WO2020147235A1 - 一种基于相位锁定值构建脑功能网络的驾驶疲劳检测方法 - Google Patents

一种基于相位锁定值构建脑功能网络的驾驶疲劳检测方法 Download PDF

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WO2020147235A1
WO2020147235A1 PCT/CN2019/088445 CN2019088445W WO2020147235A1 WO 2020147235 A1 WO2020147235 A1 WO 2020147235A1 CN 2019088445 W CN2019088445 W CN 2019088445W WO 2020147235 A1 WO2020147235 A1 WO 2020147235A1
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function network
brain function
channels
test time
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王洪涛
刘旭程
吴聪
唐聪
裴子安
岳洪伟
陈鹏
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Wuyi University Fujian
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    • 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
    • A61B5/25—Bioelectric electrodes therefor
    • A61B5/279—Bioelectric electrodes therefor specially adapted for particular uses
    • A61B5/291—Bioelectric electrodes therefor specially adapted for particular uses for electroencephalography [EEG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A61B5/18—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state for vehicle drivers or machine operators
    • 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

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  • the present invention relates to the field of driving fatigue detection, and more specifically, to a driving fatigue detection method for constructing a brain function network based on a phase lock value.
  • the detection method based on facial features detects whether the driver is in a fatigued state by detecting eye activities such as the driver's blink amplitude, frequency, and average closing time, frequent nodding, long-term head immobility, and other facial features. Most of these detection methods are based on machine vision and have the advantages of easy placement of detection equipment and good timeliness of detection. They are the most commonly used methods for driving fatigue detection.
  • Detection method based on driving behavior indirectly by monitoring the driving characteristics of the vehicle such as the steering wheel rotation angle, accelerator pedal, brake, etc., as well as the speed, acceleration, control stability, and deviation of the driving route during the driving process. Determine whether the driver has entered a state of fatigue.
  • the advantage is that it does not need to touch the human body and is convenient to use, and the device occupies a small space inside the vehicle.
  • the EEG signal can directly reflect the person's physical and mental activities, and is known as the "gold standard" for detecting driving fatigue.
  • the methods for EEG signals power spectrum or entropy methods are mostly used.
  • the power spectral density method is to transform the EEG signal from the time domain to the frequency domain for analysis. For each frequency band, the energy change from wakefulness to fatigue can be analyzed. When the human brain enters a state of fatigue, the energy of the ⁇ and ⁇ bands of the EEG signal will increase, while the energy of the ⁇ and ⁇ bands will decrease. The ratio of the energy in the corresponding frequency bands can amplify this trend to judge the driver Mental state.
  • Entropy can be used to measure the degree of chaos in the system.
  • Entropy-based methods include approximate entropy, sample entropy, wavelet entropy, etc.
  • wavelet transform has the characteristics of time-frequency localization
  • wavelet entropy is an entropy value calculated from the signal sequence after wavelet decomposition, which can accurately reflect the complexity of brain waves.
  • the above-mentioned driving fatigue detection methods have the following shortcomings: the fatigue detection methods based on facial features are easily affected by the environment, brightness, angle, and some other uncontrollable factors still limit the performance of the algorithm to a certain extent. Facial features based on computer vision The extraction method is very easy to accept and be deceived by artificial forged signals; the driving behavior method is powerless on non-standard roads, the accuracy rate is not enough, and it is easy to produce false alarms; in the detection method based on physiological information, EEG can directly reflect For information such as human physical activity and mental state, the brain realizes information interaction through the interconnection and cluster work of different regions. The state of human consciousness and behavior is not determined by a single region, but is often determined by multiple parts of the whole brain. The regional cooperation is completed, but the driving fatigue detection method based on power spectrum and entropy does not involve brain regional information, and it is impossible to comprehensively and systematically study the mechanism of driving fatigue.
  • the present invention aims to overcome the above-mentioned defects of the prior art and provide a driving fatigue detection method based on the phase lock value to construct a brain function network, and the detection reliability and accuracy are high.
  • the technical solution adopted by the present invention is to provide a driving fatigue detection method based on the phase lock value to construct a brain function network, which is characterized in that it includes the following steps:
  • EEG signal acquisition device uses the EEG signal acquisition device to collect the EEG signals of the subject during the waking time and the test time respectively; the position of each electrode in the EEG signal acquisition device is used as a brain function network node, and the number of electrodes is Number of nodes N;
  • the frequency of theta wave is 4-8Hz
  • the frequency of the alpha wave is 8-13Hz
  • the frequency of the beta wave is 13- 30Hz
  • each brain function network node is used as a channel; calculate the phase lock value PLV during the awake time for every two channels to obtain the coupling relationship between every two channels during the awake time; Divide into multiple test time series, calculate the phase lock value PLV in the test time series for each two channels to obtain the coupling relationship between every two channels in the test time series; the phase lock value PLV represents the connection between the two channels Intensity, the phase lock value PLV in the awake time and the test time series is used to form the functional connection matrix of the channel during the awake time and the test time series; the calculation of PLV uses the formula (1):
  • connection strength threshold Set the connection strength threshold and compare it with the value of each element in the functional connection matrix to obtain the connection relationship between the channels in the awake time and test time series; the element value greater than or equal to the connection strength threshold is the connection between the two channels , Otherwise there is no connection between the two channels;
  • connection relationship between channels form the brain function network during the waking time and the test time series; compare and analyze the difference in the three sub-bands of the brain function network topology between the waking time and the test time series to judge Test whether you are in a state of driving fatigue in the time series.
  • the EEG signals of driving during the awake time and the test time are collected, denoising, decomposed and reconstructed, and then the awake time and test time series are calculated for each two channels.
  • the phase lock value PLV in the awake time and the test time series are formed according to the PLV, and the connection strength threshold is set and compared with each element value in the function connection matrix to obtain the awake time and
  • the connection between the channels in the test time series and the formation of the brain function network of the subject during the waking time and the test time series are compared and analyzed for the differences in the brain function network topology between the awake time and the test time series in the three sub-bands. To determine whether it is in a driving fatigue state in the test time series, the reliability and accuracy of the detection are high.
