Navegando por Orientadores "PEREIRA JÚNIOR, Antonio"

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Acesso aberto (Open Access)
Análises de atividades oscilatórias de EEG durante treinamento cognitivo e análise espectral de Holo-Hilbert
(Universidade Federal do Pará, 2022-07-19) SOUZA, Suzana Cescon de; GOMES, Bruno Duarte; http://lattes.cnpq.br/4932238030330851; PEREIRA JÚNIOR, Antonio; http://lattes.cnpq.br/3239362677711162
In this work we developed a protocol for the analysis of a cognitive training (TC), in order to raise the performance in bulldozer operators of Vale S.A. This research took part of the POAD (High performance operators' program) Innovation Project of the Vale Technological Institute ITV). The protocols of the TC were based in Neurofeedback (NFB), in order to develop the ability to self-regulate cerebral frequencies, based on electroencephalogram (EEG) analysis. In this research, the Holo-Hilbert Spectral Analysis (HHSA) for the study of amplitude modulation (AM) band frequency (FM) of the rhythms that compose the cerebral frequencies. The HHSA was based on empirical mode decomposition (EMD) in two layers. First the EEG signal has been decomposed in a series of intrinsic mode function in modulated frequency (IMFs) and then every IMF modulated in frequency have been decomposed in a sel of IMFs modulated in amplitude. thus. the present work explores the eflicicney of an TC bas s on modulated frequency and amplitude variations in EEG data obtained via NFB. The results show statistical relevance for the group-who went through the TC showing the effects of the application of TC, Moreover validate the efficacy of HHSA in the extraction of informative characteristics from the signals
Acesso aberto (Open Access)
Classificação de eletroencefalogramas epiléticos em estado de repouso com aplicação de classificadores lineares e um atributo derivado da densidade espectral de potência
(Universidade Federal do Pará, 2019-12-04) FIEL, José de Santana; PEREIRA JÚNIOR, Antonio; http://lattes.cnpq.br/3239362677711162
Millions of Brazilians are affected with epilepsy and the access to early diagnosis is crucial for their adequate treatment. However, epilepsy diagnosis depends on the evaluation of longduration electroencephalographic (EEG) recordings performed by trained professionals, turning it in a time-consuming process which is not readily available for many patients. Thus, the present work proposes a methodology for automatic EEG classification of epileptic subjects which uses short-duration EEG recordings obtained with the patient at rest. The system is based on machine learning algorithms that use an attribute extracted from the power spectral density of EEG signals. This attribute is an estimate of functional connectivity between EEG channel pairs and is called debiased weighted phase-lag index. The classification algorithms were linear discriminant analysis (LDA) and support vector machines (SVM). EEG signs were acquired during the interictal state, i.e., between seizures and had no epileptiform activity. Recordings of 11 epileptic patients and 7 healthy subjects were used to evaluate the method’s performance. Both algorithms reached their maximum classification performances, 100 % accuracy and area under the receiver operating characteristic (AUROC) curve, when a feature vector with 190 attributes was used as input. The results show the efficacy of the proposed system, given its high classification performance.
Acesso aberto (Open Access)
Introdução à neurociência computacional com a linguagem python
(Universidade Federal do Pará, 2023-12-29) NASCIMENTO, Weverson Vieira do; PEREIRA JÚNIOR, Antonio; http://lattes.cnpq.br/3239362677711162
This work presents a proposal for an introductory course on computational neuroscience, using the Python programming language. The brain is a complex organ, and there is significant interest in understanding the biological mechanisms underlying its functioning. Computational neuroscience is one of the fields of study that seeks to contribute to this understanding. The introductory course is aimed at undergraduate students interested in acquiring basic knowledge in Computational Neuroscience. The course initially provides a theoretical foundation in both neurophysiology and mathematics, as well as algorithmic concepts, to enable students from diverse backgrounds to benefit from its content with minimal prerequisites. The course then introduces models of neurons, ranging from simple to more elaborate ones, and explores how these neurons connect with each other, including some well-known neural connection circuits and how learning is implemented in these neuron networks. It also includes content on artificial intelligence, such as neural and neuromorphic networks, the latter using the models mentioned initially. The course utilizes interactive Python code, which is free and open-source, for simulating the presented content.