000200202 001__ 200202
000200202 005__ 20190812205802.0
000200202 037__ $$aCONF
000200202 245__ $$aSubject-oriented training for motor imagery brain-computer interfaces
000200202 269__ $$a2014
000200202 260__ $$c2014
000200202 336__ $$aConference Papers
000200202 520__ $$aSuccessful operation of motor imagery (MI)-based brain-computer interfaces (BCI) requires mutual adaptation between the human subject and the BCI. Traditional training methods, as well as more recent ones based on co-adaptation, have mainly focused on the machine-learning aspects of BCI training. This work presents a novel co-adaptive training protocol shifting the focus on subject-related performances and the optimal accommodation of the interactions between the two learning agents of the BCI loop. Preliminary results with 8 able-bodied individuals demonstrate that the proposed method has been able to bring 3 naive users into control of a MI BCI within a few runs and to improve the BCI performances of 3 experienced BCI users by an average of 0.36 bits/sec.
000200202 6531_ $$abrain computer interface
000200202 6531_ $$amotor imagery
000200202 6531_ $$asubject-oriented training
000200202 700__ $$0242173$$g192137$$aPerdikis, Serafeim
000200202 700__ $$0242179$$g192497$$aLeeb, Robert
000200202 700__ $$0240030$$g149175$$aMillán, José del R.
000200202 7112_ $$dAugust 25-30, 2014$$cChicago, Illinois, USA$$a36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
000200202 773__ $$tProceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
000200202 8564_ $$zn/a$$yn/a$$uhttps://infoscience.epfl.ch/record/200202/files/Perdikis1848.pdf$$s1188160
000200202 909C0 $$xU12103$$pCNBI$$0252018
000200202 909C0 $$0252517$$xU12599$$pCNP
000200202 909CO $$qGLOBAL_SET$$pconf$$pSTI$$ooai:infoscience.tind.io:200202
000200202 917Z8 $$x192137
000200202 917Z8 $$x192137
000200202 937__ $$aEPFL-CONF-200202
000200202 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000200202 980__ $$aCONF