to the training mode at the corresponding time of the partition point;
  • the mode switching is performed at the break point, thereby avoiding the confusion of the mode switching, and further improving the teaching efficiency and quality.
  • an eighth embodiment of the teaching method for improving attention of the present application is provided.
  • the teaching method for improving attention further includes:
  • Step S70 Acquire a training result of the user in the training mode
  • Step S80 classifying and storing the EEG feature, the attention value, and the training result when the user is training;
  • Step S90 compressing and encrypting the EEG feature, the attention value and the training result, and generating an attention analysis report.
  • the EEG collecting terminal sends the EEG feature, the training result, and the attention value during the user training to the cloud server, and the cloud server classifies and stores the data according to the label of the user, the data collection environment, and the like.
  • the data compression is encrypted and stored in the EEG database, and an attention analysis report is generated, and the generated attention analysis report is sent by e-mail to the terminal of the student teacher's parent and the academic office, wherein the attention analysis report includes attention value, The phase change curve of attention value and the brain wave data and the curve analysis of the feature data.
  • cloud services can be redesigned by API (Application Programming Interface application programming interface).
  • the teacher can personally set the seat layout according to the class situation and the student learning situation according to the actual situation of the class, and connect and display the connection status of the corresponding device terminal.
  • the teaching method for improving attention obtains the training result of the user in the training mode, and then classifies and stores the electroencephalogram characteristic, the attention value and the training result during user training, and finally
  • the EEG feature, the attention value and the training result are compressed and encrypted, and an attention analysis report is generated; the attention analysis report is generated, which facilitates the user to understand the change of attention of the students in the classroom teaching, and is beneficial to the user. Analysis of force.
