摘要
脑-机接口(BCI)系统建立大脑与外部设备之间的直接交流通路,结合快速序列视觉呈现(RSVP)范式能够实现利用人类视觉系统进行高流通量图像目标检索.近些年来,RSVP-BCI系统在范式编码、脑电(EEG)解码和系统应用方面的研究取得了长足的进步.对范式编码的研究揭示不同范式参数对系统性能的影响,促进提升系统性能;脑电解码的研究在提升算法分类性能的同时推动少训练、零训练样本、多模态等场景下的应用;对RS-VP-BCI系统应用的研究实现推动系统走向实际应用并拓宽了应用领域.同时,系统仍面临着迈向实际时可应用领域范围窄、脑电跨域解码难题以及计算机视觉飞速进步带来的挑战.该文对RSVP-BCI近年来的相关研究进展进行了回顾与总结,并对未来的发展方向进行了展望.
Abstract
Brain-Computer Interface (BCI) system establishes a direct communication pathway between the brain and external devices, and combined with the Rapid Serial Visual Presentation (RSVP) paradigm, it can achieve high-throughput target image retrieval by utilizing the human visual system. In recent years, the RSVP-BCI system has made significant progress in research on paradigm, ElectroEncephaloGram (EEG) decoding, and system applications. Research on paradigm reveals the impact of different paradigm parameters on system performance, promoting the improvement of system performance; The research on EEG decoding improves the classification performance of algorithms and promotes applications in scenarios such as few training, zero training samples, and multimodality; The research on the RSVP-BCI system application has driven the system towards practical applications and expanded its application fields. However, the system also faces challenges such as limited practical applications, difficulties in cross-domain decoding of EEG, and the rapid progress of computer vision. This article reviews and summarizes the research progress of RSVP-BCI in recent years, and looks forward to the future development direction.
维普
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