000177843 001__ 177843
000177843 005__ 20190812205614.0
000177843 037__ $$aCONF
000177843 245__ $$aLatency Correction of Error Potentials Between Different Experiments Reduces Calibration Time for Single-Trial Classification
000177843 269__ $$a2012
000177843 260__ $$c2012
000177843 336__ $$aConference Papers
000177843 520__ $$aOne fundamental limitation of EEG-based brain- computer interfaces is the time needed to calibrate the system prior to the detection of signals, due to the wide variety of issues affecting the EEG measurements. For event-related potentials (ERP), one of these sources of variability is the application performed: Protocols with different cognitive workloads might yield to different latencies of the ERPs. In this sense, it is still not clear the effect that these latency variations have on the single-trial classification. This work studies the differences in the latencies of error potentials across three experiments with increasing cognitive workloads. A delay-correction algorithm based on the cross-correlation of the averaged signals is presented, and tested with a single-trial classification of the signals. The results showed that latency variations exist between different protocols, and that it is feasible to re-use data from previous experiments to calibrate a classifier able to detect the signals of a new experiment, thus reducing the calibration time.
000177843 700__ $$aIturrate, Iñaki
000177843 700__ $$0241256$$g137762$$aChavarriaga, Ricardo
000177843 700__ $$aMontesano, Luis
000177843 700__ $$aMinguez, Javier
000177843 700__ $$aMillán, José del R.$$g149175$$0240030
000177843 7112_ $$dAugust 28 - September 1, 2012$$cSan Diego$$a34th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'12)
000177843 8564_ $$zn/a$$yn/a$$uhttps://infoscience.epfl.ch/record/177843/files/IturrateChMoMiMi12.pdf$$s1892676
000177843 909C0 $$xU12103$$pCNBI$$0252018
000177843 909C0 $$0252517$$xU12599$$pCNP
000177843 909CO $$qGLOBAL_SET$$pconf$$pSTI$$ooai:infoscience.tind.io:177843
000177843 917Z8 $$x137762
000177843 917Z8 $$x137762
000177843 937__ $$aEPFL-CONF-177843
000177843 973__ $$rREVIEWED$$sACCEPTED$$aEPFL
000177843 980__ $$aCONF