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

CSFL-GDBN-Based Brain-Computer Interface Method for Steady-state Motor Visual Evoked Potentials

A technology of visual evoked potential and computer interface, which is applied in the field of brain-computer interface, can solve the problems of limiting the development of brain-computer interface technology, easy to lose useful features of signals, and unstable classification accuracy, so as to shorten the signal sampling time, signal Effect of short sampling time, improved reliability and feasibility

Active Publication Date: 2019-05-21
XI AN JIAOTONG UNIV
View PDF8 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

But at the same time, due to the extremely complex brain-computer interface, the method of artificial design features of CCA is very easy to lose useful features in the signal, resulting in unstable classification accuracy among individuals, and it takes 3-5 seconds of acquisition time to achieve accurate classification , which limits the further development of SSMVEP-based brain-computer interface technology

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
  • CSFL-GDBN-Based Brain-Computer Interface Method for Steady-state Motor Visual Evoked Potentials
  • CSFL-GDBN-Based Brain-Computer Interface Method for Steady-state Motor Visual Evoked Potentials
  • CSFL-GDBN-Based Brain-Computer Interface Method for Steady-state Motor Visual Evoked Potentials

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0047] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0048] The CSFL-GDBN-based steady-state motor visual evoked potential brain-computer interface method includes the following steps:

[0049] Step 1), refer to figure 1 , respectively place measuring electrodes A1, A2, A3 on the occipital regions O1, O2, Oz of the subject's head X, place the reference electrode D on the unilateral earlobe of the subject's head X, place The ground electrode E is placed on the forehead Fpz of X, the output terminals of the measuring electrodes A1, A2, A3 are connected to the input terminals F1, F2, F3 of the EEG collector F, and the output terminal of the reference electrode D is connected to the input terminal of the collector F F4, the output terminal of the ground electrode E is connected to the input terminal F5 of the collector F, the output terminal of the collector F is connected to the input terminal 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

Steady-state motor visual evoked potential brain-computer interface method based on CSFL-GDBN, first connect the hardware, and then collect labeled SSMVEP data to train CSFL-GDBN so that it can effectively classify SSMVEP signals, CSFL-GDBN It is formed by stacking GRBM and RBM. Multiple GRBM trainings are performed on data from different channels in the underlying input data layer, and the signal features of each channel are extracted. Next, the extracted sub-channel features are performed on the next layer of feature fusion layer. Fusion, and finally classify the fusion features after abstraction to obtain the stimulus target information of SSMVEP; the present invention can automatically extract signal features, and it is not easy to lose useful information. The multi-channel fusion mechanism makes the extracted features include the space in the multi-channel EEG signal Information has the advantages of fast recognition speed and stable recognition accuracy among individuals.

Description

technical field [0001] The invention relates to the technical field of brain-computer interface, in particular to a CSFL-GDBN (Channel Separated Feature Learning Gaussian Deep Belief Networks)-based brain-computer interface method for steady-state motor visual evoked potentials. Background technique [0002] The brain-computer interface is a human-computer interaction system that establishes between the human brain and the outside world without relying on the conventional brain information output pathway. As a kind of human-computer interface, the brain-computer interface opens up a new way for the brain to communicate and control information with the outside world because it does not rely on conventional brain output pathways, enabling people to directly control external devices through the brain. Due to its great application potential in the fields of game entertainment, rehabilitation medicine, aerospace, military and other fields, brain-computer interface technology has ...

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): G06F3/01G06K9/00G06K9/62
CPCG06F3/015G06F2203/011G06F2218/12G06F18/2414
Inventor 谢俊贾亚光徐光华罗爱玲李敏韩兴亮
Owner XI AN JIAOTONG UNIV
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