000199783 001__ 199783
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000199783 037__ $$aCONF
000199783 245__ $$aRewards-Driven Control of Robot Arm by Decoding EEG Signals
000199783 269__ $$a2014
000199783 260__ $$c2014
000199783 336__ $$aConference Papers
000199783 520__ $$aDecoding the user intention from non-invasive EEG signals is a challenging problem. In this paper, we study the feasibility of predicting the goal for controlling the robot arm in self-paced reaching movements, i.e., spontaneous movements that do not require an external cue. Our proposed system continuously estimates the goal throughout a trial starting before the movement onset by online classification and generates optimal trajectories for driving the robot arm to the estimated goal. Experiments using EEG signals of one healthy subject (right arm) yield smooth reaching movements of the simulated 7 degrees of freedom KUKA robot arm in planar center-out reaching task with approximately 80 % accuracy of reaching the actual goal.
000199783 700__ $$0246728$$g216104$$aTanwani, Ajay Kumar
000199783 700__ $$0240030$$g149175$$aMillán, José del R.
000199783 700__ $$0240594$$g115671$$aBillard, Aude
000199783 7112_ $$dAugust 26-30, 2014$$cChicago, USA$$aIEEE Engineering in Medicine and Biology Society Conference (EMBC)
000199783 773__ $$tProceedings of 36th IEEE Engineering in Medicine and Biology Society Conference (EMBC)
000199783 8564_ $$zn/a$$yn/a$$uhttps://infoscience.epfl.ch/record/199783/files/Tanwani_EMBC_14.pdf$$s633646
000199783 909C0 $$xU10660$$pLASA$$0252119
000199783 909C0 $$0252409$$xU12367$$pNCCR-ROBOTICS
000199783 909C0 $$xU12103$$pCNBI$$0252018
000199783 909C0 $$xU12599$$pCNP$$0252517
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000199783 937__ $$aEPFL-CONF-199783
000199783 973__ $$rREVIEWED$$sACCEPTED$$aEPFL
000199783 980__ $$aCONF