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. 2025 May 8:13:1591316.
doi: 10.3389/fbioe.2025.1591316. eCollection 2025.

Vibration stimulation enhances robustness in teleoperation robot system with EEG and eye-tracking hybrid control

Wenbin Zhang #  1 , Tianjie Wang #  2 , Chaolong Qin  3 , Baoguo Xu  3 , Hexuan Hu  1 , Tong Wang  2 , Ying Shen  2
Affiliations

Vibration stimulation enhances robustness in teleoperation robot system with EEG and eye-tracking hybrid control

Wenbin Zhang et al. Front Bioeng Biotechnol. .

Abstract

Introduction: The application of non-invasive brain-computer interfaces (BCIs) in robotic control is limited by insufficient signal quality and decoding capabilities. Enhancing the robustness of BCIs without increasing the cognitive load remains a major challenge in brain-control technology.

Methods: This study presents a teleoperation robotic system based on hybrid control of electroencephalography (EEG) and eye movement signals, and utilizes vibration stimulation to assist motor imagery (MI) training and enhance control signals. A control experiment involving eight subjects was conducted to validate the enhancement effect of this tactile stimulation technique.

Results: Experimental results showed that during the MI training phase, the addition of vibration stimulation improved the brain region activation response speed in the tactile group, enhanced the activation of the contralateral motor areas during imagery of non-dominant hand movements, and demonstrated better separability (p = 0.017). In the robotic motion control phase, eye movement-guided vibration stimulation effectively improved the accuracy of online decoding of MI and enhanced the robustness of the control system and success rate of the grasping task.

Discussion: The vibration stimulation technique proposed in this study can effectively improve the training efficiency and online decoding rate of MI, helping users enhance their control efficiency while focusing on control tasks. This tactile enhancement technology has potential applications in robot-assisted elderly care, rehabilitation training, and other robotic control scenarios.

Keywords: brain-computer interface; eye tracking; motor imagery; teleoperation robot; vibrotactile stimulation.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Actual scenes of local control (a), visual control interface (b), and remote robot platform (c).
FIGURE 2
FIGURE 2
System framework of brain-controlled teleoperation robot.
FIGURE 3
FIGURE 3
Schematic diagram of the time structure for a single trial of MI training.
FIGURE 4
FIGURE 4
The time structure for a single trial of robot control.
FIGURE 5
FIGURE 5
The feature distribution of all subjects in TA (a) and VA (b) training tasks.
FIGURE 6
FIGURE 6
The average ERSP spatial distribution in VA training (a) and TA training (b) of all subjects during MI.
FIGURE 7
FIGURE 7
The average ERD curve of all subjects in VA (a) and TA (b) training.
FIGURE 8
FIGURE 8
The robot motion trajectories of subject S1 during the process of capturing target A (a), target B (b), and target C (c), respectively.
See this image and copyright information in PMC

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