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Motor imagery electroencephalogram signal recognition method based on EMD and DCNN

An EEG signal and motor imagery technology, applied in the field of human-computer interaction, can solve problems such as poor model generalization ability, high requirements for signal acquisition and processing, and limited expression ability of complex functions, achieving good stability and recognition accuracy high effect

Pending Publication Date: 2021-03-19
重庆邮智机器人研究院有限公司 +1
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Problems solved by technology

However, only the EMD pattern recognition method is mainly based on the "shallow learning" algorithm, which has high requirements for signal acquisition and processing, limited expression ability for complex functions, and poor generalization ability of the model.

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  • Motor imagery electroencephalogram signal recognition method based on EMD and DCNN
  • Motor imagery electroencephalogram signal recognition method based on EMD and DCNN
  • Motor imagery electroencephalogram signal recognition method based on EMD and DCNN

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Embodiment Construction

[0049] Embodiments of the present invention are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation modes, and various modifications or changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the diagrams provided in the following embodiments are only schematically illustrating the basic concept of the present invention, and the following embodiments and the features in the embodiments can be combined with each other in the case of no conflict.

[0050] Wherein, the accompanying drawings are for illustrative purposes only, and represent only schematic diagrams, rather than physical drawings, and should...

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Abstract

The invention relates to a motor imagery electroencephalogram signal recognition method based on EMD and DCNN, and belongs to the field of human-computer interaction. The method comprises the following steps: S1, performing empirical mode decomposition on an electroencephalogram signal to obtain an intrinsic mode function (IMF); S2, automatically extracting electroencephalogram signal features byutilizing the IMF components with obvious DCNN fusion feature information; S3, recognizing the features of the electroencephalogram signals through a classifier, so as to achieve the recognition of the electroencephalogram signals. When the electroencephalogram signals are processed, electroencephalogram signal features can be effectively extracted, and the electroencephalogram signals can be accurately and effectively classified and recognized.

Description

technical field [0001] The invention belongs to the field of human-computer interaction, and relates to a motor imagery EEG signal recognition method based on EMD and DCNN. Background technique [0002] The Brain-Computer Interface (BCI) analyzes the input EEG signal, decodes the user's intention into a control command to control the output device, and realizes the interaction between the human brain and external devices. The core of brain-computer interface technology is the recognition of EEG signals. However, EEG signals have the characteristics of nonlinearity and non-stationarity. How to effectively extract the characteristics of EEG signals becomes the key to the recognition of EEG signals. [0003] At present, the feature extraction of EEG signal mainly adopts time-frequency domain feature analysis method. EEG feature extraction methods based on time-frequency domain analysis mainly include STFT, WT, and WPT. for higher resolution. The EMD algorithm can adaptively ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F2218/08G06F2218/12G06F18/2415G06F18/214
Inventor 黄超张毅郑凯
Owner 重庆邮智机器人研究院有限公司
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