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A Review on Signal Processing Approaches to Reduce Calibration Time in EEG-Based Brain-Computer Interface.
Frontiers in Neuroscience ( IF 4.3 ) Pub Date : 2021-08-19 , DOI: 10.3389/fnins.2021.733546
Xin Huang 1 , Yilu Xu 2 , Jing Hua 2 , Wenlong Yi 2 , Hua Yin 2 , Ronghua Hu 3 , Shiyi Wang 4
Affiliation  

In an electroencephalogram- (EEG-) based brain-computer interface (BCI), a subject can directly communicate with an electronic device using his EEG signals in a safe and convenient way. However, the sensitivity to noise/artifact and the non-stationarity of EEG signals result in high inter-subject/session variability. Therefore, each subject usually spends long and tedious calibration time in building a subject-specific classifier. To solve this problem, we review existing signal processing approaches, including transfer learning (TL), semi-supervised learning (SSL), and a combination of TL and SSL. Cross-subject TL can transfer amounts of labeled samples from different source subjects for the target subject. Moreover, Cross-session/task/device TL can reduce the calibration time of the subject for the target session, task, or device by importing the labeled samples from the source sessions, tasks, or devices. SSL simultaneously utilizes the labeled and unlabeled samples from the target subject. The combination of TL and SSL can take advantage of each other. For each kind of signal processing approaches, we introduce their concepts and representative methods. The experimental results show that TL, SSL, and their combination can obtain good classification performance by effectively utilizing the samples available. In the end, we draw a conclusion and point to research directions in the future.

中文翻译:

在基于 EEG 的脑机接口中减少校准时间的信号处理方法综述。

在基于脑电图 (EEG) 的脑机接口 (BCI) 中,受试者可以使用他的 EEG 信号以安全便捷的方式直接与电子设备进行通信。然而,对噪声/伪影的敏感性和 EEG 信号的非平稳性导致受试者/会话间的高度可变性。因此,每个主题通常花费漫长而乏味的校准时间来构建特定于主题的分类器。为了解决这个问题,我们回顾了现有的信号处理方法,包括转移学习 (TL)、半监督学习 (SSL) 以及 TL 和 SSL 的组合。跨对象 TL 可以为目标对象转移来自不同源对象的标记样本量。此外,跨会话/任务/设备 TL 可以减少目标会话、任务、通过从源会话、任务或设备导入带标签的样本。SSL 同时利用来自目标对象的标记和未标记样本。TL 和 SSL 的组合可以相互利用。对于每种信号处理方法,我们介绍它们的概念和代表性方法。实验结果表明,TL、SSL 及其组合可以通过有效利用现有样本获得良好的分类性能。最后得出结论并指出未来的研究方向。我们介绍他们的概念和代表方法。实验结果表明,TL、SSL 及其组合可以通过有效利用现有样本获得良好的分类性能。最后得出结论并指出未来的研究方向。我们介绍他们的概念和代表方法。实验结果表明,TL、SSL 及其组合可以通过有效利用现有样本获得良好的分类性能。最后得出结论并指出未来的研究方向。
更新日期:2021-08-19
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