WO2021046949A1 - 一种驾驶疲劳相关的eeg功能连接动态特性的分析方法 - Google Patents
一种驾驶疲劳相关的eeg功能连接动态特性的分析方法 Download PDFInfo
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Definitions
- the invention relates to the technical field of driving fatigue analysis, in particular to a method for analyzing the dynamic characteristics of EEG functional connections related to driving fatigue.
- FC fatigue-related EEG functional connection
- the purpose of the present invention is to provide a method for analyzing the dynamic characteristics of driving fatigue-related EEG functional connections, and applying the dynamic FC analysis framework to the driving fatigue research, so as to obtain driving fatigue-related brains on a fine time scale.
- a method for analyzing the dynamic characteristics of EEG functional connections related to driving fatigue including:
- the correlation includes spatio-temporal global efficiency, spatio-temporal local efficiency, and temporal-spatial proximity centers degree.
- preprocessing of EEG data using independent component analysis and wavelet packet transform includes:
- the blinking artifact data includes horizontal EOG HEOG data and vertical EOG VEOG data;
- the EEG data is divided into awake state data and fatigue state data.
- the construction of the preprocessed EEG data into a time brain network with dynamic characteristics based on the sliding window method includes:
- the static network being a binary NxN matrix, where N represents the number of electrodes of the EEG cap;
- the PLI value above the threshold is set to 1, and the PLI value below the threshold is set to 0, thereby forming a binary adjacency network as a snapshot of the temporal brain network;
- the static network is arranged according to the time sequence to form a time-brain network with dynamic characteristics.
- the measuring the spatiotemporal topology of the temporal brain network based on the time efficiency analysis framework includes:
- the temporal and spatial proximity centrality is used to evaluate the temporal and spatial characteristics of the node-level temporal brain network.
- An analysis device for the dynamic characteristics of the EEG functional connection related to driving fatigue including:
- the preprocessing unit is used to preprocess the EEG data using independent component analysis and wavelet packet transform
- the construction unit is used to construct the preprocessed EEG data into a dynamic time brain network based on the sliding window method
- a spatiotemporal topology calculation unit configured to measure the spatiotemporal topology of the time brain network based on a time efficiency analysis framework
- the statistical analysis unit is used to perform statistical analysis on the spatio-temporal topology of the time-brain network to obtain the correlation between the driving fatigue-related behavior performance and the dynamic characteristics of the time-brain network.
- the correlation includes the spatio-temporal global efficiency and the spatio-temporal Local efficiency and temporal proximity to centrality.
- the pre-processing unit includes:
- a blink artifact data collection unit for the blink artifact data including horizontal electro-oculogram HEOG data and vertical electro-oculogram VEOG data;
- a filtering unit configured to use independent component analysis to find and delete components in the EEG data that are highly related to the blinking artifact data
- the baseline removal unit is used to remove the baseline of the filtered EEG data
- a decomposition unit configured to use wavelet packet transform to decompose the EEG data into three standard frequency bands, which are alpha frequency band, beta frequency band and ⁇ frequency band;
- the data division unit is used to divide the EEG data into awake state data and fatigue state data according to the test time.
- construction unit includes:
- the sliding window processing unit is used to select a suitable window length and step length, the sliding window sequentially traverses the entire time series, and the length of the time series is the length of the experiment for collecting EEG data;
- the PLI calculation unit is configured to use the phase lag index to estimate the PLI value of the functional connection in each of the static networks;
- the binarization calculation unit adopts the sparsity method to set the PLI value higher than the threshold to 1, and the PLI value lower than the threshold to 0, thereby forming a binarized adjacency network, which is used as the time Snapshot of the brain network;
- the sparsity calculation unit is used to select an appropriate sparsity and interval, and use sparsity methods to reserve the required functional connections in each static network;
- the dynamic characteristic building unit is used for arranging the static network according to the time sequence to form a time brain network with dynamic characteristics.
