The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
A Noninvasive Brain-Actuated Wheelchair Based on a P300 Neurophysiological Protocol and Automated Navigation
Open accessAbstract
This paper describes a new noninvasive brain-actuated wheelchair that relies on a P300 neurophysiological protocol and automated navigation. When in operation, the user faces a screen displaying a real-time virtual reconstruction of the scenario and concentrates on the location of the space to reach. A visual stimulation process elicits the neurological phenomenon, and the electroencephalogram (EEG) signal processing detects the target location. This location is transferred to the autonomous navigation system that drives the wheelchair to the desired location while avoiding collisions with obstacles in the environment detected by the laser scanner. This concept gives the user the flexibility to use the device in unknown and evolving scenarios. The prototype was validated with five healthy participants in three consecutive steps: screening (an analysis of three different groups of visual interface designs), virtual-environment driving, and driving sessions with the wheelchair. On the basis of the results, this paper reports the following evaluation studies: 1) a technical evaluation of the device and all functionalities; 2) a users' behavior study; and 3) a variability study. The overall result was that all the participants were able to successfully operate the device with relative ease, thus showing a great adaptation as well as a high robustness and low variability of the system.
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Citations
Cites
Brain–computer interfaces for communication and control
Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00057-3 · Cited by 7,927
Neuronal ensemble control of prosthetic devices by a human with tetraplegia
Hochberg, L. R., Serruya, M. D., Friehs, G. M., Mukand, J. A., Saleh, M., Caplan, A. H., Branner, A., Chen, D., Penn, R. D., & Donoghue, J. P. (2006) · Nature · doi:10.1038/nature04970 · Cited by 3,295
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BCI2000: A General-Purpose Brain-Computer Interface (BCI) System
Schalk, G., McFarland, D. J., Hinterberger, T., Birbaumer, N., & Wolpaw, J. R. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.827072 · Cited by 3,024
A spelling device for the paralysed
Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchoubey, B., Kübler, A., Perelmouter, J., Taub, E., & Flor, H. (1999) · Nature · doi:10.1038/18581 · Cited by 1,577
Self-citationControl of a two-dimensional movement signal by a noninvasive brain-computer interface in humans
Wolpaw, J. R., & McFarland, D. J. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0403504101 · Cited by 1,512
An EEG-based brain-computer interface for cursor control
Wolpaw, J. R., McFarland, D. J., Neat, G. W., & Forneris, C. A. (1991) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(91)90040-b · Cited by 1,114
Characterization of N200 and P300: selected studies of the Event-Related Potential
Patel, S. H., & Azzam, P. N. (2005) · International Journal of Medical Sciences · doi:10.7150/ijms.2.147 · Cited by 794
A comparison of classification techniques for the P300 Speller
Krusienski, D. J., Sellers, E. W., Cabestaing, F., Bayoudh, S., McFarland, D. J., Vaughan, T. M., & Wolpaw, J. R. (2006) · Journal of Neural Engineering · doi:10.1088/1741-2560/3/4/007 · Cited by 762
Toward enhanced p300 speller performance
Krusienski, D. J., Sellers, E. W., McFarland, D. J., Vaughan, T. M., & Wolpaw, J. R. (2008) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.07.017 · Cited by 749 · cited 1×
“For each of these subproblems, the stepwise linear discriminant analysis (SWLDA) was used, extensively studied for P300 classification problems [23], and used with very good results in online communication using visual stimulation [28] and auditory stimulation [29].”
Method
Cited by
Brain Computer Interfaces, a Review
Nicolas-Alonso, L. F., & Gomez-Gil, J. (2012) · Sensors · doi:10.3390/s120201211 · Cited by 2,143
EEG-Based Brain-Controlled Mobile Robots: A Survey
Bi, L., Fan, X.-A., & Liu, Y. (2013) · IEEE Transactions on Human-Machine Systems · doi:10.1109/tsmcc.2012.2219046 · Cited by 386
Progress in Brain Computer Interface: Challenges and Opportunities
Saha, S., Mamun, K. A., Ahmed, K., Mostafa, R., Naik, G. R., Darvishi, S., Khandoker, A. H., & Baumert, M. (2021) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2021.578875 · Cited by 365
Life can be worth living in locked-in syndrome
Lulé, D., Zickler, C., Häcker, S., Bruno, M. A., Demertzi, A., Pellas, F., Laureys, S., & Kübler, A. (2009) · Progress in Brain Research · doi:10.1016/s0079-6123(09)17723-3 · Cited by 217
Self-citationTowards Independence: A BCI Telepresence Robot for People With Severe Motor Disabilities
Leeb, R., Tonin, L., Rohm, M., Desideri, L., Carlson, T., & Millan, J. D. R. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2419736 · Cited by 187
Metadata
- Authors
- Iturrate, I., Antelis, J. M., Kubler, A., & Minguez, J.
- Venue
- IEEE Transactions on Robotics
- Published
- 2009-06
- Volume
- 25
- Issue
- 3
- Pages
- 614-627
- ISSN
- 1552-3098 (ISSN-L)
- 1941-0468
- OpenAlex
- W2128637458
- Cited by
- 512
- References
- 36
- FWCI
- 14.92
- Type
- journal article
- Language
- en
- Open access
- green
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- Iturrate, I., Antelis, J. M., Kubler, A., & Minguez, J. (2009). A Noninvasive Brain-Actuated Wheelchair Based on a P300 Neurophysiological Protocol and Automated Navigation. IEEE Transactions on Robotics. https://doi.org/10.1109/tro.2009.2020347
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