WO2021027593A1 - 基于脑电数据的汽车控制方法、装置和存储介质 - Google Patents

基于脑电数据的汽车控制方法、装置和存储介质 Download PDF

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WO2021027593A1
WO2021027593A1 PCT/CN2020/106059 CN2020106059W WO2021027593A1 WO 2021027593 A1 WO2021027593 A1 WO 2021027593A1 CN 2020106059 W CN2020106059 W CN 2020106059W WO 2021027593 A1 WO2021027593 A1 WO 2021027593A1
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eeg
user
data
attention index
attention
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PCT/CN2020/106059
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English (en)
French (fr)
Inventor
韩璧丞
李嘉宁
杨钊祎
黄柏维
孙东圣
阿迪斯
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浙江强脑科技有限公司
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Publication of WO2021027593A1 publication Critical patent/WO2021027593A1/zh

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    • 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
    • G06F2203/00—Indexing scheme relating to G06F3/00 - G06F3/048
    • G06F2203/01—Indexing scheme relating to G06F3/01
    • G06F2203/012—Walk-in-place systems for allowing a user to walk in a virtual environment while constraining him to a given position in the physical environment

Definitions

  • This application relates to the field of brain control technology, and in particular to an automobile control method, device and computer readable storage medium based on brain electricity data.
  • Concentration also known as attention, refers to a person's mental state when concentrating on a certain thing or activity. Under normal circumstances, concentration makes people's mental activities toward a certain thing, selectively accepting certain information, and inhibiting other activities and other information, and concentrate all the mental energy on the pointed thing. Children's attention training in some recreational activities helps children grow and exercise.
  • the main purpose of this application is to provide a vehicle control method, device and computer readable storage medium based on EEG data, aiming to provide a method for calculating the user’s attention index based on the user’s EEG data, and according to the user’s attention
  • the force index controls the running speed of the car.
  • the vehicle control method based on EEG data includes the following steps:
  • the EEG data of the user includes EEG signal values of the EEG.
  • the step of calculating the user's attention index based on the user's EEG data includes:
  • the EEG signal value, frequency domain signal and energy value of the EEG are identified and scored through a preset neural network model to obtain the user's attention index.
  • the energy value is an alpha frequency energy value, a beta frequency energy value, a delta frequency energy value, and a theta frequency energy value.
  • the step of identifying and scoring the EEG signal value, frequency domain signal and energy value of the EEG signal through a preset neural network model, and obtaining the attention index of the user includes:
  • the attention index score is weighted and summed to obtain the attention index of the user.
  • the step of identifying and scoring the EEG signal value, frequency domain signal and energy value of the EEG signal through a preset neural network model, and obtaining the attention index of the user includes:
  • the EEG signal value, frequency domain signal and energy value of the EEG are identified and scored through the corresponding neural network model to determine the attention index of the user.
  • the step of controlling the running speed of the car according to the user's attention index includes:
  • the vehicle is controlled to run at the operating speed.
  • the step of determining the corresponding running speed according to the user's attention index according to a preset rule includes:
  • the running speed corresponding to the index range According to the running speed corresponding to the index range, the running speed corresponding to the attention index is obtained.
  • the present application also provides an EEG data-based automobile control device.
  • the EEG data-based automobile control device includes: a memory, a processor, and a A car control program based on EEG data running on the processor, and when the EEG data-based car control program is executed by the processor, the steps of the above-mentioned EEG data-based car control method are implemented.
  • this application also provides a computer-readable storage medium on which is stored a car control program based on EEG data, and the car control program based on EEG data is processed When the device is executed, the steps of the above-mentioned car control method based on EEG data are realized.
  • This application provides an automobile control method, device and computer storage medium based on brain electricity data.
  • the user's EEG data is obtained; the user's attention index is calculated according to the user's EEG data; the running speed of the car is controlled according to the user's attention index.
  • the present application can calculate the user's attention index based on the user's EEG data, and then convert the user's attention index into a corresponding running speed, so that the vehicle can run at the running speed.
  • This application can have an intuitive representation of the attention situation calculated based on the user’s EEG data through the vehicle speed, so that the user can clearly and intuitively know their current attention situation through the speed, and then can be based on The way to increase the speed of the vehicle is to train one's own attention.
  • the way of car control such as racing can enhance the user's entertainment in the training process, so that the user can not only train the attention, but also produce entertainment effects.
  • FIG. 1 is a schematic diagram of the device structure of the hardware operating environment involved in the solution of the embodiment of the present application;
  • FIG. 2 is a schematic flowchart of a first embodiment of a vehicle control method based on EEG data in this application;
  • FIG. 3 is a schematic flowchart of a second embodiment of a vehicle control method based on EEG data in this application;
  • FIG. 4 is a schematic flowchart of a third embodiment of a vehicle control method based on EEG data in this application;
  • FIG. 5 is a schematic flowchart of a fourth embodiment of a vehicle control method based on EEG data in this application;
  • FIG. 6 is a schematic flowchart of a fifth embodiment of a vehicle control method based on EEG data in this application;
  • FIG. 7 is a schematic flowchart of a sixth embodiment of a vehicle control method based on EEG data in this application.
  • FIG. 1 is a schematic diagram of the device structure of the hardware operating environment involved in the solution of the embodiment of the present application.
  • the terminal in the embodiment of the present application may be a PC, or a terminal device with data processing functions, such as a smart phone, a tablet computer, and a portable computer.
