Open AccessAutomatic eye blink artifact removal for EEG based on a sparse coding technique for assessing major mental disorders
Author(s) -
Raheel Zafar,
Abdul Qayyum,
Wajid Mumtaz
Publication year - 2019
Publication title -
journal of integrative neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.336
H-Index - 33
eISSN - 1757-448X
pISSN - 0219-6352
DOI - 10.31083/j.jin.2019.03.164
Subject(s) - computer science , artifact (error) , artificial intelligence , pattern recognition (psychology) , electroencephalography , classifier (uml) , coding (social sciences) , neural coding , computer vision , speech recognition , mathematics , psychology , statistics , psychiatry
In the electroencephalogram recorded data are often confounded with artifacts, especially in the case of eye blinks. Different methods for artifact detection and removal are discussed in the literature, including automatic detection and removal. Here, an automatic method of eye blink detection and correction is proposed where sparse coding is used for an electroencephalogram dataset. In this method, a hybrid dictionary based on a ridgelet transformation is used to capture prominent features by analyzing independent components extracted from a different number of electroencephalogram channels. In this study, the proposed method has been tested and validated with five different datasets for artifact detection and correction. Results show that the proposed technique is promising as it successfully extracted the exact locations of eye blinking artifacts. The accuracy of the method (automatic detection) is 89.6% which represents a better estimate than that obtained by an extreme machine learning classifier.