Multichannel electroencephalogram data fusion and dimension descending method

Abstract

The invention discloses a multichannel electroencephalogram data fusion and dimension descending method. The multichannel electroencephalogram data fusion and dimension descending method comprises the following steps of (1) reading in multichannel electroencephalogram data; (2) performing kernel density estimation on the electroencephalogram data by using a Parzen window to obtain an estimation value of the electroencephalogram data; (3) performing kernel transformation on the electroencephalogram data by using a polynomial kernel function, mapping the electroencephalogram data to corresponding kernel space to form kernel matrixes and fusing all the kernel matrixes corresponding to electroencephalogram of all channels into a synthetic kernel matrix by using different weight numbers; (4) calculating an eigenvalue and an eigenvector of the synthetic kernel matrix; and (5) performing entropy component analysis on the eigenvalue of the synthetic kernel matrix G and the eigenvector of the synthetic kernel matrix G by using a map of kernel entropy principal component analysis (KECA) to obtain low-dimension eigenvalue and eigenvector data and implement fusion and dimension descending of the multichannel electroencephalogram data. By the multichannel electroencephalogram data fusion and dimensional descending method, the electroencephalogram data of each channel are subjected to kernel function mapping, and effective fusion and dimension descending of the multichannel electroencephalogram data can be implemented through multi-kernel entropy component analysis.

Description

technical field [0001] The invention belongs to the technical field of multi-channel electrophysiological signal data processing methods and applications, and relates to a multi-channel electroencephalogram signal data fusion dimensionality reduction method. Background technique [0002] Multi-channel EEG signals have been widely used in the diagnosis of brain diseases and brain science research, and the extraction and fusion of multi-channel EEG signal data has become a key link in the analysis of EEG signal data. At the same time, due to the inevitable information redundancy in the EEG signal data, it is also very important to reduce the dimensionality of the EEG signal data while merging the multi-channel EEG signal data. [0003] At present, the typical data dimensionality reduction methods that have been proposed for the dimensionality reduction of EEG data include principal component analysis algorithm, kernel principal component analysis algorithm, and kernel entropy ...

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Problems solved by technology

[0004] Aiming at the deficiencies in the prior art, the object of the present invention is to propose a multi-channel EEG data fusion dimensionality reduction method, which can process single-channel EEG data or multi-channel EEG signals in series Due to the lack of a single eigenvector of data, the effective fusion and dimensionality reduction of multi-channel EEG signal data can be realized by performing kernel function mapping on the EEG signal data of each channel and analyzing the multi-core entropy components.

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