An outlier detection/rejection method for single-trial EEG signals classification

We address the key issue of the presence of outliers and artifacts inherently present in various electroencephalography (EEG) recordings. Indeed, in almost all brain-computer interface (BCI) systems, EEG signals may contain artifacts and noisy components frequently caused by improper imagination or by a loss of concentration during the imagination process. In this paper, a method is firstly proposed to detect the outliers from training and as a second step to reject those artifacts and outliers on the basis of training. In particular, the method proposed can operate for all kind of EEG signals contaminated by noise; in addition to remove the need of human expertise. Experimental results conducted on BCI competition 2003 dataset III show significant improvement of performance accuracy compared with the standard classification scheme.

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