  • the embodiment of the present application further provides a computer readable storage medium.
  • the computer readable storage medium of the present application stores an instruction program for improving attention, and the instruction program for improving attention is executed by the processor to implement the following steps:
  • the EEG features are analyzed, and the EEG features are scored according to pre-designed rules;
  • the EEG features are scored based on the calculation results and the pre-designed sub-rules.
  • Filtering is performed on the data to be filtered based on a second preset function, wherein the filter is used to remove low frequency, high frequency, and power frequency interference noise, and separate rhythm waves of respective frequency bands.
  • the prompt to switch to the training mode is sent to the management terminal.
  • the classification stores the EEG feature, the attention value, and the training result when the user is training
  • the EEG feature, the attention value, and the training result are compression-encrypted, and an attention analysis report is generated.
  • the technical solution of the present application which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM as described above). , a disk, an optical disk, including a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the methods described in the various embodiments of the present application.
  • a terminal device which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.

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Abstract

一种提高注意力的教学方法、教学装置及计算机可读存储介质。该方法包括以下步骤：获取学生的脑电波数据；获取脑电采集设备采集到用户的脑电波数据，基于所述脑电波数据计算注意力值（S10）；若所述注意力值小于第一预设阈值，则切换至训练模式（S20）；获取用户训练时的脑电特征，基于所述脑电特征输出并显示对应的动画效果，以调整用户注意力（S30）。实现了在监测用户注意力的同时对用户注意力进行训练，从而提高了用户注意力。

Description

提高注意力的教学方法、装置及计算机可读存储介质
本申请要求于2018年05月11日提交中国专利局、申请号为201810447302.1、申请名称为“提高注意力的教学方法、装置及计算机可读存储介质”的中国专利申请的优先权，其全部内容通过引用结合在申请中。
技术领域
本申请涉及教育信息化技术领域，尤其涉及一种提高注意力的教学方法、装置及计算机可读存储介质。
背景技术
脑电信号(Electroencephalograph，EEG)伴随我们生命的始终，是脑细胞群的自发性、节律性电活动在大脑皮层和头皮的总体反应，可以通过放置在头皮上的电极检测得到，EEG按照不同的频率可分为δ、θ、α、β四种节律波。很多国外的学者专家经过大量实验分析发现，人体脑电波中的α波段是在安静、觉醒状态下的主要活动频率。
目前，教学系统只包含播放富媒体课件，亦或监测学生注意力水平，没有在检测学生注意力水平的同时训练学生在课堂中提升注意力，所以，目前的教学系统不能提升学生的注意力。
上述内容仅用于辅助理解本申请的技术方案，并不代表承认上述内容是现有技术。
发明内容
本申请的主要目的在于提供一种提高注意力的教学方法、装置及计算机可读存储介质，旨在解决目前的教学系统不能提升学生的注意力的问题。
为实现上述目的，本申请提供一种提高注意力的教学方法，所述方法包括以下步骤：
获取脑电采集设备采集到用户的脑电波数据，基于所述脑电波数据计算注意力值；
若所述注意力值小于第一预设阈值，则切换至训练模式；
获取用户训练时的脑电特征，基于所述脑电特征输出并显示对应的动画效果，以调整用户注意力。
可选地，所述获取用户训练时的脑电特征，基于所述脑电特征输出并显示对应的动画效果，以调整用户注意力水平的步骤包括：
