000200203 001__ 200203
000200203 005__ 20190812205802.0
000200203 037__ $$aCONF
000200203 245__ $$aPrediction of Command Delivery Time for BCI
000200203 269__ $$a2014
000200203 260__ $$c2014
000200203 336__ $$aConference Papers
000200203 520__ $$aOne of the challenges in using brain computer interfaces over extended periods of time is the uncertainty in the system. This uncertainty can be due to the user's internal states, the non stationarity of the brain signals, or the variation of the class discriminative information over time. Therefore, the users are often unable to maintain the same accuracy and time efficiency in delivering BCI commands. In this paper, we tackle the issue of variation in BCI command delivery time for a motor imagery task with the aim of providing assistance through adaptive shared control. This is important mainly because having long delivery of mental commands leads to uncertainty in the user's intent classification and limits the responsiveness of the system. In order to address this issue, we separate the trials into “long” and “short” groups so that we have the same number of trials in each group. We demonstrate that using only a few samples at the beginning of the trial, we are able to predict whether the current trial will be short or long with high accuracies (70% - 86%). Eventually, this prediction enables us to tune the shared control parameters to overcome the issue of uncertainty.
000200203 6531_ $$aBrain computer interface (BCI)
000200203 6531_ $$aEEG
000200203 6531_ $$aAdaptive shared controls
000200203 700__ $$0245183$$g204823$$aSaeedi, Sareh
000200203 700__ $$0244057$$g206121$$aCarlson, Tom
000200203 700__ $$0241256$$g137762$$aChavarriaga, Ricardo
000200203 700__ $$aIturrate, Inaki$$0247943$$g212988
000200203 700__ $$0240030$$g149175$$aMillán, José del R.
000200203 7112_ $$dOctober 5-8, 2014$$cSan Diego, CA, USA$$aIEEE International Conference on Systems, Man, and Cybernetics
000200203 8564_ $$zn/a$$yn/a$$uhttps://infoscience.epfl.ch/record/200203/files/SarehSMC2014.pdf$$s1157029
000200203 909C0 $$xU12367$$pNCCR-ROBOTICS$$0252409
000200203 909C0 $$0252517$$xU12599$$pCNP
000200203 909C0 $$xU12103$$pCNBI$$0252018
000200203 909CO $$qGLOBAL_SET$$pconf$$pSTI$$ooai:infoscience.tind.io:200203
000200203 917Z8 $$x204823
000200203 917Z8 $$x137762
000200203 917Z8 $$x137762
000200203 917Z8 $$x204823
000200203 917Z8 $$x212988
000200203 917Z8 $$x137762
000200203 937__ $$aEPFL-CONF-200203
000200203 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000200203 980__ $$aCONF