- spatiotemporal topology calculation unit includes:
- a time distance calculation unit for calculating the time distance of a pair of nodes on a time scale, where the time distance represents the minimum number of time windows defined as a time-space path passed;
- the space-time global efficiency calculation unit is used to calculate the space-time global efficiency
- the space-time local efficiency calculation unit is used to calculate the space-time local efficiency
- the spatiotemporal feature evaluation unit is used to evaluate the spatiotemporal features of the node-level temporal brain network using the temporal proximity centrality.
- a device for analyzing the dynamic characteristics of the EEG functional connection related to driving fatigue comprising at least one control processor and a memory for communicating with the at least one control processor; the memory stores the memory that can be processed by the at least one control processor.
- the instructions executed by the driver, the instructions are executed by the at least one control processor, so that the at least one control processor can execute the method for analyzing the dynamic characteristics of the EEG functional connection related to driving fatigue as described in any one of the above.
- a computer-readable storage medium characterized in that: the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to make a computer execute the driving fatigue-related EEG described in any one of the above The analysis method of the dynamic characteristics of the functional connection.
- the one or more technical solutions provided in the embodiments of the present invention have at least the following beneficial effects: by introducing time characteristics into the static network of driving fatigue, constructing a time brain network with dynamic characteristics, and obtaining the time during driving fatigue through analysis and statistics.
- the analysis method of the present invention has more accurate analysis results, which is beneficial to reveal the reorganization of the information transmission function between the brain regions related to driving fatigue on the fine time scale. More critical dynamic characteristics.
- Figure 1 is a flow chart of the overall method of an embodiment of the present invention.
- Figure 2 is a flowchart of a preprocessing method according to an embodiment of the present invention.
- FIG. 3 is a flowchart of constructing a time-brain network with dynamic characteristics according to an embodiment of the present invention
- FIG. 4 is a flowchart of measuring the spatio-temporal topology of a time brain network according to an embodiment of the present invention
- FIG. 5 is a schematic diagram of a unit architecture in an apparatus according to an embodiment of the present invention.
- Figure 6 is a schematic diagram of connections in a device of an embodiment of the present invention.
- an embodiment of the present invention provides a method for analyzing the dynamic characteristics of EEG functional connections related to driving fatigue, including:
- S4 Perform statistical analysis on the spatio-temporal topology of the time-brain network to obtain the correlation between the driving fatigue-related behavior performance and the dynamic characteristics of the time-brain network.
- the correlation includes spatio-temporal global efficiency, spatio-temporal local efficiency, and spatio-temporal Proximity to centrality.
- step S1 includes:
- S14 Use wavelet packet transform to decompose the EEG data into three standard frequency bands, which are alpha frequency band, beta frequency band, and ⁇ frequency band;
- S15 Divide the EEG data into awake state data and fatigue state data according to the test time.
- step S2 includes:
- S21 representing the preprocessed EEG data as a static network, the static network being a binary NxN matrix, where N represents the number of electrodes of the EEG cap;
- the binarization calculation unit adopts the sparsity method to set the PLI value higher than the threshold to 1, and the PLI value lower than the threshold to 0, so as to form a binarized adjacency network, which is used as the total value.
- S25 Arranging the static network according to the time sequence to form a time-brain network with dynamic characteristics.
- step S3 includes:
- S31 Calculate the time distance of the pair of nodes on a time scale, where the time distance represents a minimum number of time windows defined as a time-space path passed;
- connection (FC) in driving fatigue research is static, that is, a representative brain network is constructed with a time scale of a few minutes in a fatigue state. Since a key component of driving fatigue is task time itself, it is emphasized that the brain regulates fatigue. Because of the accumulation characteristics of the process of performance degradation, static brain network research lacks more critical dynamic characteristics about the reorganization of information transmission functions between brain regions related to driving fatigue on fine time scales.
- the experimental setting of the embodiment of the present invention is that the subject conducts simulated driving for 90 minutes, and a virtual guided vehicle is set up in front of the subject.
- the virtual guided vehicle generates braking signals at random intervals and requires the subject to pass the system. Respond to the braking signal to maintain a safe distance.
