High-speed spelling with a noninvasive brain–computer interface
Open accessAbstract
Significance: Brain–computer interface (BCI) technology provides a new communication channel. However, current applications have been severely limited by low communication speed. This study reports a noninvasive brain speller that achieved a multifold increase in information transfer rate compared with other existing systems. Based on extremely precise coding of frequency and phase in single-trial steady-state visual evoked potentials, this study developed a new joint frequency-phase modulation method and a user-specific decoding algorithm to implement synchronous modulation and demodulation of electroencephalograms. The resulting speller obtained high spelling rates up to 60 characters (∼12 words) per minute. The proposed methodological framework of high-speed BCI can lead to numerous applications in both patients with motor disabilities and healthy people.
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EEG spellers using visually evoked potentials have reached 60 characters per minute but have usability drawbacks: they occupy the eyes, are usually not self-paced, and need flashing light panels.
“EEG spellers that use visually evoked potentials have achieved speeds of 60 characters per minute 18, but have important usability limitations, as they tie up the eyes, are not typically self-paced, and require panels of flashing lights on a screen.”
From High-performance brain-to-text communication via handwriting (2021) · cites this paper as [18]
The emergence of interfaces based on evoked responses (P300, SSVEP) as the most efficient BCI solution for communication has promoted the use of machine learning because the margin for humans to learn to regulate evoked potentials is considered narrow.
“On the one hand, the emergence of interfaces based on evoked responses (P300, steady-state visually evoked potentials [SSVEP]) as the most efficient BCI solution for communication [13–16] has promoted the use of machine learning because the margin for humans to learn to regulate evoked potentials is considered to be narrow.”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [16]
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Cites
Brainard, D. H. (1997) · Spatial Vision · doi:10.1163/156856897x00357 · Cited by 19,308 · see citing works
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
Farwell, L. A., & Donchin, E. (1988) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(88)90149-6 · Cited by 3,228 · see citing works
Brain–machine interfaces: past, present and future
Lebedev, M. A., & Nicolelis, M. A. L. (2006) · Trends in Neurosciences · doi:10.1016/j.tins.2006.07.004 · Cited by 1,875 · see citing works
Wavelets and filter banks: theory and design
Vetterli, M., & Herley, C. (1992) · IEEE Transactions on Signal Processing · doi:10.1109/78.157221 · Cited by 1,693 · see citing works
Human Brain Electrophysiology: Evoked Potentials and Evoked Magnetic Fields in Science and Medicine
Regan, D. M. (1989) · Medical Entomology and Zoology · Cited by 1,303 · see citing works
Herrmann, C. S. (2001) · Experimental Brain Research · doi:10.1007/s002210100682 · Cited by 1,080 · see citing works
Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives
Vialatte, F.-B., Maurice, M., Dauwels, J., & Cichocki, A. (2010) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2009.11.005 · Cited by 1,066 · cited 1× · see citing works
“Recently, the BCI speller using steady-state VEPs (SSVEPs) has attracted increasing attention due to its high communication rate and little user training (4, 10, 11).”
BackgroundFrequency 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
Self-citationChen, X., Wang, Y., Gao, S., Jung, T., & Gao, X. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/4/046008 · Cited by 815 · see citing works
Self-citationA practical VEP-based brain-computer interface
Wang, Y., Wang, R., Gao, X., Hong, B., & Gao, S. (2006) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2006.875576 · Cited by 622 · see citing works
Self-citationSome characteristics of average steady-state and transient responses evoked by modulated light
Regan, D. (1966) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(66)90088-5 · Cited by 550 · see citing works
Muller-Putz, G. R., Scherer, R., Brauneis, C., & Pfurtscheller, G. (2005) · Journal of Neural Engineering · doi:10.1088/1741-2560/2/4/008 · Cited by 535 · see citing works
Visual 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 451 · see citing works
