Version 1
: Received: 22 January 2024 / Approved: 22 January 2024 / Online: 23 January 2024 (07:30:51 CET)
How to cite:
Kremenska, A.; Lekova, A. New Nodes for Node‐RED Library within OpenBCI Category for EEG‐Based Brain‐Machine Interface Design and Integration in IoT. Preprints2024, 2024011608. https://doi.org/10.20944/preprints202401.1608.v1
Kremenska, A.; Lekova, A. New Nodes for Node‐RED Library within OpenBCI Category for EEG‐Based Brain‐Machine Interface Design and Integration in IoT. Preprints 2024, 2024011608. https://doi.org/10.20944/preprints202401.1608.v1
Kremenska, A.; Lekova, A. New Nodes for Node‐RED Library within OpenBCI Category for EEG‐Based Brain‐Machine Interface Design and Integration in IoT. Preprints2024, 2024011608. https://doi.org/10.20944/preprints202401.1608.v1
APA Style
Kremenska, A., & Lekova, A. (2024). New Nodes for Node‐RED Library within OpenBCI Category for EEG‐Based Brain‐Machine Interface Design and Integration in IoT. Preprints. https://doi.org/10.20944/preprints202401.1608.v1
Chicago/Turabian Style
Kremenska, A. and Anna Lekova. 2024 "New Nodes for Node‐RED Library within OpenBCI Category for EEG‐Based Brain‐Machine Interface Design and Integration in IoT" Preprints. https://doi.org/10.20944/preprints202401.1608.v1
Abstract
The growing of Brain-Computer Interface (BCI) applications is closely related to the increasing accessibility of Electroencephalography (EEG) hardware (EEG headsets), which are noninvasive, portable, wireless and often with open software. However, there is a limited number of BCI software platforms tailored to help inexperienced programmers in the development of BCI applications. Only few of them integrate BCI applications with IoT devices and services. To address these challenges, a model for visual node-based programming has been designed utilizing BrainFlow library within the Node-RED platform, which can be applied to more than 20 biosensors. New Nodes for Node-RED Library within OpenBCI Category (openBCI toolkit for Node-RED) have been developed for design of EEG-Based Brain-Machine Interface and Integration in IoT. The proposed toolkit have been implemented and validated through a case study for controlling a robotic arm by OpenBCI headset. The results from the pilot experiment demonstrated that through concentration levels classified by BrainFlow performance metrics, the control of a TinkerKit Braccio robot arm is possible.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
