EEG-based real-time BCI system using drones for attention visualization

Attention management is crucial for cognitive development, especially in children. This study presents a novel brain-computer interface (BCI) system that uses EEG signals to classify attention states. It analyzes these signals using a waveform ratio feature extraction method and visualizes attention levels through a drone's altitude. The system provides real-time feedback via a GUI and incorporates gamified elements like drone control to enhance engagement and training efficacy. Experimental results show that positive response mechanisms significantly improve focus and motivation, demonstrating the system's potential to transform traditional attention training methods.

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Publication Details

Journal
Computer Methods in Biomechanics & Biomedical Engineering
Published
2025-02-13
DOI
https://doi.org/10.1080/10255842.2025.2459272
Citations
3
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
4.29
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article

EEG-based real-time BCI system using drones for attention visualization

Chengyuan Shen, Linfeng Sui, Ran Zhang, Lan Xu et al.
3 citations
Computer Methods in Biomechanics & Biomedical Engineering
EEG and Brain-Computer Interfaces
4.29
article

EEG-based real-time BCI system using drones for attention visualization

Chengyuan Shen, Linfeng Sui, Ran Zhang, Lan Xu, Jianting Cao
article en
3 citations

Abstract

Attention management is crucial for cognitive development, especially in children. This study presents a novel brain-computer interface (BCI) system that uses EEG signals to classify attention states. It analyzes these signals using a waveform ratio feature extraction method and visualizes attention levels through a drone's altitude. The system provides real-time feedback via a GUI and incorporates gamified elements like drone control to enhance engagement and training efficacy. Experimental results show that positive response mechanisms significantly improve focus and motivation, demonstrating the system's potential to transform traditional attention training methods.

Computer Methods in Biomechanics & Biomedical Engineering
Saitama Institute of Technology (JP), RIKEN Center for Advanced Intelligence Project (JP), Shandong University of Science and Technology (CN)
Openalex Percentile: Top 7%
EEG and Brain-Computer Interfaces
4.29
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