阅读说明：本技术 一种驾驶疲劳相关的eeg功能连接动态特性的分析方法 (Analysis method for dynamic characteristics of driving fatigue related EEG function connection ) 是由 王洪涛 刘旭程 李霆 唐聪 裴子安 岳洪伟 陈鹏 许弢 于 2019-09-11 设计创作，主要内容包括：本发明公开了一种驾驶疲劳相关的EEG功能连接动态特性的分析方法,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,通过将时间特性引入驾驶疲劳的静态网络中,构建具有动态特性的时间脑网络,通过分析统计可以获得驾驶疲劳期间时间脑网络的时空重组规律,具有更准确的分析结果,有利于揭示在精细时间尺度上驾驶疲劳相关的大脑区域间信息传递功能重组的更关键的动力学特性。(The invention discloses an analysis method of EEG function connection dynamic characteristics related to driving fatigue, which uses independent component analysis and wavelet packet transformation to preprocess EEG data; constructing the preprocessed EEG data into a time brain network with dynamic characteristics based on a sliding window method; measuring a spatiotemporal topology of the temporal brain network based on a temporal efficiency analysis framework; the time brain network with the dynamic characteristics is constructed by introducing the time characteristics into a static network of the driving fatigue, and the time brain network with the dynamic characteristics can be obtained by analyzing and counting, so that the time brain network with the more accurate analysis result is obtained, and the more key dynamic characteristics of information transfer function recombination between brain areas related to the driving fatigue on a fine time scale can be disclosed.)

1. A method for analyzing EEG functional connection dynamics related to driving fatigue, comprising:

preprocessing EEG data using independent component analysis and wavelet packet transformation;

constructing the preprocessed EEG data into a time brain network with dynamic characteristics based on a sliding window method;

measuring a spatiotemporal topology of the temporal brain network based on a temporal efficiency analysis framework;

and carrying out statistical analysis on the space-time topology of the time brain network to obtain the correlation between the driving fatigue related behavior expression and the dynamic characteristics of the time brain network, wherein the correlation comprises space-time global efficiency, space-time local efficiency and space-time adjacent centrality.

2. The method of analyzing EEG functional connection dynamics related to driving fatigue as claimed in claim 1, wherein said pre-processing EEG data using independent component analysis and wavelet packet transformation comprises:

acquiring blink artifact data, wherein the blink artifact data comprises horizontal electro-oculogram HEOG data and vertical electro-oculogram VEOG data;

finding and removing components of EEG data that are highly correlated with said blink artifact data using independent component analysis;

removing the baseline of the screened EEG data;

decomposing the EEG data into three standard frequency bands by wavelet packet transform, wherein the three standard frequency bands are respectively an alpha frequency band, a beta frequency band and a theta frequency band;

the EEG data is divided into awake state data and fatigue state data by test time.

3. The method for analyzing EEG function connection dynamics related to driving fatigue as claimed in claim 1, wherein said constructing the preprocessed EEG data into temporal brain network of dynamics based on sliding window method comprises:

representing the preprocessed EEG data as a static network, the static network being a binary NxN matrix, wherein N represents a number of electrodes of an EEG cap;

selecting a proper window length and step length, and sequentially traversing the whole time sequence by sliding a window, wherein the length of the time sequence is the experimental duration for collecting EEG data;

estimating PLI values of functionally connected in each of said static networks using phase lag indices;

setting the PLI value higher than the threshold value to be 1 and the PLI value lower than the threshold value to be 0 by adopting a sparsity method, thereby forming a binary adjacent network which is taken as a snapshot of the time brain network;

and arranging the static networks according to a time sequence to form a time brain network with dynamic characteristics.

4. The method of analyzing EEG functional connection dynamics related to driving fatigue as claimed in claim 1, wherein said measuring the spatiotemporal topology of the temporal brain network based on a time-efficiency analysis framework comprises:

calculating a temporal distance of pairs of nodes on a time scale, the temporal distance representing a minimum number of time windows defined as being traversed by the spatio-temporal path;

calculating the space-time global efficiency;

calculating the local efficiency of time and space;

spatio-temporal characteristics of the node-level temporal brain network are evaluated using spatio-temporal proximity centrality.

