An EEG Fatigue Detection Method Based on Joint Quantitative Evaluation of Sample and Feature Quality

A technology for fatigue detection and quantitative evaluation, applied in diagnostic recording/measurement, medical science, diagnosis, etc., can solve the problems of lack of reliability in the quality of EEG data, achieve the effect of improving robustness and accuracy, and improving recognition effect

Active Publication Date: 2022-04-26
HANGZHOU DIANZI UNIV
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Problems solved by technology

However, due to the influence of cross-period acquisition and the position of the electrode cap in the current EEG acquisition process, the quality of the collected EEG data lacks reliability.

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  • An EEG Fatigue Detection Method Based on Joint Quantitative Evaluation of Sample and Feature Quality
  • An EEG Fatigue Detection Method Based on Joint Quantitative Evaluation of Sample and Feature Quality
  • An EEG Fatigue Detection Method Based on Joint Quantitative Evaluation of Sample and Feature Quality

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Embodiment Construction

[0059] The present invention will be further described below in conjunction with accompanying drawing.

[0060] The present invention solves the important problem of mining the important features of EEG signals in fatigue detection based on the following starting point: we believe that in fatigue detection, EEG signals are unsteady signals, and the samples contain more noise. In the learning process, the quality of each sample is characterized and the feature dimension of each sample is selected, so as to select samples and features that are conducive to model training, and a model with better robustness will be obtained. Therefore, we can choose samples and features with better quality for learning, which is of great significance for improving the accuracy of fatigue detection.

[0061] Such as figure 1 and 2 As shown, an EEG fatigue detection method for joint quantitative evaluation of sample and feature quality, the specific steps are as follows:

[0062] Step 1. Using t...

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Abstract

The invention provides an EEG fatigue detection method for joint quantitative evaluation of sample and feature quality. The steps of the present invention are as follows: 1. A plurality of subjects respectively collect EEG data under the simulated driving system. 2. Perform preprocessing and feature extraction on all the EEG data obtained in step 1. 3. Establish a machine learning model to realize the EEG fatigue detection of the joint quantitative evaluation of sample and feature quality. 4. Obtain the description factor v for measuring sample quality and the description factor θ for features. 5. Fatigue regression prediction is performed on the new subjects' EEG data. After the present invention embeds v and θ into the least squares model, the obtained weight description factor for measuring sample quality and features provides an effective tool for implementing EEG data sample selection and feature selection, and gives higher quality samples and features. The weight of the weight can accurately obtain the fatigue status of the subject according to the EEG data.

Description

technical field [0001] The invention belongs to the technical field of EEG signal processing, and in particular relates to an EEG fatigue detection method for joint quantitative evaluation of samples and feature quality. Background technique [0002] With the increasing development of my country's traffic, it is of great significance to study more practical and objective driving fatigue detection to improve active traffic safety. Comprehensive fatigue detection methods at home and abroad are mainly divided into two types: subjective evaluation and objective detection. The subjective evaluation method is mainly to judge whether you are in a state of fatigue by recording subjective questionnaires such as the Pearson Fatigue Scale and the Stanford Sleep Scale. The field of objective detection is mainly divided into: detection based on vehicle behavior characteristics, detection based on driver characteristic behavior characteristics, and detection based on physiological electr...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/18A61B5/372A61B5/369A61B5/16
CPCA61B5/18A61B5/7264A61B5/7225A61B5/7203
Inventor 彭勇李幸张怿恺
Owner HANGZHOU DIANZI UNIV
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