Use of Force Feedback Device in a Hybrid Brain-Computer Interface Based on SSVEP, EOG and Eye Tracking for Sorting...

Area of Science:

• Biomedical Engineering
• Human-Computer Interaction
• Robotics

Background:

• Brain-computer interfaces (BCIs) are gaining popularity due to advancements in EEG recording devices and decreasing costs.
• Existing non-medical BCIs often have limited command generation capabilities.
• Hybrid systems integrating multiple biosignals can overcome these limitations.

Purpose of the Study:

• To develop and evaluate a hybrid BCI system combining Steady-State Visual Evoked Potential (SSVEP), electrooculography (EOG), and eye tracking.
• To assess the impact of a force feedback system on robot control accuracy.
• To investigate the feasibility of using the hybrid BCI for item sorting tasks.

Main Methods:

• Development of a hybrid BCI system integrating SSVEP, EOG, and eye tracking.
• Implementation of a force feedback system for enhanced control.
• Testing the system with a virtual and real industrial robot for positioning tasks.
• Evaluation of item sorting capabilities using the hybrid BCI.

Main Results:

• The hybrid BCI system demonstrated improved robot tip positioning accuracy when the force feedback system was enabled.
• Testing on both virtual and real industrial robots yielded comparable results, validating the system's performance.
• Successful item sorting was achieved using bio-signals from the human operator via the BCI system.

Conclusions:

• Hybrid BCI systems integrating SSVEP, EOG, and eye tracking, coupled with force feedback, offer enhanced control and accuracy in robotic applications.
• The developed system is effective for both precise robot positioning and complex tasks like item sorting.
• This research highlights the potential of advanced BCIs for sophisticated human-robot interaction.