Detecting MMN in Infants EEG with Singular Value Decomposition
Författare
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
Avdelning/ar
- Matematisk statistik
- Institutionen för psykologi
- Statistical Signal Processing Group
Publiceringsår
2005
Språk
Engelska
Sidor
4227-4230
Publikation/Tidskrift/Serie
27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005
Dokumenttyp
Konferensbidrag
Förlag
IEEE - Institute of Electrical and Electronics Engineers Inc.
Ämne
- 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
Forskningsgrupp
- Statistical Signal Processing Group
ISBN/ISSN/Övrigt
- ISBN: 0-7803-8741-4