Abstract
This paper describes a hybrid brain-computer interface (BCI) technique that combines the P300 potential, the steady state visually evoked potential (SSVEP), and event related de-synchronization (ERD) to solve a complicated multi-task problem consisting of humanoid robot navigation and control along with object recognition using a low-cost BCI system. Our approach enables subjects to control the navigation and exploration of a humanoid robot and recognize a desired object among candidates. This study aims to demonstrate the possibility of a hybrid BCI based on a low-cost system for a realistic and complex task. It also shows that the use of a simple image processing technique, combined with BCI, can further aid in making these complex tasks simpler. An experimental scenario is proposed in which a subject remotely controls a humanoid robot in a properly sized maze. The subject sees what the surrogate robot sees through visual feedback and can navigate the surrogate robot. While navigating, the robot encounters objects located in the maze. It then recognizes if the encountered object is of interest to the subject. The subject communicates with the robot through SSVEP and ERD-based BCIs to navigate and explore with the robot, and P300-based BCI to allow the surrogate robot recognize their favorites. Using several evaluation metrics, the performances of five subjects navigating the robot were quite comparable to manual keyboard control. During object recognition mode, favorite objects were successfully selected from two to four choices. Subjects conducted humanoid navigation and recognition tasks as if they embodied the robot. Analysis of the data supports the potential usefulness of the proposed hybrid BCI system for extended applications. This work presents an important implication for the future work that a hybridization of simple BCI protocols provide extended controllability to carry out complicated tasks even with a low-cost system.
Highlights
Electroencephalogram (EEG)-based brain-computer interface (BCI) have been of huge interest because of their potential uses
The protocols using P300 or state visually evoked potential (SSVEP) are categorized as reactive BCI, which enables users to control an application by detecting indirectly modulated brain signals related to specific external stimuli
The surrogate robot recognizes whether the detected object is the object which the user is looking through the P300 potential-based BCI
Summary
Electroencephalogram (EEG)-based BCIs have been of huge interest because of their potential uses. Several EEG-based BCI protocols have shown great promise, such as P300 potentials [3,4,5,9], SSVEP [10,11,12], motor imagery [6,7,8] and ERD/ERS [13,14]-based protocols. SSVEP is a response to visual stimulation at a specific frequency. The protocols using P300 or SSVEP are categorized as reactive BCI, which enables users to control an application by detecting indirectly modulated brain signals related to specific external stimuli. Motor imagery or ERD/ERS represent consciously intended brain signals without external events, which is classified as an active
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