FuRIA: A Novel Feature Extraction Algorithm for Brain-Computer Interfaces using Inverse Models and Fuzzy Regions of Interest

In this paper, we propose a new feature extraction algorithm for brain-computer interfaces (BCIs). This algorithm is based on inverse models and uses the novel concept of fuzzy region of interest (ROI). It can automatically identify the relevant ROIs and their reactive frequency bands. The activity in these ROIs can be used as features for any classifier. A first evaluation of the algorithm, using a support vector machine (SVM) as classifier, is reported on data set IV from BCI competition 2003. Results are promising as we reached an accuracy on the test set ranging from 85 % to 86 % whereas the winner of the competition on this data set reached 84%.

[1]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.

[2]  R.J. Jimenez-Alaniz,et al.  Segmenting Brain MRI using Adaptive Mean Shift , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  José del R. Millán,et al.  Non-invasive estimation of local field potentials for neuroprosthesis control , 2005, Cognitive Processing.

[4]  Benoît Macq,et al.  Inverse Problem Applied to BCI's : Keeping Track of the EEG's Brain Dynamics Using Kalman Filtering , 2006 .

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

[6]  M Congedo,et al.  Classification of movement intention by spatially filtered electromagnetic inverse solutions , 2006, Physics in medicine and biology.

[7]  Lei Ding,et al.  Motor imagery classification by means of source analysis for brain–computer interface applications , 2004, Journal of neural engineering.

[8]  Klaus-Robert Müller,et al.  Classifying Single Trial EEG: Towards Brain Computer Interfacing , 2001, NIPS.

[9]  K.-R. Muller,et al.  BCI meeting 2005-workshop on BCI signal processing: feature extraction and translation , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[10]  Gert Pfurtscheller,et al.  EEG event-related desynchronization (ERD) and synchronization (ERS) , 1997 .

[11]  J D Watson,et al.  Nonparametric Analysis of Statistic Images from Functional Mapping Experiments , 1996, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[12]  Fusheng Yang,et al.  BCI competition 2003-data set IV:An algorithm based on CSSD and FDA for classifying single-trial EEG , 2004, IEEE Transactions on Biomedical Engineering.

[13]  Madan M. Gupta,et al.  Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems , 2003 .

[14]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  R D Pascual-Marqui,et al.  Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. , 2002, Methods and findings in experimental and clinical pharmacology.

[16]  M. Buss,et al.  EEG Source Localization for Brain-Computer-Interfaces , 2005, Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005..