000200115 001__ 200115
000200115 005__ 20190812205802.0
000200115 0247_ $$2doi$$a10.3217/978-3-85125-378-8-68
000200115 037__ $$aCONF
000200115 245__ $$aComparing BCI performance using scalp EEG-and inverse solution-based features
000200115 269__ $$a2014
000200115 260__ $$c2014
000200115 336__ $$aConference Papers
000200115 520__ $$aSeveral studies have proposed the use of inverse solutions based features to improve the decoding performance of brain-computer interfaces. Most of these studies have compared the performance of inverse solutions features over scalp activity in a small set of electrodes. However, the estimated sources are indeed a linear combination of scalp-wide activity. Therefore, this comparison may be biased against surface EEG. Performance comparison in three ERP-based protocols show that classifiers combining larger sets of EEG electrodes may perform comparably, and previous reports may have overestimated the advantages of using inverse solution based features.
000200115 700__ $$0242180$$g187220$$aGoel, Mohit Kumar
000200115 700__ $$0241256$$g137762$$aChavarriaga, Ricardo
000200115 700__ $$aMillán, José del R.$$g149175$$0240030
000200115 7112_ $$dSeptember 16-19, 2014$$cGraz, Austria$$a6th International Brain-Computer Interface Conference 2014
000200115 8564_ $$zn/a$$yn/a$$uhttps://infoscience.epfl.ch/record/200115/files/InverseERP.pdf$$s1095290
000200115 909C0 $$xU12103$$pCNBI$$0252018
000200115 909C0 $$0252517$$xU12599$$pCNP
000200115 909CO $$qGLOBAL_SET$$pconf$$pSTI$$ooai:infoscience.tind.io:200115
000200115 917Z8 $$x137762
000200115 917Z8 $$x149175
000200115 917Z8 $$x149175
000200115 937__ $$aEPFL-CONF-200115
000200115 973__ $$rREVIEWED$$sACCEPTED$$aEPFL
000200115 980__ $$aCONF