Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Electroencephalogram recognizing method combing convolutional neural network with long and short time memory network

A convolutional neural network, long-short-term memory technology, applied in biological neural network models, neural architecture, diagnostic recording/measurement, etc.

Inactive Publication Date: 2018-04-27
CHONGQING UNIV OF POSTS & TELECOMM
View PDF10 Cites 85 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the EEG signal is a typical time-series signal, and this method ignores the hidden useful information between the EEG signals.

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 recognizing method combing convolutional neural network with long and short time memory network
  • Electroencephalogram recognizing method combing convolutional neural network with long and short time memory network
  • Electroencephalogram recognizing method combing convolutional neural network with long and short time memory network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0047] The technical solutions in the embodiments of the present invention will be described clearly and in detail below with reference to the drawings in the embodiments of the present invention. The described embodiments are only some of the embodiments of the invention.

[0048] The technical scheme that the present invention solves the problems of the technologies described above is:

[0049] like figure 1 As shown, the CNN-LSTM-based EEG recognition method provided in this embodiment includes the following steps:

[0050] (1) To collect EEG signal data, the Emotion EEG signal acquisition device is used in the EEG signal acquisition device. Emotiv contains a total of 16 electrodes, of which CMS and DRL are two reference electrodes, and the electrodes are placed according to the international 10-20 standard electrode placement method. The sampling frequency of the signal is 128Hz. After the collected EEG signal is amplified and filtered, it is transmitted to the computer...

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 claims an electroencephalogram recognizing method combing a convolutional neural network (CNN) with a long and short time memory network (LSTM). The method comprises the following steps:firstly, acquiring electroencephalogram signal data by using an Emotive acquiring instrument, and carrying out pretreatment such as mean removal, filtering and normalization on the acquired electroencephalogram signals; secondly, inputting pretreated data into a convolution layer and a pooling layer to extract space features; and finally, directly connecting the rear of the pooling layer to LSTM,extracting temporal order information of electroencephalogram data, and finishing a classifying task through Dropout and a fully connected layer. The temporal and spatial features of electroencephalogram signals can be fully utilized, the space and temporal order information of the electroencephalogram data are extracted, thus, the classifying accuracy of the electroencephalogram signals is improved, and a new way is provided for research on electroencephalogram recognition.

Description

technical field [0001] The invention belongs to the field of feature extraction and recognition of EEG signals, in particular to an EEG recognition method combining a convolutional neural network (CNN) and a long-short-term memory network (LSTM). Background technique [0002] Brain-Computer Interface (BCI) is a new interaction method that can realize the communication between human brain and computer or other devices without relying on muscles and peripheral nerves. It has great practical value in the medical field, cognitive science, psychology, military field and life entertainment, etc., especially in the medical field, BCI technology can help those who are born or acquired muscle nerve damage Lateral sclerosis, etc.) patients communicate with the outside world normally and even recover. [0003] Electroencephalogram signal (EEG) processing is the key technology of BCI system, including three parts: EEG signal preprocessing, feature extraction and classification. Common...

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): A61B5/0476G06N3/04
CPCA61B5/7264A61B5/369G06N3/045
Inventor 蔡军魏畅唐贤伦昌泉陈晓雷曹慧英万亚利李佳歆
Owner CHONGQING UNIV OF POSTS & TELECOMM
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
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