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Noninvasive Brain-Computer Interfaces Based on Sensorimotor Rhythms
Open accessAbstract
Brain-computer interfaces (BCIs) have been explored in the field of neuroengineering to investigate how the brain can use these systems to control external devices. We review the principles and approaches we have taken to develop a sensorimotor rhythm EEG based brain-computer interface (BCI). The methods include developing BCI systems incorporating the control of physical devices to increase user engagement, improving BCI systems by inversely mapping scalp-recorded EEG signals to the cortical source domain, integrating BCI with noninvasive neuromodulation strategies to improve learning, and incorporating mind-body awareness training to enhance BCI learning and performance. The challenges and merits of these strategies are discussed, together with recent findings. Our work indicates that the sensorimotor-rhythm-based noninvasive BCI has the potential to provide communication and control capabilities as an alternative to physiological motor pathways.
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Cites
Independent component analysis, A new concept?
Comon, P. (1994) · Signal Processing · doi:10.1016/0165-1684(94)90029-9 · Cited by 8,324
Solutions of Ill-Posed Problems.
Bell, J. B., Tikhonov, A. N., & Arsenin, V. Y. (1978) · Mathematics of Computation · doi:10.2307/2006360 · Cited by 8,096
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
Event-related EEG/MEG synchronization and desynchronization: basic principles
Pfurtscheller, G., & Lopes da Silva, F. H. (1999) · Clinical Neurophysiology · doi:10.1016/s1388-2457(99)00141-8 · Cited by 7,328
The information capacity of the human motor system in controlling the amplitude of movement.
Fitts, P. M. (1954) · Journal of Experimental Psychology · doi:10.1037/h0055392 · Cited by 6,466
The information capacity of the human motor system in controlling the amplitude of movement.
Fitts, P. M. (1992) · Journal of Experimental Psychology General · doi:10.1037//0096-3445.121.3.262 · Cited by 6,296
Nitsche, M. A., & Paulus, W. (2000) · The Journal of Physiology · doi:10.1111/j.1469-7793.2000.t01-1-00633.x · Cited by 5,656
Analysis of Discrete Ill-Posed Problems by Means of the L-Curve
Hansen, P. C. (1992) · SIAM Review · doi:10.1137/1034115 · Cited by 3,903
Frequency Dependence of Signal Power and Spatial Reach of the Local Field Potential
Leski, S., Lindén, H., Tetzlaff, T., Pettersen, K. H., & Einevoll, G. T. (2013) · PubMed · doi:10.18154/rwth-conv-084442 · Cited by 3,667
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
Research works printed but not linked: 1
- mason, 2006, Evaluating the performance of self-paced brain-computer interface technology, Neil Squire Soc
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A comprehensive review of EEG-based brain–computer interface paradigms
Abiri, R., Borhani, S., Sellers, E. W., Jiang, Y., & Zhao, X. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aaf12e · Cited by 999
EEG source imaging enhances the decoding of complex right-hand motor imagery tasks
Edelman, B. J., Baxter, B., & He, B. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2467312 · Cited by 370
Self-citationNoninvasive 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
Self-citationHS-CNN: a CNN with hybrid convolution scale for EEG motor imagery classification
Dai, G., Zhou, J., Huang, J., & Wang, N. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab405f · Cited by 342
Electrophysiological source imaging: A noninvasive window to brain dynamics
He, B., Sohrabpour, A., Brown, E., & Liu, Z. (2018) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-062117-120853 · Cited by 297
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Metadata
- Authors
- He, B., Baxter, B., Edelman, B. J., Cline, C. C., & Ye, W. W.
- Affiliations
- University of Minnesota (US)
- Corresponding author
- He, Bin
- Venue
- Proceedings of the IEEE
- Published
- 2015-06
- Volume
- 103
- Issue
- 6
- Pages
- 907-925
- ISSN
- 0018-9219 (ISSN-L)
- 1558-2256
- PMC
- PMC8323842
- OpenAlex
- W2147854680
- Cited by
- 233
- References
- 177
- Citations by year
- 2026: 12 · 2025: 18 · 2024: 28 · 2023: 24 · 2022: 15 · 2021: 21 · 2020: 17 · 2019: 20 · 2018: 33 · 2017: 27 · 2016: 14 · 2015: 4
- FWCI
- 15.63
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the IEEE (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation (CBET-1264782, DGE-1069104)
- National Institute of Health (EB006433, EY023101)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Keywords
- non-invasive brain-computer interface, sensorimotor rhythm, EEG, cortical source localization, non-invasive neuromodulation, motor control, communication aids
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