EURASIP Journal on Advances in Signal Processing
P300 detection based on feature extraction in on-line brain-computer interface
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Comparison of classification methods for P300 brain-computer interface on disabled subjects
Computational Intelligence and Neuroscience - Special issue on Selected Papers from the 4th International Conference on Bioinspired Systems and Cognitive Signal Processing
Brain-computer interface research at Katholieke Universiteit Leuven
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
Efficient penetration depth approximation using active learning
ACM Transactions on Graphics (TOG)
Optimized dissimilarity space embedding for labeled graphs
Information Sciences: an International Journal
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In this paper, we present a novel scheme for linear feature extraction in classification. The method is based on the maximization of the mutual information (MI) between the features extracted and the classes. The sum of the MI corresponding to each of the features is taken as an heuristic that approximates the MI of the whole output vector. Then, a component-by-component gradient-ascent method is proposed for the maximization of the MI, similar to the gradient-based entropy optimization used in independent component analysis (ICA). The simulation results show that not only is the method competitive when compared to existing supervised feature extraction methods in all cases studied, but it also remarkably outperform them when the data are characterized by strongly nonlinear boundaries between classes.