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Electroencephalography adaptive classification and decoding techniques

机译：脑电图自适应分类和解码技术

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Electroencephalography (EEG) classification is an essential component of Brain Computer Interface (BCI), which allows to communicate from the human mind to computer, and thus to communicate even for subjects with physical disabilities. There are various classes of classification methods related to EEG-BCI. Researchers have classified these types into four fundamental categories. The first is an adaptive classification approaches. The second is based on using matrices and tensor class of classification. The third is about the use of transfer learning, and final the fourth is about the use deep learning mechanisms. Given this background, this research framework provides a concise survey of adaptive classification methods employed for EEG based Brain Computer Interface. As indicated to, the adaptive classifiers, are dynamic classifiers where there parameters are incrementally re-evaluated and updated over time as new EEG data become available. In addition, the research frame has picked to establish an overall review to this specific category of classifier since, adaptive type of classifiers, have indicated to be superior to other static types of classifiers, as in reference to limited supervision or unsupervised adaptation, Lotte et. al. [1].
机译：脑电图（EEG）分类是大脑计算机接口（BCI）的重要组成部分，该接口允许从人的大脑到计算机进行通信，从而甚至可以为身体残障的受试者进行通信。有许多与EEG-BCI相关的分类方法。研究人员将这些类型分为四个基本类别。第一种是自适应分类方法。第二种是基于使用矩阵和张量类的分类。第三个是关于转移学习的使用，最后是关于深度学习机制的使用。在这种背景下，该研究框架对基于EEG的脑计算机接口所采用的自适应分类方法进行了简要概述。如所指出的，自适应分类器是动态分类器，其中随着新的EEG数据变得可用，随着时间的过去逐步重新评估和更新参数。此外，研究框架已选择对该分类器的这一特定类别建立总体评估，因为适应性分类器已表明优于其他静态类型的分类器，例如在有限监督或无监督适应方面，Lotte等。 al。 [1]。

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