- the time interval from the braking command generated in the virtual guided vehicle to the subject's braking operation is considered as the reaction time (RT), and the vehicle's speed change (SV) is also collected as a quantitative evaluation of the subject's behavior index.
- RT reaction time
- SV vehicle's speed change
- the data collection method is that the subject wears a 24-channel wireless EEG cap with an improved international 10-20 electrode placement system (HD-72, Cognionics, Inc., USA) to record EEG data at 250 Hz.
- the reference electrodes are right and left.
- the EEG signal is filtered by a band-pass filter (between 2 and 100 Hz).
- blinking artifacts were recorded by electrodes placed on the outer corner of the eye (horizontal electro-oculogram, HEOG) and above and below the right eye (vertical electro-oculogram, VEOG).
- ICA Use independent component analysis
- the dynamic analysis framework of brain network is to use the optimal sliding window to characterize the time-ordered static network.
- the phase lag index (PLI) is used to estimate FC, because it has advantages in minimizing the influence of common source signal and volume conduction.
- the PLI value of FC is between 0 and 1, and the larger the number, the larger the value. The stronger the connection.
- using the commonly used sparsity method select a sparsity of 5%-15%, with an interval of 1%, and threshold the matrix into a binarized adjacency network, which is equivalent to comparing the PLI value of FC with the threshold, and the setting is greater than the threshold.
- the choice of window length can be 3-6 seconds, and the choice of step size can be 2-4 seconds.
- the window length is chosen to be 4 Second, the step length is selected as 4 seconds.
- the most awake and fatigued state are determined by statistical comparison of the subjects’ behavioral performance in terms of reaction time and speed change.
- the spatio-temporal topology of the time brain network is the spatio-temporal topology of the time brain network
- the time distance is defined as the minimum number of time windows passed by the time-space path. It is worth noting that the time-dependent path is Metrics in the space and time domains, and the time distance is represented by the time domain. Therefore, the time distance is a positive integer, and its range is between 1 and T.
- the spatiotemporal efficiency analysis framework is used to measure the spatiotemporal topology of the time brain network.
- the spatiotemporal efficiency measures the interaction and information transmission functions between the entire nodes in the dynamic system, and the spatiotemporal global efficiency The ability to capture the dynamics of the entire network and the information flow is spread throughout the entire life cycle. The calculation method is as follows:
- G is a spatiotemporal network with a mathematical structure NxNxT, and It is the efficiency of space-time global efficiency at time t, and the value of t is a positive integer not greater than T.
- Ranges from 0 to 1 It means that all nodes are connected in the snapshot of the spatiotemporal brain network, ⁇ i ⁇ j (t) means the time distance from i to j at time t. .
- the temporal and spatial local efficiency measures the overall elasticity of the dynamic network to randomly remove nodes in a local range:
- G(i,t) is a sub-spatiotemporal network including all neighbors of node i at time t.
- the range is from 0 to 1. It is an index of a dynamic network, used to measure the information dissemination capacity of a local scale.
- the temporal and spatial proximity centrality is used to evaluate the temporal and spatial characteristics of the node-level temporal brain network.
- the centrality measures the ability of node i to reach other nodes C c (i, t) is expressed as:
- the temporal and spatial proximity centrality also indicates the importance of nodes in the entire temporal network.
- the integral space-time proximity centrality represents the area under the curve of node i in the entire sparsity range.
- the reference network increases the randomness of the dynamic brain network, showing different information dissemination efficiencies. Calculating such a reference network helps to reveal the neural mechanism of dynamic FC with different topologies.
- a two-step randomization method is used: random edge (RE) and random connection (RC).
- the RE method randomly reconnects all edges in the dynamic network under certain constraints, which destroys the topology of the dynamic brain network.
- the RC method randomly redistributes all connections in the network, which eliminates the distribution of the number of connections at each edge.
- the application of these two methods destroys the main structure of the dynamic network.
