Time-Frequency Features Combination to Improve Single-Trial EEG Classification

In this paper, we propose a combination of two simple feature extraction methods from time and frequency domain to improve singe-trial EEG classification in self-paced BCI. ‘Bereitschaftspotential’ (BP) features from time domain and event-related desynchronization (ERD) features from frequency domain are merged and feed into four different classifiers which are probabilistic neural network (PNN), support-vector machine (SVM), K-nearest neighbor (KNN), and Parzen classifier (PC). Results using BCI competition 2003 [1] dataset IV are showing that the combined features are quite discriminative as we reached an accuracy on the test set ranging from 82% to 85% whereas the winner of the competition on this data set reached 84% using three types of features [2,3].

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

[2]  B. Arnaldi,et al.  FuRIA: A Novel Feature Extraction Algorithm for Brain-Computer Interfaces using Inverse Models and Fuzzy Regions of Interest , 2007, 2007 3rd International IEEE/EMBS Conference on Neural Engineering.

[3]  Adam Krzyżak,et al.  Methods of combining multiple classifiers and their applications to handwriting recognition , 1992, IEEE Trans. Syst. Man Cybern..

[4]  Lucas C. Parra,et al.  Second Order Bilinear Discriminant Analysis for single trial EEG analysis , 2007, NIPS.

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

[6]  Klaus-Robert Müller,et al.  The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials , 2004, IEEE Transactions on Biomedical Engineering.

[7]  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.

[8]  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.

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

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

[11]  J. Wolpaw,et al.  Brain-computer communication: unlocking the locked in. , 2001, Psychological bulletin.

[12]  Klaus-Robert Müller,et al.  Combining Features for BCI , 2002, NIPS.