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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]. © 2019 Institution of Engineering and Technology. All rights reserved.
