Evaluation of the NeuroSky MindFlex EEG headset brain waves data

Katona, József; Farkas, I.; Ujbányi, Tibor; Dukan, Peter; Kővári, Attila

doi:10.1109/sami.2014.6822382

Cited by 63 publications

(27 citation statements)

References 2 publications

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“…Las interfaces BCI (Brain Computer Interface), en sus orígenes, eran bastante costosas, pero con el paso del tiempo se han desarrollado diferentes instrumentos de éste tipo, tales como: MindFlex de Mattel [12], MindSet de NeuroSky [13], Muse de InteraXon [14] y el Emotiv EPOC e Insight, que disminuyen los costos de forma considerable, permitiendo el incremento en su uso en investigaciones en otros ámbitos de las ciencias, como en el marketing con el análisis de pautas publicitarias [15], en pedagogía para detectar problemas para mejorar los estilos de aprendizaje de estudiantes de ingeniería [16], en robótica con el control mental de los robots [17], entre otros. Éstos han tenido gran acogida porque con ellos se obtienen resultados de mayor precisión pues se registran las actividades cerebrales inconscientes de la persona, donde ya se conoce que surge la gran mayoría de la toma de decisiones y de la determinación de las acciones.…”

Section: Metodología Para La Captura De Neuroseñalesunclassified

Registro de neuroseñales con una Interfaz Cerebro-Computador para estimar el nivel estrés en un estudiante durante una clase

Cueva

Cortés²,

Delgado

et al. 2017

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ResumenIntroducción− El presente trabajo muestra un estudio de carácter individual que captura, registra y analiza el nivel de estrés de un estudiante universitario durante una clase que involucra una evaluación. La información del estrés se estimó por medio de una interfaz cerebro-computador comercial y de bajo costo. Esto permite solucionar el problema de obtener fácilmente medidas cuantitativas y no solo cualitativas.Objetivo− Analizar el comportamiento de las neuroseñales para estimar el nivel de estrés en un estudiante ante algunos eventos verbales y no verbales generados por un docente.Metodología− Se desarrolló un diseño experimental de carácter individual tomando como perturbaciones al nivel de estrés eventos tales como preguntas, limitaciones de tiempo y gestos.Resultados− Se logró poner en evidencia algunos eventos que causan estrés en los estudiantes producidos por el lenguaje verbal y no verbal del docente al impartir la clase.Conclusiones− Se recomienda a los docentes moderar su lenguaje corporal durante las evaluaciones evitando acciones que emulen afanes o presiones en tiempos innecesarios. Palabras clave− Neuroseñales, estrés, clase, educación, emociones, ICC AbstractIntroduction− This work shows an individual study of the capture, recording, and analysis of the level of stress of a university student during a class that involves an evaluation. The stress information was estimated using a commercial and low-cost computer-brain interface. This allows solving the problem of easily obtaining quantitative and not only qualitative measures.Objective− The aim of this article is to analyze the behavior of neural signals to estimate the level of stress in a student to some verbal and nonverbal events generated by a teacher.Methodology− An experimental design of individual character was developed taking as disturbances the level of stress, events such as questions, time limits, and gestures.Results− Some events that caused stress in students produced by the verbal and non-verbal language of the teacher when teaching the class were evidenced.Conclusions− Teachers are encouraged to moderate their body language during assessments by avoiding actions that emulate anxieties or pressures in unnecessary times.

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Section: Metodología Para La Captura De Neuroseñalesunclassified

Registro de neuroseñales con una Interfaz Cerebro-Computador para estimar el nivel estrés en un estudiante durante una clase

Cueva

Cortés²,

Delgado

et al. 2017

INGE CUC

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“…If mean ( ̅ ), standard deviation ( ), skewness ( ), kurtosis ( ), entropy ( ) has the sum of data and is pixel data i then color moment can be calculated by equation (1)(2)(3)(4)(5). …”

Section: B Signal Processingmentioning

confidence: 99%

Brainwaves feature classification by applying K-Means clustering using single-sensor EEG

Azhari

Hernandez

2016

Int. J. Adv. Intell. Informatics

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The use of brainwave signal is a step in the introduction of the individual identity using biometric technology based on characteristics of the body. Brainwave signal has unique characteristics and different on each individual because the brainwave cannot be read or copied by people so it is not possible to have a similarity of one person with another person. To be able to process the identification of individual characteristics, which obtained from the signal brainwave, required a pattern of brain activity that is prominent and constant. Cognitive activity testing using a single-sensor EEG (Electroencephalogram) divided into two categories, called the activity of cognitive involving the ability of the right brain (creativity, imagination, holistic thinking, intuition, arts, rhythms, nonverbal, feelings, visualization, tune of songs, daydreaming) and the left brain (logic, analysis, sequences, linear, mathematics, language, facts, think in words, word of songs, computation) give a different cluster based on two times the test on mathematical activities (no cluster slices of experiment 1 and experiment 2). The result showed that cognitive activity based on math activity can provide a signal characteristic that can be used as the basis for a brain-computer interface applications development by utilizing EEG single-sensor.

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“…A Windows Forms Application has been developed to evaluate and visualize the brain wave data of the MindFlex EEG headset, described in the previous chapter [17]. This program can run on a PC and was implemented in C#.…”

Section: The Implementation Of the Brain Visualization And Evalumentioning

confidence: 99%