Electroencephalographic (EEG) control of three-dimensional movement
Open accessAbstract
Brain-computer interfaces (BCIs) can use brain signals from the scalp (EEG), the cortical surface (ECoG), or within the cortex to restore movement control to people who are paralyzed. Like muscle-based skills, BCIs' use requires activity-dependent adaptations in the brain that maintain stable relationships between the person's intent and the signals that convey it. This study shows that humans can learn over a series of training sessions to use EEG for three-dimensional control. The responsible EEG features are focused topographically on the scalp and spectrally in specific frequency bands. People acquire simultaneous control of three independent signals (one for each dimension) and reach targets in a virtual three-dimensional space. Such BCI control in humans has not been reported previously. The results suggest that with further development noninvasive EEG-based BCIs might control the complex movements of robotic arms or neuroprostheses.
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Previous research shows the promising capability of controlling a virtual cursor in 3D space.
“Previous research shows the promising capability of controlling a virtual cursor in 3D space31”
From Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks (2016) · cites this paper as [31]
The extent and impact of subject learning effects in noninvasive MI BCI training remain rather disputable.
“Notwithstanding a few exceptions of longitudinal and translational studies in which thorough neuroimaging evidence is also provided [9,42], the extent and impact of subject learning effects in noninvasive MI BCI training remain rather disputable.”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [42]
Users have reported reaching a state of proficiency through learning where BCI control becomes "automatic," as they no longer need to engage explicitly in MI.
“Interestingly, users have reported reaching a state of proficiency through learning where BCI control becomes “automatic,” as they no longer need to engage explicitly in MI [23,40–42,50,60,61].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [42]
These works report on longitudinal training and involve end-users.
“These works report on longitudinal training and involve end-users.”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [42]
[10] and [42] substantiate learning effects with event-related desynchronization/synchronization maps and SMR topographies, respectively, over 3-4 time points throughout the training period.
“Furthermore, [10] and [42] substantiate learning effects with event-related desynchronization/synchronization (ERD/ERS) maps and SMR topographies, respectively, over 3–4 time points throughout the training period.”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [42]
The most complete evidence of subject learning with obvious translational implications is offered in [9], [10], and [42].
“The most complete evidence of subject learning with obvious translational implications is offered in [9], [10], and [42].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [42]
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Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482 · see citing works
Haykin, S. (1986) · Cited by 12,603 · see citing works
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820 · see citing works
Rogers, S. (1996) · Control Engineering Practice · doi:10.1016/0967-0661(96)82838-3 · Cited by 10,013 · 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
Self-citationThe brainweb: phase synchronization and large-scale integration
Varela, F., Lachaux, J.-P., Rodriguez, E., & Martinerie, J. (2001) · Nature Reviews Neuroscience · doi:10.1038/35067550 · Cited by 4,761 · see citing works
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 · see citing works
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 3,022 · see citing works
Self-citationLearning to control a brain–machine interface for reaching and grasping by primates
Carmena, J. M., Lebedev, M. A., Crist, R. E., O'Doherty, J. E., Santucci, D. M., Dimitrov, D. F., Patil, P. G., Henriquez, C. S., & Nicolelis, M. A. L. (2003) · PLoS Biology · doi:10.1371/journal.pbio.0000042 · Cited by 1,761 · see citing works
Cortical control of a prosthetic arm for self-feeding
Velliste, M., Perel, S., Spalding, M. C., Whitford, A. S., & Schwartz, A. B. (2008) · Nature · doi:10.1038/nature06996 · Cited by 1,591 · see citing works
Direct Cortical Control of 3D Neuroprosthetic Devices
Taylor, D. M., Tillery, S. I. H., & Schwartz, A. B. (2002) · Science · doi:10.1126/science.1070291 · Cited by 1,542 · see citing works
Control 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 · see citing works
Self-citationAutomaticity: A Theoretical and Conceptual Analysis.