  • the EEG acquisition device in step s1 is a wireless stem electrode EEG acquisition device, including 24 electrodes, and the frequency of collecting signals is 250 Hz; the functional connection matrix is 24*24; and the test time is 90 minutes.
  • the improved international 10-20 placement standard is used to place the electrodes.
  • the electrode names are: AFp3h, AFpz, AFp4h, AFF3, AFFz, AFF4, FFC5h, FFC3h, FFCz, FFC4h, FFC6h, CCP5h, CCP1, CCPz, CCP2, CCP6h, PO3, POz, PO4, PO7, O1h, Oz, O2h, PO8, the wireless stem electrode EEG acquisition equipment is published in the literature (Klem G H, Lüders H O, Jasper H H, et al.
  • test time of each driving fatigue detection experiment is set at 90 minutes to ensure that the subjects are from the awake state to the fatigue state EEG signals throughout the process.
  • the subject uses the simulated driving system to drive, and the simulated driving system randomly sends out braking commands to record the time interval between seeing the braking command and responding to the subject; set the time interval threshold, if the response is correct If the time interval is greater than or equal to the time interval threshold, the EEG signal during the test period before this time interval will be used as the EEG signal during the awake period, and the EEG signal during the test period after this time interval will be used as the EEG signal The signal to be detected for fatigue.
  • the time interval threshold is derived from the training experiment. Due to the individual differences of the subjects, the time interval threshold is not uniform. Therefore, it is necessary to obtain the time interval threshold for individual subjects through the training experiment before the test experiment, that is, before step S1.
  • the calculation method It is the average value of the response time interval during the training experiment when the subject is externally fatigued (such as yawning) or the vehicle's driving path deviates from the normal running track; when driving fatigue, the subject is seeing The time interval between the brake command and the response will be longer.
  • the test time is distinguished by the time interval threshold, and only the EEG signal during the test time after this time interval can be processed as the signal to be detected for fatigue. Reduce the data processed.
  • the subject uses a simulated driving system to drive, also known as car driving simulation, or virtual driving, which refers to the use of modern high-tech means such as: 3D image instant generation technology, vehicle dynamics simulation physical system, and large field of view display technology (such as multi-channel stereo projection system), six-degree-of-freedom motion platform (or three-degree-of-freedom motion platform), user input hardware system, stereo sound, central control system, etc., allowing the experiencer to feel close to reality in a virtual driving environment
  • the effect of visual, auditory and somatosensory car driving experience is suitable for the needs of research institutions for automotive engineering, traffic engineering, human factors engineering research and as a simulation experiment platform in the field of automotive research.
  • an independent component analysis method is used to perform denoising processing on the EEG signal, so as to remove the interference of the ocular electrical signal and improve the signal-to-noise ratio of the EEG signal.
  • wavelet packet transform is used to decompose the EEG signal, and then reconstructed into three sub-band signals.
  • connection strength threshold in step s5 is 0.2.
  • a complex network analysis method is used to quantitatively analyze the differences in the brain function network topology of the three sub-bands during the awake time and the test time sequence from both functional integration and functional differentiation;
  • the functional integration includes features Path length and global efficiency,
  • functional differentiation includes local efficiency and clustering coefficient;
  • the clustering coefficient represents the degree of clustering of brain function network nodes, which is calculated using formula (2);
  • C i represents the average number of clusters of the brain function network
  • k i represents the number of adjacent nodes of node i
  • adjacent nodes are nodes connected to node i
  • E i is the closed-loop triangle existing in adjacent nodes of node i Number; the other two nodes connected to node i, if there are edges between the three nodes, it is a closed-loop triangle;
  • the characteristic path length reflects the information transmission ability inside the brain function network, and is calculated using formula (3):
  • L is the characteristic path length
  • L ij is the shortest path length between node i and node j, that is, the number of shortest edges used from node i to node j
  • N is the number of nodes
  • the global efficiency is the average value of the reciprocal of the shortest path length L ij , which is used to measure the ability of the brain function network to transmit and process information. It is calculated using formula (4):
  • N is the number of nodes
  • Lij is the shortest path length between node i and node j
  • the local efficiency is used to measure the local information transmission and processing ability, and it is calculated by formula (5):
  • E global (G i ) is the global efficiency of node i, namely E g .
  • the present invention has the following beneficial effects: by separately collecting EEG signals during awake time and during test time, and performing denoising processing, decomposition and reconstruction on the EEG signals, and then separately analyzing each two
  • the channel calculates the phase lock value PLV during the awake time and the test time series, and forms the functional connection matrix of the channel during the awake time and the test time series according to the PLV, sets the connection strength threshold and connects with each element value in the function connection matrix Make comparisons to obtain the connection relationship between channels in the awake time and the test time series respectively, and form the brain function network of the subject during the waking time and the test time series, and compare and analyze the brain function network during the awake time and the test time series
  • the difference of the topological structure in the three sub-bands is used to determine whether the test time series is in a driving fatigue state, and the reliability and accuracy of the detection are high.
  • FIG. 1 is a diagram of the electrode position of the improved international 10-20 system used in the driving fatigue detection method for constructing a brain function network based on the phase lock value of this embodiment.
  • Figure 2a is a schematic diagram of the functional connection matrix during awake time
  • Figure 2b is a schematic diagram of the functional connection matrix during test time
  • the row and column lines are channels
  • the gradient bar on the right of the grid is the phase lock value PLV.
  • Figure 3a is a brain function network diagram during awake time
  • Figure 3b is a brain function network diagram during a test time
  • Figure 3c is a comparison diagram of the difference between Figure 3a and Figure 3b.
  • FIG. 4 is a schematic diagram of the clustering coefficients of the brain function network topology in the three sub-bands during the awake time and the test time sequence in this embodiment.
  • FIG. 5 is a schematic diagram of the global efficiency of the brain function network topology in the three sub-bands during the awake time and the test time sequence in this embodiment.