对所述脑电特征进行分析，并根据预设计分规则对所述脑电特征进行评分；
将所述评分与第二预设阈值进行比较，以得到比较结果；
基于所述比较结果加载对应的所述动画效果的文件，并播放所述文件内容。
可选地，所述对所述脑电特征进行分析，并根据预设计分规则对所述脑电特征进行评分的步骤包括：
获取所述脑电特征对应的Alpha波、Beta波、Delta波、Gamma波及Theta波；
计算所述Alpha波、Beta波、Delta波、Gamma波及Theta波对应的能量值在频域上的均值、标准差、比值及乘积，以得到计算结果；
基于所述计算结果及所述预设计分规则对所述脑电特征进行评分。
可选地，所述获取脑电采集设备采集到用户的脑电波数据的步骤之后，所述提高注意力的教学方法还包括：
基于第一预设函数去除脑电波数据中心电、眼电以及随机噪声，以得到待滤波数据；
基于第二预设函数对所述待滤波数据利用滤波器滤波，其中，所述滤波器用于对低频、高频以及工频干扰噪声的去除，并且分离出各个频段的节律波。
可选地，所述获取用户训练时的脑电特征，基于所述脑电特征输出并显示对应的动画效果，以调整用户注意力的步骤之后，所述提高注意力的教学方法还包括：
若所述训练模式结束，则切换至普通教学模式。
可选地，所述若所述注意力值小于第一预设阈值，则切换至训练模式的步骤包括：
若所述注意力值小于第一预设阈值，则将切换至训练模式的提示发送至管理终端。
可选地，所述若所述注意力值小于第一预设阈值，则切换至训练模式的步骤还包括：
若所述注意力值小于第一预设阈值，则获取当前播放内容对应隔断点；
在所述隔断点对应时间自动切换至训练模式。
可选地，所述提高注意力的教学方法还包括：
获取用户在所述训练模式的训练结果；
分类存储用户训练时的所述脑电特征、所述注意力值及所述训练结果；
将所述脑电特征、所述注意力值及所述训练结果进行压缩加密，并生成注意力分析报告。
此外，为实现上述目的，本申请还提供一种提高注意力的教学装置，提高注意力的教学装置包括：存储器、处理器及存储在所述存储器上并可在所述处理器上运行的提高注意力的教学程序，所述提高注意力的教学程序被所述处理器执行时实现上述任一项提高注意力的教学方法的步骤。
此外，为实现上述目的，本申请还提供一种计算机可读存储介质，所述计算机可读存储介质上存储有提高注意力的教学程序，所述提高注意力的教学程序被处理器执行时实现上述任一项提高注意力的教学方法的步骤。
本申请通过获取脑电采集设备采集到用户的脑电波数据，基于所述脑电波数据计算注意力值，然后若所述注意力值小于第一预设阈值，则切换至训练模式，最后获取用户训练时的脑电特征，基于所述脑电特征输出并显示对应的动画效果，以调整用户注意力；由此实现了在监测用户注意力的同时对用户注意力进行训练，从而提高了用户注意力。
附图说明
图1是本申请实施例方案涉及的硬件运行环境中提高注意力的教学装置所属终端的结构示意图；
图2为本申请提高注意力的教学方法第一实施例的流程示意图；
图3为本申请提高注意力的教学方法第二实施例中所述获取用户训练时的脑电特征，基于所述脑电特征输出并显示对应的动画效果，以调整用户注意力水平步骤的细化流程示意图；
图4为本申请提高注意力的教学方法第三实施例中所述对所述脑电特征进行分析，并根据预设计分规则对所述脑电特征进行评分步骤的细化流程示意图；
图5为本申请提高注意力的教学方法第四实施例的流程示意图；
图6为本申请提高注意力的教学方法第五实施例的流程示意图；
图7为本申请提高注意力的教学方法第七实施例中所述若所述注意力值小于第一预设阈值，则切换至训练模式步骤的流程示意图；
图8为本申请提高注意力的教学方法第八实施例的流程示意图。
本申请目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。
具体实施方式
应当理解，此处所描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。
如图1所示，图1是本申请实施例方案涉及的硬件运行环境中装置所属终端的结构示意图。
本申请实施例终端可以是PC。如图1所示，该终端可以包括：处理器1001，例如CPU，网络接口1004，用户接口1003，存储器1005，通信总线1002。其中，通信总线1002用于实现这些组件之间的连接通信。用户接口1003可以包括显示屏（Display）、输入单元比如键盘（Keyboard），可选用户接口1003还可以包括标准的有线接口、无线接口。网络接口1004可选的可以包括标准的有线接口、无线接口（如WI-FI接口）。存储器1005可以是高速RAM存储器，也可以是稳定的存储器（non-volatile memory），例如磁盘存储器。存储器1005可选的还可以是独立于前述处理器1001的存储装置。
可选地，终端还可以包括摄像头、RF（Radio Frequency，射频）电路，传感器、音频电路、WiFi模块等等。其中，传感器比如光传感器、运动传感器以及其他传感器。具体地，光传感器可包括环境光传感器及接近传感器，其中，环境光传感器可根据环境光线的明暗来调节显示屏的亮度，接近传感器可在移动终端移动到耳边时，关闭显示屏和/或背光。作为运动传感器的一种，重力加速度传感器可检测方向上（一般为三轴）加速度的大小，静止时可检测出重力的大小及方向，可用于识别移动终端姿态的应用（比如横竖屏切换、相关游戏、磁力计姿态校准）、振动识别相关功能（比如计步器、敲击）等；当然，移动终端还可配置陀螺仪、气压计、湿度计、温度计、红外线传感器等其他传感器，在此不再赘述。
本领域技术人员可以理解，图1中示出的终端结构并不构成对终端的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置。