Moors, A., & Houwer, J. D. (2006) · Psychological Bulletin · doi:10.1037/0033-2909.132.2.297 · Cited by 1,422 · see citing works
Instant neural control of a movement signal
Serruya, M. D., Hatsopoulos, N. G., Paninski, L., Fellows, M. R., & Donoghue, J. P. (2002) · Nature · doi:10.1038/416141a · Cited by 1,415 · see citing works
Correlated neuronal activity and the flow of neural information
Salinas, E., & Sejnowski, T. J. (2001) · Nature Reviews Neuroscience · doi:10.1038/35086012 · Cited by 1,333 · see citing works
A brain–computer interface using electrocorticographic signals in humans
Leuthardt, E. C., Schalk, G., Wolpaw, J. R., Ojemann, J. G., & Moran, D. W. (2004) · Journal of Neural Engineering · doi:10.1088/1741-2560/1/2/001 · Cited by 1,188 · see citing works
Self-citationAmerican Electroencephalographic Society Guidelines for Standard Electrode Position Nomenclature
&NA; (1991) · Journal of Clinical Neurophysiology · doi:10.1097/00004691-199104000-00007 · Cited by 1,179 · see citing works
Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex
Chapin, J. K., Moxon, K. A., Markowitz, R. S., & Nicolelis, M. A. L. (1999) · Nature Neuroscience · doi:10.1038/10223 · Cited by 1,168 · see citing works
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 1,117 · see citing works
Self-citationSpatial filter selection for EEG-based communication
McFarland, D. J., McCane, L. M., David, S. V., & Wolpaw, J. R. (1997) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/s0013-4694(97)00022-2 · Cited by 937 · see citing works
Self-citationAmerican Electroencephalographic Society guidelines for standard electrode position nomenclature
Sharbrough, F. W. (1991) · Journal of Clinical Neurophysiology · Cited by 918 · see citing works
Motor areas in the frontal lobe of the primate
DUM, R., & STRICK, P. (2002) · Physiology & Behavior · doi:10.1016/s0031-9384(02)00929-0 · Cited by 653 · see citing works
Emergence of a stable cortical map for neuroprosthetic control
Ganguly, K., & Carmena, J. M. (2009) · PLoS Biology · doi:10.1371/journal.pbio.1000153 · Cited by 578 · see citing works
Dum, R. P., & Strick, P. (2005) · Journal of Neuroscience · doi:10.1523/jneurosci.3902-04.2005 · Cited by 568 · see citing works
Two-dimensional movement control using electrocorticographic signals in humans
Schalk, G., Miller, K. J., Anderson, N., Wilson, J. A., Smyth, M. D., Ojemann, J. G., Moran, D. W., Wolpaw, J. R., & Leuthardt, E. C. (2008) · Journal of Neural Engineering · doi:10.1088/1741-2560/5/1/008 · Cited by 481 · see citing works
Self-citationMultichannel EEG-based brain-computer communication
Wolpaw, J. R., & McFarland, D. J. (1994) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(94)90135-x · Cited by 449 · see citing works
Self-citationSelecting the signals for a brain–machine interface
Andersen, R. A., Musallam, S., & Pesaran, B. (2004) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2004.10.005 · Cited by 329 · see citing works
Network Oscillations: Emerging Computational Principles
Sejnowski, T. J., & Paulsen, O. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.3737-05d.2006 · Cited by 284 · see citing works
Complex Organization of Human Primary Motor Cortex: A High-Resolution fMRI Study
Meier, J. D., Aflalo, T., Kästner, S., & Graziano, M. S. A. (2008) · Journal of Neurophysiology · doi:10.1152/jn.90531.2008 · Cited by 282 · see citing works
Motor Learning with Unstable Neural Representations
Rokni, U., Richardson, A. G., Bizzi, E., & Seung, H. S. (2007) · Neuron · doi:10.1016/j.neuron.2007.04.030 · Cited by 270 · see citing works
Emulation of computer mouse control with a noninvasive brain–computer interface
McFarland, D. J., Krusienski, D. J., Sarnacki, W. A., & Wolpaw, J. R. (2008) · Journal of Neural Engineering · doi:10.1088/1741-2560/5/2/001 · Cited by 202 · see citing works
Self-citationMorash, V. S., Bai, O., Furlani, S. J., Lin, P. T., & Hallett, M. (2008) · Clinical Neurophysiology · doi:10.1016/j.clinph.2008.08.013 · Cited by 194 · see citing works
Biological and Biochemical Basis of Behavior.
