A feature recognition method of motor imaginary EEG signal based on CBLSTM algorithm model

A technology of EEG signal and motor imagery, applied in character and pattern recognition, neural learning method, biological neural network model, etc., can solve the problem of long experiment time

Active Publication Date: 2019-03-15
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

Li et al. used the OWPT method to extract features from MI-EEG, and then classified them with the LSTM algorithm. Finally, after experimental verification, it was found that the recognition rate was much higher than the accuracy rate of AR+LDA. Due to the limitations of OWPT itself, the experiment cost longer time

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  • A feature recognition method of motor imaginary EEG signal based on CBLSTM algorithm model
  • A feature recognition method of motor imaginary EEG signal based on CBLSTM algorithm model
  • A feature recognition method of motor imaginary EEG signal based on CBLSTM algorithm model

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[0027] 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.

[0028] The technical scheme that the present invention solves the problems of the technologies described above is:

[0029] A method for feature recognition of motor imagery EEG signals based on a convolutional bidirectional long-short-term memory algorithm model, comprising the following steps:

[0030] S1: First use the signal acquisition instrument to collect the original EEG signal;

[0031]S2: Perform preprocessing such as filtering and amplification on the collected EEG signals, and use MPCA to reduce the dimensionality of the multi-dimensional EEG signals to reduce the amount of calculation;

[0032] S3: Considering the complex spatiotemporal characteristics of EEG signals, an algorithm model that...

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Abstract

The invention claims a feature recognition method of motor imaginary electroencephalogram signal based on CBLSTM algorithm model. The algorithm model comprises the following steps S1: collecting EEG signals; S2, preprocessing the original EEG signal; S3, extracting frequency domain characteristics of EEG signals by using convolution neural network; S4, extracting time domain features of EEG signals by using a two-way long-short-term network; 5, classify that EEG signals by the softmax regression method; S6: Output the final EEG signal classification result. The invention effectively improves the recognition rate of a plurality of EEG signals.

Description

technical field [0001] The invention belongs to the field of recognition of EEG signals in brain-computer interfaces, and mainly relates to a recognition method for multi-type motor imagery EEG signals using a combination algorithm model of convolutional neural network and bidirectional long-short-term memory. Background technique [0002] Non-invasive brain-computer interface (Brain-computer interfaces, BCIs) technology provides a convenient way of life for people with disabilities, and this control method has strong feasibility and practicality. Used to control computers and other smart devices. EEG signals play an integral role in this, and can be used to detect whether drivers are driving tired or not, and can also be used to help stroke patients recover their functions. [0003] Considering that the transmission of EEG signals is mainly completed through the cooperation of multiple neurons, this paper uses a multi-channel brain-computer interface device for research. ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F2218/12G06F18/2411
Inventor 胡章芳崔婷婷罗元张毅魏博
Owner CHONGQING UNIV OF POSTS & TELECOMM
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