The Development of Brain-Computer Interface Applications Controlled by the Emotiv Insight Portable Headset Based on Analyzing the EEG Signals Using NODE-Red and Python Programming Software Tools

Rușanu, Oana Andreea

doi:10.1007/978-3-031-42467-0_82

Cited by 1 publication

(1 citation statement)

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“…While BCI software platforms do exist, there is a limited number of systems specifically tailored to help inexperienced programmers in the development of BCI applications: NeuroBlock [1], Blockly block framework for dynamic brain-based virtual environment [2], BrainFlow [3], Neuromore Studio [4], NeuroScale [5], NeuroPype [6], EmotivBCI Node-RED toolkit [7]. From them, software technologies for BCI programming in the Internet of Things (IoT) are only two: NeuroScale [5], designed to integrate with various popular EEG devices commonly used in research and clinical settings, while EmotivBCI Node-RED toolkit [7,8] is device specific. However, the BCI toolkit for programing EEG-based BCI applications is only for Emotiv EPOC+ and Insight devices.…”

Section: Introductionmentioning

confidence: 99%

New Nodes for Node‐RED Library within OpenBCI Category for EEG‐Based Brain‐Machine Interface Design and Integration in IoT

Kremenska,

Lekova

2024

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%