A Task‐Level AR‐BCI for Enhanced Interactive Experiences

ABSTRACT Augmented reality (AR) technology can provide immersive and natural interactive interfaces for brain‐computer interface (BCI) systems. The control architecture of existing AR‐BCIs is at joint‐level (JL) or action‐level (AL), which brings a huge user burden and poor interactive experiences. A task‐level (TL) BCI control method was proposed in this study to enhance interactive experiences. The TL AR‐BCI system based on steady‐state visual evoked potentials was implemented controlling a robotic arm to grab and drop blocks. The online experiment of ten subjects shows TL AR‐BCI can effectively reduce the number of control steps and stimulation time while maintaining the same performance as JL and AL AR‐BCIs. The performance of three AR‐BCIs (JL, AL, TL) was calculated (mean accuracy: 90.66%, 92.52% and 92.2%. Mean information transfer rates: 77.56, 80.06, and 82.71 bits/min. Mean numbers of control steps: 35.48, 17.32, and 13.05. Mean stimulation time: 0.97, 0.97 and 0.89 s). The results show that TL AR‐BCI can effectively reduce the number of control steps and stimulation time while maintaining the same performance as JL and AL AR‐BCIs.

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Publication Details

Journal
Electronics Letters
Published
2025-01-01
DOI
https://doi.org/10.1049/ell2.70465
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
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article

A Task‐Level AR‐BCI for Enhanced Interactive Experiences

Xinzhou Xie, Songyun Xie, Dalu Zheng, Qing Liu
Electronics Letters
EEG and Brain-Computer Interfaces
article

A Task‐Level AR‐BCI for Enhanced Interactive Experiences

Xinzhou Xie, Songyun Xie, Dalu Zheng, Qing Liu
article en

Abstract

ABSTRACT Augmented reality (AR) technology can provide immersive and natural interactive interfaces for brain‐computer interface (BCI) systems. The control architecture of existing AR‐BCIs is at joint‐level (JL) or action‐level (AL), which brings a huge user burden and poor interactive experiences. A task‐level (TL) BCI control method was proposed in this study to enhance interactive experiences. The TL AR‐BCI system based on steady‐state visual evoked potentials was implemented controlling a robotic arm to grab and drop blocks. The online experiment of ten subjects shows TL AR‐BCI can effectively reduce the number of control steps and stimulation time while maintaining the same performance as JL and AL AR‐BCIs. The performance of three AR‐BCIs (JL, AL, TL) was calculated (mean accuracy: 90.66%, 92.52% and 92.2%. Mean information transfer rates: 77.56, 80.06, and 82.71 bits/min. Mean numbers of control steps: 35.48, 17.32, and 13.05. Mean stimulation time: 0.97, 0.97 and 0.89 s). The results show that TL AR‐BCI can effectively reduce the number of control steps and stimulation time while maintaining the same performance as JL and AL AR‐BCIs.

Electronics LettersVol. 61(1)
Northwestern Polytechnical University (CN)
Openalex Percentile: Top 68%
EEG and Brain-Computer Interfaces
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