EEG feature extraction methods in motor imagery brain computer interface

Abstract

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Brain-computer interface (BCI) is a link between the human brain and a computer or other peripheral devices for communication and control. The most frequently utilized BCI paradigms at the time are motor imagination (MI) BCI. In the procedure of MI-BCI, one of the most important roles is the feature extraction of EEG signals. This article examines various feature extraction approaches in four distinct domains: time, frequency, time-frequency, and spatial. Various approaches are introduced in each domain, including the ERD/ERS computation, the FFT method, the Wavelet Transform (WT), the Discrete Wavelet Transform (DWT), Common Spatial Patterns (CSP), and Sub-band Common Spatial Patterns (SBCSP). This paper also compares the advantages and disadvantages of different methods in practical application, which can provide reference for future research.