[The supernumerary robotic limbs of brain-computer interface based on asynchronous steady-state visual evoked...

Area of Science:

• Neuroscience
• Robotics
• Human-Computer Interaction

Context:

• Steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) are crucial for intelligent robotics.
• Existing SSVEP-BCI systems often lack autonomous control due to reliance on synchronized triggers and inability to distinguish user control states.

Purpose:

• To develop an accurate asynchronous state recognition method for SSVEP-BCI systems.
• To create a brain-machine fusion system for cooperative control in multitasking scenarios, particularly for individuals with disabilities.

Summary:

• A novel SSVEP asynchronous state recognition method is proposed, fusing time-frequency domain electroencephalographic (EEG) features with linear discriminant analysis (LDA).
• A brain-machine fusion system was developed for collaborative control of a wearable manipulator and robotic arm, functioning as a 'third hand'.
• Experimental results demonstrated high accuracy (93.0%) in user intent recognition for multitasking cooperative operations using the proposed asynchronous SSVEP-BCI control algorithm.

Impact:

• Provides a theoretical and practical foundation for the real-world application of asynchronous SSVEP-BCI systems.
• Enhances the control capabilities of intelligent robots and assistive devices, especially in complex environments.
• Offers significant advantages for disabled individuals by enabling collaborative control for multitasking operations.