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

Yichang Liu, Tianyu Wang, Ziyi Ye, Yawei Li, Yu-Gang Jiang, Shouyan Wang, Yanwei Fu

TL;DR

It is demonstrated 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.

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.

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

TL;DR

It is demonstrated 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.

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.
Paper Structure (25 sections, 2 equations, 6 figures, 3 tables)

This paper contains 25 sections, 2 equations, 6 figures, 3 tables.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORKS
  3. Visual Decoding from Brain Signals
  4. Robotics Control using EEG
  5. DATA COLLECTION AND SYSTEM CONFIGURATION
  6. Participants
  7. Task Paradigms
  8. Signal Acquisition and Preprocessing
  9. Robotics Platform
  10. METHODOLOGY AND EVALUATION
  11. Model Selection
  12. Feature Extraction
  13. Experimental Setup
  14. Experimental Results
  15. Offline Performance
  16. ...and 10 more sections

Figures (6)

  • Figure 1: High-level cognitive control pipeline for EEG-based robotic manipulation. The framework consists of three main components: (1) Offline EEG data collection: visual imagery (VI) and motor imagery (MI) EEG data are collected and labeled to train visual and motor decoders. (2) Online EEG acquisition: A dual-channel system is deployed in which VI-EEG is decoded into grasping intentions and MI-EEG determines placement positions, enabling real-time task-level command generation. (3) Robotic control: the trained decoders drive a robotic arm to perform the grasp and place task in real-world, demonstrating the seamless mapping from high-level visual cognition to physical manipulation.
  • Figure 2: Visual Imagery and Motor Imagery Paradigms.
  • Figure 3: Offline results for three tasks: (a) Visual Perception, (b) Visual Imagery, and (c) Motor Imagery. Points show per-subject accuracies across models and frequency settings; the dashed line denotes chance level (VP/VI: 33.3%, MI: 50%). MI consistently outperforms VP/VI, and all tasks exceed chance.
  • Figure 4: Comparison of decoding accuracies for VI and MI. Motor imagery achieves higher accuracy than the visual EEG tasks overall, and within the visual paradigm, VP slightly outperforms VI.
  • Figure 5: Evoked EEG responses to fruit visual imagery. Brain activity patterns during VI for three stimuli in Sub00 (59 channels): (a) Apple, (b) Banana, (c) Orange. Responses vary over time yet remain broadly similar across categories, reflecting the fine-grained nature of VI decoding.
  • ...and 1 more figures