The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
P300 brain computer interface: current challenges and emerging trends
Open accessAbstract
A brain-computer interface (BCI) enables communication without movement based on brain signals measured with electroencephalography (EEG). BCIs usually rely on one of three types of signals: the P300 and other components of the event-related potential (ERP), steady state visual evoked potential (SSVEP), or event related desynchronization (ERD). Although P300 BCIs were introduced over twenty years ago, the past few years have seen a strong increase in P300 BCI research. This closed-loop BCI approach relies on the P300 and other components of the ERP, based on an oddball paradigm presented to the subject. In this paper, we overview the current status of P300 BCI technology, and then discuss new directions: paradigms for eliciting P300s; signal processing methods; applications; and hybrid BCIs. We conclude that P300 BCIs are quite promising, as several emerging directions have not yet been fully explored and could lead to improvements in bit rate, reliability, usability, and flexibility.
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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
Is the P300 component a manifestation of context updating?
Donchin, E., & Coles, M. G. H. (1988) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00058027 · Cited by 3,585
Motor imagery and direct brain-computer communication
Pfurtscheller, G., & Neuper, C. (2001) · Proceedings of the IEEE · doi:10.1109/5.939829 · Cited by 1,990
Temporary suppression of visual processing in an RSVP task: an attentional blink?
Raymond, J. E., Shapiro, K. L., & Arnell, K. M. (1992) · Journal of Experimental Psychology: Human Perception and Performance · doi:10.1037/0096-1523.18.3.849 · Cited by 1,649
A P300-based brain–computer interface for people with amyotrophic lateral sclerosis
Nijboer, F., Sellers, E. W., Mellinger, J., Jordan, M. A., Matuz, T., Furdea, A., Halder, S., Mochty, U., Krusienski, D. J., Vaughan, T. M., et al. (2008) · Clinical Neurophysiology · doi:10.1016/j.clinph.2008.03.034 · Cited by 633 · cited 1×
“The BP and Intendix systems described above were validated with ALS users in their home settings, as were other P300 BCI systems (Sellers et al., 2006, 2010; Vaughan et al., 2006; Nijboer et al., 2008).”
MethodSelf-citationTownsend, G., LaPallo, B. K., Boulay, C. B., Krusienski, D. J., Frye, G. E., Hauser, C. K., Schwartz, N. E., Vaughan, T. M., Wolpaw, J. R., & Sellers, E. W. (2010) · Clinical Neurophysiology · doi:10.1016/j.clinph.2010.01.030 · Cited by 570 · cited 5×
“Several human perceptual phenomena such as attentional blink, repetition blindness, and habituation are potential sources of error in P300 detection and have been addressed in the literature (Fazel-Rezai, 2007, 2009; Citi et al., 2008; Jin et al., 2010; Townsend et al., 2010).”
BackgroundMethodResultInfluentialSelf-citationThe thought translation device (TTD) for completely paralyzed patients
Birbaumer, N., Kubler, A., Ghanayim, N., Hinterberger, T., Perelmouter, J., Kaiser, J., Iversen, I., Kotchoubey, B., Neumann, N., & Flor, H. (2000) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.847812 · Cited by 493
A 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
Self-citation
Cited by
EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces
Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M., Hung, C. P., & Lance, B. J. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aace8c · Cited by 5,115
“They are generally robust across subjects and contain well-stereotyped waveforms, enabling the exact time course of the ERP to be modeled through machine learning efficiently [40].”
BackgroundVisual and Auditory Brain–Computer Interfaces
Gao, S., Wang, Y., Gao, X., & Hong, B. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2300164 · Cited by 449
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
Lotte, F. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2404941 · Cited by 327
Lotte, F., Larrue, F., & Mühl, C. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00568 · Cited by 307
Metadata
- Authors
- Fazel-Rezai, R., Allison, B. Z., Guger, C., Sellers, E. W., Kleih, S. C., & Kübler, A.
- Venue
- Frontiers in Neuroengineering
- Publisher
- Frontiers Media SA
- Published
- 2012
- Volume
- 5
- Pages
- 14-14
- ISSN
- 1662-6443 (ISSN-L)
- OpenAlex
- W2105438329
- Cited by
- 359
- References
- 8
- FWCI
- 9.21
- Type
- journal article
- Language
- en
- Open access
- hybrid
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