Navegando por Assunto "Eletroencefalografia"

Agora exibindo 1 - 8 de 8

Acesso aberto (Open Access)
Aprendizado em conjunto aplicado à classificação da imagética motora
(Universidade Federal do Pará, 2025-01-20) JORGE, Vitor da Silva; SILVA, Cleison Daniel; http://lattes.cnpq.br/1445401605385329; https://orcid.org/0000-0001-8280-2928
Acesso aberto (Open Access)
AutoBCI: interface cérebro-máquina com configuração hiperparamétrica automatizada
(Universidade Federal do Pará, 2021-03-11) VILAS BOAS, Vitor Mendes; TEIXEIRA, Otávio Noura; http://lattes.cnpq.br/5784356232477760; SILVA, Cleison Daniel; http://lattes.cnpq.br/1445401605385329; https://orcid.org/0000-0001-8280-2928
Motor Imagery-based Brain-Computer Interfaces (MI-BCI) allow control of devices without the use of peripheral nerves and muscles, based on voluntary modulation of brain electrophysiological activity. The challenge imposed on the typical non-invasive MI-BCI is to extract patterns that describe the motor intention in signals collected by electroencephalography (EEG) and classify them to generate reliable commands to the application. For that, the selection of suitable processing techniques as well as the correct parameterization of the system are fundamental in the adjustment of effective classification models. The configuration of multiple hyperparameters in the processing chain, commonly performed manually and unspecified by the user, tends to generate rigid models that are unable to generalize well in different individuals, especially due to the high variability of MI patterns observed among them. The use of strategies to estimate these hyperparameters according to the subject’s specificities is presented as a more effective approach and has been explored in recent studies. This work proposes a structure based on Bayesian learning incorporated into a new open source MI-BCI computational platform for automatic configuration of hyperparameters. The system integrates all the basic steps of the ICM-IM subband architecture, from the acquisition to the control of a virtual application. Various processing techniques make up a large configuration space to search for particular hyperparametric instances that maximize system performance and draw the user the manual adjustment task. Data from 72 subjects in three public EEG sets were used in offline and online simulations, whose goal was to validate the operation of the implemented modules and to investigate the effects of the automatic configuration on the classification performance and on the effective control of the application. A significant improvement in the accuracy of classification was observed when using automatic configuration based models of the system compared to models generated from frequent configurations in the literature. The results suggest that the optimization of hyperparameters produces more assertive models in the classification of IM patterns of different users and tends to contribute to a more effective control of the application. It is concluded that this study contributes to the design of ICM-IM more effective in recognizing the user’s particular IM patterns by providing a complete experimental environment, customizable and uncomplicated to use by automated configuration. The option for more efficient techniques in signal processing also proved to be viable and are also considered contributions of this work.
