A combined measure to differentiate EEG signals using fractal dimension and MFDFA-Hurst (2020)
- Authors:
- USP affiliated authors: DAVID, SERGIO ADRIANI - FZEA ; INACIO JUNIOR, CLAUDIO MARCIO CASSELA - FZEA ; VALENTIM JUNIOR, CARLOS ALBERTO - FZEA
- Unidade: FZEA
- DOI: 10.1016/j.cnsns.2020.105170
- Subjects: EPILEPSIA; ELETROENCEFALOGRAFIA; MEMÓRIA; DIMENSÃO
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Communications in Nonlinear Science and Numerical Simulation
- ISSN: 1007-5704
- Volume/Número/Paginação/Ano: v. 84, art. 105170, p. 1-13, May 2020
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
DAVID, Sérgio Adriani et al. A combined measure to differentiate EEG signals using fractal dimension and MFDFA-Hurst. Communications in Nonlinear Science and Numerical Simulation, v. 84, p. 1-13, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.cnsns.2020.105170. Acesso em: 03 jan. 2026. -
APA
David, S. A., Machado, J. A. T., Inácio Junior, C. M. C., & Valentim Junior, C. A. (2020). A combined measure to differentiate EEG signals using fractal dimension and MFDFA-Hurst. Communications in Nonlinear Science and Numerical Simulation, 84, 1-13. doi:10.1016/j.cnsns.2020.105170 -
NLM
David SA, Machado JAT, Inácio Junior CMC, Valentim Junior CA. A combined measure to differentiate EEG signals using fractal dimension and MFDFA-Hurst [Internet]. Communications in Nonlinear Science and Numerical Simulation. 2020 ; 84 1-13.[citado 2026 jan. 03 ] Available from: https://doi.org/10.1016/j.cnsns.2020.105170 -
Vancouver
David SA, Machado JAT, Inácio Junior CMC, Valentim Junior CA. A combined measure to differentiate EEG signals using fractal dimension and MFDFA-Hurst [Internet]. Communications in Nonlinear Science and Numerical Simulation. 2020 ; 84 1-13.[citado 2026 jan. 03 ] Available from: https://doi.org/10.1016/j.cnsns.2020.105170 - Fractal methods and power spectral density as means to explore EEG patterns in patients undertaking mental tasks
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Informações sobre o DOI: 10.1016/j.cnsns.2020.105170 (Fonte: oaDOI API)
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