Artefatos biológicos no EEG quantitativo

Anghinah, Renato; Basile, Luis I.; Schmidt, Magali Taino; Sameshima, Koichi; Gattaz, Wagner F.

doi:10.1590/s0004-282x2006000200017

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…Artefatos têm características que podem ser, relativamente, fáceis de serem identificadas, tais como movimentos oculares, movimentos musculares e até mesmo por interferência externa eletromagnética (Aminoff , 2003;Anghinah et al, 2006).…”

Section: Eeg Quantitativounclassified

Aplicação de redes neurais artificiais paraconsistentes como método de auxílio no diagnóstico da doença de Alzheimer

Lopes¹

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The visual analysis of EEG has shown useful in helping the diagnosis of Alzheimer disease (AD) when the diagnosis remains uncertain, being used in some clinical protocols. However, such analysis is subject to the inherent equipment imprecision, patient movement, electrical records, and physician interpretation of the visual analysis variation. The Artificial Neural Network (ANN) could be a helpful tool, appropriate to address problems such as prediction and pattern recognition. In this work, it has use a new class of ANN, the Paraconsistent Artificial Neural Network (PANN), which is capable of handling uncertain, inconsistent, and paracomplet information, for recognizing predetermined patterns of EEG and to assess its value as a possible auxiliary method for AD diagnosis. Thirty three patients with Alzheimer's disease and thirty four controls patients of EEG records were obtained during relaxed wakefulness. It was considered as normal patient pattern, the background EEG activity between 8.0 Hz and 12.0 Hz (with an average frequency of 10 Hz), allowing a range of 0.5 Hz. The PANN was able to recognize waves that belonging to their respective bands of clinical use (theta, delta, alpha, and beta), leading to an agreement with the clinical diagnosis at 82% of sensitivity and at 61% of specificity. Supported with these results, the PANN could be a promising tool to manipulate EEG analysis, bearing in mind the following considerations: the growing interest of specialists in EEG analysis visual and the ability of the PANN to deal directly imprecise, inconsistent and paracomplet data, providing an interesting quantitative and qualitative analysis.

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Section: Eeg Quantitativounclassified

Aplicação de redes neurais artificiais paraconsistentes como método de auxílio no diagnóstico da doença de Alzheimer

Lopes¹

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“…São caracterizadas por ondas de baixa amplitude e alta frequência, observadas enquanto se está acordado. As ondas Beta estão relacionadas a um estado de consciência, atividade mental, pensamento lógico, planejamento, ou seja, são apresentadas durante o dia a dia para completar tarefas conscientes; .2.2.3 Artefatos em EEG Os sinais de EEG possuem artefatos que, de acordo comAnghinah et al (2006), são definidos como "todo potencial elétrico proveniente de outra fonte que não seja o cérebro". Potencial Relacionado ao Evento A cada instante no tempo o cérebro apresenta um estado funcional global resultante da interação entre as novas informações recebidas pelo cérebro e a atividade espontânea cerebral, tendo como consequência a iniciação de uma série de diferentes e breves estados cerebrais que manifestam diferentes passos e aspectos no processamento da informação (D.LEHMANN, 1989).Os Potenciais Relacionados ao Evento, ou Event-Related Potential (ERP), ou Potencial Evocado, são "pequenos potenciais elétricos gerados nas estruturas cerebrais em resposta a eventos específicos ou estímulos" (SUR; SINHA, 2009).…”

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Um estudo sobre predição de musicalidade por meio da análise de sinais de EEG

Ribeiro¹

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Owing to its multidimensional nature and complexity, music requires a higher neural demand and the process of plasticity causes structural differences between the brains of people who practice (musicians) and do not practice (non-musicians) musical instruments. Recent studies have been conducted in order to understand these differences and the way the brain processes music. Musics are able to extract the components found in conventional studies of evoked potential in Electroencephalogram (EEG) and, from the selection of acoustic features extracted from the audio signals, it is possible to determine the time instants of the music where there is a high contrast (triggers), considered as stimuli in EEG signal analysis. The main objective of this work is to develop a computational model capable of classifying, through supervised learning, the EEG signals recorded by a group of volunteers in musicians and nonmusicians. This model is composed of the following 6 steps: (1-2) Extraction and selection of acoustic features; (3) Selection of triggers; (4-5) Processing and analysis of EEG signals; and (6) Classification. Experiments were carried out with 26 volunteers, 13 non-musicians and 13 amateur musicians, who listened to two classical music (Hungarian Dance No.5, from Johannes Brahms, and The Barber of Seville-Overture, from Gioachino Rossini), while it was performed the acquisition of EEG signals using dry electrodes. For the classification were proposed two scenarios that composed the input data of the classifiers, varying the latency in which the EEG signal was observed. As classifiers, the k-NN and the Neural Network were used. It was possible to obtain as a result an assertive rate above 80% for both songs using the k-NN.

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