  • 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 and communication between these components.
  • the user interface 1003 may include a display screen (Display) and an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.
  • the network interface 1004 may optionally include a standard wired interface and 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 a magnetic disk memory.
  • the memory 1005 may also be a storage device independent of the foregoing processor 1001.
  • the terminal may also include a camera, RF (Radio Frequency, radio frequency) circuits, sensors, audio circuits, Wi-Fi modules, etc.
  • sensors such as light sensors, motion sensors and other sensors.
  • the light sensor may include an ambient light sensor and a proximity sensor.
  • the ambient light sensor can adjust the brightness of the display screen according to the brightness of the ambient light.
  • the proximity sensor can turn off the display screen and/or when the mobile terminal is moved to the ear. Backlight.
  • the gravity acceleration sensor can detect the magnitude of acceleration in various directions (usually three-axis), and can detect the magnitude and direction of gravity when it is stationary.
  • the mobile terminal can be used for applications that recognize the posture of the mobile terminal (such as horizontal and vertical screen switching, Related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, percussion), etc.; of course, the mobile terminal can also be equipped with other sensors such as gyroscope, barometer, hygrometer, thermometer, infrared sensor, etc. No longer.
  • terminal structure shown in FIG. 1 does not constitute a limitation on the terminal, and may include more or fewer components than shown in the figure, or combine some components, or arrange different components.
  • the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a car control program based on brain electricity data.
  • the network interface 1004 is mainly used to connect to a background server and perform data communication with the background server;
  • the user interface 1003 is mainly used to connect to a client (user side) and perform data communication with the client;
  • the processor 1001 can be used to call a car control program based on EEG data stored in the memory 1005 and perform the following operations:
  • the processor 1001 may call a car control program based on brain electricity data stored in the memory 1005, and also perform the following operations:
  • the EEG data of the user includes EEG signal values of the EEG.
  • the processor 1001 may call a car control program based on brain electricity data stored in the memory 1005, and also perform the following operations:
  • the EEG signal value, frequency domain signal and energy value of the EEG are identified and scored through a preset neural network model to obtain the user's attention index.
  • the processor 1001 may call a car control program based on brain electricity data stored in the memory 1005, and also perform the following operations:
  • the energy value is alpha frequency energy value, beta frequency energy value, delta frequency energy value and ⁇ frequency energy value.
  • the processor 1001 may call a car control program based on brain electricity data stored in the memory 1005, and also perform the following operations:
  • the attention index score is weighted and summed to obtain the attention index of the user.
  • the processor 1001 may call a car control program based on brain electricity data stored in the memory 1005, and also perform the following operations:
  • the EEG signal value, frequency domain signal and energy value of the EEG are identified and scored through the corresponding neural network model to determine the attention index of the user.
  • the processor 1001 may call a car control program based on brain electricity data stored in the memory 1005, and also perform the following operations:
  • the vehicle is controlled to run at the operating speed.
  • the processor 1001 may call a car control program based on brain electricity data stored in the memory 1005, and also perform the following operations:
  • the running speed corresponding to the index range According to the running speed corresponding to the index range, the running speed corresponding to the attention index is obtained.
  • the specific embodiments of the vehicle control device based on EEG data of the present application are basically the same as the following embodiments of the vehicle control method based on EEG data, and will not be repeated here.
  • Fig. 2 is a schematic flowchart of a first embodiment of a vehicle control method based on EEG data according to this application.
  • the EEG data-based vehicle control method includes:
  • Step S100 obtain the user's EEG data
  • the EEG data of the user is obtained, and the EEG data of the user includes the EEG signal value of the EEG.
  • the EEG signal value of EEG is a graph obtained by magnifying and recording the spontaneous biopotential of the brain from the scalp through a sophisticated electronic instrument. It is the result of the spontaneous and rhythmic electrical activity of brain cells and the nearby part. A potential difference of 100 microvolts, magnified by 1 to 2 million times by a precision instrument and traced out with a clear curve, is an important source of information for studying brain activity, and is the result of the common activities of many neurons.
  • the basic characteristics of EEG signal include frequency, period, amplitude, phase and so on.
  • the waveform of EEG is very irregular. According to its frequency, amplitude and physiological characteristics, it is divided into the following 4 basic waveforms:
  • alpha wave The frequency of alpha wave is 8 ⁇ 13Hz per second, and the amplitude is 20 ⁇ 100 ⁇ V. Appears when you are normally quiet, awake and closed your eyes. When you open your eyes or accept other stimuli, it disappears immediately and presents a fast wave, which is called alpha wave block.
  • ⁇ wave The frequency of ⁇ wave is 14 ⁇ 30Hz per second, and the amplitude is 5 ⁇ 20 ⁇ V. This wave can appear when you open your eyes, hear a sound suddenly, or think about a problem. It is generally believed that ⁇ wave is the performance of cerebral cortex excitement.
  • Theta wave frequency is 4 ⁇ 7Hz per second, and the amplitude is 100 ⁇ 150 ⁇ V. It appears when drowsiness, lack of O2 or deep anesthesia.
  • the frequency of delta wave is 0.5 ⁇ 3Hz per second, and the amplitude is 20 ⁇ 200 ⁇ V. It can appear when adults are sleeping, but not when they are awake; it can also appear under deep anesthesia and O2 deficiency.
  • the rhythm of the EEG changes with the activity state of the cerebral cortex.
