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A Less Common Algorithmic Complexity Approach to EEG Signal Processing for Machine Learning

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dc.contributor.author IAPASCURTA, Victor
dc.date.accessioned 2023-11-14T08:42:09Z
dc.date.available 2023-11-14T08:42:09Z
dc.date.issued 2023
dc.identifier.citation IAPASCURTA, Victor. A Less Common Algorithmic Complexity Approach to EEG Signal Processing for Machine Learning. In: 6th International Conference on Nanotechnologies and Biomedical Engineering: proc. of ICNBME-2023, 20–23, 2023, Chisinau, vol. 2: Biomedical Engineering and New Technologies for Diagnosis, Treatment, and Rehabilitation, 2023, p. 181-190. ISBN 978-3-031-42781-7. e-ISBN 978-3-031-42782-4. en_US
dc.identifier.isbn 978-3-031-42781-7
dc.identifier.isbn 978-3-031-42782-4
dc.identifier.uri https://doi.org/10.1007/978-3-031-42782-4_20
dc.identifier.uri http://repository.utm.md/handle/5014/24786
dc.description Acces full text - https://doi.org/10.1007/978-3-031-42782-4_20 en_US
dc.description.abstract Electroencephalography (EEG) is a widely used neuroimaging technique that records the electrical activity of the brain. EEG analysis provides valuable insights into brain dynamics and understanding of neural processes. As EEG data analysis relies heavily on signal processing and statistical analysis, it is crucial to have a robust framework for analyzing EEG data that produces reliable results. One very useful framework for EEG data analysis is the use of algorithmic complexity measures. Algorithmic complexity is a measure of the complexity of a given sequence of data such as the EEG waveform. It provides a way to quantify the amount of randomness and predictability within EEG data. Along with traditional complexity measures like Sample Entropy, Hurst Exponent, Multiscale Entropy, etc., there is a less-known approach involving Kolmogorov-Chaitin algorithmic complexity, which is a mathematical approach used for measuring the complexity of a string of information. It is based on the idea that a complex string of information cannot be compressed or represented by a simpler algorithm. The advantages of using Kolmogorov-Chaitin complexity include its objectivity, non-linearity, ability to capture content and robustness. This paper presents the basics of the later approach and shows how it can be used for machine learning on EEG data. en_US
dc.language.iso en en_US
dc.publisher Springer Nature Switzerland en_US
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject signal processing en_US
dc.subject electroencephalography en_US
dc.subject Kolmogorov-Chaitin algorithmic complexity en_US
dc.subject machine learning en_US
dc.title A Less Common Algorithmic Complexity Approach to EEG Signal Processing for Machine Learning en_US
dc.type Article en_US


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  • 2023
    6th International Conference on Nanotechnologies and Biomedical Engineering, September 20–23, 2023, Chisinau, Moldova

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Attribution-NonCommercial-NoDerivs 3.0 United States Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States

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