Brain-Computer Interface (BCI) literature - a bibliometric study

Hamadicharef, Brahim

doi:10.1109/isspa.2010.5605421

Cited by 20 publications

(6 citation statements)

References 18 publications

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“…DISCUSSION BCI is changing the interaction between people and systems. Technological improvements have led to innovative solutions prompting an explosion of BCI research and technology development [39]. Current research is developing new applications that use non-invasive and rapid-setup EEG solutions which allow long-term data collection.…”

Section: Bci2000 and Openvibe Acquisitionsmentioning

confidence: 99%

Creamino: A Cost-Effective, Open-Source EEG-Based BCI System

Guermandi

Placati

Scarselli

et al. 2019

IEEE Trans. Biomed. Eng.

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This paper presents an open source framework called Creamino. It consists of an Arduino-based cost-effective quick-setup EEG platform built with off-the shelf components and a set of software modules that easily allow users to connect this system to Simulink or BCI-oriented tools (such as BCI2000 or OpenViBE) and set up a wide number of neuroscientific experiments. Creamino is capable of processing multiple EEG channels in real-time and operates under Windows, Linux and Mac OS X in real-time on a standard PC. Its objective is to provide a system that can be readily fabricated and used for neurophysiological experiments and, at the same time, can serve as the basis for development of novel BCI platforms by accessing and modifying its open source hardware and software libraries. Schematics, gerber files, bill of materials, source code, software modules, demonstration videos and instructions on how to use these modules are available free of charge for research and educational purposes online at https://github.com/ArcesUnibo/creamino. Application cases show how the system can be used for neuroscientific or BCI experiments. Thanks to its low production cost and its compatibility with open-source BCI tools, the system presented is particularly suitable for use in BCI research and educational applications.

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Section: Bci2000 and Openvibe Acquisitionsmentioning

confidence: 99%

Creamino: A Cost-Effective, Open-Source EEG-Based BCI System

Guermandi

Placati

Scarselli

et al. 2019

IEEE Trans. Biomed. Eng.

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“…G. wAR-RLS: The Closed-Form Solution Substituting ( 10), ( 14), (15), and ( 18) into (2), it follows that…”

Section: F Conditional Probability Distribution Adaptationmentioning

confidence: 99%

Switching EEG Headsets Made Easy: Reducing Offline Calibration Effort Using Active Weighted Adaptation Regularization

Wu,

Lawhern,

Hairston

et al. 2017

Preprint

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Electroencephalography (EEG) headsets are the most commonly used sensing devices for Brain-Computer Interface. In real-world applications, there are advantages to extrapolating data from one user session to another. However, these advantages are limited if the data arise from different hardware systems, which often vary between application spaces. Currently, this creates a need to recalibrate classifiers, which negatively affects people's interest in using such systems. In this paper, we employ active weighted adaptation regularization (AwAR), which integrates weighted adaptation regularization (wAR) and active learning, to expedite the calibration process. wAR makes use of labeled data from the previous headset and handles class-imbalance, and active learning selects the most informative samples from the new headset to label. Experiments on single-trial event-related potential classification show that AwAR can significantly increase the classification accuracy, given the same number of labeled samples from the new headset. In other words, AwAR can effectively reduce the number of labeled samples required from the new headset, given a desired classification accuracy, suggesting value in collating data for use in wide scale transfer-learning applications.

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“…En los últimos años existe un crecimiento significativo en cuanto al desarrollo e investigación relacionadas con las Interfaces cerebrales (e.g., Brain-Computer Interfaces o BCIs por sus siglas en inglés) [4]. Una BCI se define como un sistema basado en computadora que adquiere las señales del cerebro, las analiza y las traduce en comandos que están relacionados con un dispositivo de salida para llevar a cabo una acción deseada [10].…”

Section: Trabajos Relacionadosunclassified

Diademas cerebrales para niños con problemas de atención

Rodríguez

2016

Avances en IHC

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Las terapias de neuro-retroalimentación han demostrado ser útiles para tratar el trastorno de déficit de atención e hiperactividad (TDAH). Para realizar esta terapia, el paciente requiere utilizar una diadema cerebral que se coloca en el cuero cabelludo y/o en los lóbulos de las orejas. Sin embargo su diseño no es apropiado para el uso de niños en edad preescolar que se pueden beneficiar de las terapias de retroalimentación neuronal, en especial aquellos preescolares que tengan problemas de atención. Esta tesis propone diseñar, implementar y evaluar diademas cerebrales adecuadas para preescolares y evaluar su usabilidad y experiencia de uso en comparación con las diademas disponibles en el mercado.

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Brain-Computer Interface (BCI) literature - a bibliometric study

Cited by 20 publications

References 18 publications

Creamino: A Cost-Effective, Open-Source EEG-Based BCI System

Creamino: A Cost-Effective, Open-Source EEG-Based BCI System

Switching EEG Headsets Made Easy: Reducing Offline Calibration Effort Using Active Weighted Adaptation Regularization

Diademas cerebrales para niños con problemas de atención

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