Driving fatigue detecting method based on phase-locked value established brain function network

Abstract

The invention relates to the field of driving fatigue detection, in particular to a driving fatigue detecting method based on a phase-locked value established brain function network. The method includes the steps of collecting EEG signals during driving within the wake time and test time, denoising, decomposing and reestablishing the EEG signals, calculating the phase-locked value (PLV) in the sequence of the wake time and test time for every two channels, forming a functional connection matrix of the channels in the sequence of the wake time and test time according to the PLV, setting a connecting strength threshold, comparing the connecting strength threshold with each element value in the functional connection matrix to obtain the connection relation between the channels in the sequenceof the wake time and test time and form a brain function network of a testee in the sequence of the wake time and test time, and analyzing the difference of the brain functional network topological structure in the sequence of the wake time and test time on three sub-bands through comparison to judge whether the driving fatigue state is realized in the sequence of the test time or not. The detection reliability and accuracy are high.

Description

technical field [0001] The present invention relates to the field of driving fatigue detection, more specifically, to a driving fatigue detection method for constructing a brain function network based on phase locking values. Background technique [0002] Many countries have attached great importance to the research on detection methods related to driving fatigue. The initial research mainly started from the medical aspect, using medical equipment to study people's mental state. At the beginning of the nineteenth century, the United States was the first to investigate the rationality of the regulation on the service time of motor vehicle drivers. Afterwards, studies on driving fatigue have been carried out one after another. After years of development, the research on driving fatigue detection methods can be roughly divided into three categories: detection methods based on facial features, detection methods based on driving behavior, and detection methods based on physiolog...

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Problems solved by technology

[0008] However, the above-mentioned driving fatigue detection method has the following deficiencies: the fatigue detection method based on facial features is easily affected by the environment, brightness, angle and 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 artificially forged signals; the driving behavior method is powerless to non-standard roads, the accuracy is not enough, and it is easy to generate false positives; 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 regional cooperation is completed, but the driving fatigue detection method based on power spectrum and entropy does not involve the regional information of the brain, so it is impossible to comprehensively and systematically study the mechanism of driving fatigue

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