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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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/7/3763 |
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| author | Yeison Nolberto Cardona-Álvarez Andrés Marino Álvarez-Meza David Augusto Cárdenas-Peña Germán Albeiro Castaño-Duque German Castellanos-Dominguez |
| author_facet | Yeison Nolberto Cardona-Álvarez Andrés Marino Álvarez-Meza David Augusto Cárdenas-Peña Germán Albeiro Castaño-Duque German Castellanos-Dominguez |
| author_sort | Yeison Nolberto Cardona-Álvarez |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-11T05:23:26Z |
| format | Article |
| id | doaj.art-04955b97398842b5b6f94848e8d7a370 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T05:23:26Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-04955b97398842b5b6f94848e8d7a3702023-11-17T17:37:25ZengMDPI AGSensors1424-82202023-04-01237376310.3390/s23073763A Novel OpenBCI Framework for EEG-Based Neurophysiological ExperimentsYeison Nolberto Cardona-Álvarez0Andrés Marino Álvarez-Meza1David Augusto Cárdenas-Peña2Germán Albeiro Castaño-Duque3German Castellanos-Dominguez4Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170003, ColombiaSignal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170003, ColombiaAutomatics Research Group, Universidad Tecnológica de Pereria, Pereira 660003, ColombiaCultura de la Calidad en la Educación Research Group, Universidad Nacional de Colombia, Manizales 170003, ColombiaSignal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170003, ColombiaAn 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.https://www.mdpi.com/1424-8220/23/7/3763brain computer interfacesOpenBCIEEGdriversdistributed systemsneurophysiological |
| spellingShingle | Yeison Nolberto Cardona-Álvarez Andrés Marino Álvarez-Meza David Augusto Cárdenas-Peña Germán Albeiro Castaño-Duque German Castellanos-Dominguez A Novel OpenBCI Framework for EEG-Based Neurophysiological Experiments Sensors brain computer interfaces OpenBCI EEG drivers distributed systems neurophysiological |
| 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 | brain computer interfaces OpenBCI EEG drivers distributed systems neurophysiological |
| url | https://www.mdpi.com/1424-8220/23/7/3763 |
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