Authors:
Murielle Kirkove
;
Clémentine François
;
Aurélie Libotte
and
Jacques G. Verly
Affiliation:
University of Liège, Belgium
Keyword(s):
Electroencephalography, Ocular Artifact, Wavelet Transform, Adaptive Filtering, Blind Source Separation.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Wavelet Transform
Abstract:
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 t
o successfully identify OA's in EEG recordings.
(More)