| 摘要: |
| 随着人工智能在神经科学和人机交互等领域的深度交叉融合，脑-机接口(BCI)再次引起了国内外智能交互领域学者的关注。目的 为了提高脑-机接口系统效率，运用交互设计思维呈现符合被试心理预期的界面形式，减少被试视觉疲劳，更加高效、准确的获取实验数据。方法 对目前基于事件相关电位(ERP)、视觉稳态诱发电位(SSVEP)和运动想象(MI)等EEG-BCI实验范式的交互界面设计进行了详细地梳理和归纳。结论 通过综述为脑-机接口实验范式设计研究提供参考，改善目前EEG-BCI在交互控制领域的应用局限性，提高BCI系统可用性、易用性。更有助于促进工效学、设计学科、认知神经科学、信息科学等多学科的交叉融合，为基于脑-机交互的人工智能发展提供新方向和新思路。 |
| 关键词: 脑-机接口 界面设计 事件相关电位ERP 稳态视觉诱发电位 运动想象 |
| DOI：10.19554/j.cnki.1001-3563.2021.18.013 |
| 分类号:TB472;TP391 |
| 基金项目:国家自然科学基金项目(72001202);中央高校基本科研业务费专项资金资助项目(2020QN17);工效学会-津发科技优秀青年学者联合研究基金项(CES-Kingfar-2019-004) |
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| A Review Study of Interactive Interface Design for the Eeg-Bci Experimental Paradigm |
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SHAO Jiang1, BAI Yu-xin1, WEI Le-le1, YAO Jun1, TIAN Fang-yuan2, ZHU Zhao-hua1
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(1. China University of Mining and Technology, Xuzhou 221116, China;2. Xi’an University of Science and Technology, Xi’an 710054, China)
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| Abstract: |
| With the deep cross-fertilisation of artificial intelligence in the fields of neuroscience and human-computer interaction, brain-computer interface (BCI) has once again attracted the attention of scholars in the field of intelligent interaction at home and abroad. In order to improve the efficiency of the brain-computer interface system, interaction design thinking is used to present an interface format that meets the mental expectations of the subjects, reduces their visual fatigue and allows for more efficient and accurate access to experimental data. The design of current EEG-BCI experimental paradigms based on event-related potentials (ERP), visual steady-state visual evoked potentials (SSVEP) and motor imagery (MI) are reviewed and summarised in detail. The review provides a reference for research on the design of experimental paradigms for brain-machine interfaces, improves the limitations of current EEG-BCI applications in the field of interaction control, and increases the usability and ease of use of BCI systems. It also helps to promote the cross- fertilization of ergonomics, design disciplines, cognitive neuroscience, information science and other disciplines, and provides new directions and new ideas for the development of artificial intelligence based on brain-machine interaction. |
| Key words: brain-computer interface interface design event-related potentials(ERP) steady-state visual evoked potentials motor imager |
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