Lin Zhimin, Zeng Ying, Gao Hui, Tong Li, Zhang Chi, Wang Xiaojuan, Wu Qunjian, Yan Bin
China National Digital Switching System Engineering and Technological Research Center, Zhengzhou, China.
Key Laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
Biomed Res Int. 2017;2017:2049094. doi: 10.1155/2017/2049094. Epub 2017 Jul 20.
Target image detection based on a rapid serial visual presentation (RSVP) paradigm is a typical brain-computer interface system with various applications, such as image retrieval. In an RSVP paradigm, a P300 component is detected to determine target images. This strategy requires high-precision single-trial P300 detection methods. However, the performance of single-trial detection methods is relatively lower than that of multitrial P300 detection methods. Image retrieval based on multitrial P300 is a new research direction. In this paper, we propose a triple-RSVP paradigm with three images being presented simultaneously and a target image appearing three times. Thus, multitrial P300 classification methods can be used to improve detection accuracy. In this study, these mechanisms were extended and validated, and the characteristics of the multi-RSVP framework were further explored. Two different P300 detection algorithms were also utilized in multi-RSVP to demonstrate that the scheme is universally applicable. Results revealed that the detection accuracy of the multi-RSVP paradigm was higher than that of the standard RSVP paradigm. The results validate the effectiveness of the proposed method, and this method can provide a whole new idea in the field of EEG-based target detection.
基于快速序列视觉呈现（RSVP）范式的目标图像检测是一种典型的脑机接口系统，具有多种应用，如图像检索。在RSVP范式中，通过检测P300成分来确定目标图像。这种策略需要高精度的单次试验P300检测方法。然而，单次试验检测方法的性能相对低于多次试验P300检测方法。基于多次试验P300的图像检索是一个新的研究方向。在本文中，我们提出了一种三重RSVP范式，同时呈现三张图像，目标图像出现三次。因此，可以使用多次试验P300分类方法来提高检测精度。在本研究中，对这些机制进行了扩展和验证，并进一步探索了多RSVP框架的特性。还在多RSVP中使用了两种不同的P300检测算法，以证明该方案具有普遍适用性。结果表明，多RSVP范式的检测精度高于标准RSVP范式。结果验证了所提方法的有效性，该方法可为基于脑电图的目标检测领域提供全新思路。
