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Different types of artifacts in the electroencephalogram (EEG) signals can considerably reduce the performance of the later-stage EEG analysis algorithms for making decisions, such as those for brain-computer interfacing (BCI) classification. In this paper, we address the problem of artifact detection and removal from single-channel EEG signals. We propose a novel approach that maps the probability of an EEG epoch to be artifactual based on four different statistical measures entropy (a measure of uncertainty), kurtosis (a measure of pea
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