Reference : Performance evaluation of methods for correcting ocular artifacts in electroencephalo...
Scientific congresses and symposiums : Unpublished conference
Engineering, computing & technology : Multidisciplinary, general & others
Human health sciences : Neurology
http://hdl.handle.net/2268/134410
Performance evaluation of methods for correcting ocular artifacts in electroencephalographic (EEG) recordings
English
Kirkove, Murielle mailto [Université de Liège - ULg > > CSL (Centre Spatial de Liège) >]
François, Clémentine mailto [Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Exploitation des signaux et images >]
Libotte, Aurélie [> >]
Verly, Jacques mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Exploitation des signaux et images >]
Feb-2013
Yes
No
International
6th International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS 2013)
from 11-02-2013 to 14-02-2013
Barcelona
Spain
[en] Electroencephalography ; Ocular artifact ; Wavelet transform ; Adaptive filtering ; Blind source separation
[en] 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.
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/134410

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