The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Incorporating Feedback from Multiple Sensory Modalities Enhances Brain–Machine Interface Control
Open accessAbstract
The brain typically uses a rich supply of feedback from multiple sensory modalities to control movement in healthy individuals. In many individuals, these afferent pathways, as well as their efferent counterparts, are compromised by disease or injury resulting in significant impairments and reduced quality of life. Brain–machine interfaces (BMIs) offer the promise of recovered functionality to these individuals by allowing them to control a device using their thoughts. Most current BMI implementations use visual feedback for closed-loop control; however, it has been suggested that the inclusion of additional feedback modalities may lead to improvements in control. We demonstrate for the first time that kinesthetic feedback can be used together with vision to significantly improve control of a cursor driven by neural activity of the primary motor cortex (MI). Using an exoskeletal robot, the monkey's arm was moved to passively follow a cortically controlled visual cursor, thereby providing the monkey with kinesthetic information about the motion of the cursor. When visual and proprioceptive feedback were congruent, both the time to successfully reach a target decreased and the cursor paths became straighter, compared with incongruent feedback conditions. This enhanced performance was accompanied by a significant increase in the amount of movement-related information contained in the spiking activity of neurons in MI. These findings suggest that BMI control can be significantly improved in paralyzed patients with residual kinesthetic sense and provide the groundwork for augmenting cortically controlled BMIs with multiple forms of natural or surrogate sensory feedback.
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Cites
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
Learning 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 · cited 3×
“…a decoder based on a linear filter because of its computational simplicity and its strong presence in the literature (Serruya et al., 2002; Taylor et al., 2002; Carmena et al., 2003; Musallam et al., 2004; Hochberg et al., 2006; Kim et al., 2008; Mulliken et al., 2008; Ganguly and Carmena, 2009).”
BackgroundMethodCortical 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
Integration of Proprioceptive and Visual Position-Information: An Experimentally Supported Model
van Beers, R. J., Sittig, A. C., & Gon, J. J. D. V. D. (1999) · Journal of Neurophysiology · doi:10.1152/jn.1999.81.3.1355 · Cited by 742
Chronic, multisite, multielectrode recordings in macaque monkeys
Nicolelis, M. A. L., Dimitrov, D., Carmena, J. M., Crist, R., Lehew, G., Kralik, J. D., & Wise, S. P. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1934665100 · Cited by 740
Cognitive Control Signals for Neural Prosthetics
Musallam, S., Corneil, B. D., Greger, B., Scherberger, H., & Andersen, R. A. (2004) · Science · doi:10.1126/science.1097938 · Cited by 663
Control of limb dynamics in normal subjects and patients without proprioception
Sainburg, R. L., Ghilardi, M. F., Poizner, H., & Ghez, C. (1995) · Journal of Neurophysiology · doi:10.1152/jn.1995.73.2.820 · Cited by 662
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
Electromyographic correlates of learning an internal model of reaching movements
Thoroughman, K. A., & Shadmehr, R. (1999) · The Journal of Neuroscience · doi:10.1523/jneurosci.19-19-08573.1999 · Cited by 467
Design and Validation of a Rehabilitation Robotic Exoskeleton for Tremor Assessment and Suppression
Rocon, E., Belda-Lois, J. M., Ruiz, A. F., Manto, M., Moreno, J. C., & Pons, J. L. (2007) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2007.903917 · Cited by 349 · cited 1×
“With the recent advent of lightweight wearable robotic exoskeletons (Kiguchi et al., 2005; Rocon et al., 2007), these patient populations may be served with a cortically controlled BMI that drives these devices and, in turn, moves the affected but proprioception-intact limbs, thereby augmenting these systems with prop…”
Background
Cited by
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
Clinical translation of a high-performance neural prosthesis
Gilja, V., Pandarinath, C., Blabe, C. H., Nuyujukian, P., Simeral, J. D., Sarma, A. A., Sorice, B. L., Perge, J. A., Jarosiewicz, B., Hochberg, L. R., et al. (2015) · Nature Medicine · doi:10.1038/nm.3953 · Cited by 343
The computational and neural basis of voluntary motor control and planning
Scott, S. H. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.09.008 · Cited by 317
Closed-Loop Decoder Adaptation Shapes Neural Plasticity for Skillful Neuroprosthetic Control
Orsborn, A. L., Moorman, H. G., Overduin, S. A., Shanechi, M. M., Dimitrov, D. F., & Carmena, J. M. (2014) · Neuron · doi:10.1016/j.neuron.2014.04.048 · Cited by 280
Brain–machine interfaces from motor to mood
Shanechi, M. M. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0488-y · Cited by 263
Metadata
- Authors
- Suminski, A. J., Tkach, D. C., Fagg, A. H., & Hatsopoulos, N. G.
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2010-12-15
- Volume
- 30
- Issue
- 50
- Pages
- 16777-16787
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- OpenAlex
- W2039621768
- Cited by
- 231
- References
- 10
- FWCI
- 10.32
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Suminski, A. J., Tkach, D. C., Fagg, A. H., & Hatsopoulos, N. G. (2010). Incorporating Feedback from Multiple Sensory Modalities Enhances Brain–Machine Interface Control. The Journal of Neuroscience. https://doi.org/10.1523/jneurosci.3967-10.2010
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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