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A comparison of regression techniques for a two-dimensional sensorimotor rhythm-based brain-computer interface.

Abstract : People can learn to control electroencephalogram (EEG) features consisting of sensorimotor-rhythm amplitudes and use this control to move a cursor in one, two or three dimensions to a target on a video screen. This study evaluated several possible alternative models for translating these EEG features into two-dimensional cursor movement by building an offline simulation using data collected during online performance. In offline comparisons, support-vector regression (SVM) with a radial basis kernel produced somewhat better performance than simple multiple regression, the LASSO or a linear SVM. These results indicate that proper choice of a translation algorithm is an important factor in optimizing brain-computer interface (BCI) performance, and provide new insight into algorithm choice for multidimensional movement control.
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https://hal.inria.fr/inria-00498774
Contributor : Joan Fruitet <>
Submitted on : Thursday, July 8, 2010 - 2:27:52 PM
Last modification on : Thursday, March 5, 2020 - 5:34:54 PM

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Joan Fruitet, Dennis J Mcfarland, Jonathan R Wolpaw. A comparison of regression techniques for a two-dimensional sensorimotor rhythm-based brain-computer interface.. Journal of Neural Engineering, IOP Publishing, 2010, 7 (1), pp.16003. ⟨10.1088/1741-2560/7/1/016003⟩. ⟨inria-00498774⟩

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