5. An analysis device for driving fatigue-related EEG functional connection dynamics, characterized in that: comprises that

A pre-processing unit for pre-processing the EEG data using independent component analysis and wavelet packet transformation;

the construction unit is used for constructing the preprocessed EEG data into a time brain network with dynamic characteristics based on a sliding window method;

a spatiotemporal topology calculation unit for measuring spatiotemporal topology of the temporal brain network based on a time efficiency analysis framework;

and the statistical analysis unit is used for carrying out statistical analysis on the space-time topology of the time brain network to obtain the correlation between the driving fatigue related behavior and the dynamic characteristics of the time brain network, wherein the correlation comprises space-time global efficiency, space-time local efficiency and space-time adjacent centrality.

6. The device for analyzing EEG function connection dynamics related to driving fatigue as claimed in claim 5, wherein said preprocessing unit comprises:

the blink artifact data acquisition unit is used for acquiring blink artifact data comprising horizontal electro-oculogram HEOG data and vertical electro-oculogram VEOG data;

a screening unit for finding and deleting components of EEG data that are highly correlated with said blink artifact data using independent component analysis;

a baseline removal unit for removing a baseline of the screened EEG data;

the decomposition unit is used for decomposing the EEG data into three standard frequency bands, namely an alpha frequency band, a beta frequency band and a theta frequency band by using wavelet packet transformation;

a data dividing unit for dividing the EEG data into awake state data and fatigue state data by a test time.

7. The device for analyzing EEG function connection dynamics related to driving fatigue as claimed in claim 5, wherein said constructing unit comprises:

a matrix construction unit for representing the preprocessed EEG data as a static network, the static network being a binary NxN matrix, wherein N represents the number of electrodes of an EEG cap;

the system comprises a sliding window processing unit, a time sequence processing unit and a time sequence processing unit, wherein the sliding window processing unit is used for selecting proper window length and step length, the sliding window sequentially traverses the whole time sequence, and the length of the time sequence is the experimental time length for collecting EEG data;

a PLI calculation unit for estimating PLI values of functional connections using phase lag indices in each of said static networks;

a binarization calculating unit, which adopts a sparsity method to set the PLI value higher than a threshold value as 1 and the PLI value lower than the threshold value as 0, thereby forming a binarization adjacent network as a snapshot of the time brain network;

and the dynamic characteristic establishing unit is used for arranging the static networks according to the time sequence to form a time brain network with dynamic characteristics.

8. The apparatus for analyzing EEG function connection dynamics related to driving fatigue as claimed in claim 5, wherein said spatio-temporal topology calculating unit comprises:

a temporal distance calculation unit for calculating a temporal distance of the pair of nodes on a time scale, the temporal distance representing a minimum number of time windows defined as the spatio-temporal path passes;

the space-time global efficiency calculating unit is used for calculating the space-time global efficiency;

the space-time local efficiency calculating unit is used for calculating space-time local efficiency;

and the space-time characteristic evaluation unit is used for evaluating the space-time characteristics of the node-level time brain network by using the space-time adjacent centrality.

9. An analysis device for the dynamics of EEG functional connections related to driving fatigue, characterized by: comprises at least one control processor and a memory for communicative connection with the at least one control processor; the memory stores instructions executable by the at least one control processor to enable the at least one control processor to perform a method of analysis of driving fatigue related EEG functional connection dynamics as claimed in any one of claims 1-4.

10. A computer-readable storage medium characterized by: the computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform a method of analysis of driving fatigue related EEG function connection dynamics according to any one of claims 1-4.

Technical Field

The invention relates to the technical field of driving fatigue analysis, in particular to an analysis method for dynamic characteristics of EEG function connection related to driving fatigue.

Background

Driving fatigue has long been recognized as one of the major causes of fatal accidents worldwide, with evidence suggesting that 15% -20% of fatal traffic accidents are associated with driving fatigue, and researchers have made extensive efforts in the recent new field of neuroergonomics to understand the neurobiological basis of driving fatigue in order to develop applicable automated detection techniques and reduce fatigue-related traffic accidents in the real world.

