Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
On Relevant Dimensions in Kernel Feature Spaces
The Journal of Machine Learning Research
Editorial: Recent advances in brain-machine interfaces
Neural Networks
Trial pruning based on genetic algorithm for single-trial EEG classification
Computers and Electrical Engineering
Robust common spatial filters with a maxmin approach
Neural Computation
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Noise in electroencephalography data (EEG) is an ubiquitous problem that limits the performance of brain computer interfaces (BCI). While typical EEG artifacts are usually removed by trial rejection or by filtering, noise induced in the data by the subject's failure to produce the required mental state is very harmful. Such ''noise'' effects are rather common, especially for naive subjects in their training phase and, thus, standard artifact removal methods would inevitably fail. In this paper, we present a novel method which aims to detect such defected trials taking into account the intended task by use of Relevant Dimensionality Estimation (RDE), a new machine learning method for denoising in feature space. In this manner, our method effectively ''cleans'' the training data and thus allows better BCI classification. Preliminary results conducted on a data set of 43 naive subjects show a significant improvement for 74% of the subjects.