A Comparison of Regression Techniques for a Two-Dimensional Sensorimotor Rhythm-Based Brain Computer Interface - Fruitet, Joan (2010)
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 off-line simulation using data collected during on-line 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 BCI performance, and provide new insight into algorithm choice for multidimensional movement control.