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Trabalho de evento
Neural network classification, coherence and power spectrum analysis with stress database (2019)
Pedrino, Mateus C.; Arone, Rafael Augusto Caracciolo; Tsukahara, Victor Hugo Batista; Maciel, Carlos Dias
Fonte: Proceedings. Nome do evento: Latin American Workshop on Computational Neuroscience - LAWCN. Unidade: EESC
Assuntos: REDES NEURAIS, ELETROENCEFALOGRAFIA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
PEDRINO, Mateus C. et al. Neural network classification, coherence and power spectrum analysis with stress database. 2019, Anais.. São João del-Rei: Escola de Engenharia de São Carlos, Universidade de São Paulo, 2019. Disponível em: https://repositorio.usp.br/directbitstream/676a53c5-d578-4c89-ad96-9a717eb2f345/sysno3185483_trabalho%2012%20-%20Neural%20Network%20Classification%2C%20Coherence%20and%20Power%20Spectrum%20Analysis%20with%20Stress%20Database.pdf. Acesso em: 27 nov. 2025.
APA
Pedrino, M. C., Arone, R. A. C., Tsukahara, V. H. B., & Maciel, C. D. (2019). Neural network classification, coherence and power spectrum analysis with stress database. In Proceedings. São João del-Rei: Escola de Engenharia de São Carlos, Universidade de São Paulo. Recuperado de https://repositorio.usp.br/directbitstream/676a53c5-d578-4c89-ad96-9a717eb2f345/sysno3185483_trabalho%2012%20-%20Neural%20Network%20Classification%2C%20Coherence%20and%20Power%20Spectrum%20Analysis%20with%20Stress%20Database.pdf
NLM
Pedrino MC, Arone RAC, Tsukahara VHB, Maciel CD. Neural network classification, coherence and power spectrum analysis with stress database [Internet]. Proceedings. 2019 ;[citado 2025 nov. 27 ] Available from: https://repositorio.usp.br/directbitstream/676a53c5-d578-4c89-ad96-9a717eb2f345/sysno3185483_trabalho%2012%20-%20Neural%20Network%20Classification%2C%20Coherence%20and%20Power%20Spectrum%20Analysis%20with%20Stress%20Database.pdf
Vancouver
Pedrino MC, Arone RAC, Tsukahara VHB, Maciel CD. Neural network classification, coherence and power spectrum analysis with stress database [Internet]. Proceedings. 2019 ;[citado 2025 nov. 27 ] Available from: https://repositorio.usp.br/directbitstream/676a53c5-d578-4c89-ad96-9a717eb2f345/sysno3185483_trabalho%2012%20-%20Neural%20Network%20Classification%2C%20Coherence%20and%20Power%20Spectrum%20Analysis%20with%20Stress%20Database.pdf
Trabalho de evento-anais periodico
Graph model evolution during epileptic seizures: linear model approach (2019)
Santos, Talysson Manoel de Oliveira; Tsukahara, Victor Hugo Batista; Oliveira, Jasiara C. de; Cota, Vinicius Rosa; Maciel, Carlos Dias
Fonte: Communications in Computer and Information Science. Nome do evento: Latin American Workshop on Computational Neuroscience - LAWCN. Unidade: EESC
Assuntos: ELETROENCEFALOGRAFIA, EPILEPSIA, PROCESSAMENTO DIGITAL DE SINAIS, ENGENHARIA ELÉTRICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SANTOS, Talysson Manoel de Oliveira et al. Graph model evolution during epileptic seizures: linear model approach. Communications in Computer and Information Science. Cham, Switzerland: Springer. Disponível em: https://doi.org/10.1007/978-3-030-36636-0_12. Acesso em: 27 nov. 2025. , 2019
APA
Santos, T. M. de O., Tsukahara, V. H. B., Oliveira, J. C. de, Cota, V. R., & Maciel, C. D. (2019). Graph model evolution during epileptic seizures: linear model approach. Communications in Computer and Information Science. Cham, Switzerland: Springer. doi:10.1007/978-3-030-36636-0_12
NLM
Santos TM de O, Tsukahara VHB, Oliveira JC de, Cota VR, Maciel CD. Graph model evolution during epileptic seizures: linear model approach [Internet]. Communications in Computer and Information Science. 2019 ; 1068 20-29.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1007/978-3-030-36636-0_12
Vancouver
Santos TM de O, Tsukahara VHB, Oliveira JC de, Cota VR, Maciel CD. Graph model evolution during epileptic seizures: linear model approach [Internet]. Communications in Computer and Information Science. 2019 ; 1068 20-29.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1007/978-3-030-36636-0_12
Trabalho de evento-anais periodico
Motor learning and machine learning: predicting the amount of sessions to learn the tracing task (2019)
Souza, Eduardo Dorneles Ferreira de; Santos, Moises Rocha dos ; Silva, Lucas Cléopas Costa da; Oliveira, Alexandre César Muniz de; Almeida Neto, Areolino de; Ribeiro, Paulo Rogério de Almeida
Fonte: Communications in Computer and Information Science. Nome do evento: Latin American Workshop on Computational Neuroscience - LAWCN. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE PADRÕES, REDES NEURAIS, REABILITAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SOUZA, Eduardo Dorneles Ferreira de et al. Motor learning and machine learning: predicting the amount of sessions to learn the tracing task. Communications in Computer and Information Science. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-36636-0_2. Acesso em: 27 nov. 2025. , 2019
APA
Souza, E. D. F. de, Santos, M. R. dos, Silva, L. C. C. da, Oliveira, A. C. M. de, Almeida Neto, A. de, & Ribeiro, P. R. de A. (2019). Motor learning and machine learning: predicting the amount of sessions to learn the tracing task. Communications in Computer and Information Science. Cham: Springer. doi:10.1007/978-3-030-36636-0_2
NLM
Souza EDF de, Santos MR dos, Silva LCC da, Oliveira ACM de, Almeida Neto A de, Ribeiro PR de A. Motor learning and machine learning: predicting the amount of sessions to learn the tracing task [Internet]. Communications in Computer and Information Science. 2019 ; 1068 20-29.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1007/978-3-030-36636-0_2
Vancouver
Souza EDF de, Santos MR dos, Silva LCC da, Oliveira ACM de, Almeida Neto A de, Ribeiro PR de A. Motor learning and machine learning: predicting the amount of sessions to learn the tracing task [Internet]. Communications in Computer and Information Science. 2019 ; 1068 20-29.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1007/978-3-030-36636-0_2

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