  • This embodiment provides a driving fatigue detection method for constructing a brain function network based on a phase lock value, which is characterized in that it includes the following steps:
  • EEG signal acquisition device uses the EEG signal acquisition device to collect the EEG signals of the subject during the waking time and the test time respectively; the position of each electrode in the EEG signal acquisition device is used as a brain function network node, and the number of electrodes is Number of nodes N;
  • the frequency of theta wave is 4-8Hz
  • the frequency of the alpha wave is 8-13Hz
  • the frequency of the beta wave is 13- 30Hz
  • each brain function network node is used as a channel; calculate the phase lock value PLV during the awake time for every two channels to obtain the coupling relationship between every two channels during the awake time; Divide into multiple test time series, calculate the phase lock value PLV in the test time series for each two channels to obtain the coupling relationship between every two channels in the test time series; the phase lock value PLV represents the connection between the two channels Intensity, the phase lock value PLV in the awake time and the test time series is used to form the functional connection matrix of the channel during the awake time and the test time series.
  • the schematic diagram of the functional connection matrix during the awake time is shown in Figure 2a, in the test time series
  • the schematic diagram of the functional connection matrix is shown in Figure 2b; the calculation of PLV uses formula (1):
  • connection strength threshold Set the connection strength threshold and compare it with the value of each element in the functional connection matrix to obtain the connection relationship between the channels in the awake time and test time series; the element value greater than or equal to the connection strength threshold is the connection between the two channels , Otherwise there is no connection between the two channels;
  • the brain function network during the waking time and the test time series is formed.
  • the brain function network during the waking time is shown in Figure 3a
  • the brain function network in the test time series is shown in Figure 3b.
  • Fig. 3c is the difference comparison diagram of Fig. 3a and Fig. 3b; the difference of the brain function network topology in the three sub-bands between the awake time and the test time series is compared and analyzed to determine whether the driving fatigue state is in the test time series.
  • the EEG acquisition device in step s1 is a wireless stem electrode EEG acquisition device, including 24 electrodes, and the frequency of collecting signals is 250 Hz; the functional connection matrix is 24*24; and the test time is 90 minutes.
  • the improved international 10-20 placement standard is used to place the electrodes.
  • the electrode names are: AFp3h, AFpz, AFp4h, AFF3, AFFz, AFF4, FFC5h, FFC3h, FFCz, FFC4h, FFC6h, CCP5h, CCP1, CCPz, CCP2, CCP6h, PO3, POz, PO4, PO7, O1h, Oz, O2h, PO8, the wireless stem electrode EEG acquisition equipment is published in the literature (Klem G H, Lüders H O, Jasper H H, et al. The ten-twenty electrode system of the International Federation[J]. Electroencephalogr Clin Neurophysiol,1999,52(3):3-6.); the test time of each driving fatigue detection experiment is set at 90 minutes to ensure that the subjects are from the awake state to the fatigue state EEG signals throughout the process.
  • step s1 the subject uses the simulated driving system to drive, and the simulated driving system randomly sends out braking commands to record the time interval between seeing the braking command and responding to the subject; set the time interval threshold, if the reaction time If the interval is greater than or equal to the time interval threshold, the EEG signal during the test time before this time interval will be used as the EEG signal during the awake time, and the EEG signal during the test time after this time interval will be used as the waiting time. Check whether the fatigue signal.
  • the time interval threshold is derived from the training experiment. Due to the individual differences of the subjects, the time interval threshold is not uniform. Therefore, it is necessary to obtain the time interval threshold for individual subjects through the training experiment before the test experiment, that is, before step S1.
  • the calculation method It is the average value of the response time interval during the training experiment when the subject is externally fatigued (such as yawning) or the vehicle's driving path deviates from the normal running track; when driving fatigue, the subject is seeing The time interval between the brake command and the response will be longer.
  • the test time is distinguished by the time interval threshold, and only the EEG signal during the test time after this time interval can be processed as the signal to be detected for fatigue. Reduce the data processed.
  • the subject uses a simulated driving system to drive, also known as car driving simulation, or virtual driving, which refers to the use of modern high-tech means such as: 3D image instantaneous generation technology, vehicle dynamics simulation physical system, large field of view display technology (such as multi-channel stereo projection system), six-degree-of-freedom motion platform (or three-degree-of-freedom motion platform), user input hardware system, stereo sound, central control system, etc., allowing the experiencer to feel close to reality in a virtual driving environment
  • the effect of visual, auditory and somatosensory car driving experience is suitable for the needs of research institutions for automotive engineering, traffic engineering, human factors engineering research and as a simulation experiment platform in the field of automotive research.
  • the independent component analysis method is used to denoise the EEG signal to remove the interference of the ocular signal and improve the signal-to-noise ratio of the EEG signal.
  • step s3 the wavelet packet transform is used to decompose the EEG signal, and then reconstruct it into three sub-band signals.
  • connection strength threshold in step s5 is 0.2.
  • step s6 the complex network analysis method is used to quantitatively analyze the differences in the brain function network topology of the three sub-bands during the awake time and the test time series from the aspects of functional integration and functional differentiation.
  • the functional integration includes characteristic paths. Length and global efficiency, functional differentiation includes local efficiency and clustering coefficient;
  • the clustering coefficient represents the degree of clustering of brain function network nodes, as shown in Figure 4, calculated using formula (2);
  • C i represents the average clustering number of the brain function network
  • k i represents the number of adjacent nodes of node i
  • adjacent nodes are nodes connected to node i
  • E i is the closed-loop triangle existing in adjacent nodes of node i Number; the other two nodes connected to node i, if there are edges between the three nodes, it is a closed-loop triangle;
  • the characteristic path length reflects the information transmission ability inside the brain function network, and is calculated using formula (3):
  • L is the characteristic path length
  • L ij is the shortest path length between node i and node j, that is, the number of shortest edges used from node i to node j
  • N is the number of nodes
  • the global efficiency is the average value of the reciprocal of the shortest path length L ij , which is used to measure the ability of the brain function network to transmit and process information. As shown in Figure 5, it is calculated by formula (4):
  • N is the number of nodes
  • Lij is the shortest path length between node i and node j
  • the local efficiency is used to measure the local information transmission and processing ability, and it is calculated by formula (5):
  • E global (G i ) is the global efficiency of node i, namely E g .

