Swarm Intelligence

, Volume 10, Issue 4, pp 247–265 | Cite as

Electroencephalography as implicit communication channel for proximal interaction between humans and robot swarms

  • Luca Mondada
  • Mohammad Ehsanul Karim
  • Francesco MondadaEmail author


Search and rescue, autonomous construction, and many other semi-autonomous multirobot 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 %.


Human–robots interaction EEG SSVEP Emotiv EPOC Thymio robot 



Many thanks to Dr. Ricardo Chavarriaga, Dr. Claire Braboszcz, and Dr. Serafeim Perdikis for the constructive discussions about experiments involving EEG; to Dr. Jérôme Scherer and Prof. Marco Picasso for their help on mathematical issues in the signal processing; to the reviewers who contributed with detailed and constructive comments during the submission process; and to all subjects who were available for the experiments. This work was partially supported by the Swiss National Center of Competence in Research “Robotics.”


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of PhysicsSwiss Federal Institute of Technology ETHZZürichSwitzerland
  2. 2.Laboratoire de Systèmes RobotiquesEcole Polytechnique Fédérale de LausanneLausanneSwitzerland

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