At present, it is an effective method to collect experimental data based on electroencephalogram (EEG), extract features, and then construct an EEG Functional Connection (FC) architecture related to fatigue; FC in past fatigue studies was a static connection, i.e. a representative brain network was constructed in a time scale of several minutes under fatigue conditions. However, static network studies lack the more critical dynamics regarding inter-brain area information transfer function reorganization related to driving fatigue on a fine time scale, and thus experimental results under static FC architectures have certain limitations.

Disclosure of Invention

In order to solve the above problems, the present invention aims to provide an analysis method for dynamic characteristics of EEG function connection related to driving fatigue, which applies a dynamic FC analysis framework to driving fatigue research, thereby obtaining more critical dynamic characteristics of information transfer function reorganization between brain regions related to driving fatigue on a fine time scale, and obtaining higher recognition accuracy.

The technical scheme adopted by the invention for solving the problems is as follows:

a method of analysis of driving fatigue related EEG functional connection dynamics, comprising:

preprocessing EEG data using independent component analysis and wavelet packet transformation;

constructing the preprocessed EEG data into a time brain network with dynamic characteristics based on a sliding window method;

measuring a spatiotemporal topology of the temporal brain network based on a temporal efficiency analysis framework;

and carrying out statistical analysis on the space-time topology of the time brain network to obtain the correlation between the driving fatigue related behavior expression and the dynamic characteristics of the time brain network, wherein the correlation comprises space-time global efficiency, space-time local efficiency and space-time adjacent centrality.

Further, the preprocessing EEG data using independent component analysis and wavelet packet transformation comprises:

acquiring blink artifact data, wherein the blink artifact data comprises horizontal electro-oculogram HEOG data and vertical electro-oculogram VEOG data;

finding and removing components of EEG data that are highly correlated with said blink artifact data using independent component analysis;

removing the baseline of the screened EEG data;

decomposing the EEG data into three standard frequency bands by wavelet packet transform, wherein the three standard frequency bands are respectively an alpha frequency band, a beta frequency band and a theta frequency band;

the EEG data is divided into awake state data and fatigue state data by test time.

Further, constructing the preprocessed EEG data into a dynamic time-brain network based on a sliding window method comprises:

representing the preprocessed EEG data as a static network, the static network being a binary NxN matrix, wherein N represents a number of electrodes of an EEG cap;

selecting a proper window length and step length, and sequentially traversing the whole time sequence by sliding a window, wherein the length of the time sequence is the experimental duration for collecting EEG data;

estimating PLI values of functionally connected in each of said static networks using phase lag indices;

setting the PLI value higher than the threshold value to be 1 and the PLI value lower than the threshold value to be 0 by adopting a sparsity method, thereby forming a binary adjacent network which is taken as a snapshot of the time brain network;

and arranging the static networks according to a time sequence to form a time brain network with dynamic characteristics.

Further, the measuring the spatiotemporal topology of the temporal brain network based on the temporal efficiency analysis framework comprises:

calculating a temporal distance of pairs of nodes on a time scale, the temporal distance representing a minimum number of time windows defined as being traversed by the spatio-temporal path;

calculating the space-time global efficiency;

calculating the local efficiency of time and space;

spatio-temporal characteristics of the node-level temporal brain network are evaluated using spatio-temporal proximity centrality.

An analysis device for driving fatigue related EEG functional connection dynamics, comprising:

a pre-processing unit for pre-processing the EEG data using independent component analysis and wavelet packet transformation;

the construction unit is used for constructing the preprocessed EEG data into a time brain network with dynamic characteristics based on a sliding window method;

a spatiotemporal topology calculation unit for measuring spatiotemporal topology of the temporal brain network based on a time efficiency analysis framework;

and the statistical analysis unit is used for carrying out statistical analysis on the space-time topology of the time brain network to obtain the correlation between the driving fatigue related behavior and the dynamic characteristics of the time brain network, wherein the correlation comprises space-time global efficiency, space-time local efficiency and space-time adjacent centrality.