Self-citationBrain-Computer Interfaces Based on Visual Evoked Potentials
Wang, Y., Gao, X., Hong, B., Jia, C., & Gao, S. (2008) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/memb.2008.923958 · Cited by 423 · see citing works
Self-citationHuman Brain Electrophysiology. Evoked Potentials and Evoked Magnetic Fields in Science and Medicine
Picton, T. (1990) · Journal of Clinical Neurophysiology · doi:10.1097/00004691-199007000-00010 · Cited by 375 · see citing works
A HIGH-SPEED BRAIN SPELLER USING STEADY-STATE VISUAL EVOKED POTENTIALS
Nakanishi, M., Wang, Y., Wang, Y.-T., Mitsukura, Y., & Jung, T. (2014) · International Journal of Neural Systems · doi:10.1142/s0129065714500191 · Cited by 372 · see citing works
Self-citation(C)overt attention and visual speller design in an ERP-based brain-computer interface
Treder, M. S., & Blankertz, B. (2010) · Behavioral and Brain Functions · doi:10.1186/1744-9081-6-28 · Cited by 363 · see citing works
FREQUENCY RECOGNITION IN SSVEP-BASED BCI USING MULTISET CANONICAL CORRELATION ANALYSIS
ZHANG, Y., ZHOU, G., JIN, J., WANG, X., & CICHOCKI, A. (2013) · International Journal of Neural Systems · doi:10.1142/s0129065714500130 · Cited by 363 · see citing works
A high-ITR SSVEP-based BCI speller
Chen, X., Chen, Z., Gao, S., & Gao, X. (2014) · Brain-Computer Interfaces · doi:10.1080/2326263x.2014.944469 · Cited by 303 · see citing works
Self-citationFrequency recognition in SSVEP-based BCI using multiset canonical correlation analysisPreprint
Zhang, Y., Zhou, G., Jin, J., Wang, X., & Cichocki, A. S. (2014) · Cited by 262 · see citing works
Towards Zero Training for Brain-Computer Interfacing
Krauledat, M., Tangermann, M., Blankertz, B., & Müller, K. R. (2008) · PLoS ONE · doi:10.1371/journal.pone.0002967 · Cited by 248 · see citing works
Electrophysiological evidence for an early attentional mechanism in visual processing in humans
Russo, F. D., & Spinelli, D. (1999) · Vision Research · doi:10.1016/s0042-6989(99)00031-0 · Cited by 188 · see citing works
Visual Spatial Attention Control in an Independent Brain-Computer Interface
Kelly, S. P., Lalor, E. C., Finucane, C., McDarby, G., & Reilly, R. B. (2005) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2005.851510 · Cited by 176 · see citing works
Spüler, M., Rosenstiel, W., & Bogdan, M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0051077 · Cited by 175 · see citing works
Spelling with non-invasive Brain–Computer Interfaces – Current and future trends
Cecotti, H. (2011) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2011.08.003 · Cited by 173 · see citing works
Rapid Communication with a “P300” Matrix Speller Using Electrocorticographic Signals (ECoG)
Brunner, P., Ritaccio, A. L., Emrich, J. F., Bischof, H., & Schalk, G. (2011) · Frontiers in Neuroscience · doi:10.3389/fnins.2011.00005 · Cited by 133 · see citing works
An independent brain–computer interface using covert non-spatial visual selective attention
Zhang, D., Maÿe, A., Gao, X., Hong, B., Engel, A. K., & Gao, S. (2010) · Journal of Neural Engineering · doi:10.1088/1741-2560/7/1/016010 · Cited by 127 · see citing works
Self-citationUse of high-frequency visual stimuli above the critical flicker frequency in a SSVEP-based BMI
Sakurada, T., Kawase, T., Komatsu, T., & Kansaku, K. (2014) · Clinical Neurophysiology · doi:10.1016/j.clinph.2014.12.010 · Cited by 102 · see citing works
SSVEP recognition using common feature analysis in brain–computer interface
Zhang, Y., Zhou, G., Jin, J., Wang, X., & Cichocki, A. S. (2014) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2014.03.012 · Cited by 101 · see citing works
Text Entry by Gaze: Utilizing Eye Tracking
Majaranta, P., & Räihä, K.-J. (2007) · Text Entry Systems · doi:10.1016/b978-012373591-1/50009-7 · Cited by 96 · see citing works
Hybrid frequency and phase coding for a high-speed SSVEP-based BCI speller
Chen, X., Wang, Y., Nakanishi, M., et al. (2014) · doi:10.1109/embc.2014.6944499 · Cited by 82 · see citing works
Self-citationA High-Performance Keyboard Neural Prosthesis Enabled by Task Optimization
Nuyujukian, P., Fan, J. M., Kao, J. C., Ryu, S. I., & Shenoy, K. V. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2354697 · Cited by 61 · see citing works
Multi-phase cycle coding for SSVEP based brain-computer interfaces
Tong, J., & Zhu, D. (2015) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-14-5 · Cited by 32 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Rappaport TS (2001) Wireless Communication, Principle and Practice (Prentice Hall, Upper Saddle River, NJ), 2nd Ed.
