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, 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.

Automated summary

This paper presents a flexible and scalable OpenBCI framework for EEG data experiments using the Cyton acquisition board with updated drivers. The framework supports distributed computing tasks, multiple sampling rates, communication protocols, free electrode placement, and single marker synchronization. It provides real-time feedback and controlled execution of EEG-based clinical protocols, along with automatic background configuration and user-friendly widgets for stimuli delivery. The framework offers a promising solution for tailored neurophysiological data processing.