Further, the preprocessing unit includes:

the blink artifact data acquisition unit is used for acquiring blink artifact data comprising horizontal electro-oculogram HEOG data and vertical electro-oculogram VEOG data;

a screening unit for finding and deleting components of EEG data that are highly correlated with said blink artifact data using independent component analysis;

a baseline removal unit for removing a baseline of the screened EEG data;

the decomposition unit is used for decomposing the EEG data into three standard frequency bands, namely an alpha frequency band, a beta frequency band and a theta frequency band by using wavelet packet transformation;

a data dividing unit for dividing the EEG data into awake state data and fatigue state data by a test time.

Further, the construction unit includes:

a matrix construction unit for representing the preprocessed EEG data as a static network, the static network being a binary NxN matrix, wherein N represents the number of electrodes of an EEG cap;

the system comprises a sliding window processing unit, a time sequence processing unit and a time sequence processing unit, wherein the sliding window processing unit is used for selecting proper window length and step length, the sliding window sequentially traverses the whole time sequence, and the length of the time sequence is the experimental time length for collecting EEG data;

a PLI calculation unit for estimating PLI values of functional connections using phase lag indices in each of said static networks;

a binarization calculating unit, which adopts a sparsity method to set the PLI value higher than a threshold value as 1 and the PLI value lower than the threshold value as 0, thereby forming a binarization adjacent network as a snapshot of the time brain network;

the sparsity calculation unit is used for selecting proper sparsity and interval and reserving required functional connection in each static network by adopting a sparsity method;

and the dynamic characteristic establishing unit is used for arranging the static networks according to the time sequence to form a time brain network with dynamic characteristics.

Further, the spatiotemporal topology calculation unit includes:

a temporal distance calculation unit for calculating a temporal distance of the pair of nodes on a time scale, the temporal distance representing a minimum number of time windows defined as the spatio-temporal path passes;

the space-time global efficiency calculating unit is used for calculating the space-time global efficiency;

the space-time local efficiency calculating unit is used for calculating space-time local efficiency;

and the space-time characteristic evaluation unit is used for evaluating the space-time characteristics of the node-level time brain network by using the space-time adjacent centrality.

An analysis device for driving fatigue related EEG functional connection dynamics, comprising at least one control processor and a memory for communicative connection with the at least one control processor; the memory stores instructions executable by the at least one control processor to enable the at least one control processor to perform a method of analysis of driving fatigue related EEG function connection dynamics as described in any one of the above.

A computer-readable storage medium characterized by: the computer-readable storage medium stores computer-executable instructions for causing a computer to perform a method of analysis of driving fatigue related EEG function connection dynamics as described in any one of the above.

One or more technical schemes provided in the embodiment of the invention have at least the following beneficial effects: the time characteristic is introduced into a static network of the driving fatigue, a time brain network with dynamic characteristic is constructed, the time-space recombination rule of the time brain network during the driving fatigue can be obtained through analysis and statistics, and compared with the static FC connection in the current driving fatigue research, the analysis method provided by the invention has a more accurate analysis result and is beneficial to revealing the more key dynamic characteristic of information transfer function recombination between brain areas related to the driving fatigue on a fine time scale.

Drawings

The invention is further illustrated by the following figures and examples.

FIG. 1 is an overall method flow diagram of an embodiment of the present invention;

FIG. 2 is a flow chart of a pre-processing method of an embodiment of the present invention;

FIG. 3 is a flow chart of constructing a dynamic time-brain network according to an embodiment of the present invention;

FIG. 4 is a flow chart of measuring the spatiotemporal topology of a temporal brain network according to an embodiment of the present invention;

FIG. 5 is a block diagram of a device according to an embodiment of the present invention;

FIG. 6 is a schematic diagram of the connections in the apparatus of an embodiment of the present invention;

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. It should be noted that, if not conflicted, the various features of the embodiments of the invention may be combined with each other within the scope of protection of the invention.

