Unlock instant, AI-driven research and patent intelligence for your innovation.

Cross-subject fatigue driving classification method based on EEG sample weight adjustment

A fatigue driving and weight adjustment technology, applied in the field of EEG signal recognition, can solve the problems of time-consuming and labor-intensive EasyTL method, poor performance in the EEG field, poor classification performance, and a single training model for a single subject to improve classification performance, The effect of high classification performance

Active Publication Date: 2022-05-17
HANGZHOU DIANZI UNIV
View PDF4 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to overcome the disadvantages of poor classification performance of the cross-subject fatigue driving detection method and the time-consuming and labor-intensive defects of the separate training model for a single subject in the existing method and the poor performance of the EasyTL method in the EEG field

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Cross-subject fatigue driving classification method based on EEG sample weight adjustment
  • Cross-subject fatigue driving classification method based on EEG sample weight adjustment
  • Cross-subject fatigue driving classification method based on EEG sample weight adjustment

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0033] The present invention will be further described below in conjunction with drawings and embodiments.

[0034] like figure 1 As shown, a method for detecting fatigue driving across subjects based on EEG, the specific implementation steps are as follows:

[0035] Step 1: Process the data in the source domain and the target domain;

[0036] Firstly, band-pass filter was used to filter out the 0.1Hz to 30Hz part of the collected raw EEG data, and then ICA independent component analysis was used to remove EEG artifacts. Next, the preprocessed EEG data are as follows: figure 2 feature extraction. In the process of feature extraction, processing is performed in units of samples. figure 2 The part (a) in the middle represents the original EEG data of a subject, with a total of 1400 samples, the number of sampling channels for each sample is 61, and the number of sampling points is 100. After PSD unilateral estimation, we get figure 2 As shown in part (b), the number of ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a cross-subject fatigue state classification method based on weight adjustment of EEG samples. The present invention uses PSD as a feature extraction method, InstanceEasyTL algorithm as a classifier, through the processing and analysis of EEG signals, under the setting of cross-subjects, classifies the fatigue degree of the driver, and realizes the classification of fatigue and wakefulness. distinguish. Firstly, the data is acquired and preprocessed; secondly, PSD is used to extract the features of the EEG data; then, the new source domain and the new target domain of the experiment are set, and then classified according to the InstanceEasyTL algorithm. Compared with traditional machine learning, deep learning methods and EasyTL method based on feature alignment, it has better classification performance across subjects. In addition, this method can still maintain high classification performance when only a small proportion of data in the target domain is required.

Description

technical field [0001] The invention relates to the field of electroencephalogram signal recognition in the field of biological feature recognition, in particular to a cross-subject fatigue driving classification method based on weight adjustment of electroencephalogram samples. Background technique [0002] In the past few years, with the development of society and the advancement of technology, more and more people own private cars, and the number of traffic accidents is also increasing, which has caused great losses to people's lives and properties. Many studies have proved that an important cause of traffic accidents is fatigue driving. Therefore, if the driver's fatigue state can be detected during driving, the probability of traffic accidents can be effectively reduced. [0003] There are three existing fatigue driving detection methods, one is through the questionnaire. Drivers rate their status by filling out questionnaires. The advantage of this method is that it...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/18A61B5/369A61B5/372A61B5/00
CPCA61B5/18A61B5/7264
Inventor 曾虹张佳明李秀峰吴振华赵月孔万增戴国骏
Owner HANGZHOU DIANZI UNIV