- the definition of the small-world attribute in the static network is extended to the time-brain network with dynamic characteristics. If the following definition is satisfied, the time-brain network with dynamic characteristics is small-world in space and time:
- the spatiotemporal efficiency of the reference network for each subject is the average of the generated spatiotemporal reference network with 50 iterations in two mental states (awake and fatigue).
- FDR False Discovery Rate
- reaction time (RT) and speed change (SV) in the 5 minutes before and after are respectively expressed in the form of coordinates to form a time-response time coordinate graph and a time-speed change coordinate graph, which can be clearly seen in the first 5 minutes There is a significant difference between the two states and the last 5 minutes, which confirms that the first 5 minutes are awake and the last 5 minutes are fatigued.
- the spatiotemporal topology of brain activity is quantitatively estimated by the spatiotemporal efficiency of the awake and fatigue states, and the frequency band-efficiency graph is established according to the three standard frequency bands to show the integrated spatiotemporal global efficiency and integrated spatiotemporal localization in the entire sparse range
- the time-brain network with efficiency and dynamic characteristics exhibits an outstanding spatio-temporal small-world architecture in all three frequency bands: the standard deviation is used to indicate the degree of dispersion of the brain connection distribution across the subject. This finding can indicate the dynamics of FC under fatigue.
- the difference between the larger subjects in the reorganization, through calculations, the standard deviation of the time-space global efficiency and the time-space local efficiency in the fatigue state is significantly higher than the standard deviation in the awake state. This result is characteristic of the individual
- the theory is consistent, this feature tends to have the fragility of the accompanying biological matrix over time.
- the correlation result between the frequency band and the efficiency can be obtained.
- the nature of the nodes only nodes that show significant fatigue-related differences in each frequency band are selected for correlation testing
- the embodiment of the present invention introduces a dynamic FC analysis framework and provides an analysis method for studying the spatio-temporal reorganization of dynamic networks during driving fatigue.
- the small-world attributes existing in the static brain network are extended to the dynamic system.
- graph theory attributes global efficiency, local efficiency
- the embodiment of the present invention also provides a driving fatigue-related EEG functional connection dynamic characteristics analysis device.
- the driving fatigue-related EEG functional connection dynamic characteristics analysis device 1000 includes, but is not limited to: a preprocessing unit 1100, The unit 1200, the space-time topology calculation unit 1300, and the statistical analysis unit 1400.
- the preprocessing unit 1100 is used to preprocess the EEG data using independent component analysis and wavelet packet transform;
- the construction unit 1200 is used for constructing the preprocessed EEG data into a time-brain network with dynamic characteristics based on the sliding window method;
- the spatiotemporal topology calculation unit 1300 is configured to measure the spatiotemporal topology of the time brain network based on the time efficiency analysis framework;
- the statistical analysis unit 1400 is configured to perform statistical analysis on the spatio-temporal topology of the time-brain network to obtain the correlation between the driving fatigue-related behavior performance and the dynamic characteristics of the time-brain network, and the correlation includes the spatio-temporal global efficiency, The local efficiency of time and space and the centrality of time and space proximity.
- the embodiment of the present invention also provides a driving fatigue-related EEG functional connection dynamic characteristics analysis device.
- the driving fatigue-related EEG functional connection dynamic characteristics analysis device 2000 can be any type of smart terminal, such as a mobile phone, a tablet computer, Personal computer, etc.
- the driving fatigue-related EEG functional connection dynamic characteristic analysis device 2000 includes: one or more control processors 2010 and a memory 2020.
- one control processor 2010 is taken as an example.
- control processor 2010 and the memory 2020 may be connected through a bus or in other ways, and the connection through a bus is taken as an example in FIG. 6.
- the memory 2020 can be used to store non-transitory software programs, non-transitory computer-executable programs and modules, such as driving fatigue-related EEG functional connection dynamic characteristics in the embodiment of the present invention
- the program instructions/modules corresponding to the analysis method are, for example, the preprocessing unit 1100, the construction unit 1200, the spatiotemporal topology calculation unit 1300, and the statistical analysis unit 1400 shown in FIG. 5.
