The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Control of a Wheelchair in an Indoor Environment Based on a Brain–Computer Interface and Automated Navigation
Abstract
The concept of controlling a wheelchair using brain signals is promising. However, the continuous control of a wheelchair based on unstable and noisy electroencephalogram signals is unreliable and generates a significant mental burden for the user. A feasible solution is to integrate a brain-computer interface (BCI) with automated navigation techniques. This paper presents a brain-controlled intelligent wheelchair with the capability of automatic navigation. Using an autonomous navigation system, candidate destinations and waypoints are automatically generated based on the existing environment. The user selects a destination using a motor imagery (MI)-based or P300-based BCI. According to the determined destination, the navigation system plans a short and safe path and navigates the wheelchair to the destination. During the movement of the wheelchair, the user can issue a stop command with the BCI. Using our system, the mental burden of the user can be substantially alleviated. Furthermore, our system can adapt to changes in the environment. Two experiments based on MI and P300 were conducted to demonstrate the effectiveness of our system.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A Formal Basis for the Heuristic Determination of Minimum Cost Paths
Hart, P., Nilsson, N., & Raphael, B. (1968) · IEEE Transactions on Systems Science and Cybernetics · doi:10.1109/tssc.1968.300136 · Cited by 12,738
A Hybrid BCI System Combining P300 and SSVEP and Its Application to Wheelchair Control
Li, Y., Pan, J., Wang, F., & Yu, Z. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2270283 · Cited by 421
Self-citationA brain-computer interface for long-term independent home use
Sellers, E. W., Vaughan, T. M., & Wolpaw, J. R. (2010) · Amyotrophic Lateral Sclerosis · doi:10.3109/17482961003777470 · Cited by 400
Cited by
A Multi-Branch 3D Convolutional Neural Network for EEG-Based Motor Imagery Classification
Zhao, X., Zhang, H., Zhu, G., You, F., Kuang, S., & Sun, L. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2938295 · Cited by 305
Self-citationFeature Extraction and Classification Methods for Hybrid fNIRS-EEG Brain-Computer Interfaces
Hong, K.-S., Khan, M. J., & Hong, M. J. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00246 · Cited by 283
Hong, K.-S., & Khan, M. J. (2017) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2017.00035 · Cited by 267
Li, Y., Pan, J., Long, J., Yu, T., Wang, F., Yu, Z., & Wu, W. (2016) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2469106 · Cited by 117
Self-citationAghili, S. N., & Erfanian, A. (2023) · IEEE Access · doi:10.1109/access.2023.3313260 · Cited by 12
Metadata
- Authors
- Zhang, R., Li, Y., Yan, Y., Zhang, H., Wu, S., Yu, T., & Gu, Z.
- Venue
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2016-01
- Volume
- 24
- Issue
- 1
- Pages
- 128-139
- ISSN
- 1534-4320 (ISSN-L)
- 1558-0210
- OpenAlex
- W2241109995
- Cited by
- 239
- References
- 3
- FWCI
- 10.51
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Control of a Wheelchair in an Indoor Environment Based on a Brain–Computer Interface and Automated Navigation. https://app.cochat.ai/papers/10.1109/tnsre.2015.2439298
- The paper
- Zhang, R., Li, Y., Yan, Y., Zhang, H., Wu, S., Yu, T., & Gu, Z. (2016). Control of a Wheelchair in an Indoor Environment Based on a Brain–Computer Interface and Automated Navigation. IEEE Transactions on Neural Systems and Rehabilitation Engineering. https://doi.org/10.1109/tnsre.2015.2439298
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
