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Nonholonomic mobile system control by combining EEG-based BCI with ANFIS.

Weiwei Yu1, Huashan Feng1, Yangyang Feng1

  • 1School of Mechatronic Engineering, Northwestern Polytechnical University, Youyi Xilu 127hao, Xi'an, 710072, China.

Bio-Medical Materials and Engineering
|September 26, 2015
PubMed
Summary

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This study introduces a novel brain-computer interface (BCI) using motor imagery electroencephalography (EEG) and an Adaptive Neural Fuzzy Inference System (ANFIS) for enhanced control of mobile systems. This approach improves accuracy and efficiency without requiring system stops.

Area of Science:

  • Neuroscience and Robotics
  • Brain-Computer Interface (BCI) technology
  • Human-Computer Interaction

Background:

  • Motor imagery electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) offer non-invasive control for peripheral devices like wheelchairs.
  • Current limitations include intensive training requirements for accurate control of complex nonholonomic mobile systems.
  • Challenges exist in achieving precise and effective control without user fatigue or external stimulation.

Purpose of the Study:

  • To propose a novel approach combining motor imagery EEG with the Adaptive Neural Fuzzy Inference System (ANFIS) for improved mobile system control.
  • To enhance the human intelligence fusion with the precision of ANFIS for nonholonomic mobile systems.
  • To achieve multi-level control enabling high maneuverability without system halts or reliance on sensor data.
Keywords:
ANFISBCImotor imagery EEGnonholonomic mobile system

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Main Methods:

  • Integration of motor imagery EEG signals with an Adaptive Neural Fuzzy Inference System (ANFIS) controller.
  • Development of a multi-level control strategy for nonholonomic mobile systems.
  • Online training capability of the ANFIS controller during the control task.

Main Results:

  • Demonstrated effective multi-level control of a nonholonomic mobile robot.
  • Achieved high controllability without requiring the mobile system to stop.
  • Verified increased control accuracy and efficiency for the user through ANFIS online training.

Conclusions:

  • The proposed approach effectively fuses human motor imagery intent with ANFIS precision for advanced mobile system control.
  • This BCI method enhances user experience by enabling intuitive and efficient operation of nonholonomic robots.
  • The system's ability to train online significantly boosts control performance and user adaptability.