2021年世界机器人大赛中与事件相关的无训练分类方法综述

Huanyu Wu, Dongrui Wu
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引用次数: 2

摘要

近年来,快速序列视觉呈现(RSVP)作为一种新的事件相关电位(ERP)范式,已成为脑电图信号处理技术中最流行的形式之一。已经提出了几种改进方法来提高RSVP分析的性能。在基于RSVP的脑机接口系统中,不依赖于训练特定参数的方法家族是必不可少的。参赛团队在2021年世界机器人大赛脑机接口控制机器人大赛ERP竞赛中提出了几种有效的无训练算法框架。本文讨论了在不需要培训的情况下提高系统性能的各种方法的有效性,并建议如何在实际系统中应用这些方法。首先,适当的预处理技术将大大提高结果。然后,非深度学习算法可能比深度学习方法更稳定。此外,集成学习可以使模型更加稳定和鲁棒。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Review of training-free event-related potential classification approaches in the World Robot Contest 2021
Recently, rapid serial visual presentation (RSVP), as a new event- related potential (ERP) paradigm, has become one of the most popular forms in electroencephalogram signal processing technologies. Several improvement approaches have been proposed to improve the performance of RSVP analysis. In brain–computer interface systems based on RSVP, the family of approaches that do not depend on training specific parameters is essential. The participating teams proposed several effective training-free frameworks of algorithms in the ERP competition of the BCI Controlled Robot Contest in World Robot Contest 2021. This paper discusses the effectiveness of various approaches in improving the performance of the system without requiring training and suggests how to apply these approaches in a practical system. First, appropriate preprocessing techniques will greatly improve the results. Then, the non-deep learning algorithm may be more stable than the deep learning approach. Furthermore, ensemble learning can make the model more stable and robust.
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