  • synchronization a rhythm with a lower frequency and a higher amplitude appears, which is called synchronization.
  • alpha wave is a synchronized rhythm; when the electrical activities of neurons are not very consistent , It manifests as a high-frequency and low-amplitude rhythm, which is called desynchronization.
  • desynchronization a rhythm with a high-frequency and low-amplitude rhythm
  • ⁇ wave is blocked and ⁇ wave appears, it is a desynchronization rhythm. Therefore, the EEG signal value of EEG can reflect the state of the brain.
  • EEG data can be obtained through sensors such as EEG electrodes or other monitoring equipment.
  • Step S200 Calculate the user's attention index based on the user's EEG data
  • the user's attention index is calculated according to the user's EEG data.
  • the user's attention index can be obtained through a preset model after calculation and processing according to the user's EEG data.
  • the preset model can be a neural network model, of course, it can also be another network model. Because the EEG data reflects the current brain condition of the user, processing and analyzing the EEG data can know the user's attention situation at that time, that is, the attention index.
  • step S300 the running speed of the car is controlled according to the user's attention index.
  • the running speed of the car can be controlled according to the user's attention index.
  • the maximum attention index of the user is 100 points.
  • the attention index is the highest, it corresponds to the upper limit of the car's running speed; when the attention index is the lowest, it corresponds to the lower limit of the car's running speed, that is, the vehicle stops running.
  • the running speed of the vehicle is directly proportional to the attention index within this range, and has a positive correlation. In this way, the user's attention index can be mapped to the running speed of the vehicle in this way, and then the vehicle can be controlled according to the running speed Run it.
  • This application provides an automobile control method, device and computer storage medium based on brain electricity data.
  • the user's EEG data is obtained; the user's attention index is calculated according to the user's EEG data; the running speed of the car is controlled according to the user's attention index.
  • the present application can calculate the user's attention index based on the user's EEG data, and then convert the user's attention index into a corresponding running speed, so that the vehicle can run at the running speed.
  • This application can have an intuitive representation of the attention situation calculated based on the user’s EEG data through the vehicle speed, so that the user can clearly and intuitively know their current attention situation through the speed, and then can be based on The way to increase the speed of the vehicle is to train one's own attention.
  • the way of car control such as racing can enhance the user's entertainment in the training process, so that the user can not only train the attention, but also produce entertainment effects.
  • FIG. 3 is a schematic flowchart of a second embodiment of a vehicle control method based on EEG data in this application.
  • step S200 includes:
  • Step S210 Convert the EEG signal value of the EEG signal into a frequency domain signal through a preset Fourier transform algorithm
  • the EEG signal value of the EEG is first converted into a frequency domain signal through a preset Fourier transform algorithm.
  • the EEG signal value of EEG is a frequency-time signal, which needs to be converted into a frequency domain signal for processing by a preset Fourier transform algorithm.
  • the Fourier transform can convert the signal from the time domain to the frequency domain, so that we can observe the distribution of brainwave frequencies.
  • the frequency distribution of brain waves will vary depending on the mental, emotional state and the position of the electrodes.
  • Step S220 Perform energy conversion on the frequency domain signal to obtain an energy value
  • EEG frequency domain signal describes EEG data from the frequency domain description. Based on external stimulation and internal mental state, the frequency domain amplitude of brain waves varies greatly. EEG frequency domain signals are divided according to frequency domain, which can be divided into alpha frequency energy value, beta frequency energy value, delta frequency energy value and ⁇ frequency energy value. among them:
  • alpha frequency energy The frequency is 8 ⁇ 13Hz per second, and the amplitude is 20 ⁇ 100 ⁇ V. Appears when you are normally quiet, awake and closed your eyes. When you open your eyes or accept other stimuli, it disappears immediately and presents a fast wave, which is called alpha wave block.
  • Theta frequency energy numerical frequency is 4 ⁇ 7Hz per second, amplitude is 100 ⁇ 150 ⁇ V. It appears when drowsiness, lack of O2 or deep anesthesia.
  • ⁇ -frequency energy numerical value The frequency is 0.5 ⁇ 3Hz per second, and the amplitude is 20 ⁇ 200 ⁇ V. It can appear when adults are sleeping, but not when they are awake; it can also appear under deep anesthesia and O2 deficiency.
  • Energy conversion is performed on the frequency domain signal to obtain an energy value, that is, energy conversion is performed on the frequency domain signal, which can be converted into these four types of alpha frequency energy values, beta frequency energy values, delta frequency energy values and ⁇ frequency energy values.
  • Step S230 Recognizing and scoring the EEG signal value, frequency domain signal and energy value of the EEG signal through a preset neural network model to obtain the user's attention index.
  • the EEG signal value, frequency domain signal and four energy values of the EEG are identified and scored through a preset neural network model to obtain the user's attention index.
  • the preset neural network model is a model that calculates the attention index obtained through training based on a large number of attention samples. By repeatedly extracting features, convolution, pooling, and modifying the method of retraining the samples, an EEG energy value is obtained The corresponding relationship model with the attention index. Through this model, when the energy value is obtained, the attention index calculated by the neural network model training can be obtained by putting the energy value into this model.
  • FIG. 4 is a schematic flowchart of a third embodiment of a vehicle control method based on EEG data in this application.
