Abstract
The paper deals with the processing of numerical series using the spectral estimation methodology. The methodology is based on the Prony transformation. The conversion lets you represent the original number series as a new series. The new series is a linear combination of exponential functions. The article presents the basic concepts and relations of the Prony transformation, and also analyzes the behavior of individual components of the transformation for typical cases. The features of the Prony method are analyzed. Shown are the individual stages of the algorithm, the problems of a specific implementation. The Prony method was used to process the recordings of the electroencephalograms of the operators in their mental representation of movements. This allows us to assume the possibility of using the described methodology in identifying EEG correlates of motor imagination in the space of the roots of polynomials
Highlights
For a long time, such physical characteristics as voice samples, handwriting samples, hand vein patterns, and corneal prints were traditionally considered biometric data
The discrete Fourier transform (DFT) is widely used due to the fast Fourier transform(FFT) algorithm, which significantly speeds up data processing
The paper presents the results of calculating the Prony transformation for model and biometric data
Summary
To cite this version: M.V. Elenetz, Michael Nemirovich-Danchenko. HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. To cite this article: M V Elenets and M M Nemirovich-Danchenko 2021 J. Ser. 1862 012007 View the article online for updates and enhancements. This content was downloaded from IP address 188.162.12.237 on 09/04/2021 at 20:16
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