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A Novel OpenBCI Framework for EEG-Based Neurophysiological Experiments

An Open Brain–Computer Interface (OpenBCI) provides unparalleled freedom and flexibility through open-source hardware and firmware at a low-cost implementation. It exploits robust hardware platforms and powerful software development kits to create customized drivers with advanced capabilities. Still...

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Autores principales: Cardona-Álvarez, Yeison Nolberto, Álvarez-Meza, Andrés Marino, Cárdenas-Peña, David Augusto, Castaño-Duque, Germán Albeiro, Castellanos-Dominguez, German
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098804/
https://www.ncbi.nlm.nih.gov/pubmed/37050823
http://dx.doi.org/10.3390/s23073763
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author Cardona-Álvarez, Yeison Nolberto
Álvarez-Meza, Andrés Marino
Cárdenas-Peña, David Augusto
Castaño-Duque, Germán Albeiro
Castellanos-Dominguez, German
author_facet Cardona-Álvarez, Yeison Nolberto
Álvarez-Meza, Andrés Marino
Cárdenas-Peña, David Augusto
Castaño-Duque, Germán Albeiro
Castellanos-Dominguez, German
author_sort Cardona-Álvarez, Yeison Nolberto
collection PubMed
description An Open Brain–Computer Interface (OpenBCI) provides unparalleled freedom and flexibility through open-source hardware and firmware at a low-cost implementation. It exploits robust hardware platforms and powerful software development kits to create customized drivers with advanced capabilities. Still, several restrictions may significantly reduce the performance of OpenBCI. These limitations include the need for more effective communication between computers and peripheral devices and more flexibility for fast settings under specific protocols for neurophysiological data. This paper describes a flexible and scalable OpenBCI framework for electroencephalographic (EEG) data experiments using the Cyton acquisition board with updated drivers to maximize the hardware benefits of ADS1299 platforms. The framework handles distributed computing tasks and supports multiple sampling rates, communication protocols, free electrode placement, and single marker synchronization. As a result, the OpenBCI system delivers real-time feedback and controlled execution of EEG-based clinical protocols for implementing the steps of neural recording, decoding, stimulation, and real-time analysis. In addition, the system incorporates automatic background configuration and user-friendly widgets for stimuli delivery. Motor imagery tests the closed-loop BCI designed to enable real-time streaming within the required latency and jitter ranges. Therefore, the presented framework offers a promising solution for tailored neurophysiological data processing.
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spelling pubmed-100988042023-04-14 A Novel OpenBCI Framework for EEG-Based Neurophysiological Experiments Cardona-Álvarez, Yeison Nolberto Álvarez-Meza, Andrés Marino Cárdenas-Peña, David Augusto Castaño-Duque, Germán Albeiro Castellanos-Dominguez, German Sensors (Basel) Article An Open Brain–Computer Interface (OpenBCI) provides unparalleled freedom and flexibility through open-source hardware and firmware at a low-cost implementation. It exploits robust hardware platforms and powerful software development kits to create customized drivers with advanced capabilities. Still, several restrictions may significantly reduce the performance of OpenBCI. These limitations include the need for more effective communication between computers and peripheral devices and more flexibility for fast settings under specific protocols for neurophysiological data. This paper describes a flexible and scalable OpenBCI framework for electroencephalographic (EEG) data experiments using the Cyton acquisition board with updated drivers to maximize the hardware benefits of ADS1299 platforms. The framework handles distributed computing tasks and supports multiple sampling rates, communication protocols, free electrode placement, and single marker synchronization. As a result, the OpenBCI system delivers real-time feedback and controlled execution of EEG-based clinical protocols for implementing the steps of neural recording, decoding, stimulation, and real-time analysis. In addition, the system incorporates automatic background configuration and user-friendly widgets for stimuli delivery. Motor imagery tests the closed-loop BCI designed to enable real-time streaming within the required latency and jitter ranges. Therefore, the presented framework offers a promising solution for tailored neurophysiological data processing. MDPI 2023-04-06 /pmc/articles/PMC10098804/ /pubmed/37050823 http://dx.doi.org/10.3390/s23073763 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Cardona-Álvarez, Yeison Nolberto
Álvarez-Meza, Andrés Marino
Cárdenas-Peña, David Augusto
Castaño-Duque, Germán Albeiro
Castellanos-Dominguez, German
A Novel OpenBCI Framework for EEG-Based Neurophysiological Experiments
title A Novel OpenBCI Framework for EEG-Based Neurophysiological Experiments
title_full A Novel OpenBCI Framework for EEG-Based Neurophysiological Experiments
title_fullStr A Novel OpenBCI Framework for EEG-Based Neurophysiological Experiments
title_full_unstemmed A Novel OpenBCI Framework for EEG-Based Neurophysiological Experiments
title_short A Novel OpenBCI Framework for EEG-Based Neurophysiological Experiments
title_sort novel openbci framework for eeg-based neurophysiological experiments
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098804/
https://www.ncbi.nlm.nih.gov/pubmed/37050823
http://dx.doi.org/10.3390/s23073763
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