Publikation

Effects of eye artifact removal methods on single trial P300 detection, a comparative study

Foad Ghaderi, Su-Kyoung Kim, Elsa Andrea Kirchner

In: Journal of Neuroscience Methods Volume 221 Seiten 41-47 Elsevier 1/2014.

Abstrakt

Electroencephalographic signals are commonly contaminated by eye artifacts, even if recorded under controlled conditions. The objective of this work was to quantitatively compare standard artifact removal methods (regression, filtered regression, Infomax, and second order blind identification (SOBI)) and two artifact identification approaches for independent component analysis (ICA) methods, i.e. ADJUST and correlation . To this end, eye artifacts were removed and the cleaned datasets were used for single trial classification of P300 (a type of event related potentials elicited using the oddball paradigm). Statistical analysis of the results confirms that the combination of Infomax and ADJUST provides a relatively better performance (0.6% improvement on average of all subject) while the combination of SOBI and correlation performs the worst. Low-pass filtering the data at lower cutoffs (here 4 Hz) can also improve the classification accuracy. Without requiring any artifact reference channel, the combination of Infomax and ADJUST improves the classification performance more than the other methods for both examined filtering cutoffs, i.e., 4 Hz and 25 Hz.

Projekte

Weitere Links

130830_Effects_of_eye_artifact_removal_methods_on_single_trial_P300_detection,_a_comparative_study_Journal_Ghaderi.pdf (pdf, 292 KB)

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence