Method for analyzing dynamic characteristic of eeg functional connectivity related to driving fatigue

2:

• The method for analyzing dynamic characteristics of EEG functional connectivity related to driving fatigue of claim 1, wherein the using independent component analysis and wavelet packet transformation to preprocess EEG data comprises:
• collecting blink artifact data comprising horizontal electrooculogram HEOG data and vertical electrooculogram VEOG data;
• using independent component analysis to search and delete a component in the EEG data highly correlated with the blink artifact data;
• removing a baseline of screened EEG data;
• using wavelet packet transformation to decompose the EEG data into three standard frequency bands which are respectively frequency band α, frequency band β and frequency band θ;
• and
• dividing the EEG data into alert-state data and fatigue-state data according to a test time.

3:

• The method for analyzing dynamic characteristics of EEG functional connectivity related to driving fatigue of claim 1, wherein the constructing the preprocessed EEG data into a temporal brain network with dynamic characteristics based on a sliding window method comprises:
• expressing the preprocessed EEG data into static networks, wherein each of the static networks is a binary N×N matrix, and N represents a number of electrodes of an EEG cap;
• selecting a suitable window length and a suitable step size, and sequentially traversing a whole temporal sequence by a sliding window, wherein a length of the temporal sequence is an experimental duration for collecting the EEG data;
• estimating a PLI value of functional connectivity using a phase lag index in each of the static networks;
• using a sparsity method to set the PLI value greater than a threshold value to be 1, and set the PLI value smaller than the threshold value to be 0, thus forming a binary neighboring network which is used as a snapshot of the temporal brain network;
• and
• arranging the static networks according to the temporal sequence to form the temporal brain network with dynamic characteristics.

4:

• The method for analyzing dynamic characteristics of EEG functional connectivity related to driving fatigue of claim 1, wherein the measuring a spatiotemporal topology of the temporal brain network based on a temporal efficiency analysis framework comprises:
• calculating a temporal distance of a pair of nodes on a temporal scale, wherein the temporal distance represents a minimum number of temporal windows which are defined to be passed through by a spatiotemporal path;
• calculating a spatiotemporal global efficiency;
• calculating a spatiotemporal local efficiency;
• and
• using a spatiotemporal closeness centrality to evaluate spatiotemporal characteristics of a node-level temporal brain network.

11:

• An apparatus for analyzing dynamic characteristics of EEG functional connectivity related to driving fatigue, comprising:
• at least one control processor, and
• a memory communicated with the at least one control processor, the memory having instructions executable by the at least one control processor stored thereon, and the instructions, when executed by the at least one control processor, enable the at least one control processor to execute the steps of:
• using independent component analysis and wavelet packet transformation to preprocess EEG data;
• constructing the preprocessed EEG data into a temporal 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
• performing statistical analysis on the spatiotemporal topology of the temporal brain network to obtain a correlation between behaviors related to driving fatigue and dynamic characteristics of the temporal brain network, wherein the correlation comprises a spatiotemporal global efficiency, a spatiotemporal local efficiency and a spatiotemporal closeness centrality.

12:

• The apparatus for analyzing dynamic characteristics of EEG functional connectivity related to driving fatigue of claim 11, wherein the using independent component analysis and wavelet packet transformation to preprocess EEG data comprises:
• collecting blink artifact data comprising horizontal electrooculogram HEOG data and vertical electrooculogram VEOG data;
• using independent component analysis to search and delete a component in the EEG data highly correlated with the blink artifact data;
• removing a baseline of screened EEG data;
• using wavelet packet transformation to decompose the EEG data into three standard frequency bands which are respectively frequency band α, frequency band β and frequency band θ;
• and
• dividing the EEG data into alert-state data and fatigue-state data according to a test time.

13:

• The apparatus for analyzing dynamic characteristics of EEG functional connectivity related to driving fatigue of claim 11, wherein the constructing the preprocessed EEG data into a temporal brain network with dynamic characteristics based on a sliding window method comprises:
• expressing the preprocessed EEG data into static networks, wherein each of the static networks is a binary N×N matrix, and N represents a number of electrodes of an EEG cap;
• selecting a suitable window length and a suitable step size, and sequentially traversing a whole temporal sequence by a sliding window, wherein a length of the temporal sequence is an experimental duration for collecting the EEG data;
• estimating a PLI value of functional connectivity using a phase lag index in each of the static networks;
• using a sparsity method to set the PLI value greater than a threshold value to be 1, and set the PLI value smaller than the threshold value to be 0, thus forming a binary neighboring network which is used as a snapshot of the temporal brain network;
• and
• arranging the static networks according to the temporal sequence to form the temporal brain network with dynamic characteristics.

14:

• The apparatus for analyzing dynamic characteristics of EEG functional connectivity related to driving fatigue of claim 11, wherein the measuring a spatiotemporal topology of the temporal brain network based on a temporal efficiency analysis framework comprises:
• calculating a temporal distance of a pair of nodes on a temporal scale, wherein the temporal distance represents a minimum number of temporal windows which are defined to be passed through by a spatiotemporal path;
• calculating a spatiotemporal global efficiency;
• calculating a spatiotemporal local efficiency;
• and
• using a spatiotemporal closeness centrality to evaluate spatiotemporal characteristics of a node-level temporal brain network.

15:

• A computer-readable storage medium with computer-executable instructions stored thereon, and the computer-executable instructions when executed enable a computer to execute the steps of
• using independent component analysis and wavelet packet transformation to preprocess EEG data;
• constructing the preprocessed EEG data into a temporal 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
• performing statistical analysis on the spatiotemporal topology of the temporal brain network to obtain a correlation between behaviors related to driving fatigue and dynamic characteristics of the temporal brain network, wherein the correlation comprises a spatiotemporal global efficiency, a spatiotemporal local efficiency and a spatiotemporal closeness centrality.

16:

• The computer-readable storage medium of claim 15, wherein the using independent component analysis and wavelet packet transformation to preprocess EEG data comprises:
• collecting blink artifact data comprising horizontal electrooculogram HEOG data and vertical electrooculogram VEOG data;
• using independent component analysis to search and delete a component in the EEG data highly correlated with the blink artifact data;
• removing a baseline of screened EEG data;
• using wavelet packet transformation to decompose the EEG data into three standard frequency bands which are respectively frequency band α, frequency band β and frequency band θ;
• and
• dividing the EEG data into alert-state data and fatigue-state data according to a test time.

17:

• The computer-readable storage medium of claim 15, wherein the constructing the preprocessed EEG data into a temporal brain network with dynamic characteristics based on a sliding window method comprises:
• expressing the preprocessed EEG data into static networks, wherein each of the static networks is a binary N×N matrix, and N represents a number of electrodes of an EEG cap;
• selecting a suitable window length and a suitable step size, and sequentially traversing a whole temporal sequence by a sliding window, wherein a length of the temporal sequence is an experimental duration for collecting the EEG data;
• estimating a PLI value of functional connectivity using a phase lag index in each of the static networks;
• using a sparsity method to set the PLI value greater than a threshold value to be 1, and set the PLI value smaller than the threshold value to be 0, thus forming a binary neighboring network which is used as a snapshot of the temporal brain network;
• and
• arranging the static networks according to the temporal sequence to form the temporal brain network with dynamic characteristics.

18:

• The computer-readable storage medium of claim 15, wherein the measuring a spatiotemporal topology of the temporal brain network based on a temporal efficiency analysis framework comprises:
• calculating a temporal distance of a pair of nodes on a temporal scale, wherein the temporal distance represents a minimum number of temporal windows which are defined to be passed through by a spatiotemporal path;
• calculating a spatiotemporal global efficiency;
• calculating a spatiotemporal local efficiency;
• and
• using a spatiotemporal closeness centrality to evaluate spatiotemporal characteristics of a node-level temporal brain network.