A pulse artifact removal method considering artifact variations in the simultaneous recording of EEG and fMRI

• Published in: Neuroscience research Vol. 81-82; pp. 42 - 50
• Main Authors: Oh, Sung Suk, Han, Yeji, Lee, Jongho, Yun, Seong Dae, Kang, Joong Koo, Lee, Eun Mi, Yoon, Hyo Woon, Chung, Jun-Young, Park, HyunWook
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier Ireland Ltd 01.04.2014
• Subjects: Adult; Algorithms; Brain - physiology; Brain Mapping; Computer Simulation; Electroencephalography; Female; Humans; Individual delay and window size; Intractable partial epilepsy patients; Magnetic Resonance Imaging; Male; Pulse; Pulse artifact (PA); Simultaneous EEG and fMRI; Young Adult; Intractable partial epilepsy patients; Pulse artifact (PA); Individual delay and window size; Simultaneous EEG and fMRI
• ISSN: 0168-0102; 1872-8111
• DOI: 10.1016/j.neures.2014.01.008

•A new method removes pulse artifact (PA) in EEG signal recorded in a MR scanner.•PA is classified into a normal PA or a deformed PA.•PA is removed by subtraction of PA template from EEG signal.•For removal of normal PA, individual delay and individual window size are utilized.•For removal of deformed PA, average delay and average window size are utilized. A simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) can provide high spatiotemporal information of brain activity. However, a proper analysis of the EEG signals is often hindered by various artifacts. In particular, pulse artifact (PA) induced from the heartbeat of a subject interferes with reliable measurements of the EEG signal. A new PA removal method that takes into account the delay variation between the heartbeat and PA and the window size variation in PA is presented in order to improve the detection and suppression of PA in EEG signals. A PA is classified into either a normal PA or a deformed PA. Only normal PAs are averaged to generate a PA template that is used to remove PAs from the measured EEG signals. The performance of the proposed method was evaluated by simulated data and real EEG measurements from epilepsy patients. The results are compared with those from conventional methods.