Wavelet-based Processing of EEG Data for Brain-Computer Interfaces

Brain-Computer Interfaces based on non-invasive electroencephalographic (EEG) signals were recently made practical through sophisticated algorithms and clever systems, in such a way that the dream of effortlessly translating volition into action is coming true, albeit in a limited way. However, a low signal-to-noise ratio and the presence of frequent artefacts, such as eye blinks, contaminate the recordings and make the recognition of the underlying mental processes difficult. In this study, a novel waveletbased signal processing technique, ContinuousWavelet Regression, has been applied to refine EEG data in a wellknown setting. The recordings of spontaneous (i.e., asynchronous) signals of subjects performing highly different cognitive tasks have been processed by our algorithm, and then analyzed and classified, obtaining very promising results as compared with those obtained by previous studies.

[1]  F Babiloni,et al.  Linear classification of low-resolution EEG patterns produced by imagined hand movements. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[2]  Charles W. Anderson,et al.  Discriminating mental tasks using EEG represented by AR models , 1995, Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society.

[3]  Charles W. Anderson,et al.  EEG Subspace Representations and Feature Selection for Brain-Computer Interfaces , 2003, 2003 Conference on Computer Vision and Pattern Recognition Workshop.

[4]  A. Geva,et al.  Forecasting generalized epileptic seizures from the EEG signal by wavelet analysis and dynamic unsupervised fuzzy clustering , 1998, IEEE Transactions on Biomedical Engineering.

[5]  Minfen Shen,et al.  The investigation of time-varying synchrony of EEG during sentence learning using wavelet analysis , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

[6]  R MillanJoseDel,et al.  EEG Recognition of Imagined Hand and Foot Movements through Signal Space Projection. , 2000 .

[7]  C.W. Anderson,et al.  Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks , 1998, IEEE Transactions on Biomedical Engineering.

[8]  H. Jasper,et al.  The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology. , 1999, Electroencephalography and clinical neurophysiology. Supplement.

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

[10]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[11]  E Donchin,et al.  The mental prosthesis: assessing the speed of a P300-based brain-computer interface. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[12]  C. Torrence,et al.  A Practical Guide to Wavelet Analysis. , 1998 .

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

[14]  Ashok Razdan.  Wavelet correlation coefficient of ‘strongly correlated’ time series , 2004 .

[15]  Michael Kirby,et al.  Geometric Analysis for the Characterization of Nonstationary Time Series , 2003 .

[16]  David G. Stork,et al.  Pattern Classification , 1973 .

[17]  Johannes R. Sveinsson,et al.  Wavelet-package transformation as a preprocessor of EEG waveforms for classification , 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).