Hybrid Brain Interface for Robotic Swarms Using EEG Signals and Joystick Inputs
Brain Computer Interfaces (BCI) have gained increased attention over the last four decades because they offer intuitive control in an abundance of applications where other interfaces are insufficient or impractical. Current technologies require lengthy training sessions until users achieve an acceptable level of control. Otherwise, users only have a limited binary control. For example, only one or two operations are manipulated.
Current systems are highly complex because users have to switch among different interfaces. In some cases, considerable cross-talk across multiple interfaces makes the robustness of the systems questionable. Therefore, there is a need for a system that is capable of intuitive and effective control over additional operations.
Researchers at Arizona State University developed a hybrid BCI system that combines electroencephalogram (EEG) signals, or electrical brain activity, and joystick input to control of robotic swarms. For each user, a separate calibration procedure must be performed. The resulting system is robust across multiple subjects, it provides reliable output with minimum errors and can be used for real-time control of robots.
The main purpose is to increase the number of operations controlled by the users and to enable more agile and complicated control strategies for robotics systems, while remaining instinctive for the user. The system can be used for the intuitive and reliable control of many different robotic platforms, such as a swarm of aerial drones and ground robotics.
- Control over aerial and ground robotics
- Motor control for disabled individuals
Benefits and Advantages
- Robust – Hybrid control allows for additional and complex operations
- Adept – Real-time commands and control over different behaviors of the swarm
- Non-invasive – Easy installation and can be used by people of any background
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CategoriesApplied Technologies Computing & Information Technology Manufacturing/Construction/Mechanical Physical Science Wireless & Networking
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- Shen Yan