It should be noted that, if not conflicted, the various features of the embodiments of the invention may be combined with each other within the scope of protection of the invention. Additionally, while functional block divisions are performed in apparatus schematics, with logical sequences shown in flowcharts, in some cases, steps shown or described may be performed in sequences other than block divisions in apparatus or flowcharts.

Referring to fig. 1, an embodiment of the present invention provides a method for analyzing driving fatigue related EEG function connection dynamics, including:

s1, preprocessing EEG data using independent component analysis and wavelet packet transformation;

s2, constructing the preprocessed EEG data into a time brain network with dynamic characteristics based on a sliding window method;

s3, measuring the space-time topology of the time brain network based on a time efficiency analysis framework;

s4, carrying out statistical analysis on the time-space topology of the time brain network, and obtaining the correlation between the driving fatigue related behavior and the dynamic characteristics of the time brain network, wherein the correlation comprises the space-time global efficiency, the space-time local efficiency and the space-time adjacent centrality.

Referring to fig. 2, step S1 includes:

s11, acquiring blink artifact data, wherein the blink artifact data comprises horizontal electro-oculogram HEOG data and vertical electro-oculogram VEOG data;

s12, using independent component analysis to find and remove components of EEG data that are highly correlated with said blink artifact data;

s13, removing the baseline of the screened EEG data;

s14, decomposing the EEG data into three standard frequency bands by wavelet packet transformation, wherein the three standard frequency bands are respectively an alpha frequency band, a beta frequency band and a theta frequency band;

s15, the EEG data is divided by test time into awake state data and fatigue state data.

Referring to fig. 3, 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;

s22, selecting a proper window length and step length, and sequentially traversing the whole time sequence by sliding a window, wherein the length of the time sequence is the experimental duration for collecting EEG data;

s23, estimating the PLI value of the functional connection in each of said static networks using the phase lag index;

s24, a binarization calculating unit sets the PLI value higher than the threshold value as 1 and the PLI value lower than the threshold value as 0 by adopting a sparsity method, thereby forming a binarization adjacent network as a snapshot of the time brain network;

and S25, arranging the static networks according to the time sequence to form a time brain network with dynamic characteristics.

Referring to fig. 4, step S3 includes:

s31, calculating the time distance of the paired nodes on the time scale, wherein the time distance represents the minimum number of time windows defined as the time-space path;

s32, calculating the space-time global efficiency;

s33, calculating space-time local efficiency;

and S34, evaluating the spatio-temporal characteristics of the node-level temporal brain network by using the spatio-temporal proximity centrality.

The analytical method of the present invention is described in detail below based on the overall scheme:

the connections (FC) in past driving fatigue studies were static, i.e. a representative brain network was constructed in the fatigue state on a timescale of several minutes, and since a key component of driving fatigue was the mission time itself, emphasizing the cumulative features of the brain in regulating the process of fatigue-related performance decline changes, static brain network studies lacked more critical dynamics with respect to functional reorganization of information transfer between brain regions related to driving fatigue on a fine timescale.

The experiment of the embodiment of the invention is set up in such a way that the test subject performs simulated driving for 90 minutes, a running virtual guide vehicle is arranged in front of the test subject, the virtual guide vehicle generates braking signals at random intervals, and the test subject is required to respond to the braking signals through braking so as to keep a safe distance. The time interval from the braking instruction generated in the virtual lead vehicle to the subject performing the braking operation is regarded as a Reaction Time (RT), while also collecting the speed change (SV) of the vehicle as a quantitative index for evaluating the performance of the subject.

The data was acquired by the subjects wearing a 24 channel wireless EEG cap with a modified international 10-20 electrode placement system (HD-72, Cognionics, inc., USA) to record EEG data at 250Hz, with the reference electrodes being the right and left mastoids, and the EEG signals being filtered by a band pass filter (between 2 and 100 Hz). Blink artifacts were also recorded by electrodes placed above and below the outer canthus (horizontal electro-oculogram, HEOG) and right eye (vertical electro-oculogram, VEOG).