  • step S230 includes:
  • Step S231 Recognizing and scoring the EEG signal value, frequency domain signal and energy value of the EEG signal through a preset neural network model to obtain respective attention index scores;
  • the EEG signal value, frequency domain signal, and four energy values can be identified and scored through a preset neural network model to obtain the EEG signal value, frequency domain signal, and four energy values.
  • the corresponding attention index scores respectively. That is, the EEG signal value of the EEG obtains one attention index score correspondingly, the frequency domain signal corresponds to one attention index score, and multiple values obtain multiple corresponding attention index scores.
  • Step S232 Perform a weighted summation of the attention index scores to obtain the attention index of the user.
  • the multiple attention index scores are weighted and summed to obtain the user's attention index. That is, the weighted sum of all the attention index scores is performed to obtain a total score, which is the user's attention index.
  • FIG. 5 is a schematic flowchart of a fourth embodiment of a vehicle control method based on EEG data in this application.
  • step S230 includes:
  • Step S233 Determine a corresponding neural network model according to the types of the EEG signal value, frequency domain signal and energy value of the brain electricity;
  • the corresponding neural network model is determined according to the type of the EEG signal value, frequency domain signal and energy value of the brain electricity.
  • the neural network model corresponding to the alpha-frequency energy value and the beta-frequency energy value in the EEG signal value, frequency domain signal and energy value is a neural network model; the ⁇ frequency in the EEG signal value, frequency domain signal and energy value
  • the neural network model corresponding to the energy value and theta frequency energy value is another neural network model.
  • Different data combinations are different, the types are different, and the corresponding neural network models are also different.
  • Step S234 Recognizing and scoring the EEG signal value, frequency domain signal and energy value of the EEG signal through the corresponding neural network model to determine the attention index of the user.
  • the EEG signal value, frequency domain signal and energy value of the EEG are identified and scored through the corresponding neural network model to determine the user Attention index.
  • FIG. 6 is a schematic flowchart of a fifth embodiment of a vehicle control method based on EEG data according to this application.
  • step S300 includes:
  • Step S310 Determine the corresponding running speed according to the user's attention index according to a preset rule
  • the running speed of the car is controlled according to the user's attention index, and the corresponding running speed can be determined according to the user's attention index according to a preset rule.
  • the maximum attention index of the user is 100 points.
  • the attention index is the highest, it corresponds to the upper limit of the car's running speed; when the attention index is the lowest, it corresponds to the lower limit of the car's running speed, that is, the vehicle stops running.
  • the running speed of the vehicle is directly proportional to the attention index within this range, and has a positive correlation. In this way, the user's attention index can be mapped to the running speed of the vehicle in this way, and further, the running speed of the vehicle is determined.
  • Step S320 Control the vehicle to run at the operating speed according to the operating speed.
  • FIG. 7 is a schematic flowchart of a sixth embodiment of a vehicle control method based on EEG data in this application.
  • step S310 includes:
  • Step S311 classify the attention index according to a preset index list, and determine the value range of the attention index
  • the corresponding running speed is determined according to the user's attention index according to a preset rule, and the attention index may be classified according to a preset index list first to determine the value range of the attention index. If the attention index is 95, according to the preset index list, it can be determined that the value range of the attention index is 90-100. In this way, the attention index can be determined according to the preset index list to determine the value range of each attention index.
  • Step S312 Obtain the running speed corresponding to the attention index according to the running speed corresponding to the index range.
  • the operation speed corresponding to the attention index is obtained according to the operation speed corresponding to the index range. If the attention index is 95, the corresponding numerical range is 90-100, and the running speed corresponding to the 90-100 numerical range is 10 m/min, then the operation corresponding to the attention index of 95 can be determined The speed is 10 m/min.
  • the embodiment of the present application also proposes a computer-readable storage medium.
  • the computer-readable storage medium of the present application stores a car control program based on EEG data, and when the car control program based on EEG data is executed by a processor, the steps of the above-mentioned EEG data-based car control method are realized.
  • the method implemented when the vehicle control program based on EEG data running on the processor is executed can refer to the various embodiments of the vehicle control method based on EEG data of this application, which will not be repeated here.

