Electroencephalogram signal unmanned platform intelligent control method based on deep convolutional adversarial network

An EEG signal and unmanned platform technology, applied in neural learning methods, biological neural network models, user/computer interaction input/output, etc., can solve problems such as brain-computer interface being susceptible to interference

Active Publication Date: 2021-04-16
XIDIAN UNIV
View PDF8 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The existing brain-computer interface is susceptible to interference during the acquisition process of the original signal. How to efficiently denoise the original signal, improve the classification and recognition rate of the EEG signal, and realize efficient and stable unmanned platform control has become a huge challenge.

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
  • Electroencephalogram signal unmanned platform intelligent control method based on deep convolutional adversarial network
  • Electroencephalogram signal unmanned platform intelligent control method based on deep convolutional adversarial network
  • Electroencephalogram signal unmanned platform intelligent control method based on deep convolutional adversarial network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0052] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0053] An embodiment of the present invention provides an intelligent control method for an EEG signal unmanned platform based on a deep convolutional confrontation network, such as figure 1 As shown, the method is specifically implemented through the following steps: Step 101: The terminal performs noise removal on the collected EEG signal, and obtains the denoised EEG signal;

[0054] Specifically, step 101: First, use the method of denoising EEG signals based on deep recurrent neural network to improve deep self-encoding, and apply it to the denoising of multi-type EEG signals. Facilitate filtering of specific types of noise in EEG by implementing from specific loss functions and better suited accuracy metrics, and using layers other than convolution, pooling, and upsampling layers.

[0055] The deep recursive neural network is used to re...

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 an electroencephalogram signal unmanned platform intelligent control method based on a deep convolutional adversarial network. The method comprises the steps that a terminal carries out the noise removal of a collected electroencephalogram signal, and obtains a denoised electroencephalogram signal; performing deep feature extraction on the denoised electroencephalogram signal through a capsule network to obtain a deep feature signal; fusing the deep feature signal and the electroencephalogram signal and then carrying out classification and recognition to determine a corresponding control instruction signal; and the terminal performs offline and online test verification on the unmanned platform, and after verification succeeds, the unmanned platform receives and executes the control instruction signal sent by the terminal. According to the method, the existing noise data are integrated into the one-dimensional electroencephalogram signal training network, the mathematical model is simplified, the problem of insufficient noise training data is solved, the one-dimensional prediction signal is reconstructed by using the auto-encoder architecture, the attention mechanism is used for feature selection, and the calculation efficiency is improved.

Description

technical field [0001] The invention belongs to the field of EEG intelligent control, and in particular relates to an EEG signal unmanned platform intelligent control method based on a deep convolutional confrontation network. Background technique [0002] The EEG signal is produced by the collective discharge of a large number of neurons in the cerebral cortex, which is the combined effect of a large number of neuron activities. Therefore, the EEG signal can be regarded as the superposition of various types of brain waves in different regions, composed of a large number of neurons. A complex composite wave formed by the electrical activity of a cell population. With the development of biosignal-related technologies, related research based on EEG signals has become a hot field. By exploring the relationship between EEG signals and brain nerves, people can discover the operating mechanism of the brain to express human intentions and movements, etc. Physiological information,...

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 Applications(China)
IPC IPC(8): G06F3/01G06K9/62G06N3/04G06N3/08
CPCY02P90/02
Inventor 秦翰林岳恒梁进蔡彬彬朱文锐延翔王诚张昱赓周慧鑫
Owner XIDIAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap