Evaluation of PSD Components and AAR Parameters as Input Features for a SVM Classifier Applied to a Robotic Wheelchair

Two distinct signal features suitable to be used as input to a Support-Vector Machine (SVM) classifier in an application involving hands motor imagery and the correspondent EEG signal are evaluated in this paper. Such features are the Power Spectral Density (PSD) components and the Adaptive Autoregressive (AAR) parameters. Different classification times (CT) and time intervals are evaluated, for the AAR-based and the PSD-based features, respectively. The best result (an accuracy of 97.1%) is obtained when using PSD components, while the AAR parameters generated an accuracy of 94.3%. The results also demonstrate that it is possible to use only two EEG channels (bipolar configuration around C3 andC4), discarding the bipolar configuration aroundCz. The algorithms were tested with a proprietary EEG data set involving 4 individuals and with a data set provided by the University of Graz (Austria) as well. The resulting classification system is now being implemented in a Brain-Computer Interface (BCI) used to guide a robotic wheelchair.

[1]  Chafic Mokbel,et al.  BRAIN IMAGING AND SUPPORT VECTOR MACHINES FOR BRAIN COMPUTER INTERFACE , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[2]  G. Pfurtscheller,et al.  Subject specific EEG patterns during motor imaginary [sic.: for imaginary read imagery] , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

[3]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[4]  G. B. Anderson,et al.  Modeling the Stationarity and Gaussianity of Spontaneous Electroencephalographic Activity , 1975, IEEE Transactions on Biomedical Engineering.

[5]  Elif Derya Übeyli,et al.  Multiclass Support Vector Machines for EEG-Signals Classification , 2007, IEEE Transactions on Information Technology in Biomedicine.

[6]  Paul Sajda,et al.  Brain-Computer Interfaces [from the guest editors] , 2008 .

[7]  Simon Haykin,et al.  Adaptive Filter Theory 4th Edition , 2002 .

[8]  Marios M. Polycarpou,et al.  Autoregressive Features for a Thought-to-Speech Converter , 2008, BIODEVICES.

[9]  Jose L Pons,et al.  Wearable Robots: Biomechatronic Exoskeletons , 2008 .

[10]  Tang Jing-tian.  Distinguishing between left and right finger movement from EEG using SVM , 2007 .

[11]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.