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A method for detecting driving fatigue based on EEG signals

An EEG signal and driving fatigue technology, applied in the field of traffic driving, can solve problems such as consuming large memory computing time

Active Publication Date: 2020-06-09
CHONGQING JINOU SCI & TECH DEV
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Problems solved by technology

Support vector machine is one of the commonly used methods. Although it can achieve good results in practical applications, it needs to use the quadratic programming method to solve it. When the number of samples is relatively large, it needs to consume a lot of memory and computing time.

Method used

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  • A method for detecting driving fatigue based on EEG signals
  • A method for detecting driving fatigue based on EEG signals
  • A method for detecting driving fatigue based on EEG signals

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Embodiment Construction

[0113] The present invention is further described above in conjunction with the accompanying drawings and specific embodiments.

[0114] (1) Select the feature quantities related to the fatigue driving state: feature selection refers to a feature set composed of a group of related feature quantities obtained after a series of corresponding processing on the original measurement signal, and then select some features from the feature set. The representative physical quantity with the best classification performance is used as a set of feature quantities to distinguish different behaviors, and then the relevant classification recognition method is used to separate different behaviors from the feature space by using the selected feature quantities. Therefore, for learning classification Both require training set samples:

[0115] T={(x 1 ,y 1 ),..., (x 1 ,y 1 )}∈(X×Y) 1 (1)

[0116] where x i ∈X=R n ,y i ∈Y=R, i=1,...,1;

[0117] For the training set that meets the requ...

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Abstract

The invention relates to a method for detecting driving fatigue based on electroencephalogram signals, which specifically includes the following aspects: (1) selecting feature quantities related to the fatigue driving state; (2) analyzing the power spectrum of the electroencephalogram signal in the fatigue driving state; (3) ) EEG signal sample entropy analysis of fatigue driving state; (4) EEG signal Kc complexity analysis of fatigue driving state; (5) support vector machine (SVM) (6) least squares support vector machine (LS-SVM) (7) Particle swarm optimization (PSO) algorithm sets the model training parameters C and g of LS-SVM. The present invention studies the electroencephalogram signals extracted under different driving states respectively from the perspective of power spectrum and using related methods in nonlinear dynamics, and shows that better results are achieved in terms of accuracy and reliability.

Description

technical field [0001] The invention belongs to the technical field of traffic driving, in particular to a method for detecting driving fatigue based on electroencephalogram signals Background technique [0002] How to select the appropriate feature quantity from many features closely related to the fatigue state plays a very important role in the accurate identification of driving fatigue. After correlative processing of the EEG signal, the selected relevant feature vectors are obtained, and then some classifiers are used to make discrimination based on these feature vectors. Support vector machine is one of the commonly used methods. Although it can achieve good results in practical applications, it needs to use the quadratic programming method to solve it. When the number of samples is relatively large, it needs to consume a lot of memory and computing time. . The present invention studies the extracted EEG signals of different driving states from the perspective of pow...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F3/01G06K9/62A61B5/00
CPCG06F3/015A61B5/72G06F2203/011A61B2503/22G06F18/2411
Inventor 金纯
Owner CHONGQING JINOU SCI & TECH DEV
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