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)
Classificação de perfis de produtividade usando redes neurais artificiais a partir de registros eletroencefalográficos: uma aplicação na Mineradora Vale S.A., Complexo S11D Eliezer Batista
(Universidade Federal do Pará, 2024-01) BASTOS, Caio de Oliveira; PEREIRA JUNIOR, Antonio; http://lattes.cnpq.br/1402289786010170; https://orcid.org/0000-0002-0808-1058; GOMES, Bruno Duarte; http://lattes.cnpq.br/4932238030330851
Results and methods from neuroscience can already be applied on a routine scale. Applied neuroscience is in use, for example, to measure and study brain activity under high demand using electroencephalography (EEG). In certain work environments, human productivity is a direct function of brain activity. The intense recruitment of cognitive functions such as sustained attention and working memory influence productivity directly. The labor demands experienced by workers in mining companies are an example. This work is a part of a bigger project called “Usando Treinamento Cognitivo para o Desenvolvimento de Operadores de Alto Desempenho” from the Vale S. A. mining company and, therefore, all of the workers that participated in this study worked for that company. We used EEG to measure productivity during a task without the constraints typically found in experiments conducted in the laboratory. To this aim, we created a machine learning algorithm to analyze the resting EEG recorded before and after a 4D simulation, where mining workers (37 ± 7 years old) specialized to operate giant high-capacity shovels. The simulated task consisted of the operator using the shovel in a digging and loading routine. The task was not planned for our research. It was part of the worker’s routine training. That is, we took advantage of the workers’ training to carry out the study. Recordings were preprocessed using a band-pass filter (0.5-100 Hz) followed by filtering using ICA (Independent Component Analysis). After each operator finish the simulation, their productivity was measured by VALE S.A technical staff. The main parameter for good productivity was the amount of ore excavated. The operators were divided into groups according to ranges of productivity. The productivity was used as a label for the learning of the algorithm that consisted of an artificial neural network of the type inception. The number of neurons and layers was optimized using Bayesian optimization. The features extracted by the inception were the input to 13 classifiers. The classifier chosen for the final algorithm was the best, that is the one providing the best accuracy in the productivity classification. The training set contained 80% of the data. A hold-out validation was used to test the accuracy of the final algorithm using 20% of the data. The resulting accuracy when the operators were divided into four groups of productivity reached 91.35%. When there were only two groups of productivity the accuracy peaked at 95.05%. Our results showed that even under no laboratory constraints – during the regular training of the operators and using resting EEG – our algorithm succeed and it is ready to be used in future field operations. We have a prototype that is patentable.
Acesso aberto (Open Access)
Detecção de potenciais corticais antecipatórios em sinais de eletroencefalografia (EEG) durante a condução de carros
(Universidade Federal do Pará, 2015-03-16) SANTOS, Fredson Carmo dos; CARVALHO, Schubert Ribeiro de; http://lattes.cnpq.br/1496976331707751; GOMES, Bruno Duarte; http://lattes.cnpq.br/4932238030330851
The recognition of the driver’s intention from electroencephalographic signals (EEG) may be useful in the development of brain computer interface (BCI) to be used in synergy with intelligent vehicles. This can be beneficial to improve the quality of interaction between the driver and the car, for example, providing a response from the smart car aligned with the intention of the driver. In this study, the anticipation is considered as the cognitive state that leads to specific actions while driving a car. Therefore, we propose to investigate the presence of anticipatory patterns in EEG signals while driving vehicles to determine two specific actions (1) left and (2) turn right, a few milliseconds before such actions take place. An experimental protocol was proposed to record EEG signals of 5 individuals as they operate a virtual reality simulator non-invasive - it was designed for this experiment - which simulates driving a virtual car. The experimental protocol is a variant of the paradigm of contingent negative variation (CNV) with Go and Nogo conditions in virtual reality training system. The results of this study indicate the presence of anticipatory patterns observed in slow cortical potentials in the time domain (medium EEG signal) and the frequency (Power Spectra and phase coherence). This opens a range of possibilities in the development of BCI systems - based on anticipatory signals - that connect the driver to the intelligent vehicle favoring a decision-making to assess the intentions of drivers may eventually prevent accidents while driving.
