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Multi-class motor imagery EEG classification using collaborative representation-based semi-supervised extreme learning machine.
Medical & Biological Engineering & Computing ( IF 3.2 ) Pub Date : 2020-07-16 , DOI: 10.1007/s11517-020-02227-4
Qingshan She 1 , Jie Zou 1 , Zhizeng Luo 1 , Thinh Nguyen 2 , Rihui Li 2 , Yingchun Zhang 2
Affiliation  

Both labeled and unlabeled data have been widely used in electroencephalographic (EEG)-based brain-computer interface (BCI). However, labeled EEG samples are generally scarce and expensive to collect, while unlabeled samples are considered to be abundant in real applications. Although the semi-supervised learning (SSL) allows us to utilize both labeled and unlabeled data to improve the classification performance as against supervised algorithms, it has been reported that unlabeled data occasionally undermine the performance of SSL in some cases. To overcome this challenge, we propose a collaborative representation-based semi-supervised extreme learning machine (CR-SSELM) algorithm to evaluate the risk of unlabeled samples by a new safety-control mechanism. Specifically, the ELM model is firstly used to predict unlabeled samples and then the collaborative representation (CR) approach is employed to reconstruct the unlabeled samples according to the obtained prediction results, from which the risk degree of unlabeled sample is defined. A risk-based regularization term is then constructed accordingly and embedded into the objective function of the SS-ELM. Experiments conducted on benchmark and EEG datasets demonstrate that the proposed method outperforms the ELM and SS-ELM algorithm. Moreover, the proposed CR-SSELM even offers the best performance while SS-ELM yields worse performance compared with its supervised counterpart (ELM).

This paper proposes a collaborative representation-based semi-supervised extreme learning machine (CR-SSELM) algorithm to evaluate the risk of unlabeled samples by a new safety-control mechanism. It is aim to solve the safety problem of SS-ELM method that SS-ELM yields worse performance than ELM. With the help of safety mechanism, the performance of our method is still better than supervised ELM method.



中文翻译:

使用基于协作表示的半监督极限学习机进行多类运动图像EEG分类。

标记和未标记的数据均已广泛用于基于脑电图(EEG)的脑机接口(BCI)。但是,标记的EEG样品通常稀少且收集起来昂贵,而未标记的样品在实际应用中被认为是丰富的。尽管半监督学习(SSL)允许我们利用标签数据和未标签数据来提高分类性能(相对于监督算法),但据报道,在某些情况下,未标签数据有时会破坏SSL的性能。为了克服这一挑战,我们提出了一种基于协作表示的半监督极限学习机(CR-SSELM)算法,以通过一种新的安全控制机制来评估未标记样本的风险。特别,首先利用ELM模型对未标记样本进行预测,然后根据获得的预测结果,采用协同表示(CR)方法对未标记样本进行重构,确定未标记样本的风险程度。然后相应地构建基于风险的正则化术语,并将其嵌入到SS-ELM的目标函数中。在基准和EEG数据集上进行的实验表明,该方法优于ELM和SS-ELM算法。此外,与受监督的同类产品(ELM)相比,拟议的CR-SSELM甚至可以提供最佳的性能,而SS-ELM的性能却更差。从中定义未标记样品的风险程度。然后相应地构建基于风险的正则化术语,并将其嵌入到SS-ELM的目标函数中。在基准和EEG数据集上进行的实验表明,该方法优于ELM和SS-ELM算法。此外,与受监督的同类产品(ELM)相比,拟议的CR-SSELM甚至可以提供最佳的性能,而SS-ELM的性能却更差。从中定义未标记样品的风险程度。然后相应地构建基于风险的正则化术语,并将其嵌入到SS-ELM的目标函数中。在基准和EEG数据集上进行的实验表明,该方法优于ELM和SS-ELM算法。此外,与受监督的同类产品(ELM)相比,拟议的CR-SSELM甚至可以提供最佳的性能,而SS-ELM的性能却较差。

本文提出了一种基于协作表示的半监督极限学习机(CR-SSELM)算法,通过一种新的安全控制机制来评估未标记样本的风险。目的是解决SS-ELM比ELM产生更差性能的SS-ELM方法的安全性问题。借助于安全机制,我们的方法的性能仍优于监督ELM方法。

更新日期:2020-07-16
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