如图1所示，作为一种计算机存储介质的存储器1005中可以包括操作服务器、网络通信模块、用户接口模块以及程序。
在图1所示的终端中，网络接口1004主要用于连接后台服务器，与后台服务器进行数据通信；用户接口1003主要用于连接客户端（用户端），与客户端进行数据通信；而处理器1001可以用于调用存储器1005中存储的程序。
在本实施例中，装置包括：存储器1005、处理器1001及存储在所述存储器1005上并可在所述处理器1001上运行的程序，其中，处理器1001调用存储器1005中存储的程序时，执行以下操作：
获取脑电采集设备采集到用户的脑电波数据，基于所述脑电波数据计算注意力值；
若所述注意力值小于第一预设阈值，则切换至训练模式；
获取用户训练时的脑电特征，基于所述脑电特征输出并显示对应的动画效果，以调整用户注意力。
进一步地，处理器1001可以调用存储器1005中存储的提高注意力的教学程序，还执行以下操作：
对所述脑电特征进行分析，并根据预设计分规则对所述脑电特征进行评分；
将所述评分与第二预设阈值进行比较，以得到比较结果；
基于所述比较结果加载对应的所述动画效果的文件，并播放所述文件内容。
进一步地，处理器1001可以调用存储器1005中存储的提高注意力的教学程序，还执行以下操作：
获取所述脑电特征对应的Alpha波、Beta波、Delta波、Gamma波及Theta波；
计算所述Alpha波、Beta波、Delta波、Gamma波及Theta波对应的能量值在频域上的均值、标准差、比值及乘积，以得到计算结果；
基于所述计算结果及所述预设计分规则对所述脑电特征进行评分。
进一步地，处理器1001可以调用存储器1005中存储的提高注意力的教学程序，还执行以下操作：
基于第一预设函数去除脑电波数据中心电、眼电以及随机噪声，以得到待滤波数据；
基于第二预设函数对所述待滤波数据利用滤波器滤波，其中，所述滤波器用于对低频、高频以及工频干扰噪声的去除，并且分离出各个频段的节律波。
进一步地，处理器1001可以调用存储器1005中存储的提高注意力的教学程序，还执行以下操作：
若所述训练模式结束，则切换至普通教学模式。
进一步地，处理器1001可以调用存储器1005中存储的提高注意力的教学程序，还执行以下操作：
若所述注意力值小于第一预设阈值，则将切换至训练模式的提示发送至管理终端。
进一步地，处理器1001可以调用存储器1005中存储的提高注意力的教学程序，还执行以下操作：
若所述注意力值小于第一预设阈值，则获取当前播放内容对应隔断点；
在所述隔断点对应时间自动切换至训练模式。
进一步地，处理器1001可以调用存储器1005中存储的提高注意力的教学程序，还执行以下操作：
获取用户在所述训练模式的训练结果；
分类存储用户训练时的所述脑电特征、所述注意力值及所述训练结果；
将所述脑电特征、所述注意力值及所述训练结果进行压缩加密，并生成注意力分析报告。
本申请进一步提供一种提高注意力的教学。参照图2，图2为本申请提高注意力的教学第一实施例的流程示意图。
在本实施例中，该提高注意力的教学包括以下步骤：
步骤S10，获取脑电采集设备采集到用户的脑电波数据，基于所述脑电波数据计算注意力值；
在本实施例中，该脑电采集设备包括采集EEG（electroencephalo-graph 脑电波）的头环，该头环能够实时采集用户的脑电波数据，该脑电波数据包括Alpha波、Beta波、Delta波、Gamma波及Theta波对应的数值，不同频率波的数值能够反映当前人体脑部的状态，例如，人在专心学习、注意力集中时，大脑频率处于Alpha波，（频率范围8-13Hz），此时脑波比较平稳，是人们学习与思考的最佳脑波状态。当学习兴奋，或精神紧张时，大脑频率处于Beta波（频率范围14Hz以上），此时脑波频率变快，幅度加大，适当的Beta波对注意力提升以及认知行为的发展有积极作用，但持续时间较短，且易疲劳。当学习疲劳、精神松弛时，大脑频率处于Theta波。
进一步地，将采集到的脑电波数据发送至注意力训练系统，例如，当数据采集频率设置为160HZ，每0.5秒将80个原始脑电数据作为一个数据包发送给注意力训练系统，注意力系统将根据脑电波数据计算用户当前注意力值，可以通过机器学习训练模型计算预测注意力数值，将注意力值发送至显示终端，并对该该注意力值实时监测。
步骤S20，若所述注意力值小于第一预设阈值，则切换至训练模式；
在本实施例中，第一预设阈值由技术人员进行设定，在处于普通教学模式时，注意力系统将检测用户在普通教学模式下的注意力值，当检测到注意力值小于第一预设阈值时，则将切换至训练模式，进一步地，若检测到用户的注意力值低于某设定的下限值时，则判定该用户注意力不集中，若检测到用户注意力值高于某上限值，则判定该用户注意力高度集中，若检测到用户注意力值在该下限值与该上限值，则判定该用户注意力集中。
进一步地，当用户为全班学生时，则将获取每个学生的注意力值，并计算平均注意力值，根据该平均注意力值判定整个班的注意力是否集中，具体地，若平均注意力值小于某一预设阈值时，则判定该班级注意力不集中，若该平均注意力值大于某一预设阈值时，则判定该班级注意力集中，当该平均注意力值小于某一预设阈值时，则将正常教学系统切换至训练系统，进训练模式。
步骤S30，获取用户训练时的脑电特征，基于所述脑电特征输出并显示对应的动画效果，以调整用户注意力。
在本实施例中，该脑电特征包括delta，theta，alpha，beta，high-beta， gamma各频率能量值，各频率能量在频域上的均值，标准差，各频段能量的比值、乘积，当然，还包括预设频率范围内的全频域信号及各频率波的频域特征，例如，获取80Hz以下全频域信号及Theta波, Alpha波、Beta波、Gamma波及Theta波段的频域特征。该频域特征包括频率波对应的均值、峰值、标准差等，通过机器学习训练模型对各频率波的脑电特征及频域特征进行分析，确定各频率波的脑电特征对应数值的权重，然后根据该权重及计分规则对脑电特征进行评分。
