Robotic Grasping and Placement Controlled by EEG-Based Hybrid Visual and Motor Imagery

TL;DR

This paper introduces a real-time BCI system that uses EEG signals for visual and motor imagery to control robotic grasping and placement, demonstrating practical human-robot interaction.

Contribution

The work presents a novel EEG-based hybrid visual and motor imagery framework enabling intention-driven robotic control without prior training on specific tasks.

Findings

Achieved 40.23% accuracy for visual intent decoding

Achieved 62.59% accuracy for motor imagery decoding

End-to-end task success rate of 20.88% in diverse scenarios

Abstract

We present a framework that integrates EEG-based visual and motor imagery (VI/MI) with robotic control to enable real-time, intention-driven grasping and placement. Motivated by the promise of BCI-driven robotics to enhance human-robot interaction, this system bridges neural signals with physical control by deploying offline-pretrained decoders in a zero-shot manner within an online streaming pipeline. This establishes a dual-channel intent interface that translates visual intent into robotic actions, with VI identifying objects for grasping and MI determining placement poses, enabling intuitive control over both what to grasp and where to place. The system operates solely on EEG via a cue-free imagery protocol, achieving integration and online validation. Implemented on a Base robotic platform and evaluated across diverse scenarios, including occluded targets or varying participant postures, the system achieves online decoding accuracies of 40.23% (VI) and 62.59% (MI), with an end-to-end task success rate of 20.88%. These results demonstrate that high-level visual cognition can be decoded in real time and translated into executable robot commands, bridging the gap between neural signals and physical interaction, and validating the flexibility of a purely imagery-based BCI paradigm for practical human-robot collaboration.