Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
The canonical correlations of matrix pairs and their numerical computation
The canonical correlations of matrix pairs and their numerical computation
A Matrix Handbook for Statisticians
A Matrix Handbook for Statisticians
Handbook of Blind Source Separation: Independent Component Analysis and Applications
Handbook of Blind Source Separation: Independent Component Analysis and Applications
Common spatiotemporal pattern analysis
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Blind source separation by nonstationarity of variance: a cumulant-based approach
IEEE Transactions on Neural Networks
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In this paper, the estimation of spatio-temporal patterns in the context of event-related potentials or evoked potentials studies in neuroscience is addressed. The proposed framework (denoted xDAWN) has the advantage to require only the knowledge of the time of stimuli onsets which are determined by the experimental setup. A theoretical analysis of the xDAWN framework shows that it provides asymptotically optimal spatial filters under weak assumptions. The loss in signal to interference-plus-noise ratio due to finite sample effect is calculated in a closed form at the first order of perturbation and is then validated by simulations. This last result shows that the proposed method provides interesting performance and outperforms classical methods, such as independent component analysis, in a wide range of situations. Moreover, the xDAWN algorithm has the property to be robust with respect to the model parameter values. Finally, validations on real electro-encephalographic data confirm the good behavior of the proposed xDAWN framework in the context of a P300 speller brain-computer interface.