Please use this identifier to cite or link to this item: http://cris.utm.md/handle/5014/2047
Title: Kolmogorov-Chaitin Algorithmic Complexity for EEG Analysis
Authors: IAPĂSCURTĂ, Victor 
FIODOROV Ion 
Keywords: electroencephalography;EEG analysis;algorithmic complexity;Block decomposition method;machine learning
Issue Date: 2022
Source: IAPĂSCURTĂ, Victor, FIODOROV, Ion. Kolmogorov-Chaitin Algorithmic Complexity for EEG Analysis. In: Electronics, Communications and Computing, Ed. 12, 20-21 octombrie 2022, Chişinău. Chișinău: Tehnica-UTM, 2023, Editia 12, pp. 214-218. DOI: 10.52326/ic-ecco.2022/CS.14
Conference: Electronics, Communications and Computing 
Abstract: 
Electroencephalography as a generally accepted method of monitoring the electrical activity of brain neurons is widely used both in diseases and in healthy conditions. The recorded electrical signal is usually obtained from several electrodes located on the scalp. While EEG recording techniques are largely standardized, the interpretation of some aspects is still an open question. There is hardly ...
URI: http://cris.utm.md/handle/5014/2047
DOI: 10.52326/ic-ecco.2022/CS.14
Appears in Collections:Proceedings Papers

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