Cited by
Sorted by citations
Exploring the impact of artificial intelligence on teaching and learning in higher education
Popenici, S. A. D., & Kerr, S. (2017) · Research and Practice in Technology Enhanced Learning · doi:10.1186/s41039-017-0062-8 · Cited by 2,019 · see citing works
High-performance brain-to-text communication via handwriting
Willett, F. R., Avansino, D. T., Hochberg, L. R., Henderson, J. M., & Shenoy, K. V. (2021) · Nature · doi:10.1038/s41586-021-03506-2 · Cited by 969 · see citing works
“Commonly used BCIs for restoring communication are either flashing electroencephalogram (EEG) spellers 18,28–32 or intracortical point-and-click BCIs 6,7,33 .”
BackgroundMethodNoninvasive neuroimaging enhances continuous neural tracking for robotic device control
Edelman, B. J., Meng, J., Suma, D., Zurn, C., Nagarajan, E., Baxter, B. S., Cline, C. C., & He, B. (2019) · Science Robotics · doi:10.1126/scirobotics.aaw6844 · Cited by 380 · see citing works
Waytowich, N., Lawhern, V. J., Garcia, J. O., Cummings, J., Faller, J., Sajda, P., & Vettel, J. M. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aae5d8 · Cited by 262 · see citing works
Using ChatGPT in academic writing is (not) a form of plagiarism: What does the literature say?
Jarrah, A. M., Wardat, Y., & Fidalgo, P. (2023) · Online Journal of Communication and Media Technologies · doi:10.30935/ojcmt/13572 · Cited by 220 · see citing works
Mahmood, M., Mzurikwao, D., Kim, Y.-S., Lee, Y., Mishra, S., Herbert, R., Duarte, A., Ang, C. S., & Yeo, W.-H. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0091-7 · Cited by 181 · see citing works
The 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
Comparing user-dependent and user-independent training of CNN for SSVEP BCI
Ravi, A., Beni, N. H., Manuel, J., & Jiang, N. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab6a67 · Cited by 165 · see citing works
Mind Reading and Writing: The Future of Neurotechnology
Roelfsema, P. R., Denys, D., & Klink, P. C. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.04.001 · Cited by 107 · see citing works
Artificial intelligence in education and assessment methods
Braiki, B. A., Harous, S., Zaki, N., & Alnajjar, F. (2020) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v9i5.1984 · Cited by 79 · see citing works
EEG Dataset for RSVP and P300 Speller Brain-Computer Interfaces
Won, K., Kwon, M., Ahn, M., & Jun, S. C. (2022) · Scientific Data · doi:10.1038/s41597-022-01509-w · Cited by 68 · see citing works
- Authors
- Chen, X., Wang, Y., Nakanishi, M., Gao, X., Jung, T.-P., & Gao, S.
- Affiliations
- Tsinghua University (CN)
- Chinese Academy of Sciences (CN)
- University of California San Diego (US)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2015-10-19
- Volume
- 112
- Issue
- 44
- Pages
- E6058-67
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC4640776
- OpenAlex
- W2132876794
- Cited by
- 969
- References
- 34
- Citations by year
- 2026: 58 · 2025: 96 · 2024: 97 · 2023: 123 · 2022: 89 · 2021: 103 · 2020: 109 · 2019: 101 · 2018: 100 · 2017: 65 · 2016: 25
- FWCI
- 38.06
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- SEMI-IR (submittedVersion)
- Funders
- National Natural Science Foundation of China (91220301, 2012AA011601, 61431007)
- Recruitment Program for Young Professionals
- Advanced Research Projects Agency
- National Key Research and Development Program of China (61431007, 2012AA011601)
- Office of Naval Research (W911NF-10-2-0022, N00014-08-1215, N00014)
- Army Research Office (W911NF-10-2-0022, W911NF-09-1-0510, W911NF)
- Army Research Laboratory (W911-NF-10-2-0022, W911NF)
- 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
- brain-computer interface, spelling, EEG, information transfer rate, steady-state visual evoked potentials, visual flicker, target identification, communication rate
- MeSH terms
- Evoked Potentials, Visual, Humans, Language, Brain-Computer Interfaces
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