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Abstract

一种基于相位锁定值构建脑功能网络的驾驶疲劳检测方法，属于驾驶疲劳检测领域，通过分别采集在清醒时间和测试时间内驾驶时的脑电信号，并对其进行去噪处理、分解和重构，然后分别对每两个通道计算在清醒时间内和测试时间序列内的相位锁定值PLV，并根据PLV形成通道在清醒时间内和测试时间序列内的功能连接矩阵，设置连接强度阈值并与功能连接矩阵中每个元素值进行比较，以分别获取清醒时间内和测试时间序列内通道间连接关系并形成受试者清醒时间内和测试时间序列内的脑功能网络，对比分析清醒时间内和测试时间序列内的脑功能网络拓扑结构在三个子频带的差异，以判断测试时间序列内是否处于驾驶疲劳状态，检测的可靠性和准确性较高。

Description

一种基于相位锁定值构建脑功能网络的驾驶疲劳检测方法 技术领域
本发明涉及驾驶疲劳检测领域，更具体地，涉及一种基于相位锁定值构建脑功能网络的驾驶疲劳检测方法。
背景技术
许多国家都重视驾驶疲劳相关的检测方法研究，最初的研究主要从医学方面入手，利用医疗设备研究人的精神状态。十九世纪初美国最早开展了机动车驾驶员服务时间管理条例的合理性的调查。之后关于驾驶疲劳的研究相继展开。经过多年的发展，驾驶疲劳检测方法的研究大致可分为三类：基于面部特征的检测方法、基于驾驶行为的检测方法、基于生理特征的检测方法。
基于面部特征的检测方法，通过检测驾驶员眨眼幅度、频率以及平均闭合时间等眼部活动、频繁点头、头部长期不动等头部特征以及其它的面部特征，判断驾驶员是否处于疲劳状态。这些检测方法大多基于机器视觉，有检测设备易于放置、检测时效性好等优点，是驾驶疲劳检测最常用的方法。
基于驾驶行为的检测方法，通过监控驾驶员操作方向盘转动角度、加速踏板、制动器等车辆控制特征，以及车辆行驶过程中的速度、加速度、控制稳定性、是否偏移驾驶路线等车辆行驶特征，间接判断驾驶员是否进入疲劳状态。优点在于无需接触人体以及使用方便，设备在车辆内部占用空间小。
基于生理特征的驾驶疲劳检测方法中，脑电信号可以直接反映人的身体及精神活动，被誉为检测驾驶疲劳的“金标准”。在针对脑电信号的方法中，大多使用功率谱或熵的方法。
功率谱密度方法是将脑电信号从时域转化为频域进行分析，针对每个频带可以分析其清醒到疲劳时的能量变化。当人的大脑进入疲劳状态时，脑电信号的δ、θ频带的能量会升高，而α、β频带的能量会降低，通过对相应频带能量的比，可以放大这种趋势从而 判断驾驶员的精神状态。
熵可以用来测量系统的混乱程度。基于熵的方法，包括近似熵、样本熵、小波熵等。其中，小波变换具有时频局部化特性，小波熵是从小波分解后的信号序列计算的一种熵值，它可准确反映脑电波的复杂程度。用小波熵值分析驾驶员疲劳状态脑电波的复杂度，并对模拟驾驶前、模拟驾驶疲劳后和休息后的脑电信号进行分析，可以判断驾驶疲劳。
但是，上述驾驶疲劳检测方法存在如下不足：基于面部特征的疲劳检测方法易受环境影响，亮度、角度以及其他的一些不可控因素仍然在一定程度上限制着算法的性能，基于计算机视觉的面部特征提取方法非常容易接受人为的伪造信号，并且被其所欺骗；驾驶行为方法则对非标准型道路无能为力，准确率不够，易产生误报；在基于生理信息的检测方法中，脑电能够直接反映人的身体活动、精神状态等信息，大脑通过不同区域的相互连接和集群工作来实现信息交互，人的意识、行为等状态也并非由某个区域单独决定，而往往是由全脑的多个区域共同协作完成的，但基于功率谱、熵的驾驶疲劳检测方法没有涉及大脑区域性的信息，无法全面、系统研究驾驶疲劳产生的机理。
发明内容
本发明旨在克服上述现有技术的缺陷，提供一种基于相位锁定值构建脑功能网络的驾驶疲劳检测方法，检测的可靠性和准确性较高。
为达到上述目的，本发明采取的技术方案是：提供一种基于相位锁定值构建脑功能网络的驾驶疲劳检测方法，其特征在于，包括如下步骤：
S1.使用脑电信号采集设备采集受试者分别在清醒时间和测试时间内驾驶时的脑电信号；其中脑电信号采集设备中每个电极的位置作为一个脑功能网络节点，电极个数为节点个数N；
S2.对脑电信号进行去噪处理，以提高脑电信号的信噪比；
S3.对去噪处理后的脑电信号进行分解和重构，按频率范围重构三个子频带波形，其中θ波频率为4-8Hz，α波频率为8-13Hz，β波频率为13-30Hz；
S4.重构后的信号中每个脑功能网络节点作为一个通道；对每两个通道计算在清醒时间内的相位锁定值PLV，以获取清醒时间内每两个通道的耦合关系；将测试时间划分为多个测试时间序列，对每两个通道计算在测试时间序列内的相位锁定值PLV，以获取测试时间序列内每两个通道的耦合关系；相位锁定值PLV表示两个通道间的连接强度，分别利用清醒时 间内和测试时间序列内相位锁定值PLV形成通道在清醒时间内和测试时间序列内的功能连接矩阵；PLV的计算使用公式(1)：
Figure PCTCN2019088445-appb-000001
其中，
Figure PCTCN2019088445-appb-000002
是两个通道在清醒时间内和测试时间序列内的相位差，每个通道在清醒时间内和测试时间序列内的相位使用希尔伯特变换获取，i∈N为脑功能网络节点，PLV值在[0，1]之间，0为通道间无连接，1为通道间完全连接；
S5.设置连接强度阈值并与功能连接矩阵中每个元素值进行比较，以分别获取清醒时间内和测试时间序列内通道间连接关系；元素值大于或等于连接强度阈值的为两个通道间连接，否则为两个通道间无连接；
S6.根据通道间连接关系，形成受试者清醒时间内和测试时间序列内的脑功能网络；对比分析清醒时间内和测试时间序列内的脑功能网络拓扑结构在三个子频带的差异，以判断测试时间序列内是否处于驾驶疲劳状态。
上述方案中，通过分别采集在清醒时间和测试时间内驾驶时的脑电信号，并对其进行去噪处理、分解和重构，然后分别对每两个通道计算在清醒时间内和测试时间序列内的相位锁定值PLV，并根据PLV形成通道在清醒时间内和测试时间序列内的功能连接矩阵，设置连接强度阈值并与功能连接矩阵中每个元素值进行比较，以分别获取清醒时间内和测试时间序列内通道间连接关系并形成受试者清醒时间内和测试时间序列内的脑功能网络，对比分析清醒时间内和测试时间序列内的脑功能网络拓扑结构在三个子频带的差异，以判断测试时间序列内是否处于驾驶疲劳状态，检测的可靠性和准确性较高。
