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EEG signal classification method based on fast multidimensional empirical mode decomposition

An empirical mode decomposition and EEG technology, applied in the field of EEG signal classification based on fast multi-dimensional empirical mode decomposition, can solve the problems of common multi-dimensional empirical mode decomposition algorithm, such as mode aliasing and low calculation efficiency, to improve the classification Accuracy, good source signal separation, strong interpretability

Inactive Publication Date: 2019-03-19
ZHEJIANG UNIV
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Problems solved by technology

[0008] The present invention provides an EEG signal classification method based on fast multi-dimensional empirical mode decomposition, which solves the problems of common multi-dimensional empirical mode decomposition algorithm mode aliasing and low calculation efficiency, and the interpretability of the decomposition results is stronger, and to a large extent Improve the classification accuracy of EEG signals

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  • EEG signal classification method based on fast multidimensional empirical mode decomposition
  • EEG signal classification method based on fast multidimensional empirical mode decomposition
  • EEG signal classification method based on fast multidimensional empirical mode decomposition

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Embodiment Construction

[0071] Taking the motor imagery classification problem of Data Set I of BCI Competition IV as an example, the implementation plan and effect of the EEG signal classification method based on fast multidimensional empirical mode decomposition are described below. This embodiment is carried out under the Matlab2017a simulation environment.

[0072] The data of Data Set I of BCI Competition IV consisted of 7 healthy subjects (a~g) facing the corresponding prompts on the computer screen, performing two types of left hand / right hand / foot motor imagery, two of them chose left hand / Foot motor imagery, another five subjects chose left / right hand motor imagery. The present embodiment adopts the data of Data Set I experimenter g of BCI Competition IV.

[0073] Each set of data contains 59 channels of EEG signals, and 200 experiments are performed, such as figure 1 As shown, each experiment included 2s quiet preparation time, 4s motor imagery task execution time, and 2s black screen re...

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Abstract

The invention discloses an EEG signal classification method based on fast multidimensional empirical mode decomposition. The method comprises: (1) collecting several groups of EEG signals and preprocessing the signals; (2) conducting the fast multidimensional empirical mode decomposition on the preprocessed signals to obtain all intrinsic mode function signals; (3) conducting spectrum analysis oneach layer of each intrinsic mode function signal and selecting a signal layer with the average power spectrum concentrated in the frequency bands of 8-12 Hz and 18-26 Hz as a new multidimensional signal; (4) making the new multidimensional signal passing through a spatial filter to extract characteristics of the EEG signals; (5) inputting the characteristics into a classifier for classification,selecting optimal parameters of a CSP according to the classification accuracy and classifying the EEG signals under different motion imaging tasks by utilizing the EEG characteristics under the optimal characteristics. The method solves the problems of pattern aliasing and low computing efficiency of a common multidimensional empirical mode decomposition algorithm, a decomposition result is moreinterpretable, and the classification accuracy of the EEG signals is improved.

Description

technical field [0001] The invention belongs to the fields of biomedical engineering and computers, and in particular relates to a method for classifying electroencephalogram signals based on fast multidimensional empirical mode decomposition. Background technique [0002] Existing studies have shown that when a person imagines a limb movement, the motor cortex area of ​​the brain related to the movement will have an electrophysiological response similar to that when the movement is performed, and the imaginary action potential will be induced. The preparation or planning of limb movements can cause a change in the activity state of a large number of nerve cells in the cortical motor center, resulting in the synchronous enhancement (event-related synchronization, ERS) or synchronous weakening (event-related desynchronization, ERD) of certain frequency components in the EEG signal. [0003] The analysis of EEG signals plays a very important role in neuroscience, psychology, b...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/0476
CPCA61B5/725A61B5/7267A61B5/369
Inventor 谢磊乔丹郎恂郑潜苏宏业
Owner ZHEJIANG UNIV
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