The advantages of the surface Laplacian in brain-computer interface research.

TitleThe advantages of the surface Laplacian in brain-computer interface research.
Publication TypeJournal Article
Year of Publication2014
AuthorsMcFarland, DJ
JournalInt J Psychophysiol
Date Published08/2014
ISSN1872-7697
Keywordsbrain-computer interface, sensorimotor rhythms, surface laplacian
Abstract

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.

URLhttp://www.ncbi.nlm.nih.gov/pubmed/25091286
DOI10.1016/j.ijpsycho.2014.07.009
Alternate JournalInt J Psychophysiol
PubMed ID25091286
PubMed Central IDPMC4312749
Grant ListP41 EB018783 / EB / NIBIB NIH HHS / United States
R01 EB000856 / EB / NIBIB NIH HHS / United States
R01 HD030146 / HD / NICHD NIH HHS / United States

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