Detecting MMN in Infants EEG with Singular Value Decomposition

Summary, in English

Mismatch negativity (MMN) is an EEG voltage fluctuation caused by the brain's automatic reaction to unexpected changes in a repetitive stimulation. In an experiment we studied 68 infants of which 2/3 were born preterm. Due to noise of large amplitude, the MMN is difficult to detect in a single infant's EEG. Therefore grand average, which is a average of many subjects EEG recordings, is sometimes used. In this paper singular value decomposition (SVD) is proposed as an alternative to grand average. Consider the SVD USigmaVT = M, where the rows of M contains noisy EEG epochs. Usually data is projected onto the leftmost column of V since this column represent the largest common component of the rows of M. When data is affected by noise of a very large amplitude we may need to choose another column of V. In this paper we propose to choose the leftmost column of V such that the elements of the corresponding column of U has approximately equal values

• Author: Johan Sandberg; Maria Sandsten; Magnus Lindgren
• Department/s: Mathematical Statistics; Department of Psychology; Statistical Signal Processing Group
• Publishing year: 2005
• Language: English
• Pages: 4227-4230
• Publication/Series: 27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005
• Links: http://dx.doi.org/10.1109/IEMBS.2005.1615397
• Document type: Conference paper
• Publisher: IEEE - Institute of Electrical and Electronics Engineers Inc.
• Topic: Probability Theory and Statistics
• Conference name: 27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005.
• Conference date: 2005-09-01 - 2005-09-04
• Conference place: Shanghai, China
• Status: Published
• Research group: Statistical Signal Processing Group
• ISBN/ISSN/Other: ISBN: 0-7803-8741-4