Data preprocessing:

independent Component Analysis (ICA) is used to find and delete components highly correlated to recorded blink artifact data, baseline is removed from the whole experimental data, the processed data is decomposed into three standard frequency bands of theta (3-7Hz), alpha (8-13Hz) and beta (14-30Hz) by Wavelet Packet Transform (WPT), which in the embodiment of the invention adopts db4 and Daubechies wavelet of decomposition level 6 to extract electroencephalogram information.

Functional connection and time brain network construction:

the brain network dynamic analysis framework is a static network characterized by an optimal sliding window in time order. In each static network, the FC is estimated using the Phase Lag Index (PLI) because it has advantages in minimizing the effects of common source signals and volume conduction, with the PLI value of FC between 0 and 1, with larger numbers indicating stronger connections. Then, by using a commonly used sparsity method, sparsity of 5% to 15% is selected at an interval of 1%, the matrix is thresholded into a binarized adjacent network, which is equivalent to comparing the PLI value of FC with a threshold value, the value greater than the threshold value is set to 1, and the value less than the threshold value is set to 0, so as to obtain the binarized adjacent network, and the binarized adjacent network is taken as a snapshot of the time brain network, so that the time brain network G ═ { G ═ G_tCan be composed of a separate static network G_tThe expression is arranged according to a time sequence t, where t represents a positive integer, each static network is a binary NxN matrix, and the number of FCs is the same as the number of static networks, in the experiment of the embodiment of the present invention, the value of N is 24.

In an embodiment of the invention the window length may be chosen to be 3-6 seconds and the step size may be chosen to be 2-4 seconds, in order to balance the dynamics of the signal and the quality of the connectivity estimate and to reduce computational complexity, the window length is chosen to be 4 seconds and the step size is chosen to be 4 seconds, the most awake and tired states are determined by statistical comparison of the subject's performance in terms of reaction time and velocity changes, and the first 5 minutes and last 5 minutes of EEG data are finally chosen as analysis data, corresponding to the most alert and tired states respectively, so that the time step size for each window is T75 in 5 minutes.

Spatiotemporal topology of the temporal brain network:

after the dynamic time brain network is constructed, the time distance of the paired nodes needs to be calculated on a time scale, the time distance is defined as the minimum number of time windows passed by the spatio-temporal path, and it is noted that the time-dependent path is a measure of the space and time domains, and the time distance is characterized by the time domain. Thus, the temporal distance is a positive integer, ranging between 1 and T.

In order to quantitatively reveal brain dynamic reorganization during driving fatigue, a space-time efficiency analysis framework is adopted to measure the space-time topology of a time brain network, conceptually speaking, the space-time efficiency measures the interaction and information transfer functions among the whole nodes in a dynamic system, the space-time global efficiency captures the dynamics of the whole network, and the capability of information flow is spread in the whole life cycle, and the calculation method comprises the following steps:

the space-time global efficiency is expressed asThen

Wherein G is a spatio-temporal network having the mathematical structure NxNxT, andthe time T is the efficiency of the space-time global efficiency, and the value of T is a positive integer not greater than T.

Thus, it is possible to provide

In the range of from 0 to 1,representing all nodes connected in a snapshot of the spatio-temporal brain network, τ_i→j(t) represents the distance in time from i to j at time t. . Spatio-temporal local efficiency measures the overall resiliency of a dynamic network to randomly remove nodes over a local range:

the spatial and temporal local efficiency is expressed asThen

Where G (i, t) is a sub-spatio-temporal network that includes all neighbors of node i at time t.Ranges from 0 to 1, which is an index of the dynamic network for measuring information dissemination ability on a local scale.

Spatio-temporal characteristics of a node-level temporal brain network are evaluated using spatio-temporal proximity centrality, which measures the ability C of a node i to reach other nodes_c(i, t) is expressed as:

spatio-temporal proximity centrality also represents the importance of a node throughout the time network. The integrated spatiotemporal proximity centrality represents the area under the curve of node i over the entire sparsity range.

