01705nas a2200157   4500008004100000022001400041245008200055210006900137260001200206520117900218653002901397653002501426653002201451100002601473856004801499       2014                        eng d  a1872-769700aThe advantages of the surface Laplacian in brain-computer interface research.0 aadvantages of the surface Laplacian in braincomputer interface r  c08/20143 a<p>Brain-computer interface (BCI) systems frequently use signal processing methods, such as spatial filtering, to enhance performance. The surface Laplacian can reduce spatial noise and aid in identification of sources. In BCI research, these two functions of the surface Laplacian correspond to prediction accuracy and signal orthogonality. In the present study, an off-line analysis of data from a sensorimotor rhythm-based BCI task dissociated these functions of the surface Laplacian by comparing nearest-neighbor and next-nearest neighbor Laplacian algorithms. The nearest-neighbor Laplacian produced signals that were more orthogonal while the next-nearest Laplacian produced signals that resulted in better accuracy. Both prediction and signal identification are important for BCI research. Better prediction of user's intent produces increased speed and accuracy of communication and control. Signal identification is important for ruling out the possibility of control by artifacts. Identifying the nature of the control signal is relevant both to understanding exactly what is being studied and in terms of usability for individuals with limited motor control.</p>10abrain-computer interface10asensorimotor rhythms10asurface laplacian1 aMcFarland, Dennis, J.  uhttp://www.ncbi.nlm.nih.gov/pubmed/25091286