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.
Gestural art: A Steady State Visual Evoked Potential (SSVEP) based Brain Computer Interface to express intentions through a robotic hand / Roberto Meattini;Umberto Scarcia;Claudio Melchiorri;Tony Belpaeme. - STAMPA. - (2014), pp. 211-216. (Intervento presentato al convegno Robot and Human Interactive Communication, 2014 RO-MAN: The 23rd IEEE International Symposium on tenutosi a Edinburgh nel 25-29 Aug. 2014) [10.1109/ROMAN.2014.6926255].
Gestural art: A Steady State Visual Evoked Potential (SSVEP) based Brain Computer Interface to express intentions through a robotic hand
MEATTINI, ROBERTO;SCARCIA, UMBERTO;MELCHIORRI, CLAUDIO;
2014
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.