The Removal Of Ocular Artifacts From EEG Signals: A Comparison of Performances For Different Methods

 
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The Removal Of Ocular Artifacts From EEG Signals: A Comparison of Performances For Different Methods

Papadelis, Christos
Klados, Manousos
Lithari, Chrysa
Bamidis, Panagiotis

The presence of electrooculographic (EOG) artifacts in the electroencephalographic (EEG) signal is a major problem in the study of brain potentials. A variety of algorithms have been proposed to reject these artifacts including methods based on regression and blind source separation (BSS) techniques. None of them has so far been established as the method of choice. In the present study, the performances of five widely used EOG artifact rejection techniques are compared. The compared methodologies include two fully automated regression methods, one based on Least Mean Square (LMS) for its optimization process, and the other on Recursive Least Square (RLS) algorithm, two BSS techniques which use respectively the Extended — Independent Component Analysis (ext — ICA) and the Second Order Blind Identification (SOBI), and finally a time-varying adaptive algorithm based on H ∞ principles (H ∞ — TV). Each algorithm was applied in real EEG data and then their performance quantified in the time domain. The performance of RLS and H ∞ — TV were poor in removing eye — blink artifacts. For the rest of the methods the results supported the use of LMS technique and suggested the need for further research examining the performance of various artifact rejection techniques in both time and frequency domain.

Article / Άρθρο
info:eu-repo/semantics/article

Artifacts
LMS
EEG
ICA
ΗΟΓ
ΗΕΓ
Παράσιτα
EOG

Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης (EL)
Aristotle University of Thessaloniki (EN)

Αγγλική γλώσσα

2009
2009-12-22T09:52:01Z


Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης, Σχολή Επιστημών Υγείας, Τμήμα Ιατρικής

IFMBE Proceedings, vol.22 [2009] p.1259-1263 [Published Version]
urn:ISSN:16800737

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info:eu-repo/semantics/openAccess



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