Device and method for denoising of electroencephalography signal using segment-based principal component analysis
AssigneeKorea University Research and Business Foundation
InventorsHyun-Chul KIM, Jong-Hwan LEE
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Abstract
Provided is a method for denoising of electroencephalography. The method for denoising of electroencephalography (EEG) includes: generating a two-dimensional data matrix (X) from a one-dimensional EEG signal (x), based on segmentation; generating an eigenvector matrix (E) from the two-dimensional data matrix (X), using principal component analysis (PCA); and removing noise in the one-dimensional EEG signal (x), based on a center-frequency and kurtosis for each of a plurality of eigenvectors. The device for denoising of electroencephalography is also provided.
Claims9
A method for denoising of electroencephalography (EEG) signal, comprising:
- generating a two-dimensional data matrix (X) from a one-dimensional EEG signal (x), based on segmentation;
- generating an eigenvector matrix (E) from the two-dimensional data matrix (X), using principal component analysis (PCA);
- and
- removing noise in the one-dimensional EEG signal (x), based on a center-frequency and kurtosis for each of a plurality of eigenvectors.
A device for denoising of electroencephalography (EEG) signal, comprising:
- a data matrix generation module for generating a two-dimensional data matrix (X) from a one-dimensional EEG signal (x), based on segmentation;
- a principal component analysis (PCA) module for generating an eigenvector matrix (E) from the two-dimensional data matrix (X), using PCA;
- and
- a noise removal module for removing noise in the one-dimensional EEG signal (x), based on a center-frequency and kurtosis for each of a plurality of eigenvectors.
Dependencies are inferred from the claim text.