A review of adaptive feature extraction and classification methods for EEG-based brain-computer interfaces

A brain-computer interface (BCI) is a system that allows its users to control external devices which are independent of peripheral nerves and muscles with brain activities. Electroencephalogram (EEG) signals are electrical signals collected from the scalp. They are frequently used in brain-computer interaction. However, EEG signals which change over time are highly non-stationary. One major challenge in current BCI research is how to extract features of time-varying EEG signals and classify the signals as accurately as possible. An effective BCI should be robust against and adaptive to the dynamic variations of brain activities. Adaptive learning in a BCI system, a rapidly developing application of machine learning, would be an effective approach to conquer the challenge. This paper reviews representative adaptive feature extraction and classification methods for EEG-based BCIs and further discusses some important open problems which can hopefully be useful to promote the research of the BCIs.