000166743 001__ 166743
000166743 005__ 20190812205527.0
000166743 037__ $$aCONF
000166743 245__ $$aMinimizing calibration time of single-trial recognition of error potentials in brain-computer interfaces
000166743 269__ $$a2011
000166743 260__ $$c2011
000166743 336__ $$aConference Papers
000166743 520__ $$aOne of the main problems of both synchronous and asynchronous EEG-based BCIs is the need of an initial calibration phase before the system can be used. This phase is necessary due to the high non-stationarity of the EEG, since it changes between sessions and users. The calibration limits the BCI systems to scenarios where the outputs are very controlled, and makes these systems non-friendly and exhausting for the users. Although it has been studied how to reduce calibration time for asynchronous signals, it is still an open issue for event- related potentials. Here, we analyze the differences between users for single-trial error-related potentials, and propose the design of classifiers based on inter-subject features to either remove or minimize the calibration time. The results show that it is possible to have a classifier with a high performance from the beginning of the experiment, which is able to adapt itself without the user noticing.
000166743 6531_ $$aBrain-computer interface
000166743 6531_ $$aError-related potentials
000166743 700__ $$0247943$$g212988$$aIturrate, Inaki
000166743 700__ $$aMontesano, Luis
000166743 700__ $$0241256$$g137762$$aChavarriaga, Ricardo
000166743 700__ $$aMillán, José del R.$$g149175$$0240030
000166743 700__ $$aMinguez, Javier
000166743 7112_ $$dAugust 30 - September 3, 2011$$cBoston, Massachusetts, USA$$aEngineering in Medicine and Biology Conference (EMBC 2011)
000166743 8564_ $$zn/a$$yn/a$$uhttps://infoscience.epfl.ch/record/166743/files/IturrateMoChMiMi11.pdf$$s474521
000166743 909C0 $$xU12103$$pCNBI$$0252018
000166743 909C0 $$0252517$$xU12599$$pCNP
000166743 909CO $$qGLOBAL_SET$$pconf$$pSTI$$ooai:infoscience.tind.io:166743
000166743 917Z8 $$x137762
000166743 917Z8 $$x137762
000166743 917Z8 $$x137762
000166743 937__ $$aEPFL-CONF-166743
000166743 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000166743 980__ $$aCONF