Integration of Amplitude and Phase Statistics for Complete Artifact Removal in Independent Components of Neuromagnetic Recordings

• Published in: IEEE transactions on biomedical engineering Vol. 55; no. 10; pp. 2353 - 2362
• Main Authors: Dammers, Jurgen, Schiek, Michael, Boers, Frank, Silex, Carmen, Zvyagintsev, Mikhail, Pietrzyk, Uwe, Mathiak, Klaus
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2008; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artifact reduction; Artificial Intelligence; Biometry - methods; Biophysics; blind source separation (BSS); Blinking; Content addressable storage; Electrocardiography; Electroencephalography; Electroencephalography - methods; Electrooculography; Factor Analysis, Statistical; Humans; Independent component analysis; independent component analysis (ICA); Linear Models; Magnetoencephalography; magnetoencephalography (MEG); Magnetoencephalography - methods; Muscles; Myocardial Contraction; Neuroscience; Particle measurements; Pattern Recognition, Automated - methods; Principal Component Analysis; Psychiatry; Signal Processing, Computer-Assisted; Statistics; Studies; Weights and Measures; magneto-encephalography (MEG); independent component analysis (ICA); Artifact reduction; blind source separation (BSS)
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2008.926677

In magnetoencephalography (MEG) and electroencephalography (EEG), independent component analysis is widely applied to separate brain signals from artifact components. A number of different methods have been proposed for the automatic or semiautomatic identification of artifact components. Most of the proposed methods are based on amplitude statistics of the decomposed MEG/EEG signal. We present a fully automated approach based on amplitude and phase statistics of decomposed MEG signals for the isolation of biological artifacts such as ocular, muscle, and cardiac artifacts (CAs). The performance of different artifact identification measures was investigated. In particular, we show that phase statistics is a robust and highly sensitive measure to identify strong and weak components that can be attributed to cardiac activity, whereas a combination of different measures is needed for the identification of artifacts caused by ocular and muscle activity. With the introduction of a rejection performance parameter, we are able to quantify the rejection quality for eye blinks and CAs. We demonstrate in a set of MEG data the good performance of the fully automated procedure for the removal of cardiac, ocular, and muscle artifacts. The new approach allows routine application to clinical measurements with small effect on the brain signal.