Reference network:

in consideration of the richness of complex structures in the dynamic FC, a certain processing method needs to be adopted to reveal the characteristics and advantages of the dynamic brain network compared with the reference network. The reference network increases the randomness of the dynamic brain network and presents different information dissemination efficiencies. Computing such a reference network helps to reveal the neural mechanisms of dynamic FCs with different topologies. In an embodiment of the invention, a two-step randomization method is used: random Edges (RE) and Random Connections (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 the connections in the network, which eliminates the distribution of the number of connections per edge. Applying these two methods destroys the main structure of the dynamic network. Extending the definition of the small world attributes in static networks to dynamic time-brain networks that are small world in space-time if the following definitions are satisfied:

or

The spatiotemporal efficiency of the reference network per subject is the average of the generated spatiotemporal reference networks with 50 iterations in the two psychological states (awake and tired).

Statistical analysis:

to investigate the effect of driving fatigue on driver control ability, one-way repeated measures ANOVA were used to calculate the reaction time and speed variations for the entire simulated driving task, i.e. by using one-way ANOVA to find out the key differences in spatio-temporal global efficiency, spatio-temporal local efficiency, spatio-temporal neighbouring centrality between awake and fatigue states, for which pearson correlations were performed to assess the correlation between fatigue-related behavioural performance and dynamic brain network characteristics, including integrated spatio-temporal global efficiency, integrated spatio-temporal local efficiency and integrated spatio-temporal compactness centrality, expressed as p, considered significantly correlated when p < 0.05. Finally, the multiple comparisons of the region features are corrected by a False Discovery Rate (FDR) of q 0.05.

And (4) analyzing results:

by plotting the values of the Reaction Time (RT) and the velocity change (SV) in the 5-minute periods before and after the first and last periods by means of coordinates, respectively, a time-reaction time plot and a time-velocity change plot are formed, and it is apparent that there is a significant difference between the first 5 minutes and the last 5 minutes, thereby confirming that the first 5 minutes defined above is a waking state and the last 5 minutes is a fatigue state, as is reasonable.

Therefore, the space-time topology of brain activity is quantitatively estimated through the space-time efficiency of the waking state and the fatigue state respectively, a frequency band-efficiency coordinate graph is established according to three standard frequency band divisions, the integrated space-time global efficiency and the integrated space-time local efficiency in the whole sparse range are displayed, and the time brain network with dynamic characteristics shows a prominent space-time small-world architecture in all three frequency bands: the finding that the degree of dispersion of brain junction distribution across subjects is expressed by standard deviation, may indicate a greater inter-subject difference in dynamic FC reorganization in fatigue, and it is computationally evident that the standard deviation of spatio-temporal global efficiency and spatio-temporal local efficiency in fatigue is significantly higher than in awake, a result consistent with the theory that individuals have characteristic features that tend to have concomitant time-varying fragility of biological matrices.

Space-time compactness center:

and calculating the comprehensive space-time adjacent centrality of 24 nodes to evaluate the node space-time characteristics of the dynamic FC, wherein the comprehensive space-time adjacent centrality of the 24 nodes in the fatigue state is generally lower than the comprehensive space-time adjacent centrality in the waking state through calculation, and meanwhile, according to the brain region corresponding to the data acquired by the EGG, the nodes in the frontal lobe and the top lobe generally display the huge difference between the waking state and the fatigue state, namely the calculation result meets p < 0.05.

Relationship between behavioral performance and network attributes:

the relationship between the behavioral measures Δ RT and Δ SV and the dynamics of the temporal brain network Δ E was studied by bivariate correlation analysis:

ΔRT＝RT_fatigue-RT_alert

ΔSV＝SV_fatigue-SV_alert

ΔE＝E_fatigue-E_alert

according to the analysis of the correlation between the behavior measures Δ RT and Δ SV in the three standard frequency bands and the dynamic characteristic Δ E, the result of the correlation of the frequency bands and the efficiency can be obtained. Regarding node properties, only nodes showing significant fatigue-related differences in each frequency band were selected for correlation testing

In the process of the analysis method, the worlds attributes existing in a static brain network are expanded to a dynamic system, time factors are introduced into the FC, and the graph theory attributes (global efficiency and local efficiency) are used as characteristics, so that higher identification accuracy rate than that of the traditional analysis method is obtained, and the feasibility of a space-time architecture and a space-time efficiency method in driving fatigue detection is proved.