Wolff, H. G. (1959) · Archives of Neurology · doi:10.1001/archneur.1959.03840060124007 · Cited by 176 · see citing works
McFarland, D. J., & Wolpaw, J. R. (2008) · Journal of Neural Engineering · doi:10.1088/1741-2560/5/2/006 · Cited by 172 · see citing works
Self-citationOtto, K. J., Johnson, M. D., & Kipke, D. R. (2006) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2005.862530 · Cited by 132 · see citing works
Aflalo, T., & Graziano, M. S. A. (2006) · Journal of Neuroscience · doi:10.1523/jneurosci.0768-06.2006 · Cited by 122 · see citing works
Large-Scale Visuomotor Integration in the Cerebral Cortex
Ledberg, A., Bressler, S. L., Ding, M., Coppola, R., & Nakamura, R. (2006) · Cerebral Cortex · doi:10.1093/cercor/bhj123 · Cited by 114 · see citing works
Prestimulus Cortical Activity is Correlated with Speed of Visuomotor Processing
Zhang, Y., Wang, X., Bressler, S. L., Chen, Y., & Ding, M. (2008) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2008.20132 · Cited by 114 · see citing works
Brain–computer interfaces for communication and control
Wolpaw, J. R., & Birbaumer, N. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511545061.036 · Cited by 92 · see citing works
Self-citationBrain–computer interface signal processing at the Wadsworth Center: mu and sensorimotor beta rhythms
McFarland, D. J., Krusienski, D. J., & Wolpaw, J. R. (2006) · Progress in brain research · doi:10.1016/s0079-6123(06)59026-0 · Cited by 91 · see citing works
Self-citationReal-time control of a robotic arm by neuronal ensembles
Fetz, E. E. (1999) · Nature Neuroscience · doi:10.1038/10131 · Cited by 73 · see citing works
Short‐lived brain state after cued motor imagery in naive subjects
Pfurtscheller, G., Scherer, R., Muller-Putz, G. R., et al. (2008) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2008.06441.x · Cited by 62 · see citing works
Interpreting single trial data using groupwise regularisation
van Gerven, M. A. J., Hesse, C. W., Jensen, O. B., & Heskes, T. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.02.041 · Cited by 51 · see citing works
EEG-based communication: analysis of concurrent EMG activity
Vaughan, T. M., Miner, L. A., McFarland, D. J., & Wolpaw, J. R. (1998) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/s0013-4694(98)00107-2 · Cited by 39 · see citing works
Self-citationA linear feature space for simultaneous learning of spatio-spectral filters in BCI
Farquhar, J. D. R. (2009) · Neural Networks · doi:10.1016/j.neunet.2009.06.035 · Cited by 35 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Brown L The New Shorter Oxford English Dictionary 1993 2 Oxford Clarenden Press 2882
- Wolpaw JR McFarland DJ Neat GW Forneris CA An EEG-based brain-computer interface for cursor control Electroencephalogr. Clin. Neurophysiol 1991 78 252 259 1707798
Cited by
Sorted by citations
High-performance neuroprosthetic control by an individual with tetraplegia
Collinger, J. L., Wodlinger, B., Downey, J. E., Wang, W., Tyler-Kabara, E. C., Weber, D. J., McMorland, A. J., Velliste, M., Boninger, M. L., & Schwartz, A. B. (2013) · The Lancet · doi:10.1016/s0140-6736(12)61816-9 · Cited by 1,848 · see citing works
A high-performance neural prosthesis enabled by control algorithm design
Gilja, V., Nuyujukian, P., Chestek, C. A., Cunningham, J. P., Yu, B. M., Fan, J. M., Churchland, M. M., Kaufman, M. T., Kao, J. C., Ryu, S. I., et al. (2012) · Nature Neuroscience · doi:10.1038/nn.3265 · Cited by 564 · see citing works
LaFleur, K., Cassady, K., Doud, A., Shades, K., Rogin, E., & He, B. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/4/046003 · Cited by 583 · see citing works
Simeral, J. D., Kim, S.-P., Black, M. J., Donoghue, J. P., & Hochberg, L. R. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/2/025027 · Cited by 496 · see citing works
Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks
