Navegando por Assunto "Electroencephalography"

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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)
Estrutura competitiva de redes neurais autoassociativas para classificação de fadiga mental através de sinais de eletroencefalografia
(Universidade Federal do Pará, 2018-12-21) FERREIRA, Mylena Nazaré Medeiros dos Reis; CASTRO, Adriana Rosa Garcez; http://lattes.cnpq.br/5273686389382860
The complexity of mental fatigue signals in healthy people is due to the absence of specific perturbations in the electroencephalographic activity, and by the singularity and variability of the cognitive profile of each individual. Identifying this mental state requires the analysis of several factors that involve the brain behavior in its regions in various frequency bands. In concern to the industry, mental fatigue compromises the efficiency of the production chain by affecting the perception (concentration and attention) of people, which increases the risk of accidents and production costs. Thus, monitoring the cognitive condition is necessary for the maintenance of the productive and cognitive performance of the evaluated subject. This work proposes the classification of fatigue using a competitive structure of Associative Neural Networks. This type of neural network allows to find the association between the input data and the reconstructed data from a compact architecture, being indicated for real-time applications. The characteristics vector used for classification is composed of the normalized information of three frequency bands (theta, beta and alpha) and four metrics that, according to the literature, differentiate mental states from electroencephalographic data in terms of Power Spectral Density. The results show the capacity and usability of autoassociative neural networks in patterns classification.