The embodiment of the present invention further provides an analysis apparatus for EEG function connection dynamics related to driving fatigue, where the analysis apparatus 1000 for EEG function connection dynamics related to driving fatigue includes, but is not limited to: a preprocessing unit 1100, a construction unit 1200, a spatio-temporal topology calculation unit 1300, and a statistical analysis unit 1400.

Wherein the pre-processing unit 1100 is configured to pre-process the EEG data using independent component analysis and wavelet packet transformation;

a constructing unit 1200, configured to construct the preprocessed EEG data into a time-brain network with dynamic characteristics based on a sliding window method;

a spatiotemporal topology calculation unit 1300 for measuring spatiotemporal topology of the temporal brain network based on a time efficiency analysis framework;

the statistical analysis unit 1400 is configured to perform statistical analysis on the temporal-spatial topology of the temporal brain network to obtain correlations between the driving fatigue-related behavior and the dynamic characteristics of the temporal brain network, where the correlations include temporal-spatial global efficiency, temporal-spatial local efficiency, and temporal-spatial proximity centrality.

It should be noted that, since the analysis apparatus for the connection dynamic characteristics of the driving fatigue related EEG function in the present embodiment and the analysis method for the connection dynamic characteristics of the driving fatigue related EEG function are based on the same inventive concept, the corresponding contents in the method embodiments are also applicable to the present apparatus embodiment, and are not described in detail herein.

The embodiment of the invention also provides analysis equipment for the connection dynamic characteristics of the EEG function related to the driving fatigue, and the analysis equipment 2000 for the connection dynamic characteristics of the EEG function related to the driving fatigue can be any type of intelligent terminal, such as a mobile phone, a tablet computer, a personal computer and the like.

Specifically, the analysis device 2000 for the driving fatigue-related EEG function connection dynamics includes: one or more control processors 2010 and memory 2020, one control processor 2010 being illustrated in fig. 6.

Control processor 2010 and memory 2020 may be coupled by a bus or other means, such as by a bus as shown in FIG. 6.

The memory 2020, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the analysis method of the driving fatigue-related EEG function connection dynamics in the embodiment of the present invention, 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 executes various functional applications and data processing of the analysis apparatus 1000 for the driving fatigue-related EEG function connection dynamics, that is, the analysis method for the driving fatigue-related EEG function connection dynamics of the above-described method embodiment, by running the non-transitory software programs, instructions, and modules stored in the memory 2020.

The memory 2020 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the analysis apparatus 1000 of the driving fatigue-related EEG function connection dynamics, and the like. Further, the memory 2020 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 2020 may optionally include memory located remotely from the control processor 2010, which may be connected via a network to the analysis device 2000 for driving fatigue related EEG functionality connectivity dynamics. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The one or more modules stored in the memory 2020, when executed by the one or more control processors 2010, perform the analysis method for the driving fatigue related EEG function connection dynamics in the above method embodiments, for example, perform the above described method steps S1 to S4 in fig. 1, and implement the functions of the unit 1100-1400 in fig. 5.

Embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions, which are executed by one or more control processors, for example, by one control processor 2010 in fig. 6, and may cause the one or more control processors 2010 to execute the analysis method for the driving fatigue related EEG function connection dynamic characteristics in the method embodiment, for example, execute the above-described method steps S1 to S4 in fig. 1, and implement the functions of the unit 1100 and 1400 in fig. 5.

The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

Through the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be implemented by software plus a general hardware platform. Those skilled in the art will appreciate that all or part of the processes of the methods of the above embodiments may be implemented by hardware related to instructions of a computer program, which may be stored in a computer readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read Only Memory (ROM), a Random Access Memory (RAM), or the like.

While the preferred embodiments of the present invention have been described in detail, it will be understood by those skilled in the art that the foregoing and various other changes, omissions and deviations in the form and detail thereof may be made without departing from the scope of this invention.