优选地，步骤s1中脑电采集设备为无线干电极脑电采集设备，包括24个电极，采集信号的频率为250Hz；功能连接矩阵为24*24；测试时间为90分钟。采用改进的国际10-20放置标准放置电极，电极名称为：AFp3h,AFpz,AFp4h,AFF3,AFFz,AFF4,FFC5h,FFC3h,FFCz,FFC4h,FFC6h,CCP5h,CCP1,CCPz,CCP2,CCP6h,PO3,POz,PO4,PO7,O1h,Oz,O2h,PO8，该无线干电极脑电采集设备被公布在文献(Klem G H,Lüders H O,Jasper H H,et al.The ten-twenty electrode system of the International Federation[J].Electroencephalogr Clin Neurophysiol,1999,52(3):3-6.)中；每次驾驶疲劳检测实验测试时间为90分钟的设置，以确保获取受试者从清醒状态进入疲劳状态整个过程的脑电信号。
优选地，步骤s1中受试者利用模拟驾驶系统进行驾驶，且模拟驾驶系统随机发出刹车命令，记录受试者在看到刹车命令和做出反应的时间间隔；设置时间间隔阈值，若反应的时间间隔大于或等于时间间隔阈值，则将从这个时间间隔往前的测试时间内的脑电信号作为清醒时间内的脑电信号，将从这个时间间隔往后的测试时间内的脑电信号作为待检测是否疲劳的信号。时间间隔阈值来源于训练实验，由于受试者的个体差异，时间间隔阈值也不统一，因此在测试实验前即步骤S1之前需要通过训练实验获得面向个体受试者的时间间隔阈值，其计算方法是训练实验过程中，受试者外在表现为疲劳状态(如打呵欠)或汽车行车路径偏离正常运行轨迹的时间段内反应的时间间隔的平均值；驾驶疲劳时，受试者在看到刹车命令和做出反应的时间间隔会变长，将测试时间利用时间间隔阈值区分开来，可以只对这个时间间隔往后的测试时间内的脑电信号作为待检测是否疲劳的信号进行处理，减少处理的数据。受试者利用模拟驾驶系统进行驾驶，也被称为汽车驾驶仿真，或虚拟驾驶，是指利用现代高科技手段如：三维图像即时生成技术、汽车动力学仿真物理系统、大视场显示技术(如多通道立体投影系统)、六自由度运动平台(或三自由度运动平台)、用户输入硬件系统、立体声音响、中控系统等，让体验者在一个虚拟的驾驶环境中，感受到接近真实效果的视觉、听觉和体感的汽车驾驶体验，适用于研究机构进行汽车工程、交通工程、人因工程研究的需要以及作为汽车研究领域的模拟实验平台。
优选地，步骤s2中，使用独立成分分析方法对脑电信号进行去噪处理，以去除眼电信号的干扰，提高脑电信号的信噪比。
优选地，步骤s3中利用小波包变换对脑电信号进行分解，然后重构为三个子频带的信号。
优选地，步骤s5中连接强度阈值为0.2。
优选地，步骤s6中利用复杂网络分析方法从功能整合与功能分化两方面分别对三个子频带在清醒时间内和测试时间序列内的脑功能网络拓扑结构的差异进行定量分析；功能整合方面包括特征路径长度和全局效率，功能分化包括局部效率和聚类系数；
聚类系数表示脑功能网络节点的聚集程度，使用公式(2)计算；
Figure PCTCN2019088445-appb-000003
其中，C i表示脑功能网络的平均聚类数，k i表示节点i的相邻节点数，相邻节点为与节点 i连接的节点，E i为对于节点i相邻节点中存在的闭环三角形数；与节点i相连接的另外两个节点，若三个节点间都存在边则为闭环三角形；
特征路径长度反应脑功能网络内部的信息传递能力，使用公式(3)计算：
Figure PCTCN2019088445-appb-000004
其中，L为特征路径长度；L ij为节点i与节点j之间的最短路径长度，即从节点i到节点j所用最短的边的数量；N为节点的个数；
全局效率为最短路径长度L ij的倒数的平均值，用来衡量脑功能网络传递和处理信息的能力，使用公式(4)计算：
Figure PCTCN2019088445-appb-000005
其中，N为节点的个数，L ij为节点i与节点j之间的最短路径长度；
局部效率用来衡量局部信息传递和处理能力，使用公式(5)计算：
Figure PCTCN2019088445-appb-000006
其中，E global(G i)为节点i的全局效率即E g。
与现有技术相比，本发明的有益效果为：通过分别采集在清醒时间和测试时间内驾驶时的脑电信号，并对其进行去噪处理、分解和重构，然后分别对每两个通道计算在清醒时间内和测试时间序列内的相位锁定值PLV，并根据PLV形成通道在清醒时间内和测试时间序列内的功能连接矩阵，设置连接强度阈值并与功能连接矩阵中每个元素值进行比较，以分别获取清醒时间内和测试时间序列内通道间连接关系并形成受试者清醒时间内和测试时间序列内的脑功能网络，对比分析清醒时间内和测试时间序列内的脑功能网络拓扑结构在三个子频带的差异，以判断测试时间序列内是否处于驾驶疲劳状态，检测的可靠性和准确性较高。
附图说明
图1为本实施例一种基于相位锁定值构建脑功能网络的驾驶疲劳检测方法中使用的改进国际10-20系统的电极位置图。
图2a为清醒时间内的功能连接矩阵示意图，图2b为测试时间内的功能连接矩阵示意图；其中行列线为通道，格网右边的渐变条框为相位锁定值PLV。
图3a为清醒时间内的脑功能网络图，图3b为测试时间内的脑功能网络图，图3c为图3a和图3b的差异对比图。
图4为本实施例中清醒时间内和测试时间序列内的脑功能网络拓扑结构在三个子频带的聚类系数的示意图。
图5为本实施例中清醒时间内和测试时间序列内的脑功能网络拓扑结构在三个子频带的全局效率的示意图。
具体实施方式
本发明附图仅用于示例性说明，不能理解为对本发明的限制。为了更好说明以下实施例，附图某些部件会有省略、放大或缩小，并不代表实际产品的尺寸；对于本领域技术人员来说，附图中某些公知结构及其说明可能省略是可以理解的。
实施例
本实施例提供一种基于相位锁定值构建脑功能网络的驾驶疲劳检测方法，其特征在于，包括如下步骤：
S1.使用脑电信号采集设备采集受试者分别在清醒时间和测试时间内驾驶时的脑电信号；其中脑电信号采集设备中每个电极的位置作为一个脑功能网络节点，电极个数为节点个数N；
S2.对脑电信号进行去噪处理，以提高脑电信号的信噪比；
