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.

Roberto Meattini, Umberto Scarcia, Claudio Melchiorri, Tony Belpaeme (2014). Gestural art: A Steady State Visual Evoked Potential (SSVEP) based Brain Computer Interface to express intentions through a robotic hand [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.
2014
The 23rd IEEE International Symposium on Robot and Human Interactive Communication
211
216
Roberto Meattini, Umberto Scarcia, Claudio Melchiorri, Tony Belpaeme (2014). Gestural art: A Steady State Visual Evoked Potential (SSVEP) based Brain Computer Interface to express intentions through a robotic hand [10.1109/ROMAN.2014.6926255].
Roberto Meattini;Umberto Scarcia;Claudio Melchiorri;Tony Belpaeme
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/396248
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