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Updated: May 7, 2026
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SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
Published on: November 24, 2015
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An SSVEP based BCI to control a humanoid robot by using portable EEG device
Summary
This study introduces a Brain Computer Interface (BCI) using EEG to control a Nao robot. The system interprets visual evoked potentials, enabling robot control with 75% accuracy without prior training.
Area of Science:
- Neuroscience
- Robotics
- Human-Computer Interaction
Background:
- Brain Computer Interfaces (BCIs) enable human-environment interaction via brain signal interpretation.
- Portable electroencephalogram (EEG) devices offer accessible methods for capturing neural data.
Purpose of the Study:
- To design a BCI system for controlling a humanoid robot (Nao) using EEG signals.
- To process neuroelectric responses to visual stimuli for generating robot commands.
Main Methods:
- Utilized Emotiv EPOC EEG device (14 electrodes, 128 Hz) to capture brain signals.
- Analyzed steady-state visually evoked potentials (SSVEP) from occipital lobe signals.
- Employed Fast Fourier Transform and a Gaussian model to detect dominant frequencies from LED stimuli.
- Developed an embedded system to generate LED flickering patterns and control signals.
Main Results:
- The BCI system successfully translated visual stimuli into directional commands for the Nao robot.
- Experimental results showed an average detection accuracy of 75% across subjects.
- The system demonstrated effective control for drawing lines in selected directions without subject training.
Conclusions:
- The developed BCI system provides a viable method for controlling humanoid robots using EEG.
- SSVEP analysis offers a promising approach for non-invasive BCI control.
- The system's performance indicates potential for applications in assistive robotics and human-robot interaction.