S3.对去噪处理后的脑电信号进行分解和重构，按频率范围重构三个子频带波形，其中θ波频率为4-8Hz，α波频率为8-13Hz，β波频率为13-30Hz；
S4.重构后的信号中每个脑功能网络节点作为一个通道；对每两个通道计算在清醒时间内的相位锁定值PLV，以获取清醒时间内每两个通道的耦合关系；将测试时间划分为多个测试时间序列，对每两个通道计算在测试时间序列内的相位锁定值PLV，以获取测试时间序列内每两个通道的耦合关系；相位锁定值PLV表示两个通道间的连接强度，分别利用清醒时 间内和测试时间序列内相位锁定值PLV形成通道在清醒时间内和测试时间序列内的功能连接矩阵，清醒时间内的功能连接矩阵示意图如图2a所示，测试时间序列内的功能连接矩阵示意图如图2b所示；PLV的计算使用公式(1)：
Figure PCTCN2019088445-appb-000007
其中，
Figure PCTCN2019088445-appb-000008
是两个通道在清醒时间内和测试时间序列内的相位差，每个通道在清醒时间内和测试时间序列内的相位使用希尔伯特变换获取，i∈N为脑功能网络节点，PLV值在[0，1]之间，0为通道间无连接，1为通道间完全连接；
S5.设置连接强度阈值并与功能连接矩阵中每个元素值进行比较，以分别获取清醒时间内和测试时间序列内通道间连接关系；元素值大于或等于连接强度阈值的为两个通道间连接，否则为两个通道间无连接；
S6.根据通道间连接关系，形成受试者清醒时间内和测试时间序列内的脑功能网络，清醒时间内的脑功能网络如图3a所示，测试时间序列内的脑功能网络如图3b所示，图3c为图3a和图3b的差异对比图；对比分析清醒时间内和测试时间序列内的脑功能网络拓扑结构在三个子频带的差异，以判断测试时间序列内是否处于驾驶疲劳状态。
通过分别采集在清醒时间和测试时间内驾驶时的脑电信号，并对其进行去噪处理、分解和重构，然后分别对每两个通道计算在清醒时间内和测试时间序列内的相位锁定值PLV，并根据PLV形成通道在清醒时间内和测试时间序列内的功能连接矩阵，设置连接强度阈值并与功能连接矩阵中每个元素值进行比较，以分别获取清醒时间内和测试时间序列内通道间连接关系并形成受试者清醒时间内和测试时间序列内的脑功能网络，对比分析清醒时间内和测试时间序列内的脑功能网络拓扑结构在三个子频带的差异，以判断测试时间序列内是否处于驾驶疲劳状态，检测的可靠性和准确性较高。
其中，如图1所示，步骤s1中脑电采集设备为无线干电极脑电采集设备，包括24个电极，采集信号的频率为250Hz；功能连接矩阵为24*24；测试时间为90分钟。采用改进的国际10-20放置标准放置电极，电极名称为：AFp3h,AFpz,AFp4h,AFF3,AFFz,AFF4,FFC5h,FFC3h,FFCz,FFC4h,FFC6h,CCP5h,CCP1,CCPz,CCP2,CCP6h,PO3,POz,PO4,PO7,O1h,Oz,O2h,PO8，该无线干电极脑电采集设备被公布在文献(Klem G H,Lüders H O,Jasper H H,et al.The ten-twenty electrode system of the International Federation[J]. Electroencephalogr Clin Neurophysiol,1999,52(3):3-6.)中；每次驾驶疲劳检测实验测试时间为90分钟的设置，以确保获取受试者从清醒状态进入疲劳状态整个过程的脑电信号。
另外，步骤s1中受试者利用模拟驾驶系统进行驾驶，且模拟驾驶系统随机发出刹车命令，记录受试者在看到刹车命令和做出反应的时间间隔；设置时间间隔阈值，若反应的时间间隔大于或等于时间间隔阈值，则将从这个时间间隔往前的测试时间内的脑电信号作为清醒时间内的脑电信号，将从这个时间间隔往后的测试时间内的脑电信号作为待检测是否疲劳的信号。时间间隔阈值来源于训练实验，由于受试者的个体差异，时间间隔阈值也不统一，因此在测试实验前即步骤S1之前需要通过训练实验获得面向个体受试者的时间间隔阈值，其计算方法是训练实验过程中，受试者外在表现为疲劳状态(如打呵欠)或汽车行车路径偏离正常运行轨迹的时间段内反应的时间间隔的平均值；驾驶疲劳时，受试者在看到刹车命令和做出反应的时间间隔会变长，将测试时间利用时间间隔阈值区分开来，可以只对这个时间间隔往后的测试时间内的脑电信号作为待检测是否疲劳的信号进行处理，减少处理的数据。受试者利用模拟驾驶系统进行驾驶，也被称为汽车驾驶仿真，或虚拟驾驶，是指利用现代高科技手段如：三维图像即时生成技术、汽车动力学仿真物理系统、大视场显示技术(如多通道立体投影系统)、六自由度运动平台(或三自由度运动平台)、用户输入硬件系统、立体声音响、中控系统等，让体验者在一个虚拟的驾驶环境中，感受到接近真实效果的视觉、听觉和体感的汽车驾驶体验，适用于研究机构进行汽车工程、交通工程、人因工程研究的需要以及作为汽车研究领域的模拟实验平台。
其中，使用独立成分分析方法对脑电信号进行去噪处理，以去除眼电信号的干扰，提高脑电信号的信噪比。
另外，步骤s3中利用小波包变换对脑电信号进行分解，然后重构为三个子频带的信号。
其中，步骤s5中连接强度阈值为0.2。
另外，步骤s6中利用复杂网络分析方法从功能整合与功能分化两方面分别对三个子频带在清醒时间内和测试时间序列内的脑功能网络拓扑结构的差异进行定量分析；功能整合方面包括特征路径长度和全局效率，功能分化包括局部效率和聚类系数；
聚类系数表示脑功能网络节点的聚集程度，如图4所示，使用公式(2)计算；
Figure PCTCN2019088445-appb-000009
其中，C i表示脑功能网络的平均聚类数，k i表示节点i的相邻节点数，相邻节点为与节点i连接的节点，E i为对于节点i相邻节点中存在的闭环三角形数；与节点i相连接的另外两个节点，若三个节点间都存在边则为闭环三角形；
特征路径长度反应脑功能网络内部的信息传递能力，使用公式(3)计算：
Figure PCTCN2019088445-appb-000010
其中，L为特征路径长度；L ij为节点i与节点j之间的最短路径长度，即从节点i到节点j所用最短的边的数量；N为节点的个数；
全局效率为最短路径长度L ij的倒数的平均值，用来衡量脑功能网络传递和处理信息的能力，如图5所示，使用公式(4)计算：
