Observation-based learning for brain–machine interfaces

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Tkach, Dennis, et al. “Observation-Based Learning for brain–machine Interfaces”. Current Opinion in Neurobiology, vol. 18, no. 6, 2008, pp. 589-94, https://doi.org/10.1016/j.conb.2008.09.016.
Tkach, D., Reimer, J., & Hatsopoulos, N. G. (2008). Observation-based learning for brain–machine interfaces. Current Opinion in Neurobiology, 18(6), 589-594. https://doi.org/10.1016/j.conb.2008.09.016
Tkach, Dennis, Jake Reimer, and Nicholas G Hatsopoulos. “Observation-Based Learning for brain–machine Interfaces”. Current Opinion in Neurobiology 18, no. 6 (2008): 589-94. https://doi.org/10.1016/j.conb.2008.09.016.
Tkach D, Reimer J, Hatsopoulos NG. Observation-based learning for brain–machine interfaces. Current Opinion in Neurobiology. 2008;18(6):589-94.
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Citations
Title Journal Journal Categories Citations Publication Date
An Auxiliary Synthesis Framework for Enhancing EEG-Based Classification With Limited Data IEEE Transactions on Neural Systems and Rehabilitation Engineering
  • Medicine: Medicine (General): Medical technology
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  • Medicine: Internal medicine: Special situations and conditions: Sports medicine
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Technology: Engineering (General). Civil engineering (General)
2023
Rapid adaptation of brain–computer interfaces to new neuronal ensembles or participants via generative modelling Nature Biomedical Engineering
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13 2021
Structure in Neural Activity during Observed and Executed Movements Is Shared at the Neural Population Level, Not in Single Neurons Cell Reports
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A comprehensive review of EEG-based brain–computer interface paradigms Journal of Neural Engineering
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Citations Analysis
The category Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry 14 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Exploiting multiple sensory modalities in brain-machine interfaces and was published in 2009. The most recent citation comes from a 2023 study titled An Auxiliary Synthesis Framework for Enhancing EEG-Based Classification With Limited Data. This article reached its peak citation in 2014, with 5 citations. It has been cited in 23 different journals, 21% of which are open access. Among related journals, the Journal of Neural Engineering cited this research the most, with 5 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year