进一步地，可以通过小游戏、图片及动画效果来训练用户注意力，例如，花朵开放、树叶生长、沉潜等游戏程序。人类类大脑在运作过程中会产生微量电流，注意力训练系统会检测到训练者当前的脑波活动状态，并结合大脑的实际情况，针对大脑薄弱的区域运用指定的的电脑游戏来协助人们锻炼大脑神经，从而达到提升大脑注意力的目的，在训练过程中，会对用户的脑电特征进行评分，通过动画效果奖励或者惩罚用户，从而给与用户神经反馈，提高用户注意力水平。
本实施提出的提高注意力的教学方法，通过获取脑电采集设备采集到用户的脑电波数据，基于所述脑电波数据计算注意力值，然后若所述注意力值小于第一预设阈值，则切换至训练模式，最后获取用户训练时的脑电特征，基于所述脑电特征输出并显示对应的动画效果，以调整用户注意力；实现了在监测用户注意力的同时对用户注意力进行训练，从而提高了用户注意力。
基于第一实施例，提出本申请提高注意力的教学方法的第二实施例，参照图3，本实施例中，步骤S30包括：
步骤S31，对所述脑电特征进行分析，并根据预设计分规则对所述脑电特征进行评分；
在本实施例中，对脑电特征进行分析包括计算计算所述Alpha波、Beta波、Delta波、Gamma波及Theta波对应的能量值在频域上的均值、标准差、比值及乘积，还可以计算各频率波对应的能量值占总能量的百分比，当然，还可以计算全频域信号对应的能量值，该全频域信号包括Alpha波、Beta波、Delta波、Gamma波及Theta波及其它频段的信号，例如，计算Alpha波、Beta波、Delta波、Gamma波及Theta波对应的能量值的总能量值，然后根据总能量值计算Alpha波、Beta波、Delta波、Gamma波及Theta波各自对应的能量值占总能量值的百分比。
进一步地，该预设计分规则由技术人员进行设定，注意力训练系统中可以存储着脑电波特征对应评分的评分表，不能范围内的脑电波特征对应的评分不一样，例如，若计算Alpha波、Beta波、Delta波、Gamma波及Theta波各自对应的能量值占总能量值的百分比，将计算得到的百分比与评分比中的能量值百分比进行比较，确定最终的评分。
步骤S32，将所述评分与第二预设阈值进行比较，以得到比较结果；
步骤S33，基于所述比较结果加载对应的所述动画效果的文件，并播放所述文件内容。
在本实施例中，该第二预设阈值由技术人员进行设定，将评分与该第二预设阈值比较，若该评分小于第二预设阈值，则将加载对应动画效果的文件，并播放文件内容，例如，若评分小于第二预设阈值，则加载惩罚主题的动画效果的文件，并播放该文件对应的内容，若评分大于该第二预设阈值时，则加载奖励主题的动画效果的文件，并播放该文件对应的内容，当然，可以根据全班同学的脑电特征，计算脑电特征对应的平均值，然后根据该平均值进行评分。
本实施例提出的提高注意力的教学方法，通过对所述脑电特征进行分析，并根据预设计分规则对所述脑电特征进行评分，然后将所述评分与第二预设阈值进行比较，以得到比较结果，最后基于所述比较结果加载对应的所述动画效果的文件，并播放所述文件内容；实现了对脑电特征进行评分，并且显示对应的动画效果，从而提高学生注意力。
基于第二实施例，提出本申请提高注意力的教学方法的第三实施例，参照图4，本实施例中，步骤S31包括：
步骤S311，获取所述脑电特征对应的Alpha波、Beta波、Delta波、Gamma波及Theta波；
步骤S312，计算所述Alpha波、Beta波、Delta波、Gamma波及Theta波对应的能量值在频域上的均值、标准差、比值及乘积，以得到计算结果；
步骤S313，基于所述计算结果及所述预设计分规则对所述脑电特征进行评分。
在本实施例中，脑电特征包括delta，theta，alpha，beta，high-beta，gamma各频率能量值、各频率能量在频域上的均值，标准差，各频段能量的比值、乘积。可以选择delta，theta，alpha，beta，high-beta，gamma各频率能量值、各频率能量在频域上的均值，标准差，各频段能量的比值中任一种或多种作为脑d特征，还可以计算各频率波对应的能量值占总能量的百分比，例如，计算Alpha波、Beta波、Delta波、Gamma波及Theta波对应的能量值的总能量值，然后根据总能量值计算Alpha波、Beta波、Delta波、Gamma波及Theta波各自对应的能量值占总能量值的百分比。
本实施例提出的基于注意力，通过获取所述脑电特征对应的Alpha波、Beta波、Delta波、Gamma波及Theta波，然后计算所述Alpha波、Beta波、Delta波、Gamma波及Theta波对应的能量值在频域上的均值、标准差、比值及乘积，以得到计算结果，最后基于所述计算结果及所述预设计分规则对所述脑电特征进行评分；实现了根据脑电特征进行评分，从而提高了用户注意力。
基于第一实施例，提出本申请提高注意力的教学方法的第四实施例，参照图5，本实施例中，步骤S10之后，还包括：
步骤S40，基于第一预设函数去除脑电波数据中心电、眼电以及随机噪声，以得到待滤波数据；
在本实施例中，脑电信号是一种随机性很强的电生理信号，各种不同的情绪和心态都会影响它的 变化。因此，脑电信号具有很高的时变敏感性，极易被无关噪声污染，从而形成各种脑电伪迹，其中影响最大的是心电以及眼电伪迹，因眨眼等造成的肌电信号干扰、因头环与皮肤摩擦造成的电势变化。这些噪音的主要特征为：在频域信号以及分频信号上的特殊峰值。数据预处理模块的主要功能是检测这些噪声并对这些噪声进行剔除。该数据清理包含三个部分，IMU动作处理，眨眼检测，波峰压缩，具体地，IMU数据是头环内置模块采集的头环物理运动数据，该数据包括头环在该时间点在三维空间三个坐标轴上的加速度，当加速度大于一定阈值，则判断该时间的数据不可信，对于不可信的数据段，直接丢弃，并在频域上进行线性插值，利用第一预设函数去除脑电波数据中心电、眼电以及随机噪声，以得到待滤波数据，例如，V = V_{0} + (t-t_{0}) * (V_{1}-V_{0}) / (t_{1}-t{0})，其中，V为在时刻t进行的插值，V_{0}和V_{1}分别是丢弃数据段的起始和结束时刻，V_{0}和V_{1}是相应时刻的电压值。
