000221632 001__ 221632
000221632 005__ 20190317000539.0
000221632 0247_ $$2doi$$a10.1007/s11721-016-0127-0
000221632 022__ $$a1935-3812
000221632 02470 $$2ISI$$a000389629200002
000221632 037__ $$aARTICLE
000221632 245__ $$aElectroencephalography as implicit communication channel for proximal interaction between humans and robot swarms
000221632 269__ $$a2016
000221632 260__ $$aNew York$$bSpringer Verlag$$c2016
000221632 300__ $$a19
000221632 336__ $$aJournal Articles
000221632 520__ $$aSearch and rescue, autonomous construction, and many other semi-autonomous multi-robot applications can benefit from proximal interactions between an operator and a swarm of robots. Most research on proximal interaction is based on explicit communication techniques such as gesture and speech. This study proposes a new implicit proximal communication technique to approach the problem of robot selection. We use electroencephalography (EEG) signals to select the robot at which the operator is looking. This is achieved using steady-state visually evoked potential (SSVEP), a repeatable neural response to a regularly blinking visual stimulus that varies predictively based on the blinking frequency. In our experiments, each robot was equipped with LEDs blinking at a different frequency, and the operator’s SSVEP neural response was extracted from the EEG signal to detect and select the robot without requiring any conscious action by the user. This study systematically investigates several parameters affecting the SSVEP neural response: blinking frequency of the LED, distance between the robot and the operator, and color of the LED. Based on these parameters, we study two signal processing approaches and critically analyze their performance on 10 subjects controlling a set of physical robots. Our results show that despite numerous artifacts, it is possible to achieve a recognition rate higher than 85% on some subjects, while the average over the ten subjects was 75%.
000221632 6531_ $$ahuman-robots interaction
000221632 6531_ $$aEEG
000221632 6531_ $$aSSVEP
000221632 6531_ $$aEmotiv EPOC
000221632 6531_ $$aThymio robot
000221632 6531_ $$a[MOBOTS]
000221632 700__ $$aMondada, Luca
000221632 700__ $$aKarim, Mohammad Ehsanul
000221632 700__ $$0240589$$aMondada, Francesco$$g102717
000221632 773__ $$j10$$k4$$q247–265$$tSwarm Intelligence
000221632 8564_ $$uhttp://rdcu.be/mlcW$$zURL
000221632 8564_ $$s1714444$$uhttps://infoscience.epfl.ch/record/221632/files/EEG-HSI.pdf$$yn/a$$zn/a
000221632 909C0 $$0252409$$pNCCR-ROBOTICS$$xU12367
000221632 909C0 $$0252016$$pLSRO
000221632 909CO $$ooai:infoscience.tind.io:221632$$pSTI$$particle$$qGLOBAL_SET
000221632 917Z8 $$x102717
000221632 917Z8 $$x102717
000221632 917Z8 $$x102717
000221632 917Z8 $$x102717
000221632 937__ $$aEPFL-ARTICLE-221632
000221632 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000221632 980__ $$aARTICLE