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BCI with Intuitive Object Control Based on Code-Modulated Visual Evoked Potentials

delete2026-01-01
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PRE
AI
H
Hanneke A. Scheppink
A
Atilla Cantürk
I
Ivan Volosyak *
DOI:10.1007/978-3-032-02728-3_46delete
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Abstract

Abstract

En 中文
In recent years, brain-computer interface (BCI) research has rapidly evolved. The current primary focus of the field is to improve the speed, accuracy and overall performance of the system, however, slowly the focus is shifting to taking applications out of the laboratory environment and into the real world. Some of these solutions involve using augmented reality (AR) or virtual reality (VR), allowing for a stimulation that is incorporated into the real world. Most of these AR or VR studies, however, often still use the generic box-like stimuli, usually describing with text or icons what action the user can execute. To create a more intuitive control option, this study investigated the feasibility of stimulation of the objects themselves. This stimulation approach has the additional benefit that the visual field is not obstructed by added stimuli. This online pilot study presented the stimuli on a monitor to test the feasibility of stimulating the objects. Specifically, the stimulation is done by flickering the objects between dark and bright states. All stimuli had different sizes, shapes and colours, to account for a real-life environment. We studied the accuracy, speed, and subjective user experience and compared those between flashing the objects and flashing the box-stimuli with text descriptions. We found that the condition in which the generic boxes with text were used was more accurate, reliable and faster than the condition in which the objects themselves were flickering. In terms of user experience, no statistical differences were found between the two conditions.
Keywords:
code-modulated visual evoked potential (c-VEP)
brain-computer interface (BCI)
EEG
integrated control

Journal

A
ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2025, PT II
IF:
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Papers:
53
Citations:
0

Organization

No organization information available
Cited Papers

Cited Papers

Towards solving of the Illiteracy phenomenon for VEP-based brain-computer interfaces
err2020-04-30
err0
errOAAI
errIvan Volosyak; Aya Rezeika; Mihaly Benda; Felix Gembler; Piotr Stawicki
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Brain-computer interfaces based on code-modulated visual evoked potentials (c-VEP): a literature review
err2021-11-26
err45
errOAAI
errMartinez-Cagigal, Victor; Thielen, Jordy; Santamaria-Vazquez, Eduardo; Perez-Velasco, Sergio; Desain, Peter; Hornero, Roberto
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BNCI Horizon 2020: towards a roadmap for the BCI community
err2015-02-10
err0
errOAAI
errClemens Brunner; Niels Birbaumer; Benjamin Blankertz; Christoph Guger; Andrea Kübler; Donatella Mattia; José del R. Millán; Felip Miralles; Anton Nijholt; Eloy Opisso; Nick Ramsey; Patric Salomon; Gernot R. Müller-Putz
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IF0
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err0
PREAI
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Brain-computer interfaces for communication and control
err2002-06-01
err6.0K
PREAI
errWolpaw, JR; Birbaumer, N; McFarland, DJ; Pfurtscheller, G; Vaughan, TM
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VEP-Based Brain-Computer Interfaces: Time, Frequency, and Code Modulations
err2009-11-01
err207
PREAI
errBin, Guangyu; Gao, Xiaorong; Wang, Yijun; Hong, Bo; Gao, Shangkai
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Estimating and approaching the maximum information rate of noninvasive visual brain-computer interface
err2024-04-01
err0
errOAAI
errNanlin Shi; Yining Miao; Changxing Huang; Xiang Li; Yonghao Song; Xiaogang Chen; Yijun Wang; Xiaorong Gao
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Brain-Computer Interface Integrated With Augmented Reality for Human-Robot Interaction
err2023-12-01
err20
PREAI
errFang, Bin; Ding, Wenlong; Sun, Fuchun; Shan, Jianhua; Wang, Xiaojia; Wang, Chengyin; Zhang, Xinyu
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