步骤S50，基于第二预设函数对所述待滤波数据利用滤波器滤波，其中，所述滤波器用于对低频、高频以及工频干扰噪声的去除，并且分离出各个频段的节律波。
在本实施例中，该第二预设函数包括butter函数与filtfilt函数，可以利用滤波器对待滤波数据进行滤波，首先通过带通滤波，然后再进行带阻滤波，低频干扰主要为基线漂移，由测量时电极和人体接触不良、放大器温漂或呼吸引 起，高频干扰主要是采集中存在的射频干扰和肌电干扰。可以用巴特沃斯滤波器进行带通滤波，调用butter函数与filtfilt函数对待滤波数据进行滤波。进一步地，可以使用数字陷波器对50Hz（还可以是60Hz）工频干扰去除，利用FIR数字滤波器分离各种节律波。
本实施例提出的提高注意力的教学方法，通过基于第一预设函数去除脑电波数据中心电、眼电以及随机噪声，以得到待滤波数据，然后基于第二预设函数对所述待滤波数据利用滤波器滤波，其中，所述滤波器用于对低频、高频以及工频干扰噪声的去除，并且分离出各个频段的节律波；实现了对脑电波数据进行去噪及滤波，从而保证了检测的准确性。
基于第四实施例，提出本申请提高注意力的教学方法的第五实施例，参照图6，本实施例中，步骤S30之后，还包括：
步骤S60，若所述训练模式结束，则切换至普通教学模式；
在本实施例中，若训练模式结束，则自动切换至普通教学模式，并且将用户在该训练模式的训练结果进行存储并在终端显示，该训练结果包括用户训练时Alpha波、Beta波、Delta波、Gamma波及Theta波等频率波的能量值、脑电特征、脑电特征对应的评分及注意力值等，用户可以在显示终端看到自己脑电波的变化反应的注意力的变化，将这些数据进行存储，从而能够进行对用户的注意力进行分析。
本实施例提出的提高注意力的教学方法，通过若所述训练模式结束，则切换至普通教学模式；实现了在训练模式与普通教学模式的切换，从而进一步提高了用户的注意力、
基于第一实施例，提出本申请提高注意力的教学方法的第六实施例，本实施例中，步骤S20包括：
步骤S21，若所述注意力值小于第一预设阈值，则将切换至训练模式的提示发送至管理终端。
在本实施例中，当注意力值小于第一预设阈值时，将切换至训练模式的提示消息发送至管理终端，管理者可以利用管理终端切换至训练模式，并在训练模式对应的界面选择训练模式对应的场景，例如，可以选择小游戏或者动画特效等，例如，当管理者在管理终端显示界面点击训练模式中小游戏训练时，则将小游戏对应的场景进行显示，用户可以根据小游戏进行测试，脑电波采集装置实时对用户脑电波进行采集，脑电波监测装置实时监测用户脑电波的变化，将以不同形式在软件界面中警示，教师可以迅速地看到哪些同学注意力不集中。
本实施例提出的提高注意力的教学方法，通过若所述注意力值小于第一预设阈值，则将切换至训练模式的提示发送至管理终端；实现了能够在用户注意力值小于第一预设阈值时，提示管理者切换模式，从而提高了用户体验。
基于以上实施例，提出本申请提高注意力的教学方法的第七实施例，参照图7，本实施例中，步骤S20还包括：
步骤S22，若所述注意力值小于第一预设阈值，则获取当前播放内容对应隔断点；
步骤S23，在所述隔断点对应时间自动切换至训练模式。
在本实施例中，该隔断点可由技术人员进行设定，将普通模式教学中的内容分成很多部分，该隔断点是指用户在普通教学模式中各部分内容之间的临界点，可以在该临界点对应数据的位置用特殊标识标记，每部分内容用隔断点连接，每个隔断点对应不同的时间，若注意力小于第一预设阈值，则识别当前播放内容所在隔断点的时间，并在该时间切换至训练模式，例如，普通教学模式中课件内容分为5部分，当老师教第二部分时，计算得到的脑电波对应的注意力值小于第一预设阈值，则获取该部分内容对应的隔断点，并获取该隔断点对应的时间，并在该时间切换为训练模式。
本实施楼梯处的提高注意力的教学方法，通过若所述注意力值小于第一预设阈值，则获取当前播放内容对应隔断点，然后在所述隔断点对应时间自动切换至训练模式；实现了在隔断点进行模式切换，从而避免了模式切换的混乱，进一步提高了教学效率及质量。
基于第七实施例，提出本申请提高注意力的教学方法的第八实施例，参照图8，本实施例中，该提高注意力的教学方法还包括：
步骤S70，获取用户在所述训练模式的训练结果；
步骤S80，分类存储用户训练时的所述脑电特征、所述注意力值及所述训练结果；
步骤S90，将所述脑电特征、所述注意力值及所述训练结果进行压缩加密，并生成注意力分析报告。
在本实施例中，脑电采集终端将用户训练时的脑电特征、训练结果及注意力值等发送至云服务器，云服务器将数据根据用户、数据采集的环境等标签进行分类与存储，将数据压缩加密后存入脑电数据库，并生成注意力分析报告，将生成的注意力分析报告通过电子邮件发至学生老师家长和教务处所在终端，其中，该注意力分析报告包括注意力值、注意力数值阶段性的变化曲线及脑电波数据、特征数据的曲线图分析等。当然，云服务可通过重新设计API（Application Programming Interface 应用程序编程接口）替代。
进一步地，还支持学生成绩的录入和导入，通过对学生上课情况的历史数据与学生的学习成果的进行分析，分析数据，横向对比不同学生、不同班级、不同课程、不同老师的注意力水平，纵向对比学生或班级一段时间内的注意力波动，给出提高学生学习效率的方法与建议。教师可以根据课堂实际情况在检测终端上根据班级人数，学生学习情况个性化设置座位布局并连接和显示相应设备终端的连接情况。