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Abstract

一种基于脑电数据的汽车控制方法、装置及计算机可读存储介质，具体包括：获得用户的脑电数据；根据用户的脑电数据计算用户的注意力指数；根据用户的注意力指数控制汽车的运行速度。

Description

基于脑电数据的汽车控制方法、装置和存储介质
本申请要求2019年8月12日申请的，申请号为201910742649.3，名称为“基于脑电数据的汽车控制方法、装置和存储介质”的中国专利申请的优先权，在此将其全文引入作为参考。
技术领域
本申请涉及脑控技术领域，尤其涉及一种基于脑电数据的汽车控制方法、装置和计算机可读存储介质。
 背景技术
随着经济的发展，生活水平的提高，儿童游玩设施和场所逐渐增多。一些娱乐设施希望在提供给用户愉快感受的同时，也能够对用户进行一些能力的训练。如注意力，动手能力、思考能力等。专注力，又称为注意力，指一个人专心于某一事物、或活动时的心理状态。在正常情况下，专注力使人们的心理活动朝向某一事物，有选择地接受某些信息，而抑制其它活动和其它信息，并集中全部的心理能量用于所指向的事物。儿童在一些娱乐活动中进行注意力的训练，有助于儿童的成长和锻炼。
但是目前，市面上，还没有一款基于用户的脑电数据，反应用户实时的注意力情况，并根据用户的实时的注意力情况控制儿童游玩汽车速度的设备，用于训练儿童的注意力，无法实现提供给用户一种新的娱乐设施，用于辅助用于增加娱乐的同时，还能够进行注意力的训练。
 技术解决方案
本申请的主要目的在于提供一种基于脑电数据的汽车控制方法、装置和计算机可读存储介质，旨在实现提供一种基于用户的脑电数据计算用户的注意力指数，并根据用户的注意力指数控制汽车的运行速度。
为实现上述目的，本申请提供一种基于脑电数据的汽车控制方法，所述基于脑电数据的汽车控制方法包括以下步骤：
获得用户的脑电数据；
根据用户的脑电数据计算用户的注意力指数；
根据用户的注意力指数控制汽车的运行速度。
在一实施例中，所述用户的脑电数据包括脑电EEG信号值。
在一实施例中，所述根据用户的脑电数据计算用户的注意力指数的步骤包括：
将所述脑电EEG信号值通过预设傅立叶变换算法转换为频域信号；
对所述频域信号进行能量转换，获得能量数值；
对所述脑电EEG信号值、频域信号和能量数值通过预设的神经网络模型进行识别评分，获得用户的注意力指数。
在一实施例中，所述能量数值为α频能量数值、β频能量数值、δ频能量数值和θ频能量数值。
在一实施例中，所述对所述脑电EEG信号值、频域信号和能量数值通过预设的神经网络模型进行识别评分，获得用户的注意力指数的步骤包括：
将所述脑电EEG信号值、频域信号和能量数值分别通过预设的神经网络模型进行识别评分，获得分别对应的注意力指数分值；
将所述注意力指数分值进行加权求和，获得用户的注意力指数。
在一实施例中，所述对所述脑电EEG信号值、频域信号和能量数值通过预设的神经网络模型进行识别评分，获得用户的注意力指数的步骤包括：
根据所述脑电EEG信号值、频域信号和能量数值的类型确定对应的神经网络模型；
对所述脑电EEG信号值、频域信号和能量数值通过所述对应的神经网络模型进行识别评分，确定用户的注意力指数。
在一实施例中，所述根据用户的注意力指数控制汽车的运行速度的步骤包括：
根据用户的注意力指数按照预设规律确定对应的运行速度；
根据所述运行速度控制车辆按照所述运行速度进行行驶。
在一实施例中，所述根据用户的注意力指数按照预设规律确定对应的运行速度的步骤包括：
对所述注意力指数按照预设指数列表进行分类，确定所述注意力指数的数值范围；
根据指数范围所对应的运行速度，获得所述述注意力指数所对应的运行速度。
此外，为实现上述目的，本申请还提供一种基于脑电数据的汽车控制装置，所述基于脑电数据的汽车控制装置包括：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基于脑电数据的汽车控制程序，所述基于脑电数据的汽车控制程序被所述处理器执行时实现如上所述的基于脑电数据的汽车控制方法的步骤。
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基于脑电数据的汽车控制程序，所述基于脑电数据的汽车控制程序被处理器执行时实现上述的基于脑电数据的汽车控制方法的步骤。
本申请提供一种基于脑电数据的汽车控制方法、装置和计算机存储介质。在该方法中，获得用户的脑电数据；根据用户的脑电数据计算用户的注意力指数；根据用户的注意力指数控制汽车的运行速度。通过上述方式，本申请能够根据用户的脑电数据计算出用户的注意力指数，进而将用户的注意力指数转化为对应的运行速度，让车辆按照运行速度进行运行。本申请能够对基于用户的脑电数据的计算出来的注意力情况通过车辆速度的方式有个直观的表现，让用户能够清楚明了很直观通过速度的方式知道自己当时的注意力情况，进而能够根据提高车辆速度的方式对自己的注意力进行训练，同时，通过赛车等汽车控制的方式能增强用户训练过程中的娱乐性，使用户既能进行注意力的训练，又能产生娱乐效果。
 附图说明
图1是本申请实施例方案涉及的硬件运行环境的装置结构示意图；
图2为本申请基于脑电数据的汽车控制方法第一实施例的流程示意图；
图3为本申请基于脑电数据的汽车控制方法第二实施例的流程示意图；
图4为本申请基于脑电数据的汽车控制方法第三实施例的流程示意图；
图5为本申请基于脑电数据的汽车控制方法第四实施例的流程示意图；
图6为本申请基于脑电数据的汽车控制方法第五实施例的流程示意图；
