Ongoing EEG artifact correction using blind source separation.

Objective: Analysis of the electroencephalogram (EEG) for epileptic spike and seizure detection or brain-computer interfaces can be severely hampered by the presence of artifacts. The aim of this study is to describe and evaluate a fast automatic algorithm for ongoing correction of artifacts in continuous EEG recordings, which can be applied offline and online.

Methods: The automatic algorithm for ongoing correction of artifacts is based on fast blind source separation.

Fast evaluation of interictal spikes in long-term EEG by hyper-clustering.

Purpose: The burden of reviewing long-term scalp electroencephalography (EEG) is not much alleviated by automated spike detection if thousands of events need to be inspected and mentally classified by the reviewer. This study investigated a novel technique of clustering and 24-h hyper-clustering on top of automated detection to assess whether fast review of focal interictal spike types was feasible and comparable to the spikes types observed during routine EEG review in epilepsy monitoring.

Methods: Spike detection used a transformation of scalp EEG into 29 regional source activities and adaptive thresholds to increase sensitivity.