Figure PCTCN2019088445-appb-000011
其中，N为节点的个数，L ij为节点i与节点j之间的最短路径长度；
局部效率用来衡量局部信息传递和处理能力，使用公式(5)计算：
Figure PCTCN2019088445-appb-000012
其中，E global(G i)为节点i的全局效率即E g。
显然，本发明的上述实施例仅仅是为清楚地说明本发明技术方案所作的举例，而并非是对本发明的具体实施方式的限定。凡在本发明权利要求书的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本发明权利要求的保护范围之内。

Claims (7)

  1. 一种基于相位锁定值构建脑功能网络的驾驶疲劳检测方法，其特征在于，包括如下步骤：
    S1.使用脑电信号采集设备采集受试者分别在清醒时间和测试时间内驾驶时的脑电信号；
    其中脑电信号采集设备中每个电极的位置作为一个脑功能网络节点，电极个数为节点个数N；
    S2.对脑电信号进行去噪处理，以提高脑电信号的信噪比；
    S3.对去噪处理后的脑电信号进行分解和重构，按频率范围重构三个子频带波形，其中θ波频率为4-8Hz，α波频率为8-13Hz，β波频率为13-30Hz；
    S4.重构后的信号中每个脑功能网络节点作为一个通道；对每两个通道计算在清醒时间内的相位锁定值PLV，以获取清醒时间内每两个通道的耦合关系；将测试时间划分为多个测试时间序列，对每两个通道计算在测试时间序列内的相位锁定值PLV，以获取测试时间序列内每两个通道的耦合关系；相位锁定值PLV表示两个通道间的连接强度，分别利用清醒时间内和测试时间序列内相位锁定值PLV形成通道在清醒时间内和测试时间序列内的功能连接矩阵；PLV的计算使用公式(1)：
    Figure PCTCN2019088445-appb-100001
    其中，
    Figure PCTCN2019088445-appb-100002
    是两个通道在清醒时间内和测试时间序列内的相位差，每个通道在清醒时间内和测试时间序列内的相位使用希尔伯特变换获取，i∈N为脑功能网络节点，PLV值在[0，1]之间，0为通道间无连接，1为通道间完全连接；
    S5.设置连接强度阈值并与功能连接矩阵中每个元素值进行比较，以分别获取清醒时间内和测试时间序列内通道间连接关系；元素值大于或等于连接强度阈值的为两个通道间连接，否则为两个通道间无连接；
    S6.根据通道间连接关系，形成受试者清醒时间内和测试时间序列内的脑功能网络；对比分析清醒时间内和测试时间序列内的脑功能网络拓扑结构在三个子频带的差异，以判断测试时间序列内是否处于驾驶疲劳状态。
  2. 根据权利要求1所述的一种基于相位锁定值构建脑功能网络的驾驶疲劳检测方法，其特征在于，步骤s1中脑电采集设备为无线干电极脑电采集设备，包括24个电极，采集信号的频率为250Hz；功能连接矩阵为24*24；测试时间为90分钟。
  3. 根据权利要求1所述的一种基于相位锁定值构建脑功能网络的驾驶疲劳检测方法，其特 征在于，步骤s1中受试者利用模拟驾驶系统进行驾驶，且模拟驾驶系统随机发出刹车命令，记录受试者在看到刹车命令和做出反应的时间间隔；设置时间间隔阈值，若反应的时间间隔大于或等于时间间隔阈值，则将从这个时间间隔往前的测试时间内的脑电信号作为清醒时间内的脑电信号，将从这个时间间隔往后的测试时间内的脑电信号作为待检测是否疲劳的信号。
  4. 根据权利要求1所述的一种基于相位锁定值构建脑功能网络的驾驶疲劳检测方法，其特征在于，步骤s2中，使用独立成分分析方法对脑电信号进行去噪处理，以去除眼电信号的干扰，提高脑电信号的信噪比。
  5. 根据权利要求1所述的一种基于相位锁定值构建脑功能网络的驾驶疲劳检测方法，其特征在于，步骤s3中利用小波包变换对脑电信号进行分解，然后重构为三个子频带的信号。
  6. 根据权利要求1所述的一种基于相位锁定值构建脑功能网络的驾驶疲劳检测方法，其特征在于，步骤s5中连接强度阈值为0.2。
  7. 根据权利要求1所述的一种基于相位锁定值构建脑功能网络的驾驶疲劳检测方法，其特征在于，步骤s6中利用复杂网络分析方法从功能整合与功能分化两方面分别对三个子频带在清醒时间内和测试时间序列内的脑功能网络拓扑结构的差异进行定量分析；功能整合方面包括特征路径长度和全局效率，功能分化包括局部效率和聚类系数；
    聚类系数表示脑功能网络节点的聚集程度，使用公式(2)计算；
    Figure PCTCN2019088445-appb-100003
    其中，C i表示脑功能网络的平均聚类数，k i表示节点i的相邻节点数，相邻节点为与节点i连接的节点，E i为对于节点i相邻节点中存在的闭环三角形数；与节点i相连接的另外两个节点，若三个节点间都存在边则为闭环三角形；
    特征路径长度反应脑功能网络内部的信息传递能力，使用公式(3)计算：
    Figure PCTCN2019088445-appb-100004
    其中，L为特征路径长度；L ij为节点i与节点j之间的最短路径长度，即从节点i到节 点j所用最短的边的数量；N为节点的个数；
    全局效率为最短路径长度L ij的倒数的平均值，用来衡量脑功能网络传递和处理信息的能力，使用公式(4)计算：
    Figure PCTCN2019088445-appb-100005
    其中，N为节点的个数，L ij为节点i与节点j之间的最短路径长度；
    局部效率用来衡量局部信息传递和处理能力，使用公式(5)计算：
    Figure PCTCN2019088445-appb-100006
    其中，E global(G i)为节点i的全局效率即E g。
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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CN116211319A (zh) * 2023-02-22 2023-06-06 北京工业大学 一种基于图注意力网络和稀疏编码的静息态多通道脑电信号识别方法