Meng, J., Zhang, S., Bekyo, A., Olsoe, J., Baxter, B., & He, B. (2016) · Scientific Reports · doi:10.1038/srep38565 · Cited by 484 · see citing works
Noninvasive 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
Lotte, F., Larrue, F., & Mühl, C. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00568 · Cited by 307 · see citing works
Huang, D., Qian, K., Fei, D.-Y., Jia, W., Chen, X., & Bai, O. (2012) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2012.2190299 · Cited by 237 · see citing works
Noninvasive Brain-Computer Interfaces Based on Sensorimotor Rhythms
He, B., Baxter, B., Edelman, B. J., Cline, C. C., & Ye, W. W. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2407272 · Cited by 233 · see citing works
Brain–Computer Interface Technologies in the Coming Decades
Lance, B. J., Kerick, S. E., Ries, A. J., Oie, K. S., & McDowell, K. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2184830 · Cited by 231 · 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
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
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
Friedrich, E. V. C., Suttie, N., Sivanathan, A., Lim, T., Louchart, S., & Pineda, J. A. (2014) · Frontiers in Neuroengineering · doi:10.3389/fneng.2014.00021 · Cited by 99 · see citing works
Data augmentation for self-paced motor imagery classification with C-LSTM.
Freer, D., & Yang, G.-Z. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab57c0 · Cited by 96 · see citing works
Fifer, M. S., Acharya, S., Benz, H. L., Mollazadeh, M., Crone, N. E., & Thakor, N. V. (2012) · IEEE Pulse · doi:10.1109/mpul.2011.2175636 · Cited by 74 · see citing works
EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features
Wu, D., Lance, B. J., Lawhern, V. J., Gordon, S., Jung, T.-P., & Lin, C.-T. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2017.2699784 · Cited by 64 · see citing works
Innovations in Prosthetic Interfaces for the Upper Extremity
Kung, T. A., Bueno, R. A., Alkhalefah, G. K., Langhals, N. B., Urbanchek, M. G., & Cederna, P. S. (2013) · Plastic and Reconstructive Surgery · doi:10.1097/prs.0b013e3182a97e5f · Cited by 71 · see citing works
Hiremath, S. V., Chen, W., Wang, W., Foldes, S., Yang, Y., Tyler-Kabara, E. C., Collinger, J. L., & Boninger, M. L. (2015) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2015.00040 · Cited by 53 · see citing works
Systems Neuroengineering: Understanding and Interacting with the Brain
Edelman, B. J., Johnson, N., Sohrabpour, A., Tong, S., Thakor, N., & He, B. (2015) · Engineering · doi:10.15302/j-eng-2015078 · Cited by 45 · see citing works
- Authors
- McFarland, D. J., Sarnacki, W. A., & Wolpaw, J. R.
- Affiliations
- New York State Department of Health (US)
- Corresponding author
- Sarnacki, William A
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2010-05-11
- Volume
- 7
- Issue
- 3
- Pages
- 036007
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- PMC
- PMC2907523
- OpenAlex
- W1989927577
- Cited by
- 454
- References
- 45
- Citations by year
- 2026: 7 · 2025: 13 · 2024: 22 · 2023: 17 · 2022: 20 · 2021: 25 · 2020: 23 · 2019: 23 · 2018: 43 · 2017: 25 · 2016: 42 · 2015: 37 · 2014: 31 · 2013: 36 · 2012: 40
- FWCI
- 22.63
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Neural Engineering (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Institute of Biomedical Imaging and Bioengineering (EB 00856, R01EB000856)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD030146, HD30146)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- brain-computer interface, EEG, motor learning, neural prostheses, robotic arm control, frequency bands
- MeSH terms
- Algorithms, Brain, Electroencephalography, Evoked Potentials, Humans, Imagination, Male, Motion, Spinal Cord Injuries, User-Computer Interface, Computer Peripherals
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