Artifact detection in sleep EEG recording

An investigation was conducted with the aim of achieving a completely automatic detection of artifacts resulting from muscular activity and ocular movements superimposed on an electroencephalogram (EEG) signal. Muscular artifacts were detected by a pattern-recognition approach; different features were tested to achieve reliable and real-time results. Pattern classes were constructed based on selected features and using the K-means clustering algorithm. Fisher´s linear discriminant, Mahalanobis distance, and the Q-NN rule were the methods tested within the task of supervised classification, using the pattern classes found in the clustering step. Ocular artifacts were detected by an algorithm which compares simultaneously recorded EOG (electrooculogram) and EEG signals. Whenever a significant correlation between them is found, an ocular artifact is detected. If one or both of those artifact types are detected, the actual segment is replaced by an EEG simulation using an AR (autoregressive) modeled system driven by Gaussian white noise at its input