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SSVEP electroencephalogram signal recognition method based on MVMD-CCA

A technology of MVMD-CCA and EEG signals, applied in the field of pattern recognition, can solve the problems of large influence and low classification accuracy

Inactive Publication Date: 2020-08-25
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

[0006] At present, the recognition of SSVEP EEG signals is greatly affected by non-related brain activities and artifacts in EEG signals, and the classification accuracy is not high

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  • SSVEP electroencephalogram signal recognition method based on MVMD-CCA
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  • SSVEP electroencephalogram signal recognition method based on MVMD-CCA

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Embodiment Construction

[0055] The present invention will be described in detail below with reference to the accompanying drawings and examples.

[0056] The present invention provides the SSVEP EEG signal recognition method based on MVMD-CCA, its principle is as follows figure 1 shown, including the following steps:

[0057] S1. Collect multi-channel steady-state visual evoked potential SSVEP EEG signals as the EEG signals to be identified. In the embodiment of the present invention, a multi-lead electrode cap is used to collect EEG signals stimulated by SSVEP as EEG signals to be identified.

[0058] S2. Set the number K of components with decomposition, construct a variational problem, and use the ADMM algorithm to solve the variational problem, and decompose the EEG signal to be identified into K multivariate modulation components.

[0059] S3. Define a reference signal according to the visual stimulation frequency that induces the EEG signal to be recognized.

[0060] S4. Find the specific st...

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Abstract

The invention discloses an SSVEP electroencephalogram signal recognition method based on MVMD-CCA. The method can decompose an electroencephalogram signal into a plurality of multivariate modulation components, reduces the impact of non-correlated brain activity and artifacts in the electroencephalogram signal, and improves classification precision. The method comprises the following steps: collecting a multichannel SSVEP electroencephalogram signal as an electroencephalogram signal to be recognized; and setting the number K of to-be-decomposed components, constructing a variational problem, solving the variational problem by adopting an ADMM algorithm, and decomposing the electroencephalogram signal to be recognized into K multivariate modulation components; defining a reference signal according to the visual stimulus frequency for inducing the electroencephalogram signal to be recognized; solving a weighted correlation coefficient of the specific stimulus frequency fi; and taking thefrequency corresponding to the maximum weighted correlation coefficient as the inductive stimulus frequency of the electroencephalogram signal to be recognized.

Description

technical field [0001] The invention relates to the technical field of pattern recognition, in particular to an SSVEP electroencephalogram signal recognition method based on MVMD-CCA. Background technique [0002] Brain-computer interface is a technology that directly uses signals generated by human brain activities to communicate with external environmental devices without relying on peripheral neuromuscular tissue and other pathways. In recent years, brain-computer interface has gradually become a research hotspot in the fields of brain science, biomedicine, and artificial intelligence. It has received attention from various fields in the world, and many countries have launched related research programs. Brain-computer interface technology has great potential for development in many fields: in the field of rehabilitation medicine, it can help patients with amyotrophic lateral sclerosis, stroke and other diseases to perform rehabilitation training, and help patients recover...

Claims

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Application Information

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IPC IPC(8): A61B5/0476A61B5/0478A61B5/0484G06K9/62G06N20/00
CPCG06N20/00A61B5/291A61B5/378A61B5/369G06F18/24
Inventor 翟弟华王康胡乐云夏元清戴荔邹伟东张金会闫莉萍崔冰孙中奇郭泽华
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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