Analysis of Feature Extraction Algorithms Used in Brain-Computer Interfaces

Abstract

A brain-computer interface (BCI) is a system for communication between humans or animals and computers, which sends messages or commands from brain activities to the external devices without peripheral nerves and muscles activities. Feature extraction is crucial in a BCI system, for it determines whether the user's intent can be interpreted as an accurate command. We examined the performance of the representative algorithms including Fast Fourier transform (FFT), Wavelet transform (WT) and Independent component analysis (ICA). Experimental results show that these algorithms are effective to identify the target characteristics. Improvements of these methods or their integrations can contribute to enhance the efficiency of the BCI systems.