Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Fatigue detection and regulation method based on electroencephalogram-eye movement bimodal signal

A fatigue detection, dual-modal technology, used in electrotherapy, eye testing equipment, diagnostic recording/measurement, etc., can solve the problem of inability to continuously monitor the fatigue level of high-load workers, and achieve high efficiency and stability. Clutter, the effect of improving robustness

Active Publication Date: 2018-12-18
BEIJING MECHANICAL EQUIP INST
View PDF19 Cites 14 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In view of the above analysis, the embodiment of the present invention aims to provide a fatigue detection and control method based on EEG-eye movement dual modes, to solve the problem that the existing technology cannot continuously monitor the fatigue degree of high-load workers and provide neural control in a timely manner. The problem

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
  • Fatigue detection and regulation method based on electroencephalogram-eye movement bimodal signal
  • Fatigue detection and regulation method based on electroencephalogram-eye movement bimodal signal
  • Fatigue detection and regulation method based on electroencephalogram-eye movement bimodal signal

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0058] A specific embodiment of the present invention discloses a kind of, such as figure 1 shown, including the following steps:

[0059] S1. Obtain the EEG monitoring data and eye movement monitoring data of the operator;

[0060] S2. Perform feature extraction (dual-mode signal feature extraction) on the EEG monitoring data and eye movement monitoring data to obtain corresponding EEG features and eye movement features;

[0061] S3. Perform feature fusion of the EEG features and eye movement features (dual-mode signal feature fusion), and input the fusion results into a pre-built fatigue prediction model based on EEG-eye movement dual modes (dual-mode signal fatigue prediction model ), determine whether neuromodulation is needed; if neuromodulation is required, perform transcranial direct current stimulation on the operator; if no neuromodulation is required, determine that the fatigue status of the operator does not affect work performance.

[0062] When implemented, such...

Embodiment 2

[0065] To perform optimization on the basis of the above-mentioned embodiments, the fatigue prediction model of the EEG-eye movement dual mode needs to be established in advance. The process of fatigue detection and regulation of operators is as follows: image 3 shown. Regarding its establishment method, such as Figure 4 shown, including the following steps:

[0066] S01. Obtain a test set including N groups of EEG monitoring data, eye movement monitoring data and corresponding fatigue levels. Specifically, the Neuroscan 64-lead EEG acquisition system was used to collect the EEG monitoring data of the workers. The electrodes used were Ag / AgCl electrodes, with the forehead as the ground and the left ear mastoid as the reference. When in use, set the EEG sampling frequency of the Neuroscan 64-lead EEG acquisition system to 1000 Hz, the band-pass filter range to 0.1-100 Hz, and use a 50 Hz notch filter to remove power frequency interference. The Eyelink 1000plus eye trackin...

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 relates to a fatigue detection and regulation method based on an electroencephalogram and eye movement bimodal signal, belongs to the technical field of fatigue detection and regulation,and solves the problems in the prior art that the fatigue degree of a high-load operator cannot be continuously monitored and nerve regulation is not timely performed. A fatigue prediction model based on electroencephalogram-eye movement bimodal uses a machine learning method to analyze bimodal fusion features, that is, a fusion result of electroencephalogram features and eye movement features, recognizes the brain fatigue state of the operator, and according to the detection result, judges whether or not neuromodulation is required. If not required, the operator can continue to work and maintain high job performance. If necessary, a transverse brain intervention is applied by using transcranial direct current stimulation. Through bidirectional closed-loop fatigue monitoring and adaptiveregulation, the excitability of a cerebral cortex functional area is adjusted, thereby improving the physiological state of the operator and ensuring the efficiency and stability of the job performance.

Description

technical field [0001] The invention relates to the technical field of fatigue detection and regulation, in particular to a method for fatigue detection and regulation based on EEG-eye movement dual modes. Background technique [0002] In a complex operating environment, high-load task operators, such as astronauts and pilots, need to maintain a high degree of concentration at all times to ensure stable and efficient work performance. As the load of tasks and information increases, the occupancy rate of mental cognitive resources of operators will be greatly increased, which can easily lead to mental fatigue. [0003] Changes in the physiological state of workers may cause the central nervous system to respond slowly, resulting in inattention, decreased alertness and work ability, misjudgments and missed judgments of information, and even operational errors causing safety hazards. [0004] In order to effectively improve the mental fatigue state of long-time, high-load task...

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
IPC IPC(8): A61B5/18A61B5/0476A61B5/11A61B3/113A61B3/11A61B5/00A61N1/20A61B5/0496
CPCA61N1/20A61B3/112A61B3/113A61B5/1103A61B5/168A61B5/18A61B5/7203A61B5/725A61B2503/22A61B5/398A61B5/369
Inventor 奕伟波范新安陈远方张利剑
Owner BEIJING MECHANICAL EQUIP INST
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com