A Study of Various Feature Extraction Methods on a Motor Imagery Based Brain Computer Interface System.
BACKGROUND
Brain Computer Interface (BCI) systems based on Movement Imagination (MI) are widely used in recent decades. Separate feature extraction methods are employed in the MI data sets and classified in Virtual Reality (VR) environments for real-time applications.


METHODS
This study applied wide variety of features on the recorded data using Linear Discriminant Analysis (LDA) classifier to select the best feature sets in the offline mode. The data set was recorded in 3-class tasks of the left hand, the right hand, and the foot motor imagery.


RESULTS
The experimental results showed that Auto-Regressive (AR), Mean Absolute Value (MAV), and Band Power (BP) features have higher accuracy values,75% more than those for the other features.


CONCLUSIONS
These features were selected for the designed real-time navigation. The corresponding results revealed the subject-specific nature of the MI-based BCI system; however, the Power Spectral Density (PSD) based α-BP feature had the highest averaged accuracy.
Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran.
Seyed Navid
SN
Resalat
Radiation Research Center, Faculty of Paramedicine, AJA University of Medical Sciences, Tehran, Iran.
Valiallah
V
Saba
Automatic data processing; Brain-Computer Interface (BCI); Electroencephalography