000255921 001__ 255921
000255921 005__ 20190822150644.0
000255921 0247_ $$a10.1109/SMC.2017.8122612$$2doi
000255921 02470 $$2DOI$$a10.1109/SMC.2017.8122612
000255921 037__ $$aCONF
000255921 245__ $$aInverse solutions for brain-computer interfaces: Effects of regularisation on localisation and classification
000255921 260__ $$c2017
000255921 269__ $$a2017
000255921 336__ $$aConference Papers
000255921 520__ $$aEstimation of intracranial sources, using inverse solutions methods, has been proposed as a mean to improve performance in non-invasive brain-computer interfaces. These methods estimate the activity of a large number of neural sources from a smaller number of scalp electroencephalography (EEG) channels. This is a highly undetermined problem and regularisation constraints need to be applied. In this paper we compared the effect of several regularisation constraints and parameters in the localisation error and classification performance. Results on three event-related potential protocols-rapid serial visual processing, P300-speller and error-related potentials-showed no significant difference in the maximum performance between minimum norm or weighted minimum norm regularisation constraints. Standardised methods despite yielding lower localisation error resulted in decreased classification performance. Noteworthy, testing on data acquired in different days than the training suggests that discriminant features extracted from intracranial sources are stable across sessions.
000255921 700__ $$g187220$$aGoel, Mohit Kumar$$0242180
000255921 700__ $$g149175$$aMillán, José del R.$$0240030
000255921 700__ $$0241256$$aChavarriaga Lozano, Ricardo$$g137762
000255921 7112_ $$dOctober 5-8, 2017$$cBanff, Canada$$a2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
000255921 773__ $$t2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)$$q258-263
000255921 8564_ $$uhttps://infoscience.epfl.ch/record/255921/files/GoelChMi17.pdf$$s1611435
000255921 8560_ $$fbeatrice.marselli@epfl.ch
000255921 909C0 $$xU12367$$pNCCR-ROBOTICS$$mjoelle.mottier@epfl.ch$$0252409
000255921 909C0 $$xU12103$$pCNBI$$mricardo.chavarriaga@epfl.ch$$0252018
000255921 909CO $$qGLOBAL_SET$$pconf$$pSTI$$ooai:infoscience.epfl.ch:255921
000255921 960__ $$aricardo.chavarriaga@epfl.ch
000255921 961__ $$amanon.velasco@epfl.ch
000255921 973__ $$aEPFL$$rREVIEWED
000255921 980__ $$aCONF
000255921 981__ $$aoverwrite