Advanced search
Add to list

Gestural art: a Steady State Visual Evoked Potential (SSVEP) based brain computer interface to express intentions through a robotic hand

Author
Organization
Abstract
We present an automated solution for the acquisition, processing and classification of electroencephalography (EEG) signals in order to remotely control a remotely located robotic hand executing communicative gestures. The Brain-Computer Interface (BCI) was implemented using the Steady State Visual Evoked Potential (SSVEP) approach, a low-latency and low-noise method for reading multiple non-time-locked states from EEG signals. As EEG sensor, the low-cost commercial Emotiv EPOC headset was used to acquire signals from the parietal and occipital lobes. The data processing chain is implemented in OpenViBE, a dedicated software platform for designing, testing and applying Brain-Computer Interfaces. Recorded commands were communicated to an external server through a Virtual Reality Peripheral Network (VRPN) interface. During the training phase, the user controlled a local simulation of a dexterous robot hand, allowing for a safe environment in which to train. After training, the user's commands were used to remotely control a real dexterous robot hand located in Bologna (Italy) from Plymouth (UK). We report on the robustness, accuracy and latency of the setup.
Keywords
EEG, COMMUNICATION

Citation

Please use this url to cite or link to this publication:

MLA
Meattini, R et al. “Gestural Art: a Steady State Visual Evoked Potential (SSVEP) Based Brain Computer Interface to Express Intentions Through a Robotic Hand.” 2014 23RD IEEE INTERNATIONAL SYMPOSIUM ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION (IEEE RO-MAN). NEW YORK: IEEE, 2014. 211–216. Print.
APA
Meattini, R., Scarcia, U., Melchiorri, C., & Belpaeme, T. (2014). Gestural art: a Steady State Visual Evoked Potential (SSVEP) based brain computer interface to express intentions through a robotic hand. 2014 23RD IEEE INTERNATIONAL SYMPOSIUM ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION (IEEE RO-MAN) (pp. 211–216). Presented at the 23rd IEEE International Symposium on Robot and Human Interactive Communication (IEEE RO-MAN), NEW YORK: IEEE.
Chicago author-date
Meattini, R, U Scarcia, C Melchiorri, and Tony Belpaeme. 2014. “Gestural Art: a Steady State Visual Evoked Potential (SSVEP) Based Brain Computer Interface to Express Intentions Through a Robotic Hand.” In 2014 23RD IEEE INTERNATIONAL SYMPOSIUM ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION (IEEE RO-MAN), 211–216. NEW YORK: IEEE.
Chicago author-date (all authors)
Meattini, R, U Scarcia, C Melchiorri, and Tony Belpaeme. 2014. “Gestural Art: a Steady State Visual Evoked Potential (SSVEP) Based Brain Computer Interface to Express Intentions Through a Robotic Hand.” In 2014 23RD IEEE INTERNATIONAL SYMPOSIUM ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION (IEEE RO-MAN), 211–216. NEW YORK: IEEE.
Vancouver
1.
Meattini R, Scarcia U, Melchiorri C, Belpaeme T. Gestural art: a Steady State Visual Evoked Potential (SSVEP) based brain computer interface to express intentions through a robotic hand. 2014 23RD IEEE INTERNATIONAL SYMPOSIUM ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION (IEEE RO-MAN). NEW YORK: IEEE; 2014. p. 211–6.
IEEE
[1]
R. Meattini, U. Scarcia, C. Melchiorri, and T. Belpaeme, “Gestural art: a Steady State Visual Evoked Potential (SSVEP) based brain computer interface to express intentions through a robotic hand,” in 2014 23RD IEEE INTERNATIONAL SYMPOSIUM ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION (IEEE RO-MAN), Heriot Watt Univ, Edinburgh, SCOTLAND, 2014, pp. 211–216.
@inproceedings{8197683,
  abstract     = {We present an automated solution for the acquisition, processing and classification of electroencephalography (EEG) signals in order to remotely control a remotely located robotic hand executing communicative gestures. The Brain-Computer Interface (BCI) was implemented using the Steady State Visual Evoked Potential (SSVEP) approach, a low-latency and low-noise method for reading multiple non-time-locked states from EEG signals. As EEG sensor, the low-cost commercial Emotiv EPOC headset was used to acquire signals from the parietal and occipital lobes. The data processing chain is implemented in OpenViBE, a dedicated software platform for designing, testing and applying Brain-Computer Interfaces. Recorded commands were communicated to an external server through a Virtual Reality Peripheral Network (VRPN) interface. During the training phase, the user controlled a local simulation of a dexterous robot hand, allowing for a safe environment in which to train. After training, the user's commands were used to remotely control a real dexterous robot hand located in Bologna (Italy) from Plymouth (UK). We report on the robustness, accuracy and latency of the setup.},
  author       = {Meattini, R and Scarcia, U and Melchiorri, C and Belpaeme, Tony},
  booktitle    = {2014 23RD IEEE INTERNATIONAL SYMPOSIUM ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION (IEEE RO-MAN)},
  isbn         = {978-1-4799-6765-0},
  issn         = {1944-9445},
  keywords     = {EEG,COMMUNICATION},
  language     = {eng},
  location     = {Heriot Watt Univ, Edinburgh, SCOTLAND},
  pages        = {211--216},
  publisher    = {IEEE},
  title        = {Gestural art: a Steady State Visual Evoked Potential (SSVEP) based brain computer interface to express intentions through a robotic hand},
  year         = {2014},
}

Web of Science
Times cited: