Feature Extraction of Electroencephalography Signals Using Fast Fourier Transform
Abstract: This article
discusses a method within the area of brain-computer interface. The proposed
method is to use the features extracted from the Electroencephalograph signal
and a three hidden-layer artificial neural network to map the brain signal
features to the computer cursor movement. The evaluated features are the root
mean square and the average power spectrum. The empirical evaluation using 200
records taken from 2003 BCI Competition dataset shows that the current approach
can accurately classify a simple cursor movement within 92.5% accuracy in a
short computation time.
Keywords: Electroencephalography
(EEG); Brain Com- puter Interface (BCI); Fast Fourier Transform (FFT)
Author: Hindarto, Sumarno
Journal Code: jptinformatikagg160014