图7为本申请基于脑电数据的汽车控制方法第六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。
 本发明的实施方式
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的装置结构示意图。
本申请实施例终端可以是PC，也可以是智能手机、平板电脑、便携计算机等具有数据处理功能的终端设备。
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。
在一实施例中，终端还可以包括摄像头、RF（Radio Frequency，射频）电路，传感器、音频电路、Wi-Fi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各个方向上（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中可以包括操作系统、网络通信模块、用户接口模块以及基于脑电数据的汽车控制程序。
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中存储的基于脑电数据的汽车控制程序，并执行以下操作：
获得用户的脑电数据；
根据用户的脑电数据计算用户的注意力指数；
根据用户的注意力指数控制汽车的运行速度。
进一步地，处理器1001可以调用存储器1005中存储的基于脑电数据的汽车控制程序，还执行以下操作：
所述用户的脑电数据包括脑电EEG信号值。
进一步地，处理器1001可以调用存储器1005中存储的基于脑电数据的汽车控制程序，还执行以下操作：
将所述脑电EEG信号值通过预设傅立叶变换算法转换为频域信号；
对所述频域信号进行能量转换，获得能量数值；
对所述脑电EEG信号值、频域信号和能量数值通过预设的神经网络模型进行识别评分，获得用户的注意力指数。
进一步地，处理器1001可以调用存储器1005中存储的基于脑电数据的汽车控制程序，还执行以下操作：
所述能量数值为α频能量数值、β频能量数值、δ频能量数值和θ频能量数值。
进一步地，处理器1001可以调用存储器1005中存储的基于脑电数据的汽车控制程序，还执行以下操作：
将所述脑电EEG信号值、频域信号和能量数值分别通过预设的神经网络模型进行识别评分，获得分别对应的注意力指数分值；
将所述注意力指数分值进行加权求和，获得用户的注意力指数。
进一步地，处理器1001可以调用存储器1005中存储的基于脑电数据的汽车控制程序，还执行以下操作：
根据所述脑电EEG信号值、频域信号和能量数值的类型确定对应的神经网络模型；
对所述脑电EEG信号值、频域信号和能量数值通过所述对应的神经网络模型进行识别评分，确定用户的注意力指数。
进一步地，处理器1001可以调用存储器1005中存储的基于脑电数据的汽车控制程序，还执行以下操作：
根据用户的注意力指数按照预设规律确定对应的运行速度；
根据所述运行速度控制车辆按照所述运行速度进行行驶。
进一步地，处理器1001可以调用存储器1005中存储的基于脑电数据的汽车控制程序，还执行以下操作：
对所述注意力指数按照预设指数列表进行分类，确定所述注意力指数的数值范围；
根据指数范围所对应的运行速度，获得所述述注意力指数所对应的运行速度。
本申请基于脑电数据的汽车控制设备的具体实施例与下述基于脑电数据的汽车控制方法各实施例基本相同，在此不作赘述。
参照图2，图2为本申请基于脑电数据的汽车控制方法第一实施例的流程示意图，所述基于脑电数据的汽车控制方法包括：
步骤S100，获得用户的脑电数据；
在本实施例中，获得用户的脑电数据，所述用户的脑电数据包括脑电EEG信号值。脑电EEG信号值是通过精密的电子仪器，从头皮上将脑部的自发性生物电位加以放大记录而获得的图形，是将脑细胞自发性、节律性电活动所产生与临近部位的5-100微伏电位差，通过精密仪器放大100-200万倍并以清晰曲线描记出来的波形，是研究大脑活动的一种重要的信息来源，是许多神经元共同活动的结果。EEG信号的基本特征包括频率、周期、幅值、相位等。脑电图的波形很不规则，根据其频率、振幅和生理特征分为下列4种基本波形：
1.α波频率每秒8～13Hz，振幅20～100μV。正常安静、清醒闭目时出现。睁开眼睛或接受其他刺激时，立即消失而呈现快波，称为α波阻断。
2.β波频率每秒14～30Hz，振幅5～20μV。睁眼视物，或突然听到音响，或思考问题时可出现此波。一般认为β波是大脑皮层兴奋的表现。
3.θ波频率每秒4～7Hz，振幅100～150μV。在困倦、缺O2或深度麻醉时出现。
4.δ波频率每秒0.5～3Hz，振幅20～200μV。成人睡眠时可出现，清醒时无此波；在深度麻醉和缺O2亦可时出现。
不同的特性的波在不同的状态下信号的表现不同。
脑电图的节律随大脑皮层活动状态的不同而变化。当大脑皮层许多神经元的电活动步调趋于一致，就出现频率较低而振幅较高的节律，称为同步化，如α波即是同步化节律；当神经元的电活动不大一致时，就表现为高频率低振幅的节律，称为去同步化，如α波阻断而出现β波时即是去同步化节律。因此，脑电EEG信号值能反应脑部状态。
脑电数据可以通过脑电电极等传感器获得，也可以通过其他监测设备获得。
步骤S200，根据用户的脑电数据计算用户的注意力指数；
根据用户的脑电数据计算用户的注意力指数，具体的，根据用户的脑电数据通过计算处理后，可以通过预设的模型，获得用户的注意力指数。预设的模型，可以为神经网络模型，当然，也可以为其他网络模型。因为脑电数据反映用户的当时的脑部状况，因此，对脑电数据进行处理分析，可以得知用户当时的注意力情况，即注意力指数。
步骤S300，根据用户的注意力指数控制汽车的运行速度。
