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J Neural Eng. 2010 Feb;7(1):16003. doi: 10.1088/1741-2560/7/1/016003. Epub 2010 Jan 14.

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

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  • 1Ecole Normale Supérieure, 45 rue d'Ulm, 75230 Paris Cedex 05, France.


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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