A study on ocular and facial muscle artifacts in EEG signals for BCI applications

Reyes, Carmina E.; Rugayan, Janine Lizbeth C.; Jason, Carl; Rullan, G.; Oppus, Carlos; Tangonan, Gregory L.

doi:10.1109/tencon.2012.6412241

Cited by 13 publications

(6 citation statements)

References 2 publications

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“…Finally, the frown was used to stop and enter in ERP mode. Reyes et al [13] investigated the possibility of using brain activity (generated by facial gestures and eye movements) in the purpose of controlling assistive devices.…”

Section: Introductionmentioning

confidence: 99%

Wink based facial expression classification using machine learning approach

et al. 2020

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Facial expression may establish communication between physically disabled people and assistive devices. Different types of facial expression including eye wink, smile, eye blink, looking up and looking down can be extracted from the brain signal. In this study, the possibility of controlling assistive devices using the individual's wink has been investigated. Brain signals from the five subjects have been captured to recognize the left wink, right wink, and no wink. The brain signals have been captured using Emotiv Insight which consists of five channels. Fast Fourier transform and the sample range have been computed to extract the features. The extracted features have been classified with the help of different machine learning algorithms. Here, support vector machine (SVM), linear discriminant analysis (LDA) and K-nearest neighbor (K-NN) have been employed to classify the features sets. The performance of the classifier in terms of accuracy, confusion matrix, true positive and false positive rate and the area under curve (AUC)-receiver operating characteristics (ROC) have been evaluated. In the case of sample range, the highest training and testing accuracies are 98.9% and 96.7% respectively which have been achieved by two classifiers namely, SVM and K-NN. The achieved results indicate that the person's wink can be utilized in controlling assistive devices.

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Section: Introductionmentioning

confidence: 99%

Wink based facial expression classification using machine learning approach

et al. 2020

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“…Si se considera necesario, también puede someterse a un proceso de eliminación de artefactos, que no son más que ruido provocado por movimientos musculares, mandibulares, oculares… [11]. Para decodificar el movimiento de las extremidades el usuario deberá realizar una serie de pruebas, ya sea movimientos de los brazos o ciclos de marcha, por lo que resulta obvio que la existencia de artefactos musculares es inevitable.…”

Section: Tratamiento Y Adecuación De La Señalunclassified

Uso de interfaces cerebro-computador para la decodificación de la cinemática de miembro superior e inferior

Alarcón-Domínguez¹

2017

DOCTUMH

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El desarrollo a principios del siglo XX de la técnica de la electroencefalografía por parte de Hans Berger abrió las puertas a la investigación de las señales cerebrales, aunque hubo que esperar hasta finales de siglo para que tuvieran lugar las primeras investigaciones importantes en este campo. En la actualidad, el enfoque más importante de las interfaces cerebro-computador (BCI) es el médico. Esta tecnología tiene la posibilidad de ayudar a aquellos individuos que presenten algún tipo de discapacidad motora a volver a interaccionar con el entorno de forma normal. Dentro de ese enfoque, en este artículo se destaca la opción de poder decodificar el movimiento de las extremidades superiores e inferiores a partir de las señales cerebrales registradas. Esto permitiría que el individuo pudiera controlar dispositivos electrónicos de rehabilitación o prótesis robóticas a través de la interfaz cerebro- computador. Así mismo la metodología de este procedimiento presenta numerosas posibilidades y combinaciones por lo que aquellos investigadores que estén interesados disponen de un campo muy amplio en el que desarrollar su actividad.

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“…The EEG is classically described as occurring between 0 and 30Hz, and recent work and precise notch digital filtering has defined a gamma frequency greater than 24Hz extending to 50-60Hz [6]. (1) is shown below [7].…”

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confidence: 99%

EEG Brainwave Analysis for Research on Meditation Influence to the Concentration

Jo¹

2014

The Journal of the Korea institute of electronic communication

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Many people complain their big or little stress due to the complicated city life in modern times, so they are exposed of the mental illness. Especially, not only students and office workers but also most people suffer from degradation of efficiency at work and keeping the high quality of life because of the insufficiency of concentration ability. To improve the concentration ability, the meditation is a substitution. The influence of meditation for the concentration ability is experimented with EEG brainwave. Some experienced meditators are participated for the experiments, and the left and right portion of prefrontal lobe, AF3 and AF4, are measured and analyzed. As a result, the changes of rhythmic activity of a unique pattern and power spectra are observed.

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A study on ocular and facial muscle artifacts in EEG signals for BCI applications

Cited by 13 publications

References 2 publications

Wink based facial expression classification using machine learning approach

Wink based facial expression classification using machine learning approach

Uso de interfaces cerebro-computador para la decodificación de la cinemática de miembro superior e inferior

EEG Brainwave Analysis for Research on Meditation Influence to the Concentration

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