https://www.selleckchem.com/btk.html
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 peakedness), sk
Respect!
Kommentar
Delen