- the control processor 2010 runs the non-transitory software programs, instructions and modules stored in the memory 2020 to execute various functional applications and data processing of the driving fatigue-related EEG functional connection dynamic characteristics analysis device 1000, that is, to realize the above method
- the method for analyzing the dynamic characteristics of the EEG functional connection related to driving fatigue of the embodiment
- the memory 2020 may include a storage program area and a storage data area, where the storage program area can store an operating system and an application program required by at least one function; the storage data area can store the analysis device 1000 connected with the dynamic characteristics of the EEG function related to driving fatigue Use the created data, etc.
- the memory 2020 may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices.
- the memory 2020 may optionally include memories remotely provided with respect to the control processor 2010, and these remote memories may be connected to the analysis device 2000 of the dynamic characteristics of the driving fatigue-related EEG function via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.
- the one or more modules are stored in the memory 2020, and when executed by the one or more control processors 2010, the method for analyzing the dynamic characteristics of the EEG functional connection related to driving fatigue in the above method embodiment is executed, For example, the method steps S1 to S4 in FIG. 1 described above are executed to realize the functions of the units 1100-1400 in FIG. 5.
- the embodiment of the present invention also provides a computer-readable storage medium, the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are executed by one or more control processors, for example, as shown in FIG. 6
- the execution of one control processor 2010 of the above-mentioned one or more control processors 2010 can make the above-mentioned one or more control processors 2010 execute the method of analyzing the dynamic characteristics of driving fatigue-related EEG functional connection in the above-mentioned method embodiment, for example, execute the method in FIG. 1 described above Steps S1 to S4 implement the functions of the units 1100-1400 in FIG. 5.
- the device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the objectives of the solutions of the embodiments.
- each implementation manner can be implemented by means of software plus a general hardware platform.
- All or part of the processes in the methods of the above embodiments can be implemented by computer programs instructing relevant hardware.