本实施例提出的提高注意力的教学方法，通过获取用户在所述训练模式的训练结果，然后分类存储用户训练时的所述脑电特征、所述注意力值及所述训练结果，最后将所述脑电特征、所述注意力值及所述训练结果进行压缩加密，并生成注意力分析报告；实现了生成注意力分析报告，方便用户了解课堂教学中学生的注意力变化，有利于用户注意力的分析。
此外，本申请实施例还提出一种计算机可读存储介质。本申请计算机可读存储介质上存储有提高注意力的教学程序，所述提高注意力的教学程序被处理器执行时实现如下步骤：
获取脑电采集设备采集到用户的脑电波数据，基于所述脑电波数据计算注意力值；
若所述注意力值小于第一预设阈值，则切换至训练模式；
获取用户训练时的脑电特征，基于所述脑电特征输出并显示对应的动画效果，以调整用户注意力。
进一步地，该提高注意力的教学程序被所述处理器执行时，还实现如下步骤：
对所述脑电特征进行分析，并根据预设计分规则对所述脑电特征进行评分；
将所述评分与第二预设阈值进行比较，以得到比较结果；
基于所述比较结果加载对应的所述动画效果的文件，并播放所述文件内容。
进一步地，该提高注意力的教学程序被所述处理器执行时，还实现如下步骤：
获取所述脑电特征对应的Alpha波、Beta波、Delta波、Gamma波及Theta波；
计算所述Alpha波、Beta波、Delta波、Gamma波及Theta波对应的能量值在频域上的均值、标准差、比值及乘积，以得到计算结果；
基于所述计算结果及所述预设计分规则对所述脑电特征进行评分。
进一步地，该提高注意力的教学程序被所述处理器执行时，还实现如下步骤：
基于第一预设函数去除脑电波数据中心电、眼电以及随机噪声，以得到待滤波数据；
基于第二预设函数对所述待滤波数据利用滤波器滤波，其中，所述滤波器用于对低频、高频以及工频干扰噪声的去除，并且分离出各个频段的节律波。
进一步地，该提高注意力的教学程序被所述处理器执行时，还实现如下步骤：
若所述训练模式结束，则切换至普通教学模式。
进一步地，该提高注意力的教学程序被所述处理器执行时，还实现如下步骤：
若所述注意力值小于第一预设阈值，则将切换至训练模式的提示发送至管理终端。
进一步地，该提高注意力的教学程序被所述处理器执行时，还实现如下步骤：
若所述注意力值小于第一预设阈值，则获取当前播放内容对应隔断点；
在所述隔断点对应时间自动切换至训练模式。
进一步地，该提高注意力的教学程序被所述处理器执行时，还实现如下步骤：
获取用户在所述训练模式的训练结果；
分类存储用户训练时的所述脑电特征、所述注意力值及所述训练结果；
将所述脑电特征、所述注意力值及所述训练结果进行压缩加密，并生成注意力分析报告。
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者系统不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者系统所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者系统中还存在另外的相同要素。
上述本申请实施例序号仅仅为了描述，不代表实施例的优劣。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在如上所述的一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端设备(可以是手机，计算机，服务器，空调器，或者网络设备等)执行本申请各个实施例所述的方法。
以上仅为本申请的优选实施例，并非因此限制本申请的专利范围，凡是利用本申请说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本申请的专利保护范围内。

Claims (20)

  1. 一种提高注意力的教学方法，其中，所述提高注意力的教学方法包括以下步骤：
    获取脑电采集设备采集到用户的脑电波数据，基于所述脑电波数据计算注意力值；
    若所述注意力值小于第一预设阈值，则切换至训练模式；
    获取用户训练时的脑电特征，基于所述脑电特征输出并显示对应的动画效果，以调整用户注意力。
  2. 如权利要求1所述的提高注意力的教学方法，其中，所述获取用户训练时的脑电特征，基于所述脑电特征输出并显示对应的动画效果，以调整用户注意力水平的步骤包括：
    对所述脑电特征进行分析，并根据预设计分规则对所述脑电特征进行评分；
    将所述评分与第二预设阈值进行比较，以得到比较结果；
    基于所述比较结果加载对应的所述动画效果的文件，并播放所述文件内容。
  3. 如权利要求2所述的提高注意力的教学方法，其中，所述对所述脑电特征进行分析，并根据预设计分规则对所述脑电特征进行评分的步骤包括：
    获取所述脑电特征对应的Alpha波、Beta波、Delta波、Gamma波及Theta波；
    计算所述Alpha波、Beta波、Delta波、Gamma波及Theta波对应的能量值在频域上的均值、标准差、比值及乘积，以得到计算结果；
    基于所述计算结果及所述预设计分规则对所述脑电特征进行评分。
  4. 如权利要求1所述的提高注意力的教学方法，其中，所述获取脑电采集设备采集到用户的脑电波数据的步骤之后，所述提高注意力的教学方法还包括：
    基于第一预设函数去除脑电波数据中心电、眼电以及随机噪声，以得到待滤波数据；
    基于第二预设函数对所述待滤波数据利用滤波器滤波，其中，所述滤波器用于对低频、高频以及工频干扰噪声的去除，并且分离出各个频段的节律波。
