Linear Discriminant Analysis on Brain Computer Interface

This report analyses the application of Linear Discriminant Analysis in Brain Computer Interface technology. It is aimed to obtain an objective evaluation of the discrimination capability achieved when different filtering windows are considered in order to differentiate between three different cerebral activities. In this report the following issues are discussed: quantification of the discrimination capability between the employed cerebral activities, identification of the frequency bands with the highest discrimination power, methodology to weight the amplitude of the previous frequency bands in order to reduce the dimensionality of the feature space and facilitate posterior analysis, without lost of intrinsic characteristics of each cerebral activity, determination of the best preprocess window. Linear Discrimination Analysis is employed in order to reduce the dimensionality of the input feature space; bilateral contrasts between features, inferred from each cerebral activity, are used to determine the discrimination power.

[1]  F. Harris.  On the use of windows for harmonic analysis with the discrete Fourier transform , 1978, Proceedings of the IEEE.

[2]  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.

[3]  J.B. Allen,et al.  A unified approach to short-time Fourier analysis and synthesis , 1977, Proceedings of the IEEE.

[4]  Brian D. Ripley,et al.  Pattern Recognition and Neural Networks , 1996 .

[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]  Ernst Fernando Lopes Da Silva Niedermeyer,et al.  Electroencephalography, basic principles, clinical applications, and related fields , 1982 .

[7]  Jonathan R Wolpaw,et al.  Brain–computer interfaces as new brain output pathways , 2007, The Journal of physiology.

[8]  Gert Pfurtscheller,et al.  Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.

[9]  H. Flor,et al.  The thought translation device (TTD) for completely paralyzed patients. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[10]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[11]  M J Stokes,et al.  EEG-based communication: a pattern recognition approach. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[12]  C. Rosenberg,et al.  Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, 3rd Ed. , 1994 .

[13]  J J Vidal,et al.  Toward direct brain-computer communication. , 1973, Annual review of biophysics and bioengineering.

[14]  A Kostov,et al.  Parallel man-machine training in development of EEG-based cursor control. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.