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Electroencephalography signal characteristic extraction method based on small training samples

A training sample and feature extraction technology, which is applied to instruments, character and pattern recognition, computer components, etc., can solve the problems of low stability of feature values ​​and poor discrimination

Inactive Publication Date: 2012-10-31
BEIJING UNIV OF TECH
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Problems solved by technology

The disadvantage is that the eigenvalue stability of the constructed eigenvector is low, and the discrimination is relatively poor, especially when the sample is small.

Method used

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  • Electroencephalography signal characteristic extraction method based on small training samples
  • Electroencephalography signal characteristic extraction method based on small training samples
  • Electroencephalography signal characteristic extraction method based on small training samples

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Embodiment Construction

[0053] Specific embodiments of the present invention will be further described below in conjunction with the accompanying drawings. Figure 4 It is a flow chart of EEG signal classification based on small training samples in the embodiment of the present invention.

[0054] The present invention adopts the g.tec electroencephalograph produced by the Austrian g.tec company and the PC as the hardware platform. g.tec electroencephalograph includes electrode cap, EEG amplifier, A / D converter, etc. The invention collects the EEG signal through the electrode cap, and the EEG signal is amplified by the EEG amplifier and converted by A / D, then transmitted to the PC through the USB port, and stored in the memory in the form of the signal voltage amplitude. The EEG data sent by the electroencephalograph is received and processed through the application program written by MATLAB2009a. The specific operation is as follows:

[0055] (1) Acquisition of EEG signals

[0056] In this embod...

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Abstract

The invention relates to a characteristic extraction method of imagining action potential in a BCI (Brain-Computer Interface) device and particularly relates to a characteristic extraction method combining a regularization method and a CSSD (Common Special Subspace Decomposition) algorithm. In the method provided by the invention, regularization parameters are led; a covariance matrix of trainingdata of a target experimenter and a covariance matrix of training data of an auxiliary experimenter are combined to form a regularization covariance matrix under the action of the regularization parameters; a regularization space filter is constructed; and characteristic analysis is carried out on the test data of the target experimenter by utilizing the regularization space filter. By using the method in the invention, the problems that characteristic value is unstable, classification accuracy rate is low and the like in the CSSD algorithm are solved when a small-sample problem is processed.

Description

technical field [0001] The invention belongs to the fields of pattern recognition and intelligent systems and brain-computer interfaces, in particular to the extraction of motor imagery EEG signal feature vectors in Brain-Computer Interface (Brain-Computer Interface, BCI) system devices. ) method to improve the Common Special Subspace Decomposition (CSSD), and finally combined with the K-Nearest Neighbor (KNN) classification algorithm for feature extraction and classification. Background technique [0002] Currently, there are a variety of diseases that can damage the neuromuscular pathways that allow the brain to communicate and control the external environment, such as cerebral palsy, multiple sclerosis, and stroke. These disorders cause a person to lose some or all of their voluntary muscle control. With the development of science and technology, modern life support technology can make patients survive for a long time, but the quality of life of patients is low, causing ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06K9/66
Inventor 李明爱陆婵婵马建勇杨金福阮晓钢李骧崔燕龚道雄于建均
Owner BEIJING UNIV OF TECH
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