000166740 001__ 166740
000166740 005__ 20180913060701.0
000166740 037__ $$aCONF
000166740 245__ $$aLearning dictionaries of spatial and temporal EEG primitives for brain-computer interfaces
000166740 269__ $$a2011
000166740 260__ $$c2011
000166740 336__ $$aConference Papers
000166740 520__ $$aSparse methods are widely used in image and audio processing for denoising and classification, but there have been few previous applications to neural signals for brain-computer interfaces (BCIs). We used the dictionary- learning algorithm K-SVD, coupled with Orthogonal Matching Pursuit, to learn dictionaries of spatial and temporal EEG primitives. We applied these to P300 and ErrP data to denoise the EEG and better estimate the underlying P300 and ErrP signals. This methodology improved single-trial classification performance across 13 of 14 subjects, indicating that some of the background noise in EEG signals, presumably from neural or muscular sources, is highly structured. Furthermore, this structure can be captured via dictionary learning and sparse coding algorithms, and exploited to improve BCIs.
000166740 700__ $$aHamner, Benjamin
000166740 700__ $$0241256$$aChavarriaga, Ricardo$$g137762
000166740 700__ $$0240030$$aMillán, José del R.$$g149175
000166740 7112_ $$aWorkshop on Structured Sparsity: Learning and Inference, ICML 2011$$cBellevue, Washington, USA$$dJuly 2, 2011
000166740 8564_ $$s246484$$uhttps://infoscience.epfl.ch/record/166740/files/HamnerChMi11.pdf$$yn/a$$zn/a
000166740 909C0 $$0252018$$pCNBI$$xU12103
000166740 909C0 $$0252517$$pCNP$$xU12599
000166740 909CO $$ooai:infoscience.tind.io:166740$$pconf$$pSTI
000166740 917Z8 $$x137762
000166740 917Z8 $$x137762
000166740 917Z8 $$x137762
000166740 917Z8 $$x137762
000166740 937__ $$aEPFL-CONF-166740
000166740 973__ $$aEPFL$$rREVIEWED$$sACCEPTED
000166740 980__ $$aCONF