Acesso aberto (Open Access)
Efeitos da terapia motora baseada em movimentos de dança nas funções da teoria da mente e do ritmo Mu de pessoas com doença de Parkinson
(Universidade Federal do Pará, 2023-08) VILHALVA, Jade Thalia Rodrigues; KREJCOVA, Lane Viana; http://lattes.cnpq.br/2604693973864638; BAHIA, Carlomagno Pacheco; http://lattes.cnpq.br/0910507988777644; https://orcid.org/0000-0003-3794-4710
Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects brain regions whose neural circuitry is responsible for controlling voluntary movements. In addition to motor symptoms, PD patients have non-motor symptoms that drastically affect their quality of life. These include cognitive alterations, among which deficits in working memory, deficits in executive functions and in the ability to deduce the mental states of others (Theory of Mind: ToM) stand out, and may also be related to the functions of mirror neurons (MN). The MN are neurons activated when a person performs or observes a given action, thus performing “internal” simulation of the observed acts, a necessary process for the ability to recognize emotions and intentions in the ToM. Their activity is influenced by prior training of observed motor actions and can be recorded using electroencephalography (EEG) through changes in the Mu band wave amplitudes (alpha 1 waves) detected when an individual observes the actions of another person. The present work investigated the effects of motor therapy on electroencephalographic activity and its correlations with MT functions in patients affected by PD. For this purpose, electroencephalographic evaluations were performed to investigate desynchronization patterns characteristic of mirror neuron activity, in addition to the Reading the Mind in the Eyes (RME) and Faux Pas Recognition (FPR) tests. We evaluated patients diagnosed with PD (n=09), under pharmacological regimen, Hoehn and Yahr 2-4, of both sexes and with mean age of 62.9 ± 7.1 years and mean of 5.8 ± 1.3 years of diagnosis , in time windows before joining the project after twelve months of participation in 2 weekly sessions of motor therapy in dance. Tabulated data were analyzed using Student's t-test. No significant differences were observed in the evaluation parameters of the FPR test in the Test and Retest temporal windows, whereas in the RMT test the average score obtained by the participants in the Test was 9.7 points, while in the Retest the average was 11.3 points with observed significance (p=0.0148), whereas electroencephalographic statistical analysis (TRPs) showed significant results in the level of desynchronization of alpha 1 waves (p=0.014 and p=0.010) during specific electrophysiological evaluation. The data showed that although the individuals did not show improvement in performance in most components of the analyzed TM tests, the electrophysiological results indicate alteration of specific cortical activity related to the activation of the mirror neuron system, influenced by motor therapy in dance, which may configure then, as an adjuvant therapeutic option in the management of motor and non-motor symptoms of PD.
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.
Acesso aberto (Open Access)
Investigação do efeito ictiotóxico do extrato etanólico da raíz de Spilanthes acmella (jambú) em zebrafish através da análise eletrofisiológica e comportamental
(Universidade Federal do Pará, 2013-12-09) RIBEIRO, Layza Costa; ROCHA, Fernando Allan de Farias; http://lattes.cnpq.br/3882851981484245
Among the various species of medicinal plants, we can find the Spilanthes acmella species, popularly known as Jambu that stands out due to its numerous applications in the folk medicine field. Traditional medicine recommends the use of its leaves and flowers in the preparation of infusions to treat anemia, dyspepsia, malaria, mouth diseases (tooth pain) and throat diseases, against scurvy and also as antibiotic and anesthetic, being its main effects attributed to espilantol, which is an important representative of the substances present in these plants. Some studies have been performed using the espilantol, providing some information of the effect of this substance, as its immunomodulatory effect and because of its functional interaction with monocytes, granulocytes and killer cells. However, there are still no electrophysiological studies about its ictiotoxic action using, for example, the EEG to demonstrate its action at the central nervous system level or electromyogram to verify the occurrence of their effects in the Zebrafish muscles, evoking the need for this research. Based on this, the present study aimed to investigate the ictiotoxic action of the ethanol extract of the Spilanthes acmella root in Zebrafish by electrophysiological and behavioral analysis. The results showed that the ethanol extract of Spilanthes acmella is a potent inducer of central excitability in zebrafish, this being evidenced by changes in electrical activity patterns seen in the EEG of the animals subjected to the extract and by increasing brain activity seen in the spectrogram. The extract also caused changes, in a lesser extent, on the electromyographic tracings of zebrafish subjected to the same concentration of the extract, with the appearance of scattered muscle contractions and brief myoclonus. And the behavioral findings from the delimitation of three stages of behavior, which began with the increased excitability of the animal and resulted in the seizure and death of the fish, served to corroborate the electrophysiological findings that the ethanol extract of Spilanthesacmella acts as a potent substance acting on the nervous system of zebrafish, with convulsant activity.