Abstract
In this study, we present a method to remove ocular artifacts from electroencephalographic (EEG) recordings. This method is based on the detection of the EOG activation periods from a reference EOG channel, definition of covariance matrices containing the nonstationary information of the EOG, and applying generalized eigenvalue decomposition (GEVD) onto these matrices to rank the components in order of resemblance with the EOG. An iterative procedure is further proposed to remove the EOG components in a deflation fashion. © Springer-Verlag Berlin Heidelberg 2009.
Cite
CITATION STYLE
Gouy-Pailler, C., Sameni, R., Congedo, M., & Jutten, C. (2009). Iterative subspace decomposition for ocular artifact removal from EEG recordings. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5441, pp. 419–426). https://doi.org/10.1007/978-3-642-00599-2_53
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
