A low-cost EEG system-based hybrid brain-computer interface for humanoid robot navigation and recognition

Area of Science:

• Neuroscience
• Robotics
• Human-Computer Interaction

Background:

• Brain-computer interfaces (BCI) offer potential for controlling external devices.
• Integrating multiple BCI paradigms can enhance control capabilities.
• Low-cost systems are crucial for widespread BCI adoption.

Purpose of the Study:

• To develop and evaluate a hybrid BCI system for complex humanoid robot navigation and object recognition.
• To demonstrate the feasibility of a low-cost hybrid BCI for realistic tasks.
• To investigate the synergy of image processing with BCI for simplified task execution.

Main Methods:

• A hybrid BCI combining P300, steady-state visually evoked potential (SSVEP), and event-related desynchronization (ERD) was implemented.
• Subjects controlled a humanoid robot's navigation and object recognition via BCI.
• A simple image processing technique was integrated with the BCI system.

Main Results:

• Hybrid BCI performance in robot navigation was comparable to manual keyboard control.
• Object recognition accuracy allowed selection of favorite objects from 2-4 choices.
• Subjects reported a sense of embodiment while performing tasks.

Conclusions:

• A hybrid BCI system, even low-cost, can enable complex humanoid robot navigation and object recognition tasks.
• The integration of BCI with image processing simplifies complex human-robot interaction.
• The proposed hybrid BCI approach holds significant potential for future applications requiring extended controllability.