Detecting MMN in Infants EEG with Singular Value Decomposition.

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Subject classification (UKÄ) – MANDATORY

  • Psychology
  • Probability Theory and Statistics
Original languageEnglish
Journal[Publication information missing]
Publication statusPublished - 2005
Publication categoryResearch
Peer-reviewedYes
Event27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Shanghai
Duration: 2005 Sep 12005 Sep 4

Bibliographic note

Abstract: 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 USV2= 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.