6533b853fe1ef96bd12ac28b
RESEARCH PRODUCT
Unsupervised Eye Blink Artifact Identification in Electroencephalogram
Vijanth S. AsirvadamChristophe StolzAshvaany EgambaramTahamina BegumNasreen BadruddinEric Fauvetsubject
Artifact (error)medicine.diagnostic_testbusiness.industryComputer science05 social sciencesFeature extractionWord error ratePattern recognitionElectroencephalography050105 experimental psychologyEB Artifacts03 medical and health sciencesIdentification (information)Electroencephalogram0302 clinical medicine[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processingmedicine0501 psychology and cognitive sciences[INFO]Computer Science [cs]Artificial intelligenceAutomated ThresholdbusinessEye blink030217 neurology & neurosurgerydescription
International audience; The most prominent type of artifact contaminating electroencephalogram (EEG) signals is the eye blink (EB) artifact. Hence, EB artifact detection is one of the most crucial pre-processing step in EEG signal processing before this artifact can be removed. In this work, an approach that identifies EB artifacts without human supervision and automated varying threshold setting is proposed and evaluated. The algorithm functions on the basis of correlation between two EEG electrodes, Fp1 and Fp2, followed by EB artifact threshold determination utilizing the amplitude displacement from the mean. The proposed approach is validated and evaluated in terms of accuracy and error rate in detecting events of EB artifacts in EEG signals. Analysis has revealed that the proposed approach achieved an average of 96.6% accuracy compared to a conventional method of identifying EB artifacts with a fixed constant threshold.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-10-01 |