基于MRP的脑机接口半监督时空滤波

Jun Lv, Lei Wang
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引用次数: 3

摘要

在脑机接口(BCI)研究中,当训练轨迹数量较少时,使用时空滤波(TSF)算法无法恰当地提取运动相关电位(MRPs)的判别模式。因此,本文提出了一种半监督TSF (ssTSF)算法,该算法采用自训练方案诱导高置信度的未标记轨迹,并迭代学习mrp的判别模式。我们将TSF算法和ssTSF算法在BCI比赛i的数据上进行了比较,结果证明了ssTSF算法的有效性,特别是对于小训练集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Semi-supervised temporal-spatial filter based on MRP for brain-computer interfaces
In brain-computer interface (BCI) studies, if the number of training trails is small, the discriminative patterns of movement related potentials (MRPs) can not be appropriately extracted by temporal-spatial filter (TSF) algorithm. Thus in this paper, we proposed a semi-supervised TSF (ssTSF) algorithm which employed self-training scheme to induce the unlabelled trails with high confidences and learn the discriminative patterns of MRPs iteratively. We compared TSF and ssTSF algorithm on the data from BCI competition I. The results demonstrated the effectiveness of the ssTSF, especially for small training sets.
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