A study of EEG signals associated with intended cursor movement using asymmetry ratio

In this paper, an experiment was done to investigate the EEG signals associated with direction of cursor movement, namely 'up', 'down', 'right' and 'left' without involving motor imagery. A subject was asked to move the arrow on the computer screen virtually. The arrows were consisted of 'up* 'down', 'left' and 'right'. Power spectral density (PSD) asymmetry ratio from the EEG signals was used as input features. The backpropagation neural network (BPN) was used as classifier. Results show that the 'direction' task can be discriminated from baseline task with over 90% of accuracy. However, they cannot be discriminated from each other.

[1]  H. Lüders,et al.  American Electroencephalographic Society Guidelines for Standard Electrode Position Nomenclature , 1991, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[2]  Raveendran Paramesran,et al.  Power and asymmetry ratio of spectral bands for mental task recognition , 2001, Proceedings of the Sixth International Symposium on Signal Processing and its Applications (Cat.No.01EX467).

[3]  G Pfurtscheller,et al.  Current trends in Graz Brain-Computer Interface (BCI) research. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[4]  D J McFarland,et al.  Brain-computer interface research at the Wadsworth Center. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[5]  E Donchin,et al.  Brain-computer interface technology: a review of the first international meeting. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[6]  D.J. McFarland,et al.  The Wadsworth Center brain-computer interface (BCI) research and development program , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[7]  Charles W. Anderson,et al.  Effects of Variations in Neural Network Topology and Output Averaging on the Discrimination of Mental Tasks from Spontaneous Electroencephalogram , 1997 .

[8]  G Pfurtscheller,et al.  Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[9]  David M. Skapura,et al.  Neural networks - algorithms, applications, and programming techniques , 1991, Computation and neural systems series.

[10]  Z. Keirn,et al.  A new mode of communication between man and his surroundings , 1990, IEEE Transactions on Biomedical Engineering.