在获得用户的注意力指数后，可以根据用户的注意力指数控制汽车的运行速度。如用户的注意力指数最高为100分，当注意力指数为最高时，对应汽车运行速度的上限值；注意力指数最低时，对应汽车运行速度的下限值，即车辆停止运行。车辆的运行速度与注意力指数在这个范围内成正比例，正相关的关系，这样，用户的注意力指数就能通过这种方式映射为车辆的运行速度，进而，就能够根据该运行速度控制车辆进行运行。
本申请提供一种基于脑电数据的汽车控制方法、装置和计算机存储介质。在该方法中，获得用户的脑电数据；根据用户的脑电数据计算用户的注意力指数；根据用户的注意力指数控制汽车的运行速度。通过上述方式，本申请能够根据用户的脑电数据计算出用户的注意力指数，进而将用户的注意力指数转化为对应的运行速度，让车辆按照运行速度进行运行。本申请能够对基于用户的脑电数据的计算出来的注意力情况通过车辆速度的方式有个直观的表现，让用户能够清楚明了很直观通过速度的方式知道自己当时的注意力情况，进而能够根据提高车辆速度的方式对自己的注意力进行训练，同时，通过赛车等汽车控制的方式能增强用户训练过程中的娱乐性，使用户既能进行注意力的训练，又能产生娱乐效果。
请参阅图3，图3为本申请基于脑电数据的汽车控制方法第二实施例的流程示意图。
基于上述实施例，本实施例中，步骤S200包括：
步骤S210，将所述脑电EEG信号值通过预设傅立叶变换算法转换为频域信号；
在本实施例中，先将所述脑电EEG信号值通过预设傅立叶变换算法转换为频域信号。脑电EEG信号值为频时信号，需要通过预设傅立叶变换算法转化为频域信号进行处理。傅里叶变换可以把信号从时间域转换到频域，这样我们就可以观察得到脑波频率的分布。脑波的频率分布会由于精神、情绪状态及电极的位置而变化。
步骤S220，对所述频域信号进行能量转换，获得能量数值；
脑电频域信号是从频域方面描述对脑电数据进行描述。基于外部刺激和内在精神状态，脑波的频域幅度变化很大。脑电频域信号按照频域划分，可以分为α频能量数值、β频能量数值、δ频能量数值和θ频能量数值。其中：
1.α频能量数值频率每秒8～13Hz，振幅20～100μV。正常安静、清醒闭目时出现。睁开眼睛或接受其他刺激时，立即消失而呈现快波，称为α波阻断。
2.β频能量数值频率每秒14～30Hz，振幅5～20μV。睁眼视物，或突然听到音响，或思考问题时可出现此波。一般认为β波是大脑皮层兴奋的表现。
3.θ频能量数值频率每秒4～7Hz，振幅100～150μV。在困倦、缺O2或深度麻醉时出现。
4.δ频能量数值频率每秒0.5～3Hz，振幅20～200μV。成人睡眠时可出现，清醒时无此波；在深度麻醉和缺O2亦可时出现。
将所述频域信号进行能量转换，获得能量数值，即将频域信号进行能量转换，可以转换为这4种α频能量数值、β频能量数值、δ频能量数值和θ频能量数值。
步骤S230，对所述脑电EEG信号值、频域信号和能量数值通过预设的神经网络模型进行识别评分，获得用户的注意力指数。
将所述脑电EEG信号值、频域信号和4种能量数值通过预设的神经网络模型进行识别评分，获得用户的注意力指数。预设的神经网络模型为根据大量注意力样本通过训练得到的计算注意力指数的模型，通过对样本进行反复的提取特征，卷积，池化，修改再训练的方式，得到一个脑电能量数值与注意力指数的对应关系模型，通过这个模型，可以在获得能量数值时，通过放入这个模型中，获得该能量数值通过神经网络模型训练计算出来的注意力指数。
请参阅图4，图4为本申请基于脑电数据的汽车控制方法第三实施例的流程示意图。
基于上述实施例，本实施例中，步骤S230包括：
步骤S231，将所述脑电EEG信号值、频域信号和能量数值分别通过预设的神经网络模型进行识别评分，获得分别对应的注意力指数分值；
在本实施例中，可以将脑电EEG信号值、频域信号和4种能量数值分别通过预设的神经网络模型进行识别评分，获得这脑电EEG信号值、频域信号和4种能量数值分别对应的注意力指数分值。即脑电EEG信号值对应获得一个注意力指数分值，频域信号对应获得一个注意力指数分值，多个数值获得多个对应的注意力指数分值。
步骤S232，将所述注意力指数分值进行加权求和，获得用户的注意力指数。
将多个注意力指数分值进行加权求和，获得用户的注意力指数。即将这所有的注意力指数分值进行加权求和，获得一个总分值，该总分值即为用户的注意力指数。
请参阅图5，图5为本申请基于脑电数据的汽车控制方法第四实施例的流程示意图。
基于上述实施例，本实施例中，步骤S230包括：
步骤S233，根据所述脑电EEG信号值、频域信号和能量数值的类型确定对应的神经网络模型；
在本实施例中，根据所述脑电EEG信号值、频域信号和能量数值的类型确定对应的神经网络模型。如脑电EEG信号值、频域信号和能量数值中α频能量数值和β频能量数值对应的神经网络模型为一种神经网络模型；脑电EEG信号值、频域信号和能量数值中δ频能量数值和θ频能量数值对应的神经网络模型为另一种神经网络模型。不同的数据组合不同，类型就不同，对应的神经网络模型也就不同。
步骤S234，对所述脑电EEG信号值、频域信号和能量数值通过所述对应的神经网络模型进行识别评分，确定用户的注意力指数。
在确定脑电EEG信号值、频域信号和能量数值对应的神经网络模型后，将所述脑电EEG信号值、频域信号和能量数值通过所述对应的神经网络模型进行识别评分，确定用户的注意力指数。
请参阅图6，图6为本申请基于脑电数据的汽车控制方法第五实施例的流程示意图。
基于上述实施例，本实施例中，步骤S300包括：
步骤S310，根据用户的注意力指数按照预设规律确定对应的运行速度；
