Validating the оpenBCI Nodes through EEG-based BCI Application for Smart Home Automation Control

Abstract

Advancements in portable EEG headsets have accelerated Brain-Computer Interface (BCI) applications, particularly in smart home automation and user well-being. However, few BCI platforms support cross-disciplinary users with limited programming skills. To address this, we propose a visual, node-based BCI programming framework using BrainFlow and the OpenBCI toolkit within Node-RED. This approach enables users to stream, process, and extract EEG features, while leveraging BrainFlow APIs as backend child processes to ensure adaptability and seamless updates. The framework was validated through a case study with 14 participants, where an OpenBCI headset was used to control a robotic arm. Results revealed that specific bursts in Event-Related Synchronization (ERS) within certain frequency bands were crucial for attention-based control. θ (theta) and γ (gamma) frequency bands in the frontal lobe were highly significant, as these regions are associated focus and decision-making. Similarly, high β (beta) activity in the left central and parietal lobes demonstrated a strong correlation with motor control and sustained attention. The study also evaluated the BrainFlow 'Mindfulness' metric to assess mental state during task engagement. The average value of this metric was 0.31, with a standard deviation of 0.11, indicating a moderate relative variability with a coefficient of variation ≈ 0.364. The results also highlight the key electrodes and frequency bands involved during attention and concentration, emphasizing the potential of using EEG-based metrics and ERS burst patterns as reliable neural markers to distinguish these states.

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