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Home Robot Interaction Based on EEG Motor Imagery and Visual Perception Fusion.

Tie Hua Zhou¹, Dongsheng Li¹, Zhiwei Jian¹

¹Department of Computer Science and Technology, School of Computer Science, Northeast Electric Power University, Jilin 132013, China.

Sensors (Basel, Switzerland)

|September 13, 2025

Summary

This summary is machine-generated.

This study introduces a multimodal system for home robots to understand elderly intentions using brain (EEG) and vision data. The system achieves 83.4% accuracy, enhancing human-robot collaboration for elder care.

Keywords:

feature fusion home robot human–robot interaction motor imagery electroencephalogram (MI-EEG)scene recognition

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Area of Science:

Robotics
Artificial Intelligence
Biomedical Engineering

Background:

The global population is aging, increasing the need for assistive technologies for the elderly.
Home robots offer potential for daily life assistance, but require advanced perception capabilities.

Purpose of the Study:

To develop a multimodal human-robot interaction system for home robots to perceive elderly intentions and environments.
To enhance the collaborative interaction between humans and robots in elder care settings.

Main Methods:

Utilized Motor Imagery (MI) EEG signals with channel selection and Filter Bank co-Spatial Patterns (FBCSP) for classification.
Integrated YOLO v8 for object detection and Machine Learning for scene recognition.
Combined EEG classification with scene recognition to establish scene-intention correspondence for task recognition.

Main Results:

Achieved a recognition accuracy of 83.4% for intention-driven task types.
Demonstrated effective multimodal perception by integrating EEG and visual data.
Validated the practical application value in human-robot collaborative interaction.

Conclusions:

The proposed system provides a robust method for recognizing elderly intentions in home environments.
This technology supports the development of smarter, personalized home assistance robots.
The findings highlight the potential of multimodal perception in advanced human-robot interaction for elder care.