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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Journal of Neural Engineering, IOP Publishing, 2010, 7 (1), pp.16003. 〈10.1088/1741-2560/7/1/016003〉
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Contributeur : Joan Fruitet <>
Soumis le : jeudi 8 juillet 2010 - 14:27:52
Dernière modification le : jeudi 11 janvier 2018 - 17:08:00

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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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