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Cited by 275

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Controlling a Wheelchair Indoors Using Thought

Rebsamen, Brice, Burdet, Etienne, Guan, Cuntai, Zhang, Haihong, Teo, Chee Leong, Zeng, Qiang, Laugier, Christian, Ang, Marcelo H.·2007·IEEE Intelligent Systems

Abstract

The idea of controlling objects or machines through thought is moving from science fiction to reality. This article presents the first working prototype of a brain-controlled wheelchair that can navigate in a typical office or hospital environment. The wheelchair is based on a slow but safe brain-controlled interface using the P300 signal detected from electroencephalography. The authors adapted the system's control strategy to the interface's measured performance. To circumvent the problem caused by the interface's low information rate, a motion guidance strategy provides safe, efficient control without complex sensors or sensor processing. Experiments demonstrated that healthy subjects can safely control the wheelchair in an office-like environment without training.This article is part of a special issue on Interacting with Autonomy.

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Metadata

Authors
Rebsamen, B., Burdet, E., Guan, C., Zhang, H., Teo, C. L., Zeng, Q., Laugier, C., & Ang, M. H.
Venue
IEEE Intelligent Systems
Published
2007-03
Volume
22
Issue
2
Pages
18-24
ISSN
1541-1672 (ISSN-L)
OpenAlex
W2057151937
Cited by
275
References
5
FWCI
8.20
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Controlling a Wheelchair Indoors Using Thought. https://app.cochat.ai/papers/10.1109/mis.2007.26
The paper
Rebsamen, B., Burdet, E., Guan, C., Zhang, H., Teo, C. L., Zeng, Q., Laugier, C., & Ang, M. H. (2007). Controlling a Wheelchair Indoors Using Thought. IEEE Intelligent Systems. https://doi.org/10.1109/mis.2007.26

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.