How many people are able to control a P300-based brain-computer interface (BCI)?
Abstract
An EEG-based brain-computer system can be used to control external devices such as computers, wheelchairs or Virtual Environments. One of the most important applications is a spelling device to aid severely disabled individuals with communication, for example people disabled by amyotrophic lateral sclerosis (ALS). P300-based BCI systems are optimal for spelling characters with high speed and accuracy, as compared to other BCI paradigms such as motor imagery. In this study, 100 subjects tested a P300-based BCI system to spell a 5-character word with only 5 min of training. EEG data were acquired while the subject looked at a 36-character matrix to spell the word WATER. Two different versions of the P300 speller were used: (i) the row/column speller (RC) that flashes an entire column or row of characters and (ii) a single character speller (SC) that flashes each character individually. The subjects were free to decide which version to test. Nineteen subjects opted to test both versions. The BCI system classifier was trained on the data collected for the word WATER. During the real-time phase of the experiment, the subject spelled the word LUCAS, and was provided with the classifier selection accuracy after each of the five letters. Additionally, subjects filled out a questionnaire about age, sex, education, sleep duration, working duration, cigarette consumption, coffee consumption, and level of disturbance that the flashing characters produced. 72.8% (N=81) of the subjects were able to spell with 100% accuracy in the RC paradigm and 55.3% (N=38) of the subjects spelled with 100% accuracy in the SC paradigm. Less than 3% of the subjects did not spell any character correctly. People who slept less than 8h performed significantly better than other subjects. Sex, education, working duration, and cigarette and coffee consumption were not statistically related to differences in accuracy. The disturbance of the flashing characters was rated with a median score of 1 on a scale from 1 to 5 (1, not disturbing; 5, highly disturbing). This study shows that high spelling accuracy can be achieved with the P300 BCI system using approximately 5 min of training data for a large number of non-disabled subjects, and that the RC paradigm is superior to the SC paradigm. 89% of the 81 RC subjects were able to spell with accuracy 80-100%. A similar study using a motor imagery BCI with 99 subjects showed that only 19% of the subjects were able to achieve accuracy of 80-100%. These large differences in accuracy suggest that with limited amounts of training data the P300-based BCI is superior to the motor imagery BCI. Overall, these results are very encouraging and a similar study should be conducted with subjects who have ALS to determine if their accuracy levels are similar.
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General concerns about non-universal usability, robustness, and the role of training raised by able-bodied user studies are even more pressing with regard to end-user populations.
“As a result, the general concerns about the non-universal usability, robustness, and, especially, the role of training raised by able-bodied user studies [3–7] are even more pressing with regard to end-user populations.”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [4]
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 · see citing works
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 · see citing works
Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs
Lin, Z., Zhang, C., Wu, W., & Gao, X. (2007) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2006.889197 · Cited by 869 · see citing works
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 · see citing works
Self-citationA 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 · see citing works
Self-citationMultiple Channel Detection of Steady-State Visual Evoked Potentials for Brain-Computer Interfaces
Friman, O., Volosyak, I., & Gräser, A. (2007) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2006.889160 · Cited by 545 · see citing works
How many people are able to operate an EEG-based brain-computer interface (BCI)?
Guger, C., Edlinger, G., Harkam, W., Niedermayer, I., & Pfurtscheller, G. (2003) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2003.814481 · Cited by 526 · see citing works
Self-citationSellers, E. W., Krusienski, D. J., McFarland, D. J., Vaughan, T. M., & Wolpaw, J. R. (2006) · Biological Psychology · doi:10.1016/j.biopsycho.2006.04.007 · Cited by 504 · see citing works
Self-citationNeuper, C., Scherer, R., Wriessnegger, S. C., & Pfurtscheller, G. (2009) · Clinical Neurophysiology · doi:10.1016/j.clinph.2008.11.015 · Cited by 421 · see citing works
An improved P300-based brain-computer interface
Serby, H., Yom‐Tov, E., & Inbar, G. F. (2005) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2004.841878 · Cited by 404 · see citing works
Rapid prototyping of an EEG-based brain-computer interface (BCI)
Guger, C., Schlögl, A., Neuper, C., Walterspacher, D., Strein, T., & Pfurtscheller, G. (2001) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/7333.918276 · Cited by 312 · see citing works
Self-citationPfurtscheller, G., Leeb, R., Keinrath, C., Friedman, D. A., Neuper, C., Guger, C., & Slater, M. (2006) · Brain Research · doi:10.1016/j.brainres.2005.11.083 · Cited by 225 · see citing works
Self-citationAsynchronous P300-Based Brain--Computer Interfaces: A Computational Approach With Statistical Models
Zhang, H., Guan, C., & Wang, C. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.919128 · Cited by 175 · see citing works
High performance p300 speller for brain-computer interface
Guan, C., Thulasidas, M., & Wu, J. (2005) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas.2004.1454155 · Cited by 165 · see citing works
Proceedings of the 4th International Brain-Computer Interface Workshop and Training Course 2008
Muller-Putz, G. R., Brunner, C., Leeb, R., Scherer, R., Schlögl, A., Wriessnegger, S. C., & Pfurtscheller, G. (2006) · Cited by 38 · see citing works
Vargas, Á. D. S., Pérez, M. L., & Solís, F. O. (2006) · Revista de Neurología · doi:10.33588/rn.4311.2005629 · Cited by 5 · see citing works
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Single-trial analysis and classification of ERP components — A tutorial
Blankertz, B., Lemm, S., Treder, M., Haufe, S., & Müller, K.-R. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.048 · Cited by 1,138 · see citing works
Townsend, 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 · see citing works
Self-citationA 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 · see citing works
The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology
Blankertz, B., Tangermann, M., Vidaurre, C., Fazli, S., Sannelli, C., Haufe, S., Maeder, C., Ramsey, L., Sturm, I., Curio, G., et al. (2010) · Frontiers in Neuroscience · doi:10.3389/fnins.2010.00198 · Cited by 357 · see citing works
BCI demographics: how many (and what kinds of) people can use an SSVEP BCI?
