000163929 001__ 163929
000163929 005__ 20190812205506.0
000163929 02470 $$2ISI$$a000298735800097
000163929 037__ $$aCONF
000163929 245__ $$aSingle Trial Recognition of Anticipatory Slow Cortical Potentials: The Role of Spatio-Spectral Filtering
000163929 269__ $$a2011
000163929 260__ $$bIeee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa$$c2011
000163929 336__ $$aConference Papers
000163929 490__ $$aInternational IEEE EMBS Conference on Neural Engineering
000163929 520__ $$aSingle trial recognition of slow cortical potentials (SCPs) from full-band EEG (FbEEG) faces different challenges to classical EEG such as noisy, high magnitude (~±100µV) infra slow oscillations (ISO) with f<0.1Hz and high frequency spatial noise from a variety of artifacts. We analyze offline the anticipation related SCPs recorded from 11 subjects over two days in a variation of the Contingent Negative Variation (CNV) paradigm with Go and No-go conditions in an assistive technology framework. The results suggest that widely used spatial filters such as Common Average Referencing (CAR) and Laplacian are sub-optimal for the single trial analysis of SCPs. We show that a spatial smoothing filter (SSF), which in combination with CAR enhances the spatially distributed SCP while attenuating high frequency spatial noise. We report, first, that a narrow band filter in the range [0.1 1]Hz captures anticipation related SCP better and effectively reduces ISOs. Second, the SSF in combination with CAR outperforms CAR-alone and Laplacian spatial filters. Third, we compare linear and quadratic classifiers calculated using optimally filtered Cz electrode potentials and report that the best methods resulted in single trial classification accuracies of 83±4%, where classifiers were trained on day 1 and tested using data from day 2, to ensure generalization capabilities across days (1-7 days).
000163929 6531_ $$aSlow cortical potentials
000163929 6531_ $$aSpatio-spectral filtering
000163929 6531_ $$aAnticipatory behavior
000163929 6531_ $$aBrain computer interface
000163929 6531_ $$aElectroencephalogram
000163929 700__ $$0242177$$g176513$$aGaripelli, Gangadhar
000163929 700__ $$0241256$$g137762$$aChavarriaga, Ricardo
000163929 700__ $$aMillán, José del R.$$0240030$$g149175
000163929 7112_ $$dApril 27-May 1, 2011$$cCancun, Mexico$$a5th International Conference on Neural Engineering
000163929 773__ $$tProceedings of the 5th International Conference on Neural Engineering
000163929 8564_ $$zURL$$uhttp://ne2011.embs.org/
000163929 8564_ $$zn/a$$uhttps://infoscience.epfl.ch/record/163929/files/main.pdf$$s354852
000163929 909C0 $$xU12103$$pCNBI$$0252018
000163929 909C0 $$0252517$$xU12599$$pCNP
000163929 909CO $$qGLOBAL_SET$$pconf$$pSTI$$ooai:infoscience.tind.io:163929
000163929 917Z8 $$x176513
000163929 917Z8 $$x176513
000163929 917Z8 $$x137762
000163929 917Z8 $$x137762
000163929 937__ $$aEPFL-CONF-163929
000163929 973__ $$rREVIEWED$$sACCEPTED$$aEPFL
000163929 980__ $$aCONF