Controlling a robot using brain waves

Sudarsanan, K.; Sasipriya, S.

doi:10.1109/iccic.2014.7238512

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…It is primarily marketed as a braincomputer interface (BCI) device, allowing users to interact with various applications and devices using their brainwave activity [19]. The main feature of the NeuroSky MindSet headset is equipped with an EEG sensor, which is its primary component for measuring the brain's electrical activity [20]. This single dry electrode is strategically positioned on the FP1 area of the forehead to capture brainwave data.…”

Section: A Neurosky Mindsetmentioning

confidence: 99%

ODL-BCI: Optimal deep learning model for brain-computer interface to classify students confusion via hyperparameter tuning

Miah,

Habiba,

Kabir

2023

Preprint

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Brain-computer interface (BCI) research has gained attention in education contexts, offering the potential to monitor and enhance student’s cognitive states and this study focuses on developing an optimal deep learning model, ODL-BCI, for real-time classification of students’ concentration levels. The model incorporates hyper-parameter tuning techniques and utilizes the publicly available “Confused student EEG brainwave data” dataset. We propose a deep learning model with hyperparameters optimized through Bayesian optimization. The model’s architecture is constructed with an input layer, several hidden layers, and an output layer. The number of nodes and activation functions in the hidden layers is determined using Bayesian optimization. The learning rate is also optimized for each layer. The proposed model is evaluated and compared with several standard machine learning classifiers, including Decision Tree, AdaBoost, Bagging, MLP, Naïve Bayes, Random Forest, SVM, and XG Boost, on an EEG confusion dataset. Experimental results demonstrate that the optimized deep learning model outperforms all other classifiers, achieving an accuracy of 74 percent. The model’s effectiveness in accurately classifying students’ concentration levels highlights its potential as a valuable tool in educational settings. This research contributes to the advancement of BCI technology, providing insights into the optimization of deep learning models for EEG-based cognitive assessment. Future work involves exploring the model’s generalizability on larger datasets and extending its applicability to other BCI applications.

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Section: A Neurosky Mindsetmentioning

confidence: 99%

ODL-BCI: Optimal deep learning model for brain-computer interface to classify students confusion via hyperparameter tuning

Miah,

Habiba,

Kabir

2023

Preprint

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“…El estudio de las Interfaces Cerebro-Computador (ICC o BCI de las siglas en inglés Brain-Computer Interface) se ha desarrollado enormemente en la última década. Desde entonces se han implementado varias aplicaciones utilizando diferentes dispositivos que son capaces de capturar las señales eléctricas producidas por la interacción de las neuronas del cerebro humano [2][4][5] [8]. Durante estos últimos años, se han utilizado numerosas herramientas software que permiten interconectar dispositivos ICC con computadores [1].…”

Section: Introductionunclassified

“…El software en [8] fue de nuevo desarrollado con LabView. La monitorización del nivel de atención y la cuenta del número de parpadeos de los ojos se hace mediante bloques simples y se usan para programar los diferentes movimientos del robot.…”

Section: Introductionunclassified

Control de un robot móvil mediante interfaz cerebro-computador: un enfoque práctico

Ahumada¹,

Amitrano²,

Gómez-Bravo³

et al. 2022

Actas De Las XXXVII Jornadas De Automática 7, 8 Y 9 De Septiembre De 2016, Madrid

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Este trabajo muestra un ejemplo de la interacción entre ondas cerebrales y plataformas robóticas. Concretamente se realiza el control de un robot móvil mediante el dispositivo llamado Mindwave, producido por la compañía Neurosky. Dicho dispositivo, a pesar de poderse considerar en la categoría de bajo precio, es capaz de proporcionar los niveles de atención, entre otros, de un usuario a partir de la medida de las señales eléctricas generadas por su cerebro. La plataforma desarrollada se basa en un software diseñado en el entorno Matlab, para identificar las intenciones del usuario y transmitir las órdenes correspondientes al robot. Los resultados de las pruebas realizadas son diferentes a los reportados en la literatura, logrando una aplicación práctica, que consigue reducir los tiempos de retardo y requiere un nivel de entrenamiento reducido. Se presentan resultados de una simulación virtual. Dichos resultados han sido corroborados mediante experimentos reales.

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“…In addition, the learning process is very time-consuming. On the other hand, brainmachine interface (BMI) has provided a feasible way to control and train robots [14][15][16] quickly. Since a shared control mode is necessary for human-robot collaborative work, several workflows are designed in interleave mode.…”

Section: Introductionmentioning

confidence: 99%

Architecture design for performing grasp‐and‐lift tasks in brain–machine‐interface‐based human‐in‐the‐loop robotic system

Chang

2019

IET cyber-phys. syst.

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Controlling a robot using brain waves

Cited by 6 publications

References 11 publications

ODL-BCI: Optimal deep learning model for brain-computer interface to classify students confusion via hyperparameter tuning

ODL-BCI: Optimal deep learning model for brain-computer interface to classify students confusion via hyperparameter tuning

Control de un robot móvil mediante interfaz cerebro-computador: un enfoque práctico

Architecture design for performing grasp‐and‐lift tasks in brain–machine‐interface‐based human‐in‐the‐loop robotic system

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