Dual-tree complex wavelet and common spatial pattern combined electroencephalogram characteristic extraction method

A dual-tree complex wavelet, wavelet and common technology, applied in the direction of mechanical mode conversion, user/computer interaction input/output, medical science, etc. Information, the effect of broad application prospects

Active Publication Date: 2014-04-23
盐城市凤凰园科技发展有限公司
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  • Application Information

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Problems solved by technology

Subsequently, Sannelli, Schroder, Barachant, etc. studied the EEG data of multiple subjects imagining the movement of left and right hands, feet, etc., es

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  • Dual-tree complex wavelet and common spatial pattern combined electroencephalogram characteristic extraction method
  • Dual-tree complex wavelet and common spatial pattern combined electroencephalogram characteristic extraction method
  • Dual-tree complex wavelet and common spatial pattern combined electroencephalogram characteristic extraction method

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Embodiment Construction

[0021] The following describes in detail the present invention's EEG feature extraction method based on dual-tree complex wavelet and co-space mode in conjunction with the accompanying drawings. figure 1 for the implementation flow chart.

[0022] Such as figure 1 , the implementation of the inventive method mainly comprises the following steps:

[0023] (1) Obtain multi-channel motor imagery EEG signal EEG data;

[0024] (2) Select the corresponding electrode to obtain the EEG signal data of the corresponding channel;

[0025] (3) Calculate the sampling frequency related to the dual-tree complex wavelet multi-scale frequency division, and perform up-sampling or down-sampling on the original sampling frequency;

[0026] (4) The EEG signal of each channel is decomposed by dual-tree complex wavelet;

[0027] (5) Select relevant frequency bands and reconstruct them;

[0028] (6) Combine the reconstructed signals of each channel and input them into the CSP filter;

[0029] (...

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Abstract

The invention relates to a dual-tree complex wavelet and common spatial pattern combined electroencephalogram characteristic extraction method. The method comprises the steps that electroencephalogram signals with suitable channels are selected at first, up-sampling or down-sampling is conducted on the original frequency according to the characteristics of dual-tree complex wavelet frequency partitions, frequency bands corresponding to the frequency ranges of delta, theta, alpha and beta rhythm waves are obtained by means of dual-tree complex wavelet multi-scale decomposition, signal reconstruction is conducted in the scales, a plurality of reconstruction signals in the corresponding frequency bands are obtained, the same decomposition and reconstruction are conducted on the suitable channels, reconstruction signals of all the frequency bands of all the channels are combined to be input into a spatial filter, a six-dimensional feature vector is obtained, and motor imagery task classification is accomplished by means of a support vector machine at last. The dual-tree complex wavelet and common spatial pattern combined electroencephalogram characteristic extraction method not only conducts frequency information analysis on motor imagery electroencephalogram signals, but also can effectively overcome electrode selection defects.

Description

technical field [0001] The invention belongs to the field of electroencephalogram signal processing, and relates to an electroencephalogram feature extraction method combining dual-tree complex wavelet and co-space mode. Background technique [0002] For patients with severely impaired neuromuscular function, a new way of communicating with the outside world is urgently needed. The brain-computer interface is just such a method. It does not rely on the peripheral nervous system and muscle tissue outside the brain. It is a way to establish a direct information exchange and control channel between the human brain and computers or other electronic devices. At present, there are many types of EEG signals suitable for brain-computer interfaces, among which motor imagery EEG signals are one of the most widely used types, because motor imagery EEG signals have the advantages of no external stimulation, asynchronous communication, etc., and conform to BCI technological development ...

Claims

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Application Information

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IPC IPC(8): A61B5/048G06F3/01A61B5/374
Inventor 佘青山昌凤玲陈希豪罗志增
Owner 盐城市凤凰园科技发展有限公司
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