Blind source separation via generalized eigenvalue decomposition
The Journal of Machine Learning Research
ICA using spacings estimates of entropy
The Journal of Machine Learning Research
Brain computer interfaces as intelligent sensors for enhancing human-computer interaction
Proceedings of the 14th ACM international conference on Multimodal interaction
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The separation of the informative part from an observed dataset is a significant step for dimension reduction and features extraction. In this paper, we present an approach for adaptive artifact rejection from the electroencephalogram (EEG). The main aim of our work is to increase performance of classification algorithms which have a deal with the EEG and are used in cognitive games. We provide a method to separate the EEG into informative and noised parts, select informative one and rank its dimensions. The proposed approach is based on the theoretical relation between classification accuracy, mutual information and normalized graph cut (NC) value. The presented algorithm requires a priori known class labeled EEG dataset that are utilized for a calibration phase of the brain-computer interface (BCI). Experimental results on datasets from BCI competitions show its applicability for cognitive games.