  5. 如权利要求4所述的提高注意力的教学方法，其中，所述获取用户训练时的脑电特征，基于所述脑电特征输出并显示对应的动画效果，以调整用户注意力的步骤之后，所述提高注意力的教学方法还包括：
    若所述训练模式结束，则切换至普通教学模式。
  6. 如权利要求1所述的提高注意力的教学方法，其中，所述若所述注意力值小于第一预设阈值，则切换至训练模式的步骤包括：
    若所述注意力值小于第一预设阈值，则将切换至训练模式的提示发送至管理终端。
  7. 如权利要求1所述的提高注意力的教学方法，其中，所述若所述注意力值小于第一预设阈值，则切换至训练模式的步骤还包括：
    若所述注意力值小于第一预设阈值，则获取当前播放内容对应隔断点；
    在所述隔断点对应时间自动切换至训练模式。
  8. 如权利7所述的提高注意力的教学方法，其中，所述提高注意力的教学方法还包括：
    获取用户在所述训练模式的训练结果；
    分类存储用户训练时的所述脑电特征、所述注意力值及所述训练结果；
    将所述脑电特征、所述注意力值及所述训练结果进行压缩加密，并生成注意力分析报告。
  9. 一种提高注意力的教学装置，其中，所述提高注意力的教学装置包括：存储器、处理器及存储在所述存储器上并可在所述处理器上运行的提高注意力的教学程序，所述提高注意力的教学程序被所述处理器执行时实现以下步骤：
    获取脑电采集设备采集到用户的脑电波数据，基于所述脑电波数据计算注意力值；
    若所述注意力值小于第一预设阈值，则切换至训练模式；
    获取用户训练时的脑电特征，基于所述脑电特征输出并显示对应的动画效果，以调整用户注意力。
  10. 如权利要求9所述的提高注意力的教学装置，其中，所述提高注意力的教学程序被所述处理器执行时实现以下步骤：
    对所述脑电特征进行分析，并根据预设计分规则对所述脑电特征进行评分；
    将所述评分与第二预设阈值进行比较，以得到比较结果；
    基于所述比较结果加载对应的所述动画效果的文件，并播放所述文件内容。
  11. 如权利要求10所述的提高注意力的教学装置，其中，所述提高注意力的教学程序被所述处理器执行时实现以下步骤：
    获取所述脑电特征对应的Alpha波、Beta波、Delta波、Gamma波及Theta波；
    计算所述Alpha波、Beta波、Delta波、Gamma波及Theta波对应的能量值在频域上的均值、标准差、比值及乘积，以得到计算结果；
    基于所述计算结果及所述预设计分规则对所述脑电特征进行评分。
  12. 如权利要求9所述的提高注意力的教学装置，其中，所述提高注意力的教学程序被所述处理器执行时实现以下步骤：
    基于第一预设函数去除脑电波数据中心电、眼电以及随机噪声，以得到待滤波数据；
    基于第二预设函数对所述待滤波数据利用滤波器滤波，其中，所述滤波器用于对低频、高频以及工频干扰噪声的去除，并且分离出各个频段的节律波。
  13. 如权利要求12所述的提高注意力的教学装置，其中，所述提高注意力的教学程序被所述处理器执行时实现以下步骤：
    若所述训练模式结束，则切换至普通教学模式。
  14. 如权利要求9所述的提高注意力的教学装置，其中，所述提高注意力的教学程序被所述处理器执行时实现以下步骤：
    若所述注意力值小于第一预设阈值，则将切换至训练模式的提示发送至管理终端。
  15. 如权利要求9所述的提高注意力的教学装置，其中，所述提高注意力的教学程序被所述处理器执行时实现以下步骤：
    若所述注意力值小于第一预设阈值，则获取当前播放内容对应隔断点；
    在所述隔断点对应时间自动切换至训练模式。
  16. 如权利15所述的提高注意力的教学装置，其中，所述提高注意力的教学程序被所述处理器执行时实现以下步骤：
    获取用户在所述训练模式的训练结果；
    分类存储用户训练时的所述脑电特征、所述注意力值及所述训练结果；
    将所述脑电特征、所述注意力值及所述训练结果进行压缩加密，并生成注意力分析报告。
  17. 一种计算机可读存储介质，其中，所述计算机可读存储介质上存储有提高注意力的教学程序，所述提高注意力的教学程序被处理器执行时实现以下步骤：
    获取脑电采集设备采集到用户的脑电波数据，基于所述脑电波数据计算注意力值；
    若所述注意力值小于第一预设阈值，则切换至训练模式；
    获取用户训练时的脑电特征，基于所述脑电特征输出并显示对应的动画效果，以调整用户注意力。
  18. 如权利要求17所述的计算机可读存储介质，其中，所述提高注意力的教学程序被处理器执行时实现以下步骤：
    对所述脑电特征进行分析，并根据预设计分规则对所述脑电特征进行评分；
    将所述评分与第二预设阈值进行比较，以得到比较结果；
    基于所述比较结果加载对应的所述动画效果的文件，并播放所述文件内容。
  19. 如权利要求18所述的计算机可读存储介质，其中，所述提高注意力的教学程序被处理器执行时实现以下步骤：
    获取所述脑电特征对应的Alpha波、Beta波、Delta波、Gamma波及Theta波；
    计算所述Alpha波、Beta波、Delta波、Gamma波及Theta波对应的能量值在频域上的均值、标准差、比值及乘积，以得到计算结果；
    基于所述计算结果及所述预设计分规则对所述脑电特征进行评分。
  20. 如权利要求17所述的计算机可读存储介质，其中，所述提高注意力的教学程序被处理器执行时实现以下步骤：
    基于第一预设函数去除脑电波数据中心电、眼电以及随机噪声，以得到待滤波数据；
    基于第二预设函数对所述待滤波数据利用滤波器滤波，其中，所述滤波器用于对低频、高频以及工频干扰噪声的去除，并且分离出各个频段的节律波。
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