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BCI Software
Published in Chang S. Nam, Anton Nijholt, Fabien Lotte, Brain–Computer Interfaces Handbook, 2018
In contrast, self-contained BCI software platforms do not depend on underlying commercial software and do not have their limitations. For example, BCI2000 and OpenViBE are based on standard C++ and provide general-purpose BCI functionality at high performance and without depending on other software. The modular and general-purpose nature of these software packages lends itself well to a wide scope of systematic BCI investigations. For example, the support for a wide range of data acquisition systems in BCI2000 and OpenViBE facilitates the use of the BCI system in other environments. These systems also facilitate translating the BCI system into a clinical (and eventually commercial) application. OpenViBE is focused on providing visual data flow programming to facilitate the rapid prototyping of BCI systems. In contrast, BCI2000 is focused on providing a highly stable and performant general-purpose BCI software infrastructure that facilitates systematic and large-scale investigations.
BciPy: brain–computer interface software in Python
Published in Brain-Computer Interfaces, 2021
Tab Memmott, Aziz Koçanaoğulları, Matthew Lawhead, Daniel Klee, Shiran Dudy, Melanie Fried-Oken, Barry Oken
BCI2000 is one of the best known BCI platforms for research [8]. It is built and maintained by the National Center for Adaptive Neurotechnology (https://www.neurotechcenter.org/). While not open source, it is free for nonprofit and noncommercial usage. The software is written in C++ and relies on the orchestration between the Source, Signal Processing, User Application, and Operator modules for its operation. There are two main tutorials which may be used to model other BCI cases. These include a Mu Rhythm and P300 Matrix speller tutorial. The software is distributed with Windows installers, however, use with Linux and OSX requires compilation from source code. The release of BCPy2000 provided support for Python. This project originating from the Max Planck Institute for Biological Cybernetics in 2007 and is included with the official BCI2000 binaries.
Improving longitudinal P300-BCI performance for people with ALS using a data augmentation and jitter correction approach
Published in Brain-Computer Interfaces, 2022
Alyssa Hillary Zisk, Seyyed Bahram Borgheai, John McLinden, Roohollah Jafari Deligani, Yalda Shahriari
EEG data were recorded using a g.USBamp amplifier (g.tec Medical Technologies) with a 256 Hz sampling rate. Data were recorded from eight channels commonly used in P300 protocols, Fz*, Cz, P3, Pz, P4, PO7, PO8, and Oz [30]. However, as Fz was occupied by sensors for other studies recorded in the same session as the current experiment, it was replaced by the nearest available channel, FAF2, denoted as Fz*. All experimental protocols, data acquisition, and stimulus presentation were controlled using BCI2000 software [40].