CN117454216A (zh) * 2023-10-07 2024-01-26 惠州学院 一种基于复杂网络分析的脑电信号解码方法及系统

Families Citing this family (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109770923A (zh) * 2019-01-15 2019-05-21 五邑大学 一种基于相位锁定值构建脑功能网络的驾驶疲劳检测方法
CN110584684B (zh) * 2019-09-11 2021-08-10 五邑大学 一种驾驶疲劳相关的eeg功能连接动态特性的分析方法
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CN111227829A (zh) * 2020-02-14 2020-06-05 广东司法警官职业学院 一种基于复杂网络特征指标的脑电图信号分析方法
US11331024B2 (en) 2020-08-07 2022-05-17 Dalian University Of Technology Calibration method for critical point of mental fatigue based on self-organized criticality
CN112006683B (zh) * 2020-08-07 2021-08-10 大连理工大学 一种基于自组织临界性的精神疲劳临界点标定方法
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CN113974650B (zh) * 2021-06-29 2024-06-14 华南师范大学 一种脑电网络功能分析方法、装置，电子设备及存储介质
CN113558637B (zh) * 2021-07-05 2024-01-05 杭州电子科技大学 一种基于相位传递熵的音乐感知下脑网络构建方法
CN116098634A (zh) * 2023-01-31 2023-05-12 首都医科大学宣武医院 一种基于刺激事件的脑功能检测评估方法、装置及系统
CN116712091B (zh) * 2023-06-14 2025-02-25 北京交通大学 基于脑电复杂网络的分心驾驶状态识别方法及系统

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101810479A (zh) * 2009-12-16 2010-08-25 天津大学 复合下肢想象动作脑电的相位特征提取方法
CN105286890A (zh) * 2015-09-22 2016-02-03 江西科技学院 一种基于脑电信号的驾驶员瞌睡状态监测方法
CN105956546A (zh) * 2016-04-28 2016-09-21 杭州电子科技大学 一种基于脑电信号的情绪识别方法
WO2017152059A1 (en) * 2016-03-04 2017-09-08 University Of Tennessee Research Foundation Ambulatory seizure monitoring system and method
CN108577835A (zh) * 2018-05-17 2018-09-28 太原理工大学 一种基于微状态的脑功能网络构建方法
CN109770923A (zh) * 2019-01-15 2019-05-21 五邑大学 一种基于相位锁定值构建脑功能网络的驾驶疲劳检测方法

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101810479A (zh) * 2009-12-16 2010-08-25 天津大学 复合下肢想象动作脑电的相位特征提取方法
CN105286890A (zh) * 2015-09-22 2016-02-03 江西科技学院 一种基于脑电信号的驾驶员瞌睡状态监测方法
WO2017152059A1 (en) * 2016-03-04 2017-09-08 University Of Tennessee Research Foundation Ambulatory seizure monitoring system and method
CN105956546A (zh) * 2016-04-28 2016-09-21 杭州电子科技大学 一种基于脑电信号的情绪识别方法
CN108577835A (zh) * 2018-05-17 2018-09-28 太原理工大学 一种基于微状态的脑功能网络构建方法
CN109770923A (zh) * 2019-01-15 2019-05-21 五邑大学 一种基于相位锁定值构建脑功能网络的驾驶疲劳检测方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
CAO, RUI: "Nonlinear and Complex Network Theory in the Application of EEG Data Analysis Research", CHINESE DOCTORAL DISSERTATIONS FULL-TEXT DATABASE, 15 August 2015 (2015-08-15), pages 1 - 129, XP055718303, ISSN: 1674-022X *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116027888A (zh) * 2022-07-21 2023-04-28 西安邮电大学 一种基于plv动态脑功能网络的p300意图识别方法
CN116211319A (zh) * 2023-02-22 2023-06-06 北京工业大学 一种基于图注意力网络和稀疏编码的静息态多通道脑电信号识别方法
CN117454216A (zh) * 2023-10-07 2024-01-26 惠州学院 一种基于复杂网络分析的脑电信号解码方法及系统

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