Performance evaluation of methods for correcting ocular artifacts in electroencephalographic (EEG) recordings

The presence of ocular artifacts (OA) due to eye movements and eye blinks is a major problem for the
analysis of electroencephalographic (EEG) recordings in most applications. A large variety of methods
(algorithms) exist for detecting or/and correcting OA’s. We identified the most promising methods,
implemented them, and compared their performance for correctly detecting the presence of OA’s. These
methods are based on signal processing “tools” that can be classified into three categories: wavelet
transform, adaptive filtering, and blind source separation. We evaluated the methods using EEG signals
recorded from three healthy persons subjected to a driving task in a driving simulator. We performed a
thorough comparison of the methods in terms of the usual performances measures (sensitivity, specificity,
and ROC curves), using our own manual scoring of the recordings as ground truth. Our results show that
methods based on adaptive filtering such as LMS and RLS appear to be the best to successfully identify
OA’s in EEG recordings.