Allison, B., Luth, T., Valbuena, D., Teymourian, A., Volosyak, I., & Graser, A. (2010) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2009.2039495 · Cited by 344 · see citing works
Nijboer (2010) · Frontiers in Neuroscience · doi:10.3389/fnins.2010.00055 · Cited by 323 · see citing works
A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers
Zhang, X., Yao, L., Wang, X., Monaghan, J., McAlpine, D., & Zhang, Y. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abc902 · Cited by 296 · see citing works
Huggins, J. E., Wren, P. A., & Gruis, K. L. (2011) · Amyotrophic Lateral Sclerosis · doi:10.3109/17482968.2011.572978 · Cited by 215 · see citing works
How Many People Could Use an SSVEP BCI?
Guger, C., Allison, B. Z., Großwindhager, B., Prückl, R., Hintermüller, C., Kapeller, C., Bruckner, M., Krausz, G., & Edlinger, G. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00169 · Cited by 176 · see citing works
Self-citationThe Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users
Perdikis, S., Tonin, L., Saeedi, S., Schneider, C., & Millán, J. D. R. (2018) · PLOS Biology · doi:10.1371/journal.pbio.2003787 · Cited by 166 · see citing works
Co-adaptive calibration to improve BCI efficiency
Vidaurre, C., Sannelli, C., Müller, K.-R., & Blankertz, B. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/2/025009 · Cited by 169 · see citing works
Leeb, R., Perdikis, S., Tonin, L., Biasiucci, A., Tavella, M., Creatura, M., Molina, A., Al-Khodairy, A., Carlson, T., & Millán, J. D. R. (2013) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2013.08.004 · Cited by 166 · see citing works
Translating the Brain-Machine Interface
Thakor, N. V. (2013) · Science Translational Medicine · doi:10.1126/scitranslmed.3007303 · Cited by 128 · see citing works
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 · see citing works
Heading for new shores! Overcoming pitfalls in BCI design
Chavarriaga, R., Fried-Oken, M., Kleih, S., Lotte, F., & Scherer, R. (2017) · Brain-Computer Interfaces · doi:10.1080/2326263x.2016.1263916 · Cited by 97 · see citing works
Clinical evaluation of BrainTree, a motor imagery hybrid BCI speller
Perdikis, S., Leeb, R., Williamson, J., Ramsay, A., Tavella, M., Desideri, L., Hoogerwerf, E.-J., Al-Khodairy, A., Murray-Smith, R., & Millán, J. D. R. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/3/036003 · Cited by 85 · see citing works
Cecotti, H., & Ries, A. J. (2017) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2016.07.500 · Cited by 50 · see citing works
Context-aware adaptive spelling in motor imagery BCI
Perdikis, S., Leeb, R., & Millán, J. D. R. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/3/036018 · Cited by 49 · see citing works
A comparison study of two P300 speller paradigms for brain–computer interface
Pan, J., Li, Y., Gu, Z., & Yu, Z. (2013) · Cognitive Neurodynamics · doi:10.1007/s11571-013-9253-1 · Cited by 46 · see citing works
P300 Speller Performance Predictor Based on RSVP Multi-feature
Won, K., Kwon, M., Jang, S., Ahn, M., & Jun, S. C. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00261 · Cited by 34 · see citing works
Guo, M., Jin, J., Jiao, Y., Wang, X., & Cichockia, A. (2019) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2019.00024 · Cited by 18 · see citing works
“However, the averaged P300 response at Cz for RCP was 7.9 µV lower than the 8.8 µV achieved in SCP (Guger et al., 2009).”
ResultInfluentialBrain-computer interface: a possible help for people with locked-in syndrome,
Nolte, A. (2021) · Advances in Intelligent Systems and Computing · doi:10.1007/978-3-030-72254-8_24 · Cited by 4 · see citing works
- Authors
- Guger, C., Daban, S., Sellers, E., Holzner, C., Krausz, G., Carabalona, R., Gramatica, F., & Edlinger, G.
- Affiliations
- Guger Technologies (Austria) (AT)
- East Tennessee State University (US)
- Don Carlo Gnocchi Foundation (IT)
- Corresponding author
- Guger, Christoph
- Venue
- Neuroscience Letters
- Publisher
- Elsevier BV
- Published
- 2009-09
- Volume
- 462
- Issue
- 1
- Pages
- 94-98
- ISSN
- 0304-3940 (ISSN-L)
- OpenAlex
- W2168765820
- Cited by
- 652
- References
- 16
- Citations by year
- 2026: 9 · 2025: 18 · 2024: 13 · 2023: 19 · 2022: 23 · 2021: 30 · 2020: 33 · 2019: 47 · 2018: 51 · 2017: 46 · 2016: 42 · 2015: 47 · 2014: 51 · 2013: 51 · 2012: 63
- FWCI
- 27.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neuroscience Letters (publishedVersion)
- PubMed (publishedVersion)
- Digital Commons - East Tennessee State University (East Tennessee State University) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Keywords
- P300, brain-computer interface, P300 speller, spelling accuracy, EEG-based communication, sleep duration, user acceptance, amyotrophic lateral sclerosis, assistive technology for people with disabilities
- MeSH terms
- Adult, Biofeedback, Psychology, Brain, Electroencephalography, Female, Humans, Male, Neuropsychological Tests, Practice, Psychological, Surveys and Questionnaires, Signal Processing, Computer-Assisted, User-Computer Interface, Writing, Event-Related Potentials, P300
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