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Electroencephalogram signal feature extraction and classification method based on SCSP-LDA

A technology of feature extraction and classification method, applied in the field of biological signal processing and pattern recognition, can solve the problems of multi-channel EEG signal overlap, etc., to achieve the effect of channel sparse

Active Publication Date: 2020-05-22
YANSHAN UNIV
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Problems solved by technology

[0003] What the present invention aims to solve is the problem of multi-channel EEG signal overlap that exists during multi-channel acquisition of EEG data

Method used

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  • Electroencephalogram signal feature extraction and classification method based on SCSP-LDA
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  • Electroencephalogram signal feature extraction and classification method based on SCSP-LDA

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Embodiment Construction

[0047] Hereinafter, embodiments of the present invention will be described with reference to the drawings.

[0048] A method for feature extraction and classification of EEG signals based on SCSP-LDA of the present invention, firstly, decompose EEG data into eigenvalues, search and screen them to obtain a new feature space, and then use CSP to extract its features. Finally, LDA is used to perform feature optimization and data classification on the data after feature extraction to realize motor imagery EEG signal decoding. Its overall flow chart is as follows figure 1 As shown, the implementation steps of this method are as follows:

[0049] Step 1. Transform the CSP algorithm into a generalized eigenvalue solving problem. The specific steps are as follows:

[0050] Step 11. The CSP algorithm is regarded as an algorithm based on the generalized Rayleigh quotient, and its corresponding expression can be written as:

[0051]

[0052] In the formula, w is the spatial filter ...

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Abstract

The invention provides an electroencephalogram signal feature extraction and classification method based on SCSP-LDA. The method comprises the following steps: firstly, characteristic value decomposition is carried out on electroencephalogram data, searching and screening are carried out to obtain a new characteristic space, then characteristics of the electroencephalogram data are extracted through a CSP, and finally characteristic optimization and data classification are carried out on the data obtained after characteristic extraction through LDA to realize motor imagery EEG signal decoding.The CSP algorithm is converted into a generalized eigenvalue solving problem, an optimal spatial filter is searched by combining the generalized eigenvalue solving problem with a sparse search algorithm, a sparse common spatial pattern (SCSP) algorithm is introduced, the SCSP is a channel with the most obvious characteristic which can be effectively extracted by the algorithm to realize channel sparsity, and EEG decoding can be completely realized by combining the characteristic classification algorithm LDA.

Description

technical field [0001] The invention relates to the fields of biological signal processing and pattern recognition, in particular to an EEG feature extraction and classification method based on sparse common spatial patterns (Sparse Common Spatial Patterns, SCSP)-LDA in a brain-computer interface. Background technique [0002] Brain-computer interface (BCI) is a system that enables people to communicate with computers or other devices by collecting signals from the brain without relying on the peripheral nervous system and muscles. As a way for the human brain to communicate directly with peripheral devices, it can bring a solution to communication with the outside world for patients with movement disorders due to brain damage. Electroencephalography (EEG) is the main research direction of brain-computer interface, and the decoding of EEG is the focus of research. The Common Spatial Patterns (CSP) algorithm was first proposed by Fukunage et al., and then Romeser and his col...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06F2218/02G06F2218/08G06F18/2132G06F18/241Y02D10/00
Inventor 付荣荣田永胜王世伟
Owner YANSHAN UNIV
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