- the programs can be stored in a computer readable storage medium. At this time, it may include the process of the embodiment of the above method.
- the storage medium may be a magnetic disk, an optical disc, a read-only memory (Read Only Memory, ROM), or a random access memory (Random Access Memory, RAM), etc.
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Claims (10)
- 一种驾驶疲劳相关的EEG功能连接动态特性的分析方法,其特征在于包括:使用独立分量分析和小波包变换对EEG数据进行预处理;基于滑动窗口方法将预处理后的EEG数据构建成动态特性的时间脑网络;基于时间效率分析框架测量所述时间脑网络的时空拓扑;对所述时间脑网络的时空拓扑进行统计分析,获得驾驶疲劳相关行为表现与所述时间脑网络的动态特性之间的相关性,所述相关性包括时空全局效率、时空局部效率和时空邻近中心度。
- 根据权利要求1所述的一种驾驶疲劳相关的EEG功能连接动态特性的分析方法,其特征在于,所述使用独立分量分析和小波包变换对EEG数据进行预处理包括:采集眨眼伪影数据,所述眨眼伪影数据包括水平眼电图HEOG数据和垂直眼电图VEOG数据;使用独立分量分析查找和删除EEG数据中与所述眨眼伪影数据高度相关的成分;移除筛选后的EEG数据的基线;使用小波包变换将所述EEG数据分解为三个标准频段,分别为α频段、β频段和θ频段;按测试时间将EEG数据划分为清醒状态数据和疲劳状态数据。
- 根据权利要求1所述的一种驾驶疲劳相关的EEG功能连接动态特性的分析方法,其特征在于,所述基于滑动窗口方法将预处理后的EEG数据构建成动态特性的时间脑网络包括:将所述预处理后的EEG数据表示成静态网络,所述静态网络为二进制NxN矩阵,其中N表示EEG帽的电极数量;选择适合的窗口长度和步长,滑动窗口依次遍历整个时间序列,所述时间序列的长度为采集EEG数据的实验时长;在每个所述静态网络中使用相位滞后指数估算功能连接的PLI值;采用稀疏度方法,将高于阈值的所述PLI值设置为1,低于阈值的所述PLI值设置为0,从而构成二值化邻接网络,以此作为所述时间脑网络的快照;将所述静态网络依据时间序列排列构成具有动态特性的时间脑网络。
- 根据权利要求1所述的一种驾驶疲劳相关的EEG功能连接动态特性的分析方法,其特征在于,所述基于时间效率分析框架测量所述时间脑网络的时空拓扑包括:在时间尺度上计算成对节点的时间距离,所述时间距离表示被定义为时空路径所经过 的时间窗口的最小数量;计算时空全局效率;计算时空局部效率;使用时空邻近中心度评估节点级时间脑网络的时空特征。
- 一种驾驶疲劳相关的EEG功能连接动态特性的分析装置,其特征在于:包括预处理单元,用于使用独立分量分析和小波包变换对EEG数据进行预处理;构建单元,用于基于滑动窗口方法将预处理后的EEG数据构建成动态特性的时间脑网络;时空拓扑计算单元,用于基于时间效率分析框架测量所述时间脑网络的时空拓扑;统计分析单元,用于对所述时间脑网络的时空拓扑进行统计分析,获得驾驶疲劳相关行为表现与所述时间脑网络的动态特性之间的相关性,所述相关性包括时空全局效率、时空局部效率和时空邻近中心度。
- 根据权利要求5所述的一种驾驶疲劳相关的EEG功能连接动态特性的分析装置,其特征在于,所述预处理单元包括:眨眼伪影数据采集单元,用于所述眨眼伪影数据包括水平眼电图HEOG数据和垂直眼电图VEOG数据;筛选单元,用于使用独立分量分析查找和删除EEG数据中与所述眨眼伪影数据高度相关的成分;基线移除单元,用于移除筛选后的EEG数据的基线;分解单元,用于使用小波包变换将所述EEG数据分解为三个标准频段,分别为α频段、β频段和θ频段;数据划分单元,用于按测试时间将EEG数据划分为清醒状态数据和疲劳状态数据。
- 根据权利要求5所述的一种驾驶疲劳相关的EEG功能连接动态特性的分析装置,其特征在于,所述构建单元包括:矩阵构建单元,用于将所述预处理后的EEG数据表示成静态网络,所述静态网络为二进制NxN矩阵,其中N表示EEG帽的电极数量;滑动窗口处理单元,用于选择适合的窗口长度和步长,滑动窗口依次遍历整个时间序列,所述时间序列的长度为采集EEG数据的实验时长;PLI计算单元,用于在每个所述静态网络中使用相位滞后指数估算功能连接的PLI值;二值化计算单元,采用稀疏度方法,将高于阈值的所述PLI值设置为1,低于阈值的所述PLI值设置为0,从而构成二值化邻接网络,以此作为所述时间脑网络的快照;动态特性组建单元,用于将所述静态网络依据时间序列排列构成具有动态特性的时间脑网络。
- 根据权利要求5所述的一种驾驶疲劳相关的EEG功能连接动态特性的分析装置,其特征在于,所述时空拓扑计算单元包括:时间距离计算单元,用于在时间尺度上计算成对节点的时间距离,所述时间距离表示被定义为时空路径所经过的时间窗口的最小数量;时空全局效率计算单元,用于计算时空全局效率;时空局部效率计算单元,用于计算时空局部效率;时空特征评估单元,用于使用时空邻近中心度评估节点级时间脑网络的时空特征。
- 一种驾驶疲劳相关的EEG功能连接动态特性的分析设备,其特征在于:包括至少一个控制处理器和用于与所述至少一个控制处理器通信连接的存储器;所述存储器存储有可被所述至少一个控制处理器执行的指令,所述指令被所述至少一个控制处理器执行,以使所述至少一个控制处理器能够执行如权利要求1-4任一项所述的驾驶疲劳相关的EEG功能连接动态特性的分析方法。
- 一种计算机可读存储介质,其特征在于:所述计算机可读存储介质存储有计算机可执行指令,所述计算机可执行指令用于使计算机执行如权利要求1-4任一项所述的驾驶疲劳相关的EEG功能连接动态特性的分析方法。
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