会议MICCAI发表论文：利用动态视觉线索提升运动想象脑电信号质量的新型范式与数据集

论文“Improving Motor Imagery EEG Signal Quality with Dynamic Visual Cues: An Innovative Paradigm and Dataset”被医学图像顶会MICCAI接收

时间：2025-06-18

关键词：脑机接口与智能机器人，文章接收

  近日，硕士研究生岳晨曦在 MICCAI 2025 发表题为“Improving Motor Imagery EEG Signal Quality with Dynamic Visual Cues: An Innovative Paradigm and Dataset”的研究性文章，通讯作者为张枢教授。

  The EEG signal acquisition paradigm is fundamental to brain-computer interface (BCI) research as it directly determines the mechanisms of brain activity evoked, significantly influencing the quality of collected EEG signals. Traditional static cueing paradigms often struggle to effectively induce the motor imagery (MI) state, which can lead to inconsistent task execution and degraded EEG signal quality. This study proposes an innovative MI data acquisition paradigm employ-ing dynamic visual cues depicting real human movements to enhance engagement and more effectively induce the MI state. We build the first novel dynamic visual cueing MI dataset, comprising EEG data acquired using both dynamic and static paradigms from five subjects. We analysis our dynamic visual cueing paradigm using questionnaire, qualitative, and quantitative analyses, evaluating it from sub-jective experience, physiological phenomena, and EEG signal decoding accuracy perspectives. Experiments show that our dynamic cueing paradigm significantly enhances subjects’ task understanding and concentration, leading to greater brain activation and, consequently, improved decoding accuracy of brain states in MI-BCI tasks. By eliciting more pronounced brain state activity, our method funda-mentally improves the quality of acquired EEG signals, laying the foundation for accurate decoding of brain states, and provides an innovative perspective for the development and improvement of MI-BCI. The dataset and code will be released upon acceptance.

图1：论文框架.