在本实施例中，根据用户的注意力指数控制汽车的运行速度，可以先根据用户的注意力指数按照预设规律确定对应的运行速度。如用户的注意力指数最高为100分，当注意力指数为最高时，对应汽车运行速度的上限值；注意力指数最低时，对应汽车运行速度的下限值，即车辆停止运行。车辆的运行速度与注意力指数在这个范围内成正比例，正相关的关系，这样，用户的注意力指数就能通过这种方式映射为车辆的运行速度，进而，就确定了车辆的运行速度。
步骤S320，根据所述运行速度控制车辆按照所述运行速度进行行驶。
在确定车辆的运行速度后，再根据车辆的运行速度，控制车辆按照该运行速度进行驾驶。
请参阅图7，图7为本申请基于脑电数据的汽车控制方法第六实施例的流程示意图。
基于上述实施例，本实施例中，步骤S310包括：
步骤S311，对所述注意力指数按照预设指数列表进行分类，确定所述注意力指数的数值范围；
在本实施例中，根据用户的注意力指数按照预设规律确定对应的运行速度，可以先对所述注意力指数按照预设指数列表进行分类，确定所述注意力指数的数值范围。如注意力指数为95，则按照预设指数列表，可以确定该注意力指数的数值范围为90-100这一数值范围。这样，就可以将注意力指数按照预设指数列表，确定每个注意力指数的数值范围。
步骤S312，根据指数范围所对应的运行速度，获得所述述注意力指数所对应的运行速度。
在确定每个注意力指数的数值范围后，在根据指数范围所对应的运行速度，获得所述述注意力指数所对应的运行速度。如注意力指数为95，所对应的数值范围为90-100这一数值范围，而90-100这一数值范围对应的运行速度为10米/分钟，则可以确定注意力指数95所对应的运行速度为10米/分钟。
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基于脑电数据的汽车控制程序，所述基于脑电数据的汽车控制程序被处理器执行时实现如上所述的基于脑电数据的汽车控制方法的步骤。
其中，在所述处理器上运行的基于脑电数据的汽车控制程序被执行时所实现的方法可参照本申请基于脑电数据的汽车控制方法各个实施例，此处不再赘述。
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 (10)

  1. 一种基于脑电数据的汽车控制方法，其中，所述基于脑电数据的汽车控制方法包括以下步骤：
    获得用户的脑电数据；
    根据用户的脑电数据计算用户的注意力指数；以及
    根据用户的注意力指数控制汽车的运行速度。
  2. 如权利要求1所述的基于脑电数据的汽车控制方法，其中，所述用户的脑电数据包括脑电EEG信号值。
  3. 如权利要求2所述的基于脑电数据的汽车控制方法，其中，所述根据用户的脑电数据计算用户的注意力指数的步骤包括：
    将所述脑电EEG信号值通过预设傅立叶变换算法转换为频域信号；
    对所述频域信号进行能量转换，获得能量数值；以及
    对所述脑电EEG信号值、频域信号和能量数值通过预设的神经网络模型进行识别评分，获得用户的注意力指数。
  4. 如权利要求3所述的基于脑电数据的汽车控制方法，其中，所述能量数值为α频能量数值、β频能量数值、δ频能量数值和θ频能量数值。
  5. 如权利要求4所述的基于脑电数据的汽车控制方法，其中，所述对所述脑电EEG信号值、频域信号和能量数值通过预设的神经网络模型进行识别评分，获得用户的注意力指数的步骤包括：
    将所述脑电EEG信号值、频域信号和能量数值分别通过预设的神经网络模型进行识别评分，获得分别对应的注意力指数分值；以及
    将所述注意力指数分值进行加权求和，获得用户的注意力指数。
  6. 如权利要求3所述的基于脑电数据的汽车控制方法，其中，所述对所述脑电EEG信号值、频域信号和能量数值通过预设的神经网络模型进行识别评分，获得用户的注意力指数的步骤包括：
    根据所述脑电EEG信号值、频域信号和能量数值的类型确定对应的神经网络模型；以及
    对所述脑电EEG信号值、频域信号和能量数值通过所述对应的神经网络模型进行识别评分，确定用户的注意力指数。
  7. 如权利要求1所述的基于脑电数据的汽车控制方法，其中，所述根据用户的注意力指数控制汽车的运行速度的步骤包括：
    根据用户的注意力指数按照预设规律确定对应的运行速度；以及
    根据所述运行速度控制车辆按照所述运行速度进行行驶。
  8. 如权利要求7所述的基于脑电数据的汽车控制方法，其中，所述根据用户的注意力指数按照预设规律确定对应的运行速度的步骤包括：
    对所述注意力指数按照预设指数列表进行分类，确定所述注意力指数的数值范围；以及
    根据指数范围所对应的运行速度，获得所述述注意力指数所对应的运行速度。
  9. 一种基于脑电数据的汽车控制装置，其中，所述基于脑电数据的汽车控制装置包括：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基于脑电数据的汽车控制程序，所述基于脑电数据的汽车控制程序被所述处理器执行时实现如权利要求1至8中任一项所述基于脑电数据的汽车控制方法的步骤。
  10. 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基于脑电数据的汽车控制程序，所述基于脑电数据的汽车控制程序被处理器执行时实现如权利要求1至8中任一项所述基于脑电数据的汽车控制方法的步骤。
PCT/CN2020/106059 2019-08-12 2020-07-31 基于脑电数据的汽车控制方法、装置和存储介质 WO2021027593A1 (zh)

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